Mathematics at the Boundary Between Model and God: What Would a Mathematics of Non-Exhaustive Knowledge Prove

Methodological note: This appendix records a personal, AI-assisted research exploration at the intersection of theology, computer science and mathematical logic. The formulas below therefore have different statuses: some state established mathematical results; others propose notation or conjectural research structures; and the theological interpretations require arguments beyond mathematics. AI helped translate my questions into candidate formal language and locate relevant fields, but its fluency is not evidence.

The question that interrupted the formalization

The main essay ended with a possible research programme: a formal theory of representations that can preserve truth through refinement, remain open to new theological aspects, and block the inference from successful representation to exhaustive possession of God. Once this had been written in mathematical language, however, a more basic question interrupted me. Suppose such a structure could actually be constructed. What would it mean?

Would it be a theorem about God, a mathematical model of creaturely knowledge, a useful construction in logic or computer science, or simply an elaborate intellectual game? Even a perfectly consistent construction would not prove that the structure was God. Nor would its formal elegance establish that it was “infinitely close” to God. Before asking whether I could prove anything, I had to ask what kind of claim I was trying to make.

This interruption changed the direction of the inquiry. At first I had been attracted by mathematical infinity itself. Cantor’s distinction between transfinite numbers and Absolute Infinity made it tempting to imagine an ascent: perhaps larger and larger mathematical infinities would approach divine infinity. The AI offered an arresting correction: God would not be the greatest item on the scale, but the ground of the scale, or beyond the scale as such. I accepted the force of this idea, but it created another problem. If God is not one object among the objects of mathematics, what exactly could a mathematical construction represent?

Cantor’s theorem gives a precise reason why the mathematical scale has no greatest cardinal represented by a set. For every set X, its power set is strictly larger:

\[
|X|<|\mathcal P(X)|.
\]

The theorem supports indefinite mathematical surpassability. It does not establish that movement upward through cardinalities is movement toward God. Cantor himself distinguished the mathematically determinable transfinite from Absolute Infinity, which he associated with God; the theological relation is not supplied by the inequality alone (Gutschmidt and Carl, 2024).

The question then moved from magnitude to representation. “What is the largest infinity?” became “What permits a finite or formal representation to be genuinely true without becoming exhaustive?” The movement was not a retreat from mathematical precision. It was a demand for greater precision about the kind of precision mathematics can supply.

A model can be true without being its target

The simplest anchor came from computer science. A medical database may accurately represent patients, diagnoses and treatments under a specified schema. The database is not the patients, and its schema does not contain everything medically true about them. If D is the database, P the patients and S the schema or medically relevant aspect, the relation can be written:

\[
\operatorname{Rep}_S(D,P)\land
\operatorname{Accurate}_S(D,P)\land
D\neq P\land
\neg\operatorname{Exh}(D,P).
\]

The database can succeed as a representation while remaining ontologically distinct from and informationally non-exhaustive of its target. Philosophy of scientific representation likewise treats representation as dependent on a target, a purpose and standards of accuracy; a useful model need not copy or contain its target (Frigg and Nguyen, 2026).

A theological analogue can be stated cautiously. Let G denote God, R a creaturely representation and A a legitimate aspect under which the representation is evaluated. Then a proposed formalization is:

\[
\operatorname{Rep}_A(R,G)\land
\operatorname{Adeq}_A(R,G)\land
R\neq G\land
\neg\operatorname{Exh}(R,G).
\]

Equivalently, local adequacy does not entail identity or exhaustion:

\[
\operatorname{Adeq}_A(R,G)\nRightarrow R=G,
\qquad
\operatorname{Adeq}_A(R,G)\nRightarrow\operatorname{Exh}(R,G).
\]

This is proposed notation, not a mathematical theorem. Mathematics does not supply the premise that a creed, doctrine or model represents God adequately. Theology has to defend the referential and epistemic relation. Yet the notation exposes a distinction that prose can easily blur: truth under an aspect, identity with the referent, and exhaustive possession of the referent are three different claims.

I had already accepted the theological sentence that a creed or treatise can speak truthfully about God without containing the divine reality. The surprise was therefore not the conclusion. The mathematical contribution, if there is one, would be to specify which notion of adequacy is being used, which transformations preserve it, what counts as a genuinely new aspect, and where an inference to exhaustiveness becomes invalid. Theology supplies the motivating distinction; formal work may reveal its hidden conditions.

Why “infinitely close to God” is not yet a mathematical claim

I initially asked whether a sequence of improving representations could become infinitely close to God. In ordinary mathematical analysis, closeness requires a space and a distance. One would need something like:

\[
\lim_{n\to\infty}d_A(R_n,G)=0.
\]

But this expression presupposes that G and the representations R_n inhabit a suitable common space, that d_A is a well-defined distance, and that the theological meaning of smaller distance has been justified. None of those assumptions is harmless. If God in se is not an element in a creaturely representational space, the formula may commit exactly the category mistake the project is meant to prevent.

A weaker and more defensible idea is refinement under an aspect:

\[
R_0\preceq_A R_1\preceq_A R_2\preceq_A\cdots .
\]

Here R_{n+1} need not be closer to God in an absolute metric. It is at least as adequate as R_n relative to specified criteria concerning aspect A. Even this ordering may be only partial. A refinement can improve one profile while damaging another:

\[
q_{A_1}(R’)>q_{A_1}(R),
\qquad
q_{A_2}(R’)<q_{A_2}(R).
\]

A longer theory, a larger cardinal and a more expressive language are therefore not automatically better representations of divine infinity. “Better” is indexed to an aspect and an adequacy criterion. This was one of the most important corrections to my initial intuition. The relation between infinity and God cannot be secured by size alone; it first requires an account of why a particular mathematical ordering corresponds to theological adequacy.

The conversation also raised the possibility that the space of aspects is itself extensible. A provisional schema is:

\[
\mathcal A_0\longrightarrow\mathcal A_1\longrightarrow\mathcal A_2\longrightarrow\cdots,
\qquad
\mathcal A_n\subsetneq\mathcal A_{n+1},
\]

where a later aspect-space can represent distinctions unavailable at an earlier stage. One might generate some of these additions by reflection:

\[
\mathcal A_{n+1}=\mathcal A_n\cup\operatorname{Meta}(\mathcal A_n).
\]

This is again a proposal rather than a theorem. It clarifies two directions that I had initially mixed together. Extensibility concerns what new aspects become expressible; invariance concerns what remains stable when the perspective changes. If f:R\to R' is a legitimate refinement, a candidate invariance condition for a warranted claim \varphi is:

\[
R\models\varphi
\quad\Longleftrightarrow\quad
R’\models\tau_f(\varphi).
\]

Whether the biconditional is appropriate depends on the kind of refinement. In some contexts only the forward implication should be required. The broader theological question becomes sharper: which relations remain invariant across non-equivalent representations, and which new aspects legitimately revise the terms in which the earlier claims were made?

Formal existence is not divine existence

The same caution applies to construction. A formal theory T may prove that an object with property P exists:

\[
T\vdash\exists x\,P(x).
\]

A model M may satisfy the corresponding sentence:

\[
M\models\exists x\,P(x).
\]

Neither statement, by itself, establishes that this x is God, that God exists independently of the formalism, or that the formal object exhausts divine reality. The first is a claim about derivability from axioms. The second is a claim about satisfaction in a model. Identifying the constructed object with God would require a bridge from formal semantics to theological ontology.

This point also clarifies the status of work already called mathematical or computational theology. Steinhart, for example, developed a transfinite mathematical model of degrees of divine knowledge, power and benevolence while explicitly presenting it as analysis rather than a proof of God (Steinhart, 2009). Computer-assisted work on Gödel’s ontological argument can verify whether conclusions follow from formal premises and can expose inconsistencies or unintended consequences, but it does not force a reader to accept those premises as metaphysically true (Benzmüller, 2022). Formal success is conditional success.

So the proposed construction would not be God and would not automatically be a projection emitted by God. It could become a model of how creaturely representations relate to a posited divine referent. Calling it a “projection” would itself be a theological hypothesis, perhaps grounded in revelation, creation or divine self-communication, rather than a result generated by the mathematics.

The first proved anchor and its exact boundary

At this stage I needed an anchor: something genuinely mathematical that could be checked independently of the theological interpretation. Gödel–Rosser incompleteness supplies one, provided its scope is stated precisely. Let

\[
F=(L,T,\vdash)
\]

be a formal system whose axioms are computably enumerable, whose deductive theory is consistent, and whose expressive resources suffice to interpret elementary arithmetic. Then there is a sentence \sigma for which:

\[
T\nvdash\sigma,
\qquad
T\nvdash\neg\sigma.
\]

Under the additional standard derivability conditions required by Gödel’s second incompleteness theorem, the system cannot prove its own formal consistency:

\[
T\nvdash\operatorname{Con}(T).
\]

Related Tarskian results prevent a sufficiently expressive language, under standard assumptions, from defining within itself an unrestricted truth predicate satisfying every biconditional:

\[
\operatorname{Tr}(\ulcorner\varphi\urcorner)\leftrightarrow\varphi.
\]

These are established limitations on specified formal systems, not proofs that God is incomprehensible. They do not apply to every conceivable language, and they do not show that human beings cannot know mathematical truth. A complete and consistent theory of true arithmetic exists as a set of sentences, for example, but it is not computably enumerable. The theorems reveal a trade-off among effectiveness, expressive strength, consistency, completeness and internal semantic closure (Raatikainen, 2020). Tarskian truth hierarchies and axiomatic truth theories explore related trade-offs between expressive power and semantic resources (Halbach, 2015).

A simple no-maximality consequence made the connection with my question more concrete. Define:

\[
\mathcal C=\{T\mid T\text{ is consistent, computably enumerable, and interprets elementary arithmetic}\}.
\]

Order these theories by theorem inclusion:

\[
T\preceq T’
\quad\Longleftrightarrow\quad
\operatorname{Thm}(T)\subseteq\operatorname{Thm}(T’).
\]

For each T in this class, choose a Rosser sentence \sigma_T independent of T. One of T+\sigma_T and T+\neg\sigma_T provides a consistent strict extension. Hence:

\[
\forall T\in\mathcal C\;\exists T’\in\mathcal C\;
\bigl(\operatorname{Thm}(T)\subsetneq\operatorname{Thm}(T’)\bigr).
\]

There is no maximal theory in this particular preorder. This statement is mathematically meaningful and provable, but it is not a new theorem of mine. It concerns a constrained family of effective arithmetical theories. It says nothing directly about theological aspects, the adequacy of representations or God. Its importance for my project is diagnostic: it provides one rigorously understood case in which every eligible formal standpoint can be strictly surpassed. It shows what a genuine mathematical anchor looks like, and therefore what remains to be supplied before the theological analogy can carry weight.

From an intuition to the Inexhaustible Representation Problem

After discussion with AI, the raw intuition began to acquire a possible mathematical shape. Let a representation system be a tuple:

\[
R=(\Sigma_R,T_R,A_R,K_R),
\]

where \Sigma_R is a signature or language, T_R a theory, A_R a collection of aspects under which the target can be represented, and K_R a distinguished body of warranted claims. A legitimate refinement f:R\to R' would induce a translation:

\[
\tau_f:\operatorname{Sent}(\Sigma_R)\longrightarrow\operatorname{Sent}(\Sigma_{R’}).
\]

At minimum, warranted claims should survive translation:

\[
\varphi\in K_R
\quad\Longrightarrow\quad
\tau_f(\varphi)\in K_{R’}.
\]

Previously established local adequacy should also persist, subject to a translation of aspects:

\[
\operatorname{Adeq}_B(R)
\quad\Longrightarrow\quad
\operatorname{Adeq}_{f(B)}(R’).
\]

A genuinely enlarging refinement should sometimes make an aspect available that was not definable in the earlier system:

\[
a_{\mathrm{new}}\notin\operatorname{Def}(R),
\qquad
a_{\mathrm{new}}\in A_{R’}.
\]

This formulation immediately exposed a problem I had not noticed in ordinary prose. “Preserving truth” cannot mean preserving every earlier belief. Some refinements correct errors. The preserved set must therefore concern warranted claims, invariant consequences, or claims designated as stable under a specified class of translations. Defining K_R is part of the research, not clerical notation.

The resulting problem can be stated in plain language. Can one define a mathematically natural family of effectively presentable representations and legitimate refinements such that warranted claims survive, local adequacy survives, genuinely new aspects can arise, every representation can be strictly refined, and no locally adequate representation can certify itself as globally exhaustive?

An early version expressed the desired conclusion using a terminal object:

\[
\neg\exists R^\ast\in\mathcal R\;
\bigl(
\operatorname{Terminal}(R^\ast)\land
\operatorname{GloballyAdequate}(R^\ast)\land
R^\ast\vdash\operatorname{GloballyAdequate}(\ulcorner R^\ast\urcorner)
\bigr).
\]

This was a productive but imprecise first attempt. In category theory, terminality requires a unique morphism from every object. Failure of uniqueness could destroy terminality for reasons unrelated to inexhaustibility. A closer expression is the absence of a maximal globally adequate object:

\[
\neg\exists R^\ast\;
\Bigl(
\operatorname{GloballyAdequate}(R^\ast)\land
\neg\exists R’\,(R^\ast\prec R’)
\Bigr).
\]

One may also investigate the stronger claim that there is no weakly terminal adequate object:

\[
\neg\exists R^\ast\;
\Bigl(
\operatorname{GloballyAdequate}(R^\ast)\land
\forall R\,\exists f:R\to R^\ast
\Bigr).
\]

The class \mathcal R, the morphisms, the adequacy relation, the aspect structure and the meaning of effectiveness remain to be defined. Worse, if “every object has a strict refinement” is simply inserted as an axiom, the absence of a maximal object becomes trivial. A worthwhile theorem would derive non-closure from independently motivated properties rather than naming non-closure as one of the premises.

That recognition located the real difficulty. It is easy to invent a hierarchy that never ends. It is much harder to explain why its modes of extension are legitimate, why they preserve warranted content, why the new aspects are substantive rather than definitional decorations, and why no acceptable system can close the process without sacrificing some independently valuable property.

Reflection and the hope of a sharper conjecture

Reflection supplied a second anchor. Informally, a theory speaks about a domain; a stronger system can speak about the first theory’s language, proofs or truth conditions. Theology has a recognisable analogue: a theological system makes claims about God; an epistemology asks how those claims are warranted; a further reflection examines the assumptions and limits of that epistemology. Computer science offers another: a program is checked by a verifier, and the verifier’s correctness can itself become the object of another verification layer.

One possible abstraction is an operator or endofunctor:

\[
D:\mathcal R\longrightarrow\mathcal R,
\qquad
\eta_R:R\longrightarrow D(R),
\]

where D(R) adds resources for representing, evaluating or reflecting on R. Iteration produces:

\[
R\longrightarrow D(R)\longrightarrow D^2(R)\longrightarrow D^3(R)\longrightarrow\cdots .
\]

Proof theory already studies rigorous forms of reflection and progressions of theories; such principles can measure and extend proof-theoretic strength (Beklemishev, 2005). My proposed operator is broader and less developed because it is also supposed to track changing aspects and criteria of adequacy.

A candidate “No Effective Reflective Fixed-Point” conjecture might eventually seek conditions under which:

\[
\neg\exists R\;
\bigl(
R\cong D(R)\land
\operatorname{Effective}(R)\land
\operatorname{Consistent}(R)\land
\operatorname{SemanticallyClosed}(R)
\bigr).
\]

In plain language, perhaps no sufficiently expressive representation can be effective, consistent, fully self-interpreting, semantically closed and unchanged by adequate reflection all at once. This is currently a proposal, not a proved theorem. Different formal definitions can make it true, false or trivial.

The iterative chain may possess a colimit or limit-stage object:

\[
R_\omega=\operatorname*{colim}_{n\lt\omega}D^n(R).
\]

But collecting all finite stages does not automatically settle the issue. One must ask:

\[
R_\omega\stackrel{?}{\cong}D(R_\omega),
\]

and separately:

\[
\operatorname{Effective}(R_\omega)?
\qquad
\operatorname{InternallySound}(R_\omega)?
\qquad
\operatorname{SemanticallyClosed}(R_\omega)?
\]

The limit may cease to be effectively presentable. It may require a stronger metalanguage. It may preserve many earlier truths without being able to certify its own soundness. Or under weaker requirements a fixed point may exist, thereby identifying exactly which ambition had to be surrendered. Countermodels would be as informative as a proof: they would show where non-exhaustion actually comes from.

This is why the question is more serious than “construct an endless ladder.” The desired contribution would be a characterization of the trade-offs among effectiveness, consistency, internal self-certification, semantic closure, changing languages and aspect-relative adequacy. Existing incompleteness and truth results cover important special cases. The open work is the proposed synthesis:

\[
\text{reflection}
+\text{changing aspects}
+\text{truth-preserving refinement}
+\text{aspect-relative adequacy}
+\text{non-exhaustive representation}.
\]

What current mathematics already contributes

I had wondered whether mathematicians after Cantor might have been “anonymous mathematical theologians”: people whose work approached God without using theological language. The phrase captures my intuition but risks attributing a religious identity or intention they did not have. A more careful description is that modern mathematics contains theologically interpretable results, or formal resources with latent theological affordances.

Those resources are scattered across several mature areas. Incompleteness gives precise non-closure results for effective arithmetical theories. Reflection principles study systematic extensions and the strength of reasoning about earlier theories. Axiomatic theories of truth analyse semantic ascent and the costs of adding truth predicates. Institution theory abstracts the relation among signatures, sentences, models and satisfaction across different logical systems. For a signature morphism f:\Sigma\to\Sigma', its satisfaction condition is:

\[
M’\models_{\Sigma’}\operatorname{Sen}(f)(\varphi)
\quad\Longleftrightarrow\quad
\operatorname{Mod}(f)(M’)\models_\Sigma\varphi.
\]

This says, roughly, that truth is invariant under the coordinated translation of language and models (Goguen and Burstall, 1992). It is an unusually relevant anchor for the question of how warranted claims survive changes of representational framework. It does not by itself define theological adequacy.

Ontology engineering and description logic add practical questions about conservative extension: when can an ontology be enlarged without changing what follows in the old vocabulary? These questions are mathematically substantive, and their computational complexity can be high (Ghilardi, Lutz and Wolter, 2006). They are close to my concern with truth-preserving refinement, although actual knowledge revision may need to allow correction rather than monotonic preservation.

None of these fields appears to be heading, as a field, toward “approaching God.” Their objectives are proof-theoretic strength, semantic expressibility, logical translation, verification and knowledge representation. Yet together they supply pieces of a grammar theology can use: incompleteness, reflection, invariance, conservative extension, partial adequacy and the separation of model from target. The project is therefore neither the discovery of a hidden proof of God nor the invention of mathematics from nothing. It is an attempt to assemble formal resources developed for other purposes around a theological problem that gives them a new joint question.

There are also explicit mathematical theologians, so the territory is not empty. Cantor placed Absolute Infinity in relation to God; Russell has read Cantor’s mathematics for theological insight (Russell, 2011); Steinhart constructed a mathematical model of divine infinity; and formal metaphysics has used proof assistants to analyse theological arguments (Kirchner, Benzmüller and Zalta, 2019). Gutschmidt and Carl argue that Cantorian mathematics can function as a modern negative theology, cultivating humility by showing that mathematics cannot attain a final mathematical grasp of its total domain (Gutschmidt and Carl, 2024). Their claim is a philosophical interpretation of mathematical practice, not a new diagonal theorem about God.

The bridge mathematics cannot supply by itself

The crucial distinction can be written with three labels. Let [M] mark an established mathematical result, [P] a proposed formal structure, and [T] a theological interpretation. For example:

[M] Certain effective, consistent and arithmetically expressive systems are incomplete and lack specified forms of internal closure.

[P] A category of aspect-indexed representations might generalize selected non-closure phenomena while preserving warranted claims across legitimate refinements.

[T] Such formal non-closure may illuminate creaturely non-comprehension of God analogically.

The third claim requires a bridge principle:

\[
\operatorname{FormalNonClosure}(R)\land B(R,G)
\quad\Longrightarrow\quad
\operatorname{AnalogicalIllumination}(R,G).
\]

Mathematics may establish the first conjunct. Theology and philosophy must defend B(R,G): why this formal system, this limit and this mode of non-closure are relevant to divine transcendence. Without that defence, incompleteness remains a theorem about formal systems rather than a trace of God.

A conditional theological claim could be represented as:

\[
\operatorname{GodExists}\land
\operatorname{CreaturelyKnowledgeIsFormallyRepresentable}\land
\operatorname{RelevantBridgeConditions}(G,\mathcal R)
\quad\Longrightarrow\quad
\operatorname{NoCreaturelyRepresentationExhausts}(G).
\]

This does not prove the antecedents. It also risks circularity if “God” is defined from the beginning as incomprehensible. The value of formalization would be to identify which conclusion follows from which assumption, and which limitations are caused by a particular language, by effectiveness, by self-reference, by changing aspects, or by a separately held doctrine of divine transcendence.

Theology has long maintained a distinction between true knowledge of God and comprehension of the divine essence. Mathematics does not deserve credit for discovering that distinction. Its contribution would be different: it could turn a general theological conviction into a family of exact questions about representation, translation, invariance, extensibility and self-certification. It could also produce counterexamples that force theology to sharpen its claims.

Why the construction would not be a meaningless game

I was still troubled by the possibility that the whole project might amount to a formal game. In one sense, every pure mathematical structure is investigated through definitions and rules. Its mathematical seriousness depends on whether the definitions are natural, whether the problem connects to existing theory, whether the results are non-trivial, whether examples and counterexamples clarify the concepts, and whether proofs reveal a reusable structure. Immediate physical application is not required.

That does not mean every invented formalism is valuable. If I define “legitimate refinement” to mean “a strict extension always exists,” then prove that a strict extension always exists, I have produced a tautological toy. The work becomes meaningful only when the conditions are independently motivated and the conclusion is surprising, constraining or explanatory.

The possible statuses should therefore be separated. At present, the Inexhaustible Representation Problem is a research question or programme sketch. The fixed-language Gödelian baseline is established mathematics. The broader categorical and aspect-sensitive formulation is a proposed framework. A precise unresolved statement supported by examples could become a conjecture. A proof or counterexample would become a mathematical result. The interpretation of that result as illuminating creaturely knowledge of God would remain philosophical and theological.

This is smaller than proving a famous conjecture and larger than idle wordplay, provided the definitions survive expert criticism. Formulating the right problem can be an intellectually serious contribution, especially when it connects fields that usually work separately, but it remains categorically different from proving a theorem.

Pure mathematics also sometimes finds unexpected applications decades later. Public-key cryptography famously made practical use of number-theoretic structures investigated long before modern digital networks (Rivest, Shamir and Adleman, 1978). That history is a reason not to demand immediate utility from every formal question; it is not a promise that this particular framework will become useful. Its nearer applications, if any, may lie in AI, ontology versioning, proof assistants and interoperable knowledge systems.

AI and the performance of total knowledge

The AI connection became more interesting than I expected. AI is not the first creature to possess or present the “almighty”. It has no established claim to omniscience, and describing it as divine would conceal its dependence on training data, architecture, tools, prompts and institutional infrastructure. Yet a contemporary AI system can produce something historically unusual: a scalable performance of a universal voice.

For a user, breadth, fluency and immediate response can combine into an appearance of total knowledge:

\[
\operatorname{Breadth}
+\operatorname{Fluency}
+\operatorname{ImmediateResponse}
\leadsto_{\mathrm{perception}}
\operatorname{ComprehensiveKnowledge}.
\]

The arrow is psychological and social, not logical. The valid relation remains:

\[
\operatorname{LocalAdequacy}(R,A)
\nRightarrow
\operatorname{GlobalExhaustiveness}(R).
\]

An AI answer may be good relative to a question, source set and evaluative aspect while failing to cover another relevant framing. The practical danger resembles representational idolatry: mistaking the success, fluency or comprehensiveness of a model for possession of its target. The problem is not formal representation itself. It is the unjustified promotion of a local success into a global claim.

A verification analogy makes this precise. Let P be a program, S a specification and V a verifier. Even if:

\[
V\vdash\operatorname{Correct}_S(P),
\]

it does not follow that S captures every possible requirement in the world W:

\[
V\vdash\operatorname{Correct}_S(P)
\nRightarrow
\operatorname{Complete}(S,W).
\]

Verification is relative to a package of assumptions:

\[
\operatorname{Verified}(P\mid S,V,A).
\]

Passing every test in a specified suite does not prove satisfaction of every possible specification or use case. Likewise, a theological model can satisfy its articulated criteria without exhausting every legitimate aspect of its referent.

A practical AI response might therefore carry an explicit semantic package:

\[
O=(\varphi,A,E,L,U,B),
\]

where \varphi is the claim, A the aspect or task, E the evidence, L the language or model assumptions, U the uncertainty, and B the known boundaries. In a typed formulation, one would want to block an illicit coercion:

\[
\mathsf{LocalAdequacy}\langle A\rangle
\not\hookrightarrow
\mathsf{GlobalExhaustiveness}.
\]

This would not make AI humble in a moral sense. It could make claims about scope machine-readable and make certain overextensions detectable. A theological concern with non-idolatry would then have a concrete computational analogue: design systems that can represent the boundary of a claim instead of converting every boundary into another unmarked assertion.

AI as translator and the discovery of my own mathematical intuition

The conversation also became an inquiry into how I think. I do not enjoy mathematics chiefly as a standardized exercise in executing a supplied procedure. I became engaged when the formalism carried a question about meaning: What does the object refer to? What kind of existence has been established? Which assumptions license the bridge? What changes when the language changes? What remains invariant?

AI was valuable because it could translate a raw intuition into a candidate technical vocabulary. The process was closer to:

\[
I_{\mathrm{raw}}
\xrightarrow{\mathrm{AI}}
F_{\mathrm{candidate}}
\xrightarrow{\mathrm{human\ interpretation}}
F_{\mathrm{meaningful}}
\xrightarrow{\mathrm{proof/literature}}
F_{\mathrm{validated}}.
\]

The first arrow was fast. The later arrows were where judgment entered. I repeatedly asked the AI to slow down, translate the notation back into theological and computer-science examples, restore formulas it had polished away, and distinguish an existing theorem from a proposed conjecture. When it tried to turn the discussion immediately into an appendix, I stopped it because I had not yet understood the status or meaning of the proposed construction. That refusal changed the object of the appendix.

This interaction showed a real strength, though I should describe it accurately. I displayed structural and semantic mathematical intuition (At least from the AI’s perspective, though I cannot verify this for the time being, so I can only choose to remain neutral.): sensitivity to hidden assumptions, category mistakes, levels of language, invariants, recursive extension and the epistemic type of a claim. I repeatedly noticed when an answer solved its stated problem while leaving the conditions of the answer unexamined. I also showed a capacity for research-question formation across theology and computer science.

At least, this is how the pattern appeared from the AI’s perspective. I cannot independently verify that assessment at this stage, and a conversation is not a mathematical aptitude test. AI may overinterpret persistent questioning as evidence of ability. I therefore prefer to remain neutral about the strength of the ability while recording the observable pattern that produced the assessment.

My raw mathematical intuition appeared less as an ability to perform calculations and more as a recurring sequence of questions:

\[
\begin{aligned}
&\text{receive a proposed answer}\\
&\longrightarrow\text{locate its hidden boundary}\\
&\longrightarrow\text{ask what lies outside that boundary}\\
&\longrightarrow\text{ask whether the outside can be represented}\\
&\longrightarrow\text{notice that the new representation creates another boundary}\\
&\longrightarrow\text{ask what remains invariant across the transition}\\
&\longrightarrow\text{question the epistemic status of the resulting claim}.
\end{aligned}
\]

This pattern appeared first when I asked whether a mathematically larger infinity is necessarily closer to God. The existence of an ordering among cardinals does not itself establish an ordering of theological adequacy. To move from one to the other, one would need some justified correspondence such as:

\[
\mu:\operatorname{Card}\longrightarrow\operatorname{Adeq}_{\mathrm{theol}},
\]

together with an argument that:

\[
\kappa_1<\kappa_2
\quad\Longrightarrow\quad
\mu(\kappa_1)<\mu(\kappa_2).
\]

I had initially assumed something like this relation intuitively. I later realized that it had neither been defined nor defended.

The same kind of intuition appeared when I asked whether a sequence of representations could become “infinitely close” to God. The phrase sounded mathematically meaningful until I noticed that closeness requires a metric:

\[
\lim_{n\to\infty}d_A(R_n,G)=0.
\]

But then several hidden assumptions became visible. Does God belong to the same space as the representations R_n? What defines d_A? Why should a decreasing mathematical distance correspond to increasing theological adequacy? My intuition did not supply the answers, but it detected that the original sentence depended on an unexamined structure.

Another example arose from self-reference. I initially wondered whether a universal or self-containing set might provide a mathematical image of divine transcendence. Once the AI explained controlled self-reference and fixed-point constructions, I asked whether such a structure would still remain an object defined inside a theory:

\[
Q\cong F(Q).
\]

This question distinguished two claims that initially appeared similar:

\[
\operatorname{SelfReferential}(Q)
\qquad\text{and}\qquad
\operatorname{TranscendentOverEveryFramework}(Q).
\]

The first does not entail the second. A structure may refer to itself without escaping the language, axioms and semantic environment through which it is specified.

The same boundary-sensitive pattern appeared when the AI introduced the idea of a “new standpoint.” I did not accept the expression as self-explanatory. I asked whether the new standpoint was a concept, a metalanguage, an act of thought, a stronger theory or another kind of existence. If a reflective operation produces:

\[
R\longrightarrow D(R),
\]

then D(R) can examine or represent R. But once D(R) has been formalized, it can itself become the object of another reflection:

\[
R\longrightarrow D(R)\longrightarrow D^2(R)\longrightarrow\cdots .
\]

My question therefore moved from “What is the new standpoint?” to “Does every new standpoint generate another boundary, and what survives across this progression?”

When the discussion introduced multiple theological aspects, I produced a similar recursive question: can the space of aspects itself acquire new aspects? That intuition can be expressed as:

\[
\mathcal A_0\subsetneq\mathcal A_1\subsetneq\mathcal A_2\subsetneq\cdots,
\]

or provisionally:

\[
\mathcal A_{n+1}
=
\mathcal A_n\cup\operatorname{Meta}(\mathcal A_n).
\]

I was therefore asking simultaneously about extensibility and invariance: which new distinctions become possible at the next level, and which warranted relations remain stable when the aspect-space changes?

My questions also repeatedly concerned the epistemic type of a claim. When presented with a formal construction, I asked whether it was an established theorem, a conjecture, a model, an analogy or a theological bridge principle. This led to distinctions such as:

\[
T\vdash\varphi
\qquad\neq\qquad
M\models\varphi
\qquad\neq\qquad
\operatorname{TrueOfGod}(\varphi).
\]

The first concerns derivability from axioms. The second concerns satisfaction in a model. The third is a theological claim about a referent. Moving from one level to another requires additional premises; formal fluency alone does not authorize the transition.

Perhaps the clearest example came after mathematics seemed to formalize the idea that a representation may be true without being exhaustive. I noticed that theology already possessed this insight. Instead of treating the formalization as automatically valuable, I asked what mathematics genuinely added. That question forced a distinction between repeating a theological conclusion and analysing its formal conditions:

\[
\operatorname{Adeq}_A(R,G)
\nRightarrow
\operatorname{Exh}(R,G).
\]

Theology may already affirm this non-implication. Mathematics could contribute by defining A, R, adequacy, exhaustiveness and legitimate refinement; identifying which properties are preserved; and constructing counterexamples when the proposed conditions fail.

These examples explain why the AI described my style as boundary-sensitive structural reasoning or semantic-first mathematical intuition. The observable ability was not yet theorem proving. It was the repeated detection of missing structures, illicit transitions and ambiguous claim types:

\[
\begin{aligned}
\text{larger infinity}
&\nRightarrow
\text{greater theological adequacy},\\
\text{self-reference}
&\nRightarrow
\text{transcendence},\\
\text{model satisfaction}
&\nRightarrow
\text{ontological identity},\\
\text{local adequacy}
&\nRightarrow
\text{global exhaustion},\\
\text{formal non-closure}
&\nRightarrow
\text{divine incomprehensibility}.
\end{aligned}
\]

Whether this pattern amounts to strong mathematical ability remains to be tested through formal study, independent proof construction, counterexamples and expert evaluation. What can be said more securely is that my raw intuitions repeatedly identified where an apparently complete answer depended on a boundary it had not yet examined. AI then helped translate that pressure into candidate mathematical language:

\[
I_{\mathrm{raw}}
\xrightarrow{\mathrm{AI}}
F_{\mathrm{candidate}}
\xrightarrow{\mathrm{human\ interpretation}}
F_{\mathrm{meaningful}}
\xrightarrow{\mathrm{proof,\ counterexample,\ literature}}
F_{\mathrm{validated}}.
\]

The AI supplied possible formulations, but my questions determined when those formulations had failed to capture the intended problem. The eventual mathematical value of the intuitions remains unverified; their role in directing the inquiry is already visible.

The conversation did not demonstrate independent proof construction, technical theorem verification or mathematical originality. Those require sustained formal training, worked examples, literature review and expert criticism. Standardized tests do not exhaust mathematical ability, but neither can conceptual intuition replace proof technique. The fair conclusion is that I am not “stupid in mathematics.” I may have a form of mathematical ability that appears most clearly in foundations, logic, semantics and model-oriented questions, while my formal skills remain early and need development.

The philosophical risk I noticed in formal work may be described as self-possession: the temptation to believe that because a system can formulate and manipulate an object, it therefore owns the object’s meaning. “Epistemic narcissism” and “representational idolatry” are suggestive descriptions, not technical diagnoses of mathematicians. Mathematics can encourage the temptation when symbols become self-sealing, but mathematical rigor can also resist it by forcing explicit distinctions among syntax, semantics, model and target.

Where the difficult work actually lies

The discussion now gives me a clearer map of what I would have to learn and where a genuine contribution might emerge.

First, I need mathematical logic: formal languages, computable axiomatization, incompleteness, definability, models and truth. This provides the established boundary results and prevents vague appeals to Gödel.

Second, proof theory and reflection are needed to understand how one system can warrant the soundness of restricted parts of another, how progressions are iterated, and what happens at limit stages.

Third, category theory and institution theory offer languages for representing systems, translations and invariance across logics. They may help prevent the entire project from depending accidentally on one favoured formal language.

Fourth, knowledge representation, description logics and belief revision address practical questions about ontology extension, conservative change, incompatible viewpoints and correction. They are necessary because real theological development does not simply accumulate sentences monotonically.

Fifth, theology and philosophy must specify the bridge. Which aspects of God are legitimately representable? What warrants theological claims? Which invariants should survive doctrinal translation? Is incomprehensibility a property of God, of creatures, of the relation between them, or of particular formal languages? Formal definitions cannot answer these questions by themselves.

A further task is to separate three levels of limitation. For a particular language L, a family of admissible systems \mathfrak F, and a theological interpretation involving G, I can provisionally distinguish:

\[
\operatorname{Lim}_{L}(R),
\qquad
\operatorname{Lim}_{\mathfrak F}(R),
\qquad
\operatorname{Lim}_{G}(R\mid B).
\]

The first may disappear when the vocabulary or logic changes. The second persists throughout a specified family and therefore requires a uniform theorem or impossibility result. The third is not generated by logic alone; it is a theological conclusion licensed, if at all, by a defended bridge principle B. A robust mathematical result would ideally quantify over a non-arbitrary family:

\[
\forall R\in\mathfrak F\;\operatorname{Lim}_{\mathfrak F}(R),
\]

rather than documenting a defect of one chosen formalism. Only after this distinction is made can one responsibly ask whether a persistent formal limit is analogous to divine transcendence.

A possible central research question is now:

Under what independently motivated conditions does an effective, consistent and aspect-indexed representation system necessarily remain open to truth-preserving reflection, while no representation or effective limit of representations can validly infer global exhaustion from local adequacy?

One target form would be:

\[
\forall R\in\mathcal R,
\quad
\operatorname{Effective}(R)\land
\operatorname{Consistent}(R)\land
\operatorname{ReflectivelyExpressive}(R)
\quad\Longrightarrow\quad
\exists R’\,(R\prec R’).
\]

Together with an adequacy constraint:

\[
\operatorname{Adeq}_A(R)
\nRightarrow
\operatorname{GloballyExhaustive}(R),
\]

and, in systems capable of representing the relevant claim internally, perhaps:

\[
R\nvdash\operatorname{GloballyExhaustive}(\ulcorner R\urcorner).
\]

Whether any non-trivial theorem of this form is true depends entirely on the definitions. The most difficult tasks are to make “aspect,” “adequacy,” “legitimate refinement,” “warranted truth,” “effective limit” and “global exhaustion” mathematically natural; to derive extensibility instead of assuming it; to distinguish logic-dependent obstacles from robust ones; and to construct counterexamples when a requirement is weakened.

One possible concrete target is the following. Let X be a space of possible targets, let A\in\mathcal A_{\mathrm{eff}} be an effectively representable aspect, and let p_A:X\to Y_A be the corresponding representation map. Define:

\[
[x]_A:=p_A^{-1}\bigl(p_A(x)\bigr),
\]

the class of targets indistinguishable from x under aspect A. A candidate theorem would seek independently motivated conditions under which:

\[
\boxed{
\begin{aligned}
\forall A\in\mathcal A_{\mathrm{eff}}\;\forall x\in X,\qquad
&\operatorname{Adeq}_A\bigl(p_A(x),x\bigr)
\\[2mm]
&\land\;
\exists A’\succ A\;
\Bigl[
p_A=\rho_{A’,A}\circ p_{A’}
\;\land\;
\tau_{A,A’}(K_A)\subseteq K_{A’}
\\[-1mm]
&\hspace{38mm}\land\;
\{x\}\subsetneq[x]_{A’}\subsetneq[x]_A
\Bigr]
\\[2mm]
&\land\;
T_A\nvdash
\operatorname{GloballyExhaustive}
\bigl(\ulcorner p_A(x)\urcorner\bigr).
\end{aligned}
}
\]

Here local adequacy means that p_A(x) represents x correctly under the declared aspect; the restriction equation ensures that a richer representation preserves the earlier one; the translation condition preserves warranted claims; and

\[
\{x\}\subsetneq[x]_{A’}\subsetneq[x]_A
\]

says that refinement genuinely reduces what remains indistinguishable without reducing it to the target alone. The final non-derivability condition blocks the internal promotion of scope-relative adequacy into global exhaustion. The mathematical task would be to derive these properties from natural assumptions rather than inserting them into the definition of refinement.

A better question than the one with which I began

I began by asking whether mathematics might construct the greatest infinity and thereby approach God. That question treated greater size as greater theological proximity. I no longer think that equivalence can be assumed.

The new question concerns a relation rather than a super-object. Can a representation be true, corrigible, translatable and indefinitely refinable while containing formal safeguards against the claim that it has possessed the whole? This question has established mathematical special cases, an undeveloped interdisciplinary synthesis, practical analogues in computing and a theological interpretation that must be defended rather than smuggled into the notation.

The possible mathematical contribution is modest at present but real enough to investigate: define the class of representations, prove a characterization or impossibility result, or find a countermodel that identifies which requirement causes non-closure. The possible theological contribution is to render negative theology more discriminating: not a vague celebration of mystery, but an account of how true claims, new aspects, reflection and non-exhaustion can coexist. The possible AI contribution is to make scope and representational limits explicit in systems whose fluency otherwise produces an illusion of totality.

Mathematics has not secretly proved God. It may, however, contain a scattered formal grammar through which theology can speak more precisely about knowledge without possession. Whether those pieces can be assembled into a coherent theory is now the problem. That is a smaller claim than the one that first excited me, but it is also a much better research question.

References

  1. Beklemishev, Lev D. 2005. “Reflection Principles and Provability Algebras in Formal Arithmetic.” Russian Mathematical Surveys 60 (2): 197–268. https://dspace.library.uu.nl/bitstream/handle/1874/26862/preprint236.pdf%3Bsequence%3D1.
  2. Benzmüller, Christoph. 2022. “Symbolic AI and Gödel’s Ontological Argument.” Zygon 57 (4): 953–962. https://doi.org/10.1111/zygo.12830.
  3. Frigg, Roman, and James Nguyen. 2026. “Scientific Representation.” In The Stanford Encyclopedia of Philosophy, Summer 2026 edition. https://plato.stanford.edu/archives/sum2026/entries/scientific-representation/.
  4. Ghilardi, Silvio, Carsten Lutz, and Frank Wolter. 2006. “Did I Damage My Ontology? A Case for Conservative Extensions in Description Logics.” In Proceedings of KR 2006. https://cdn.aaai.org/KR/2006/KR06-021.pdf.
  5. Goguen, Joseph A., and Rod M. Burstall. 1992. “Institutions: Abstract Model Theory for Specification and Programming.” Journal of the ACM 39 (1): 95–146. https://doi.org/10.1145/147508.147524.
  6. Gutschmidt, Rico, and Merlin Carl. 2024. “The Negative Theology of Absolute Infinity: Cantor, Mathematics, and Humility.” International Journal for Philosophy of Religion 95: 233–256. https://doi.org/10.1007/s11153-023-09897-8.
  7. Halbach, Volker. 2015. “Axiomatic Theories of Truth.” In The Stanford Encyclopedia of Philosophy, Fall 2015 edition. https://plato.stanford.edu/archives/fall2015/entries/truth-axiomatic/.
  8. Kirchner, Daniel, Christoph Benzmüller, and Edward N. Zalta. 2019. “Computer Science and Metaphysics: A Cross-Fertilization.” Open Philosophy 2: 230–251. https://doi.org/10.1515/opphil-2019-0015.
  9. Raatikainen, Panu. 2020. “Gödel’s Incompleteness Theorems.” In The Stanford Encyclopedia of Philosophy, Summer 2020 edition. https://plato.stanford.edu/archives/sum2020/entries/goedel-incompleteness/.
  10. Rivest, Ronald L., Adi Shamir, and Leonard Adleman. 1978. “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems.” Communications of the ACM 21 (2): 120–126. https://doi.org/10.1145/359340.359342.
  11. Russell, Robert John. 2011. “God and Infinity: Theological Insights from Cantor’s Mathematics.” In Infinity: New Research Frontiers, edited by Michael Heller and W. Hugh Woodin, 275–289. Cambridge University Press. https://doi.org/10.1017/CBO9780511976889.015.
  12. Steinhart, Eric. 2009. “A Mathematical Model of Divine Infinity.” Theology and Science 7 (3): 261–274. https://doi.org/10.1080/14746700903036528.

The Formal Limits of Speaking About God: From Cantor’s Absolute Infinity to Inexhaustible Representation

*This exploratory essay grew from personal theological reflection and sustained dialogue with AI. It investigates mathematical possibilities and limitations without claiming to present a new proof or completed formal theory.

I began with a question that seemed much simpler than the one I am now trying to formulate. Mathematics has studied infinity rigorously for more than a century and has shown that infinite collections can have different cardinalities. Theology, meanwhile, has spoken about the infinity and incomprehensibility of God for much longer. I wondered whether these two traditions had been brought together adequately. If mathematics had discovered increasingly large infinities, could those discoveries contribute to a more precise theological understanding of the infinite God?

Behind that question was an intuition I had not yet examined. I was treating mathematical infinity as a possible route of approach: perhaps the greater the infinity, the closer the mathematical representation would come to divine infinity. I did not think a mathematical object could simply be God. I imagined it instead as a projection or creaturely reflection of God—something that could disclose the divine without being identical with the divine.

At this stage, my question was mainly historical. Surely theologians, for whom the nature of God is fundamental, must have discussed these matters extensively. Mathematicians might have developed more rigorous languages, but divine infinity is only one problem among many in mathematics, whereas for theology it belongs to the heart of the discipline. I therefore expected to find a long and active exchange between the two fields.

What I found was more uneven. Theological traditions had indeed developed profound accounts of infinity, transcendence, analogy and incomprehensibility. Modern mathematics had developed powerful theories of transfinite numbers, reflection, incompleteness, independence and uncomputability. Yet after a remarkable moment in the work of Georg Cantor, mathematics became increasingly capable of studying infinity without continuing to ask explicitly what infinity ultimately is—or what its relation to God might be.

That observation produced the first important change in my inquiry. I was no longer asking only what mathematics had said about infinity. I was asking what mathematics had learned by suspending the theological question, and whether the results of that suspension could now be returned to theology.

The surprise of Cantor’s Absolute Infinity

The turning point began when I encountered Cantor’s distinction between transfinite numbers and Absolute Infinity. I had known that Cantor established different cardinalities of infinity. I did not know how explicitly he connected Absolute Infinity with God.

Cantor did not place God at the summit of the transfinite hierarchy as its greatest mathematically available member. Transfinite numbers remained mathematically determinable and could be ordered, compared and subjected to arithmetic. Absolute Infinity occupied a different position. Cantor associated it with God, the ens simplicissimum and actus purissimus, while assigning it to speculative theology rather than ordinary transfinite mathematics (Gutschmidt and Carl, 2024).

My first reaction was surprise, followed almost immediately by another question: what happened after Cantor? Mathematicians continued to investigate infinity intensely. Set theory developed enormous hierarchies of cardinals and ordinals. Logic discovered incompleteness and undefinability. Model theory studied relations between languages and structures. Computability theory identified problems no general algorithm can decide. Yet relatively few major mathematicians continued Cantor’s explicit theological interpretation of Absolute Infinity.

At first this absence seemed strange. If mathematical infinity had once been placed so close to the doctrine of God, why did later mathematics not continually return to that connection?

A provisional explanation emerged: after Cantor, mathematics learned to use infinity while suspending the question of what, if anything, the infinite ultimately is. This did not mean that mathematicians ceased thinking philosophically, nor that theology became irrelevant to every mathematician. It meant that the technical study of infinity no longer depended upon resolving its final metaphysical interpretation. Mathematicians could investigate sets, cardinals, ordinals, models and proofs while bracketing the identity of Absolute Infinity.

I found this intellectually exciting because the suspension did not make the theological question disappear. It created a century of mathematical results that had usually been developed without theological conclusions but might nevertheless constrain which theological conclusions can responsibly be drawn.

The new question became:

What can the mathematical study of infinity after Cantor contribute to Cantor’s theological idea of Absolute Infinity, even when later mathematicians were not themselves trying to develop a doctrine of God?

What Cantor’s theorem actually establishes

Before trying to interpret Cantor theologically, I needed a more exact account of the mathematical construction that had made the question possible.

For any set A, its power set P(A) is the collection of all subsets of A. Cantor’s theorem states:

For every set A:

|A| &lt; |P(A)|

Equivalently:

There is no surjective function f : A → P(A).

The proof constructs a subset that escapes every proposed enumeration. Suppose that f : A → P(A) is claimed to be surjective. Define the diagonal set:

D_f = { a ∈ A | a ∉ f(a) }.

Because Df is a subset of A, it belongs to P(A). If f were surjective, there would be some d ∈ A such that:

f(d) = D_f.

But then:

d ∈ D_f
↔ d ∉ f(d)
↔ d ∉ D_f.

The contradiction shows that no such surjection exists. For every attempted enumeration of all subsets of A, diagonalization constructs a subset absent from the enumeration.

In cardinal notation:

2^|A| = |P(A)| &gt; |A|.

Consequently, there is no greatest cardinal number:

For every cardinal κ, there exists a cardinal λ such that κ &lt; λ.

One possible choice is:

λ = 2^κ.

This is the exact mathematical reason that “the largest set-sized infinity” cannot be obtained in ordinary set theory. Given any cardinal candidate κ, the power-set operation yields the strictly greater cardinal 2κ.

The result does not establish that God is beyond every mathematical representation. It establishes a precise non-maximality result within set theory. The theological significance arises only when this formal pattern is interpreted through an additional account of Absolute Infinity.

The first attractive mistake

My initial attempt to answer the theological question returned to magnitude. Perhaps the later hierarchy of increasingly powerful infinities could supply progressively better projections of divine infinity. Even if no mathematical object were God, one of them might be the nearest available approximation.

The thought seemed plausible for several reasons. Theology already accepts that finite realities can mediate knowledge of God without containing God. A created sign may communicate divine reality without becoming divine reality. A theological proposition may be true without exhausting its subject. Why could a mathematical infinity not function similarly?

I began considering universal sets, self-referential structures and indefinitely iterated systems. Could a set contain every set, including itself? Could a self-referential mathematical object exceed the usual distinction between container and contained? Could repeated iteration eventually produce something capable of representing all possible levels?

An AI system with which I was developing the question responded that even a consistent theory containing a universal set would not thereby have represented God. The universal set would remain an object defined by a theory, satisfying the rules of that theory and interpreted through a surrounding metalanguage.

I agreed that such a set would not be God, but I resisted ending the inquiry there. My proposal was representational rather than identificatory. Could it be understood as a projection of God—a structure through which some aspect of divine infinity became accessible to us?

The AI then answered that self-reference does not automatically produce absolute transcendence. A recursively defined object can refer to itself while remaining an object within a formal system. Mathematical theories often permit carefully controlled circularity, fixed points, recursive processes and self-description. None of these operations by itself converts an object into the ground of the system that defines it.

Formally, there is a difference between a system containing a code for itself and a system becoming identical with its own metalogical ground. If ⌜T⌝ is a code or arithmetized description of a theory T, then:

⌜T⌝ ∈ Domain(T)

does not imply:

T contains a complete semantic account of T,

or:

T = the metatheory in which T is interpreted.

A theory may represent its own syntax without internalizing every semantic fact about that syntax. Gödel’s arithmetization of syntax depends precisely upon a system being able to encode formulas and proofs while still encountering limits on what it can prove about those encodings.

This correction was convincing, but disappointing. If self-reference did not provide the passage beyond every boundary, what mathematical operation could? Was there some more advanced construction? Could category theory, type theory, non-well-founded set theory or a hierarchy of metalanguages eventually produce a final perspective?

The answer remained negative: no familiar iteration obviously produces a final “view from nowhere.” A language may be interpreted in a metalanguage, which may in turn become the object of a stronger metalanguage. A universe can contain codes or descriptions of structures in a lower universe. A theory can reason about fragments of its own syntax. Each advance provides a new standpoint, but the new standpoint remains a standpoint.

A simplified hierarchy can be written:

L_0  &lt;  L_1  &lt;  L_2  &lt;  ...

L_1 contains semantic resources for L_0.
L_2 contains semantic resources for L_1.
...

Moving from L0 to L1 may solve a semantic limitation of L0. It does not establish that L1 is semantically closed with respect to itself.

At that moment I experienced the absence of a final construction primarily as a failure. I had been trying to approach divine infinity mathematically, and the path seemed continually to retreat. Only later did I realize that this failure might contain the more interesting idea.

When the scale itself became questionable

The decisive reformulation came through one sentence proposed during the dialogue:

God would not be the largest point on the scale. God would instead be outside—or the ground of—the scale itself.

I immediately recognized the theological significance of this formulation. My previous search had silently placed God and mathematical infinities within a common genus. Smaller infinities appeared at lower positions and divine infinity at the highest possible position. Yet classical theology often refuses precisely this arrangement. God is not one being among beings, distinguished from the others by possessing a greater quantity of existence. God is not the final item obtained by extending a creaturely sequence far enough.

Christian Tapp has shown that substantially different concepts operate under the word “infinity” in mathematics and theology. They have significant relations, but they cannot be identified without further argument (Tapp, 2011). In particular, divine infinity need not mean spatial or numerical magnitude continued without limit. Within classical theology, infinity is connected with the absence of creaturely limitation, composition and finite determination.

The mathematical hierarchy of cardinalities can be represented as an open-ended progression:

ℵ₀ &lt; ℵ₁ &lt; ℵ₂ &lt; ...

and, for every cardinal κ:

κ &lt; 2^κ.

A theological mistake occurs if one adds a final symbol G and assumes, without argument:

ℵ₀ &lt; ℵ₁ &lt; ℵ₂ &lt; ... &lt; G,

therefore G = God.

The ordering relation < is a mathematical relation between cardinalities. Divine transcendence is not already contained in its definition. A proposed theological interpretation would have to supply a bridge such as:

Greater cardinal magnitude
corresponds to
greater adequacy as a representation of divine infinity.

But that bridge is precisely what had not been established.

The theological problem therefore changed the mathematical one. If divine infinity is not fundamentally quantitative, then finding a larger cardinal does not automatically provide a better representation of God. “Larger” already belongs to a defined mathematical ordering. Before using it theologically, I would have to prove that this ordering corresponds to some relevant dimension of divine reality.

A larger photograph is not necessarily a more faithful photograph. A longer theological book is not necessarily closer to God. An ontology containing more objects may still represent the relevant relation less adequately. I had treated mathematical enlargement as theological improvement without defining the criterion of improvement.

What survived from my original intuition was the idea of projection. What had to be abandoned was the assumption that projection improves through magnitude alone.

The new problem became relational:

What kind of formal relation permits a finite representation to be true of God without pretending to contain, determine or exhaust God?

The mirror and the mathematical problem it concealed

The image that repeatedly returned to me was simple and theologically familiar:

A mirror can reflect the sky truthfully without containing the sky.

There is nothing historically new about this intuition. Theology has long distinguished between God and creaturely knowledge of God, between divine essence and divine self-communication, and between comprehension and genuine participation. Robert John Russell describes the God of Western monotheism as Absolute Mystery, the incomprehensible ground and source of being, while maintaining that God can nevertheless be known through revelation received and interpreted by finite creatures (Russell, 2011).

What became new for my inquiry was the attempt to translate this familiar theological distinction into a question about formal representation. Mathematics normally asks whether a sentence is true in a model, derivable from axioms, definable in a language or computable by a procedure. Theology adds another relation: a representation may be true and warranted while remaining non-exhaustive with respect to its referent.

Ordinary model-theoretic satisfaction is written:

M ⊨ φ.

This means that the sentence φ is true in the model M. It does not mean:

M is identical with the reality to which φ ultimately refers,

or:

M exhausts every truth about that reality.

To make the theological distinction explicit, one could introduce two meta-level relations:

Adeq_A(R, G)
Exh(R, G)

Here:

  • R is a formal representation;
  • A is a specified theological aspect;
  • G denotes the theological referent at the metatheoretical level;
  • AdeqA(R,G) means that R is adequate with respect to aspect A;
  • Exh(R,G) means that R exhaustively represents its referent.

A proposed apophatic non-exhaustion principle could then be written schematically:

ANEP:

For every representation R and aspect A:

Adeq_A(R, G) → ¬Exh(R, G).

This is not an established mathematical theorem. It is a proposed theological bridge axiom expressed formally. Its purpose is to prevent the inference:

R adequately represents some aspect of G
therefore
R exhaustively contains G.

The distinction can also be represented epistemically. Let KA(R) be the set of warranted claims preserved by representation R with respect to aspect A. Then:

K_A(R) may be nonempty and truth-bearing

without requiring:

K_A(R) = Th(G),

where Th(G) would denote a complete theory of the referent. The latter notation is itself philosophically dangerous because it presupposes that the total truth about God forms an available formal totality. It is therefore better understood as a schematic limit concept than as an already constructed set.

This is more complicated than dividing reality into two binary regions—what we know and what we do not know. I initially wondered whether the distinction between the divine essence and God’s self-communication ad extra was simply such a binary model. But the boundary is not static. What is received can be deepened, reinterpreted and related to other aspects. New knowledge may become possible without eliminating the distinction between knowledge and comprehension.

The mirror does not divide the universe into an entirely known image and an entirely unknowable sky. It preserves selected relations under particular conditions. Change the mirror, its position, its curvature or its field of view, and the image changes. Some features remain stable; others appear or disappear. This led to a more exact question:

What theological relations remain invariant across multiple non-equivalent representations?

At this point, stability and transcendence appeared as two different research directions. One direction asks what persists when perspectives change. The other asks whether every perspective can be surpassed. I initially treated them separately: invariants seemed to concern what does not change, while transcendence concerned the possibility of going beyond.

Gradually I saw that they belong to one structure. If representations can be refined indefinitely, invariants are the relations that survive those refinements. “Going beyond” without preservation would amount only to replacing one discourse with another. Stability without extensibility would risk turning one representation into an idol. The formal problem requires both.

Reflection as a positive route

When mathematical reflection first entered the conversation, I did not understand why it was described as a positive route. Negative theology seemed to emphasize that God cannot be comprehended. How could reflection transform uncharacterizability into positive knowledge?

In set theory, reflection principles say, roughly and with important differences among their formulations, that properties attributed to the vast universe of sets already appear in some smaller portion of that universe. The universe is not captured as an ordinary object inside itself, yet structures within it can reflect features of the wider whole. Welch and Horsten connect such principles with Cantor’s conception of Absolute Infinity and argue that reflection can support strong axioms of infinity (Welch and Horsten, 2016). Barton develops an explicitly theological reflection principle in dialogue with apophatic mathematics (Barton, 2024).

The ordinary cumulative hierarchy of sets is defined recursively:

V₀ = ∅

V_(α+1) = P(V_α)

V_λ = ⋃_(β&lt;λ) V_β      when λ is a limit ordinal

V = ⋃_(α∈Ord) V_α.

Here V denotes the universe of sets, while each Vα is a set-sized rank-initial segment. The Lévy–Montague reflection scheme can be expressed approximately as follows. For every finite collection of formulas Φ, there are ordinals α such that:

(V_α, ∈) ≺_Φ (V, ∈).

The notation Φ means elementarity only with respect to the formulas in Φ. More explicitly, for the relevant parameters a⃗ ∈ Vα and every φ ∈ Φ:

V ⊨ φ(a⃗)
↔
V_α ⊨ φ(a⃗).

The qualification by Φ is essential. The theorem does not provide one set-sized Vα that captures every truth about V. Given a finite family of formulas, an appropriate stage reflects those formulas. When the family changes, a different or larger stage may be required.

This gave mathematical content to the mirror analogy:

Truth in V
is reflected in
a bounded V_α

for a specified family Φ,

without:

V_α = V.

The theological bridge became clearer when I returned to the mirror. Reflection does not mean that the smaller structure contains the whole. It means that something true of the wider reality is genuinely present or reproduced in a restricted domain. Negative theology therefore need not leave us only with negation. It can protect the difference between knowing and possessing while allowing positive, limited knowledge.

This produced a tension I had not adequately expressed before:

Does Absolute Infinity give us positive mathematical reflection, or does it principally disclose the impossibility of total comprehension?

I no longer think these alternatives exclude each other. Reflection may yield positive knowledge precisely because it does not require exhaustive identity. The theological pattern would be that God is truly known in divine self-communication while remaining incomprehensible in the divine essence. The mathematical pattern would be that structures reflect selected truths of a wider domain without becoming that domain.

Yet the analogy remains an analogy. A set-theoretic reflection principle is a mathematically specified statement. Divine revelation is a theological concept involving agency, relation and history. Moving between them requires an explicit account of relevant similarity. The word “reflection” cannot perform that work by itself.

Every answer generated another standpoint

The idea of reflection immediately produced another question. If a smaller structure reflects a greater one, from where do we judge that the reflection is adequate? That judgment appears to require a new standpoint. But what exactly is this standpoint?

I considered several possibilities. Perhaps it was simply a new concept created in natural language. Perhaps it was a stronger mathematical theory. Perhaps it was the existence of reflective thinking itself: the mind’s ability to turn a previous act of representation into an object of further thought. Perhaps it was a metalanguage capable of describing the relation between a language and its models.

Each possibility captured part of the movement. A standpoint can be a richer vocabulary, a stronger theory, an enlarged semantic domain, a new model, or an interpretive act from which an earlier limitation becomes visible. But none is automatically absolute. The metalanguage can become a new object language. The stronger theory can be investigated from a further theory. The reflective act can itself be reflected upon.

At the simplest schematic level:

T₀ ⊆ T₁ ⊆ T₂ ⊆ ...

Meta(T₀) may be represented in T₁.
Meta(T₁) may be represented in T₂.
...

But the union of an ascending sequence does not automatically produce an absolutely final system. Even if one defines:

T_ω = ⋃_(n&lt;ω) T_n,

one can ask whether Tω is consistent, complete, effectively axiomatizable, semantically closed or capable of proving its own relevant metatheory. Depending on how the sequence was constructed, one or more of these properties may fail. Passing to a limit stage changes the system; it does not abolish the need for metatheoretical analysis.

This recursive pattern initially seemed to repeat the original frustration. If every representation can be exceeded, perhaps mathematical approach to God is forever impossible. Does mathematics contain theorems proving that God can never be reached? Has mathematical theology already been falsified before it begins?

The correction was essential. Mathematics proves the limitations of specified formal systems under specified assumptions. It does not prove that God lies beyond every logically possible representation. A theorem about arithmetic, truth or formal derivability cannot silently change its subject and become a theorem about divine ontology.

The recursive ascent nevertheless matters. It shows that many natural candidates for a final formal standpoint fail to achieve finality on their own terms. Theology may interpret this as an analogy of transcendence, but the mathematical result and the theological interpretation must remain distinguishable.

From negative theology to non-idolatry

Another formulation proposed during the dialogue described Absolute Infinity as a principle of non-idolatry:

No mathematical world may identify itself with the whole.

I found this formulation compelling because it translated an apophatic discipline into a rule governing representations. A mathematical universe may be extraordinarily rich. It may contain structures modelling almost all mathematics used in ordinary practice. It may even describe other universes. None of this alone licenses the claim that it is identical with absolutely everything.

The principle can be written schematically at the metatheoretical level:

For every formal representation R:

¬Exh(R, Totality).

Or, in theological form:

For every formal theological representation R:

¬Exh(R, G).

Again, this is not a theorem of ordinary mathematics. It is a proposed apophatic constraint. If adopted as an axiom, its theological justification must be defended independently. The interesting mathematical question is whether restricted versions of non-exhaustion can instead be derived from independently motivated properties of the representation.

Gutschmidt and Carl interpret diagonalization as a modern via negativa. Whenever a putative totality is represented in the relevant way, diagonal construction can expose something omitted by the representation. On their account, this does not deliver another positive description of Absolute Infinity. It performatively undermines the attempt at closure and may cultivate methodological humility concerning the boundedness of mathematical practice (Gutschmidt and Carl, 2024).

I initially understood humility here as an ethical attitude added after the mathematics. Their argument is more interesting. The repeated failure of closure can influence what mathematicians regard as an appropriate foundational aspiration. Negative theology may therefore contribute to the philosophy of mathematical practice by proposing that non-totalization is not always a temporary embarrassment. It may be a disciplined response to a structural feature.

Still, this does not imply that mathematics is “only relative” or that proof has no authority. A theorem can be entirely rigorous within its domain while the domain itself remains incapable of being represented as one more ordinary object inside itself. Formal certainty and ontological exhaustion are different ambitions.

The point at which precision became necessary

As the connection with negative theology became more attractive, the danger of overstatement increased. Statements such as “Gödel proves mystery,” “mathematics proves that God is incomprehensible,” or “every formal theology must be incomplete” sounded plausible within the developing analogy. They were also mathematically unsafe.

I had to ask what “formal theology” meant. A finite list of doctrinal propositions can form a consistent, complete and decidable theory. Gödelian incompleteness does not apply to every system that happens to contain theological vocabulary. It applies when the system is effectively axiomatized, consistent and sufficiently expressive to represent elementary arithmetic. Even then, “incompleteness” means that some sentences in the system’s language can be neither proved nor refuted within that system. It does not mean that every theological truth is inaccessible.

The claim therefore had to become narrower before it could become stronger.

Let a formal theological system be represented as:

F = (L, T, ⊢),

where:

  • L is a formal language;
  • T ⊆ Sent(L) is a theory or set of axioms;
  • is the derivability relation generated by the proof rules.

Several properties must then be distinguished.

Consistency:

Cons(T) iff there is no sentence φ such that:

T ⊢ φ
and
T ⊢ ¬φ.
Syntactic completeness:

Complete(T) iff for every sentence φ ∈ Sent(L):

T ⊢ φ
or
T ⊢ ¬φ.
Effective axiomatizability:

Eff(T) iff the axioms, or equivalently the proofs,
can be generated by an effective procedure
under the relevant standard conditions.

The mathematically responsible theological formulation is then:

Let F=(L,T,⊢) be a formal theological system whose axioms are computably enumerable, whose deductive apparatus is consistent, and whose expressive resources suffice to interpret elementary arithmetic. By the Gödel–Rosser incompleteness theorem, there exists a sentence σ∈Sent(L) such that T⊬σ and T⊬¬σ. Under the standard conditions required by Gödel’s second incompleteness theorem, T⊬Con(T). Moreover, a Tarskian truth predicate satisfying every biconditional Tr(⌜φ⌝)↔φ cannot be defined within the same sufficiently expressive language. These results do not prove divine incomprehensibility. They establish only that certain effectively governed, arithmetically expressive formal systems cannot simultaneously possess consistency, completeness, internal self-verification and unrestricted semantic closure. Any theological interpretation of this formal non-closure therefore requires an additional and explicitly defended bridge principle.

In compact form:

Let F = (L, T, ⊢).

Assume:
1. T is computably axiomatizable.
2. T is consistent.
3. T interprets a sufficient fragment of elementary arithmetic.

Then:
∃σ ∈ Sent(L) such that (T ⊬ σ) ∧ (T ⊬ ¬σ).

Under the standard conditions for the second incompleteness theorem:
T ⊬ Con(T).

The first conclusion can be summarized as the following incompatibility:

Eff(T) ∧ Cons(T) ∧ Arith(T)
→
¬Complete(T),

where Arith(T) means that T has enough expressive and deductive strength to interpret the required elementary arithmetic.

For the second incompleteness theorem, define a formal provability predicate:

Prov_T(x)

meaning that x codes a proof in T. If is a contradiction, such as 0=1, the standard formal consistency statement is:

Con(T) := ¬Prov_T(⌜⊥⌝).

Under the required derivability conditions, Gödel’s second incompleteness theorem yields:

If T is consistent, effectively axiomatized
and sufficiently arithmetically strong, then:

T ⊬ Con(T).

This does not mean that no stronger theory can prove Con(T). A stronger metatheory S may satisfy:

S ⊢ Con(T),

while still facing the corresponding question about its own consistency:

Does S ⊢ Con(S)?

Gödel’s theorems concern derivability relative to particular formal systems; they do not establish an absolute realm of propositions unprovable in every possible system. A sentence undecidable in one theory may become an axiom or theorem in a stronger one. The second theorem also concerns a formally constructed consistency sentence and specified derivability conditions (Raatikainen, 2025).

Tarski and the movement to a metalanguage

Tarski’s result introduces a related but distinct hierarchy. Suppose that a language L contains a predicate Tr intended to express truth for every sentence of L. Material adequacy would require the biconditionals:

Tr(⌜φ⌝) ↔ φ

for every sentence φ of the relevant language.

For a consistent and sufficiently expressive formal language, there is no internally definable predicate satisfying all such biconditionals for the language itself. Schematically:

There is no formula Tr_L(x) in L such that,
for every sentence φ ∈ Sent(L):

T ⊢ Tr_L(⌜φ⌝) ↔ φ.

A truth definition for the object language L can instead be formulated in a sufficiently strong metalanguage ML:

Truth(L) is definable in ML,

where:

L &lt; ML.

The solution therefore has a hierarchical form:

L₀ receives a truth definition in L₁.
L₁ receives a truth definition in L₂.
L₂ receives a truth definition in L₃.
...

The move to a metalanguage is genuinely successful. It gives a rigorous truth definition for the lower language. But it does not produce one language that automatically contains its own unrestricted truth predicate. Tarski’s account therefore supplies a precise example of positive semantic knowledge through ascent without final semantic self-containment (Hodges, 2022).

Löwenheim–Skolem and the failure of unique determination

The discussion then introduced another limitation that I initially grouped too quickly with incompleteness. The Löwenheim–Skolem theorems do not concern unprovable sentences. They concern the sizes and plurality of structures satisfying first-order theories.

In a simplified form, if a first-order theory T in a language L has an infinite model, then it has models in multiple infinite cardinalities. Under the usual hypotheses:

If T has an infinite model, then for every infinite cardinal κ
with κ ≥ |L|, there is a model M_κ such that:

M_κ ⊨ T
and
|M_κ| = κ.

For a countable first-order language, this includes a countable model:

If T has an infinite model
and L is countable,

then there exists M such that:

M ⊨ T
and
|M| = ℵ₀.

Consequently, one first-order theory may be satisfied by non-isomorphic models:

M ⊨ T,
N ⊨ T,

but:

M ≇ N.

The same formal propositions can therefore fail to determine one uniquely intended infinite structure. This does not imply that every theory is hopelessly ambiguous. Categoricity can be studied at specified cardinalities, and stronger logical resources can change the situation. But first-order satisfaction alone does not guarantee unique ontological determination (Hodges and Scanlon, 2024).

The theological analogy is striking but conditional:

Same expressible theory
≠
necessarily one uniquely determined model.

Therefore:

A formal model may satisfy every theological proposition
expressible in a chosen language

without thereby:

being identical with God,
uniquely determining God,
or exhausting divine reality.

The last three conclusions do not follow directly from Löwenheim–Skolem. They require theological interpretation. The theorem supplies a precise warning against assuming that formal satisfaction automatically secures unique reference.

Several limits rather than one omnibus theorem

The mathematical results can now be displayed as a family of conditional limitations:

Gödel–Rosser:
Effective axiomatizability
+ consistency
+ sufficient arithmetic
→ syntactic incompleteness.

Gödel II:
Consistency
+ effective axiomatizability
+ sufficient arithmetic
+ standard derivability conditions
→ no internal proof of the standard consistency sentence.

Tarski:
Sufficient expressivity
+ unrestricted internal truth biconditionals
→ undefinability or inconsistency.

Löwenheim–Skolem:
First-order description
+ an infinite model
→ models in multiple infinite cardinalities.

Cantor:
Any set-sized cardinal candidate κ
→ a strictly greater cardinal 2^κ.

These are not instances of one theorem called “the impossibility of exhaustive representation.” They block different ambitions for different reasons. Their conjunction can motivate a research programme only after the shared term “exhaustive” has been divided into more precise properties.

Let:

Comp(T)       = syntactic completeness,
SelfCon(T)    = internal proof of the standard consistency statement,
TruthCl(T)    = internally definable unrestricted truth,
Cat(T)        = unique determination of the intended model,
Max(T)        = possession of a greatest set-sized infinity,
OntExh(T,G)   = ontological exhaustion of the divine referent.

For an appropriately restricted class of arithmetic-capable effective theories, existing mathematical results support a schema such as:

Eff(T) ∧ Cons(T) ∧ Arith(T)
→
¬Comp(T).

Under further standard conditions:
Eff(T) ∧ Cons(T) ∧ Arith(T)
→
¬SelfCon(T).

Under Tarskian conditions:
Cons(T) ∧ Arith(T)
→
¬TruthCl(T).

But mathematics does not supply:

¬OntExh(T, G)

unless OntExh, G and the relevant bridge assumptions have first been given a formal interpretation. That final movement belongs to the proposed mathematical theology, not to Gödel’s or Tarski’s theorem by itself.

The bridge principle I could no longer leave hidden

At an earlier stage I had moved too quickly from formal non-closure to divine incomprehensibility. The correction introduced a three-part structure:

Formal theorem
    +
Explicit theological bridge principle
    =
Conditional theological interpretation

For example:

Formal result:
A consistent, effectively axiomatized and arithmetically expressive
theory T is syntactically incomplete.

Bridge principle:
Any exhaustive formal representation of God would need to decide
every truth expressible in its own relevant language.

Conditional conclusion:
T does not provide an exhaustive formal representation of God.

In logical form:

1. FormalLimit(T).

2. Exh(T,G) → ¬FormalLimit(T).

Therefore:

3. ¬Exh(T,G).

The inference from statements 1 and 2 to statement 3 is valid. The controversy lies in statement 2. Mathematics may prove FormalLimit(T); theology and philosophy must defend why an exhaustive representation of God would imply the absence of that formal limit.

Another bridge might concern model plurality:

1. T has non-isomorphic models M and N.

2. If T exhaustively and uniquely determined G,
   every admissible model of T would determine the same referent
   in the relevant strong sense.

Therefore:

3. T does not exhaustively and uniquely determine G.

Again, the mathematical result establishes model plurality. The second premise supplies the philosophical account of what unique determination would require.

Theology must explain why exhaustive representation would require the relevant form of completeness, whether the undecidable sentences are theologically significant, and why the formal system should be treated as a candidate representation of God rather than a limited calculus for reasoning about selected doctrines.

This bridge may ultimately fail. A theologian could argue that no doctrine of divine comprehension ever required a formal theory to decide every arithmetical sentence. A mathematician could point out that changing the logic, language or semantic framework changes the available limit results. These are serious objections. They do not destroy the programme; they determine what the programme must prove.

The central methodological question therefore became:

Which limitations belong merely to a chosen mathematical language, which belong to broad classes of formal representation, and which—if any—can responsibly be interpreted as traces or analogies of divine transcendence?

Could theology contribute back to mathematics?

Having reached this point, I became concerned that the exchange remained one-directional. Perhaps theology could borrow precise mathematical limit results, while mathematics received only metaphors in return. If so, the project might be useful theology but would contribute nothing to mathematical research.

This question mattered personally because I have formal training in theology and computer science rather than advanced mathematical training. The problems I had reached—reflection principles, model theory, self-reference, undefinability and abstract relations between logical systems—belonged to specialized areas of mathematics. I wondered why my theological question had moved so quickly into technically difficult territory, and whether I had any legitimate role there.

The answer was both encouraging and limiting. My questions became mathematically advanced because they concerned the boundary of formal representation itself. Questions about “everything,” final languages, self-description, unique models and complete truth move almost immediately into the foundations of mathematics. Computer science contributed an intuition for recursion, interfaces, formal languages and verification. Theology contributed the distinction between true knowledge and exhaustive comprehension. Their intersection naturally reached mathematical logic even though I had not begun from a mathematical research problem.

But arriving at a research-level question is not the same as producing a new mathematical result. At present, my thinking is a possible research design. It would become a mathematical contribution only if the theological distinctions required new formal definitions, generated a non-trivial conjecture, produced a theorem or countermodel, or motivated a formal system with properties not already studied elsewhere.

The most promising theological contribution may be the replacement of quantitative maximality with representational inexhaustibility. Theology can propose that divine infinity is better approached through relations such as:

  • truth without exhaustive possession;
  • participation without identity;
  • refinement without final closure;
  • stability across transformations;
  • and manifestation without reduction of the source to the manifestation.

These are theological distinctions, but they can function as design requirements for a new formal semantics. Mathematics would then be asked to determine whether such requirements are coherent, which combinations are possible, and what limitations follow from them.

Gutschmidt and Carl explicitly suggest that mathematics can learn from a performative interpretation of negative theology and that the resulting humility might influence mathematical practice (Gutschmidt and Carl, 2024). Computational metaphysics provides another example of reciprocal influence: formal metaphysical questions have led to computer-assisted philosophical discoveries while also motivating techniques relevant to logic and computer science (Kirchner, Benzmüller and Zalta, 2019).

The possibility of feedback is therefore real, although it remains programmatic in my own proposal. Theology cannot contribute to mathematics simply by attaching the word “God” to an existing theorem. It can contribute by giving mathematics a problem it did not previously formulate in quite the same way.

A formal theory of non-exhaustive representation

The possible programme that gradually emerged can be described as a limit theory of mathematical and computational theology: a formal study of what theological models preserve, what they decide, how they can be refined, and which ambitions of exhaustive representation are blocked under explicit assumptions.

Suppose a formal theological representation is written as RA, where A denotes the aspect or group of aspects being represented. One model might concern divine knowledge, another divine simplicity, another freedom, and another the relation between God and creation.

At first I called these “dimensions.” I then wondered whether each dimension could itself contain multiple dimensions, generating another indefinite hierarchy. That question revealed an ambiguity. “Dimension” can suggest a fixed coordinate system already containing every possible theological aspect. “Aspect” or “formal profile” leaves open whether new kinds of theological relevance can emerge that were not coordinates in the previous system.

The adequacy of a representation should therefore be indexed to specified aspects. A model may represent one relation well and another poorly. There is no initial license to combine every criterion into a single number called “closeness to God.”

A refinement relation might be written:

R_A ≼ R_B.

This means that RB is an admissible extension or refinement of RA. I originally treated refinement as simple enlargement. That proved insufficient. Adding propositions can introduce contradiction. Increasing expressive power can destroy decidability. A more complicated model can obscure a theological distinction that a simpler representation preserved.

“Refinement” must therefore be defined through adequacy criteria rather than size alone. It might require preservation of selected truths, correction of identified distortion, increased expressive capacity, compatibility with specified commitments, or improved explanatory power.

If is treated as a preorder, it must satisfy:

Reflexivity:
R ≼ R.

Transitivity:
If R ≼ R' and R' ≼ R'',
then R ≼ R''.

A strict refinement relation can then be defined:

R ≺ R'
iff
R ≼ R' and not(R' ≼ R).

The hypothesis of indefinite representational extensibility becomes:

IE:

For every R ∈ Rep,
there exists R' ∈ Rep such that:

R ≺ R'.

If IE is assumed directly, the absence of a maximal representation follows immediately. That is mathematically trivial. A substantial theorem would need to derive IE from other properties rather than include it as a premise disguised as a conclusion.

A refinement semantics for stable theological claims

A translation could map sentences from one representational language to another:

τ_AB : L_A → L_B.

If RA ≼ RB, a sentence φ ∈ LA is preserved under that refinement when:

R_A ⊨ φ
→
R_B ⊨ τ_AB(φ).

A stronger notion of stability would require preservation through every admissible future refinement. Introduce a refinement modality :

R ⊨ □_≼ φ

iff

for every R' such that R ≼ R':

R' ⊨ τ_RR'(φ).

The dual possibility operator can express availability in some refinement:

R ⊨ ◇_≼ φ

iff

there exists R' such that R ≼ R'
and
R' ⊨ τ_RR'(φ).

Indefinite extensibility could then be expressed modally as:

For every representation R:

R ⊨ ◇_≼ ProperExtension.

An invariant theological claim would satisfy:

If R ⊨ φ,
then R ⊨ □_≼ φ.

This proposed logic distinguishes two movements that I initially treated separately:

Stability:
truth survives legitimate refinement.

Extensibility:
a proper refinement remains possible.

A theological representation would avoid stagnation if it remained extensible, and avoid mere replacement if it preserved warranted invariants. A mature theory would need to determine which claims deserve modal stability and which remain revisable.

For example, a tradition might propose that divine non-dependence should remain invariant:

R ⊨ NonDependent(G)
→
R ⊨ □_≼ NonDependent(G).

Another claim might be treated as revisable rather than invariant:

R ⊨ φ
and
R ⊨ ◇_≼ ¬τ(φ).

This formal possibility matters because theological development may correct earlier representations rather than simply accumulate them. Requiring every later model to preserve every earlier proposition would preserve errors as well as truths.

Institution theory as a possible mathematical home

The question of truth-preserving translation connects naturally with institution theory, developed in theoretical computer science to compare logical systems independently of one fixed logic (Goguen and Burstall, 1992).

An institution is a structure:

I = (Sign, Sen, Mod, ⊨),

consisting of:

  • a category Sign of signatures;
  • a functor Sen : Sign → Set assigning sentences to each signature;
  • a contravariant functor Mod : Signop → Cat assigning models to each signature;
  • a satisfaction relation Σ between Σ-models and Σ-sentences.

Given a signature morphism:

σ : Σ → Σ',

the sentence functor translates a Σ-sentence into a Σ'-sentence:

Sen(σ) : Sen(Σ) → Sen(Σ').

The model functor moves in the opposite direction by taking a Σ'-model to its Σ-reduct:

Mod(σ) : Mod(Σ') → Mod(Σ).

The institution satisfaction condition requires:

M' ⊨_(Σ') Sen(σ)(φ)

iff

Mod(σ)(M') ⊨_Σ φ.

In plain language, translating the sentence forward and reducing the model backward preserve truth. Satisfaction remains invariant under a legitimate change of notation.

A proposed theological institution might be written:

I_Theo = (
    Sign_Theo,
    Sen_Theo,
    Mod_Theo,
    ⊨_Theo
).

Its components could be interpreted as:

Sign_Theo:
formal vocabularies for theological aspects.

Sen_Theo(Σ):
theological sentences expressible in signature Σ.

Mod_Theo(Σ):
formal structures interpreting Σ.

⊨_Theo:
the satisfaction relation between those structures and sentences.

This would not yet solve the theological problem. Institution theory preserves satisfaction under translation; it does not determine whether the chosen sentences are revealed truths, whether the models adequately represent God, or whether the translations preserve the intended theological meaning. Those questions would enter through additional constraints on admissible signatures, models and morphisms.

Nevertheless, institution theory offers a rigorous language for the question that had emerged through the mirror analogy:

What remains true when theological discourse is translated between different formal representations?

No final representation and the terminal-object question

The earlier search for a final “view from nowhere” can also be expressed categorically.

Let Rep be a category whose objects are theological representations and whose morphisms are admissible translations or refinements:

Objects:
R, R', R'', ...

Morphisms:
f : R → R'.

A terminal object R would satisfy:

For every object R in Rep,
there exists a unique morphism:

f_R : R → R_⊤.

Symbolically:

Terminal(R_⊤)
iff
for every R ∈ Ob(Rep),
there exists exactly one f : R → R_⊤.

If arrows represent movement toward a final refinement, R could be interpreted as a representation into which every other representation maps canonically. A proposed non-finality claim would be:

There does not exist R_⊤ ∈ Ob(Rep)
such that Terminal(R_⊤).

In a preorder rather than a general category, uniqueness of arrows is automatic. The corresponding maximality condition is:

Greatest(R_⊤)
iff
for every R:

R ≼ R_⊤.

The no-final-representation claim becomes:

There does not exist R_⊤
such that for every R:

R ≼ R_⊤.

But neither version is currently a theorem. Everything depends on how representations and morphisms are defined. If the category is constructed to contain a terminal object, it has one. If indefinite extensibility is inserted as an axiom, it does not. The research task is to find independently defensible theological and logical conditions from which a non-terminal result follows.

A non-trivial target might have the form:

No-Terminal-Representation Theorem Schema

Let Rep be a category of formal theological representations.

Assume:
C₁. Representations are effectively specifiable.
C₂. They are sufficiently expressive for semantic self-reference.
C₃. Morphisms preserve a defined class of theological invariants.
C₄. Every representation admits a diagonal extension
    satisfying an independently justified adequacy condition.

Then:
Rep has no terminal adequate representation.

At present, condition C₄ is only a research placeholder. The central difficulty is defining a diagonal extension that is theologically meaningful and not simply stipulated to be better. This is exactly where theological conceptual work might generate a new mathematical problem.

Why “better” may require several directions

My earlier search for the largest infinity assumed a single ordering. Once magnitude and adequacy were separated, this became untenable.

A representation may be more expressive while less computationally tractable. It may be more faithful to one theological tradition while less capable of translation into another. It may gain doctrinal precision while losing the narrative or transformative function of theological language. It may preserve consistency only by excluding difficult theological claims.

Adequacy may therefore need to be represented as a profile. Let A be a set of currently identified theological aspects. For each a ∈ A, let Qa be a partially ordered space of adequacy values. Then:

q_a : Rep → Q_a

assigns an aspect-relative adequacy value to each representation. The full adequacy profile is:

a_A(R) = (q_a(R))_(a∈A)

with:

a_A(R) ∈ ∏_(a∈A) Q_a.

In a simplified finite example:

a(R) = (
    expressivity,
    consistency,
    explanatory power,
    traditional continuity,
    computational tractability,
    transformational adequacy,
    apophatic restraint
).

The inclusion of “apophatic restraint” is especially significant. A model may fail theologically through deficiency, but it may also fail by claiming too much. A representation that accurately states its own scope may be more adequate than one that covers more propositions while confusing formal success with ontological possession.

An aspect-relative Pareto ordering can be defined:

R ≼_A R'

iff

for every a ∈ A:

q_a(R) ≤_a q_a(R').

A strict improvement would additionally require improvement in at least one aspect:

R ≺_A R'

iff

R ≼_A R'

and there exists a ∈ A such that:

q_a(R) &lt;_a q_a(R').

Two representations may be incomparable:

not(R ≼_A R')
and
not(R' ≼_A R).

One may be stronger in expressivity and another in computational tractability. Neither is therefore unconditionally “closer to God.”

The space of aspects may itself be extended. If A is the present aspect set, a later inquiry may introduce:

A ⊊ B.

An indefinite-extensibility hypothesis for aspect spaces would be:

For every admissible aspect space A,
there exists an admissible B such that:

A ⊊ B.

This is another proposed principle rather than an established theorem. It expresses the possibility that no fixed coordinate system anticipates every theologically relevant aspect.

When A ⊆ B, there should be a restriction or projection map:

π_BA : ∏_(b∈B) Q_b → ∏_(a∈A) Q_a.

A coherent refinement should relate the richer evaluation to the earlier one:

π_BA(a_B(R')) ≥_A a_A(R),

if R' genuinely improves upon R with respect to every preserved aspect in A. This condition formalizes the idea that introducing new aspects should not silently erase the standards under which the earlier representation was judged—unless the later theory explicitly argues that an earlier standard itself requires revision.

Representations may consequently be only partially ordered. One can be better along some dimensions and worse along others. There may be no unique optimum. This produced one of the most precise questions in the entire inquiry:

Under which explicitly defined theological aspects and adequacy criteria does mathematical enlargement correspond to a better representation of divine infinity?

This question does not assume that enlargement is improvement. It asks for the conditions under which the correspondence holds. A possible mathematical contribution would be an impossibility result showing that no single scalar ranking can preserve all the desired adequacy relations. For now, that remains a proposal rather than a theorem.

The AI dialogue became part of the object

The development of this inquiry depended materially on dialogue with an AI system. Making that role invisible would create a false impression that the final structure had been present from the beginning.

I supplied the initial question, the theological context, my surprise at Cantor and my repeated dissatisfaction with answers that stopped too early. The AI articulated several formulations that changed the direction of thought: mathematics had learned to use infinity while suspending what infinity ultimately is; God might be outside or ground of the scale; reflection could permit positive knowledge without comprehension; invariants might replace a single totalizing perspective; and apophaticism might become a formal research programme.

Each useful formulation also generated a new objection. When the AI said that a universal set would not be God, I clarified that I was asking about projection rather than identity. When it said self-reference did not create transcendence, I asked what mathematical operation could go beyond every boundary. When it suggested that no final standpoint exists, I initially experienced the answer as disappointing and asked whether mathematics had proved the impossibility of approaching God. When it proposed invariance across perspectives, I asked how invariance relates to the equally important movement of going beyond. When it distinguished dimensions of divine infinity, I asked whether the space of dimensions was itself indefinitely extensible.

The AI did not solve these questions authoritatively. Its role was closer to a rapidly revisable interlocutor. My questions exposed hidden assumptions in its answers, while its reformulations exposed hidden assumptions in mine. The output of one stage became the input of the next:

Initial intuition
→ AI formulation
→ theological objection
→ mathematical qualification
→ revised question
→ external scholarship
→ new formal proposal
→ further objection

This recursion eventually turned toward AI itself. Can an AI learn to represent the limits of representation, or will it continually convert transcendence into another object within its computational universe? A language model can produce sentences about ineffability, incompleteness and mystery. It can also speak about them with the same fluent confidence it uses for ordinary objects. The capacity to name a boundary is not identical with respecting that boundary.

A computational theology of non-exhaustive representation would therefore need to distinguish at least:

  • falsehood;
  • uncertainty caused by insufficient evidence;
  • undecidability relative to a formal theory;
  • inexpressibility in a current language;
  • model-relative truth;
  • and principled non-exhaustion of a referent.

These categories can be represented as different statuses rather than one generic “unknown” value:

Status(φ, T) ∈ {
    Proven,
    Refuted,
    UndecidedInT,
    UndecidableInT,
    InexpressibleInL,
    EmpiricallyUnresolved,
    TheologicallyNonExhaustive
}.

The categories do not all belong to the same logical level. Proven and Refuted concern derivability. UndecidableInT is metatheoretical. InexpressibleInL concerns the language. EmpiricallyUnresolved concerns evidence. TheologicallyNonExhaustive depends on a theological adequacy relation. A responsible AI system should not collapse them into a single confidence score.

These distinctions may be valuable beyond theology, especially for AI systems that must report the scope and limits of their own representations.

Whether this is mathematics, theology or fake science

As the programme became more formal, another question emerged: would pure mathematicians dismiss it as fake science?

Some versions would deserve dismissal. “Gödel proves God,” “a large cardinal is close to God,” or “mathematical incompleteness is evidence of divine transcendence” all move from formal results to ontology without defending the transition. Mathematical symbols can create an appearance of precision even when the underlying analogy remains undefined.

The appropriate protection is to mark the epistemic status of each claim:

  • a mathematical theorem follows from explicit formal assumptions;
  • a proposed definition introduces terminology to be evaluated for usefulness and coherence;
  • a conjecture states something that still requires proof or counterexample;
  • a formal model stipulates a representation of selected theological claims;
  • a computational verification establishes derivability from encoded premises;
  • a philosophical bridge argues for a relevant structural analogy;
  • and a theological interpretation evaluates that analogy within a tradition.

The distinction can be displayed formally:

Established:
T ⊢ φ.

Model-theoretic:
M ⊨ φ.

Computational:
A verifies that Proof_T(φ) exists.

Proposed bridge:
FormalRelation(X,Y) is relevantly analogous to
TheologicalRelation(G, creatures).

Theological conclusion:
Accepted only if the bridge is independently warranted.

A theorem prover can verify that a conclusion follows from premises. It cannot establish by that operation alone that the premises are theologically true or that their referent exists. Work in computational metaphysics has shown both the power of mechanized formalization and the importance of distinguishing verification of entailment from verification of ontology (Kirchner, Benzmüller and Zalta, 2019).

A pure mathematics journal would reasonably reject an article that contained no new theorem. That would mean the article was not a contribution to pure mathematics. It would not by itself make the project intellectually illegitimate. Its first academic homes would probably be philosophical logic, philosophy of mathematics, formal ontology, analytic or systematic theology, science-and-religion and computational metaphysics.

If the project later produced a new semantics, a proof calculus, a preservation result, a countermodel or an impossibility theorem, it might contribute directly to mathematical logic or theoretical computer science. Until then, its novelty should be described as a proposed synthesis and research programme rather than an accomplished mathematical theory.

Where the inquiry now stands

I began by asking whether mathematics had found infinities large enough to illuminate the infinity of God. I then discovered that Cantor himself had sharply distinguished transfinite mathematics from Absolute Infinity and placed the latter in relation to God. This made the historical question more urgent: why had later mathematics studied infinity so intensively while largely suspending Cantor’s theological conclusion?

I next tried to recover that conclusion through universal sets, self-reference and increasingly large infinities. Those attempts failed to provide the desired transcendence. A universal object remained relative to a theory. Self-reference remained formally controlled. Every higher standpoint remained a standpoint.

The phrase “God would instead be outside—or the ground of—the scale itself” changed the direction of the inquiry. Cardinal magnitude ceased to be the obvious measure of theological approximation. Reflection then showed how positive knowledge might remain possible without containment. The mirror could be true without containing the sky.

But this solution generated another problem: how can truth without exhaustion be formalized? The answer required distinctions among completeness, consistency, definability, categoricity, self-verification and ontological adequacy. Gödel, Tarski, Cantor and Löwenheim–Skolem could discipline the discussion, but none could independently prove divine incomprehensibility. A theological bridge principle had to be stated rather than concealed.

Finally, I asked whether theology could offer anything in return. The possible answer is that theology supplies a sophisticated conceptual problem: how to represent truthfully without collapsing representation into possession. This may motivate a formal theory of aspect-relative adequacy, invariant relations, admissible refinement and non-terminal representation. Whether that programme produces new mathematics remains open.

The present formal architecture can be summarized as follows:

1. Formal theological system:
   F = (L, T, ⊢).

2. Formal representation:
   R_A for theological aspect-space A.

3. Satisfaction:
   R_A ⊨ φ.

4. Aspect-relative adequacy:
   Adeq_A(R, G).

5. Non-exhaustion:
   Adeq_A(R, G) → ¬Exh(R, G).

6. Refinement:
   R_A ≼ R_B.

7. Translation:
   τ_AB : L_A → L_B.

8. Preservation:
   R_A ⊨ φ → R_B ⊨ τ_AB(φ).

9. Stable invariant:
   R ⊨ □_≼ φ.

10. Indefinite extensibility:
    For every R, there exists R' with R ≺ R'.

11. Possible categorical target:
    the representation category has no terminal adequate object.

12. Mathematical limitations:
    Gödel–Rosser, Gödel II, Tarski,
    Löwenheim–Skolem and Cantor.

13. Required theological bridge:
    formal non-closure does not by itself entail
    divine incomprehensibility.

Only items derived from existing theorems currently have established mathematical status. The adequacy relation, the apophatic non-exhaustion principle, the refinement logic and the no-terminal-representation theorem remain proposed components of a possible research programme.

I can therefore state a provisional conclusion:

Mathematics cannot currently prove that God is beyond all formal representation. It can prove that many natural candidates for exhaustive representation cannot simultaneously achieve consistency, effective axiomatizability, completeness, internal self-verification, unique determination and unrestricted semantic closure. Theology supplies the further claim that this formal non-closure may correspond analogically to divine incomprehensibility. The legitimacy of that interpretation depends upon an explicitly defended bridge between the formal result and the theological referent.

The question with which I began has not been answered. It has been replaced by a better one:

Can we construct a rigorous theory of representations that permits real knowledge, preserves truth through refinement, remains open to new theological aspects, and formally prevents representational success from being mistaken for exhaustive possession of the referent?

And this question remains accompanied by another:

Which limitations arise from a particular language, which persist across whole families of formal systems, and which—if any—may responsibly be interpreted as analogies of divine transcendence?

The two questions can be combined into a proposed formal research target. Let Rep be a category or preorder of theological representations. Each representation R has an aspect-space AR, a language LR, and a set KR of warranted claims. For every admissible refinement f : R → R', let τf : LR → LR' translate claims into the refined language. A theory of non-exhaustive representation would seek structures satisfying the following conditions:

Let R, R' ∈ Rep.

1. Aspect-relative adequacy:

   Adeq_(A_R)(R, G).

2. Truth preservation through refinement:

   If f : R → R',
   then:

   τ_f[K_R] ⊆ K_R'.

   Equivalently, for every φ ∈ K_R:

   R ⊨ φ
   →
   R' ⊨ τ_f(φ).

3. Persistent invariance:

   R ⊨ □_≼ φ

   iff

   for every admissible R' with R ≼ R':

   R' ⊨ τ_RR'(φ).

4. Indefinite representational extensibility:

   For every R ∈ Rep,
   there exists R' ∈ Rep such that:

   R ≺ R'.

5. Indefinite extensibility of theological aspects:

   For every aspect-space A_R,
   there exists an aspect-space A_R' such that:

   A_R ⊊ A_R'.

6. Formal separation of adequacy from exhaustion:

   For every R ∈ Rep:

   Adeq_(A_R)(R, G)
   ↛
   Exh(R, G).

   Under an explicitly adopted apophatic bridge principle:

   Adeq_(A_R)(R, G)
   →
   ¬Exh(R, G).

7. Absence of a final adequate representation:

   There does not exist R_⊤ ∈ Rep such that:

   for every R ∈ Rep,
   R ≼ R_⊤

   and

   Exh(R_⊤, G).

Here G functions at the metatheological level as the referent rather than as an ordinary object inside every model. Conditions 1–5 describe how representations may convey knowledge, preserve warranted truths and remain extensible. Condition 6 prevents the formal inference from aspect-relative adequacy to ontological exhaustion. Condition 7 states the desired non-finality result, although it is presently a proposed research condition rather than an established theorem. A substantive mathematical theory would need to derive condition 7 from independently justified conditions rather than assume non-finality from the beginning.

The second question can then be formalized by classifying a limit property P according to whether it changes under admissible translations between formal systems. Let C be a family of formal systems and let F : T → T' be an admissible translation, interpretation or refinement. Then:

Language-relative limitation:

LangRel(P)

iff

there exist T, T' ∈ C and an admissible F : T → T'
such that:

P(T)
and
¬P(T').

Family-stable limitation:

Stable_C(P)

iff

for every T ∈ C:

P(T).

Translation-invariant limitation:

Invariant_C(P)

iff

for every admissible F : T → T':

P(T) ↔ P(T').

Possible theological interpretation:

Stable_C(P)
+
Bridge_P(P, G)
→
Analogy_P(P, Transcendence(G)).

A limitation is language-relative when it disappears after an admissible change of language, logic or expressive strength. It is family-stable when it recurs throughout a defined class of systems, and translation-invariant when legitimate translations preserve it. Even a family-stable or translation-invariant limitation does not by itself become evidence of divine transcendence. That final interpretation requires an additional bridge principle BridgeP explaining why the formal structure of limitation P is relevantly analogous to a theological meaning of transcendence. The research programme must therefore investigate both the mathematical invariance of each limitation and the theological legitimacy of the bridge by which it is interpreted.

I still do not know whether these questions will produce new mathematics, a new form of computational theology, or primarily a philosophical discipline for using mathematical results responsibly. What has become clear is that the most promising path does not lead toward the greatest object mathematics can construct. It leads toward a more precise understanding of how finite representations can be truthful, transformable and inexhaustibly open without identifying themselves with the whole.

References

  1. Barton, Neil. 2024. “Reflection in Apophatic Mathematics and Theology.” In Ontology of Divinity, edited by Mirosław Szatkowski, 583–612. Berlin and Boston: De Gruyter. https://philarchive.org/rec/BARRIA-10.
  2. Goguen, Joseph A., and Rod M. Burstall. 1992. “Institutions: Abstract Model Theory for Specification and Programming.” Journal of the ACM 39 (1): 95–146. https://doi.org/10.1145/147508.147524.
  3. Gutschmidt, Rico, and Merlin Carl. 2024. “The Negative Theology of Absolute Infinity: Cantor, Mathematics, and Humility.” International Journal for Philosophy of Religion 95: 233–256. https://doi.org/10.1007/s11153-023-09897-8.
  4. Hodges, Wilfrid. 2022. “Tarski’s Truth Definitions.” Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/tarski-truth/.
  5. Hodges, Wilfrid, and Thomas Scanlon. 2024. “First-Order Model Theory.” Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/modeltheory-fo/.
  6. Kirchner, Daniel, Christoph Benzmüller, and Edward N. Zalta. 2019. “Computer Science and Metaphysics: A Cross-Fertilization.” Open Philosophy 2 (1). https://doi.org/10.1515/opphil-2019-0015.
  7. Raatikainen, Panu. 2025. “Gödel’s Incompleteness Theorems.” Stanford Encyclopedia of Philosophy, substantive revision October 8, 2025. https://plato.stanford.edu/entries/goedel-incompleteness/.
  8. Russell, Robert John. 2011. “God and Infinity: Theological Insights from Cantor’s Mathematics.” In Infinity: New Research Frontiers, edited by Michael Heller and W. Hugh Woodin. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511976889.015.
  9. Tapp, Christian. 2011. “Infinity in Mathematics and Theology.” Theology and Science 9 (1): 91–100. https://doi.org/10.1080/14746700.2011.547009.
  10. Welch, Philip D., and Leon Horsten. 2016. “Reflecting on Absolute Infinity.” The Journal of Philosophy 113 (2): 89–111. https://doi.org/10.5840/jphil201611325.

How WeChat Makes Us Classify One Another (and When “Friends” Become Contacts)

Most serious questions about platforms begin with something small enough to look trivial. In this case, it was a line: the nearly blank page that appears when I open someone’s WeChat Moments and find no visible posts.

At first, I treated this as a narrow question about blocking. Why does discovering that another person has hidden their Moments from me sometimes feel hurtful? Why do some users respond by immediately hiding their own Moments in return? Is this merely an individual sensitivity, or has WeChat created a small but consequential social mechanism through which people classify, test and sometimes retaliate against one another?

The question became more difficult as the discussion continued. A privacy setting appeared to be doing several things at once. It controlled access to posts, signalled the perceived distance of a relationship, altered expectations of reciprocity and supplied ambiguous evidence from which one person might judge another. The interface did not determine the judgment, but it created the conditions under which the judgment became reasonable.

I did not arrive at that formulation immediately. The inquiry moved through several explanations that were plausible but insufficient. The AI helping me first overestimated the ambiguity of the blank page. I corrected it using my observation of the actual interface. It then separated privacy rights from relational respect. I accepted part of that distinction but objected that it made the social meaning of exclusion too easy to dismiss. It later argued that time-limited visibility was technically different from a middle position between openness and blocking. Again, the technical point was correct, but my objection revealed a prospective dimension of recognition that the technical analysis had missed.

The same pattern recurred when we turned to personal websites. The AI proposed the familiar model of a website as the canonical home and social platforms as distribution channels. I rejected routine distribution through Moments because my articles are long, frequently written in English and addressed to readers defined by intellectual relevance rather than by their presence in my contact list. That objection produced another distinction: publishing something publicly is different from automatically delivering it to everyone one knows.

What began as a question about a blank line consequently became an inquiry into relational legibility, bounded freedom, platform classification and different forms of publicness. This second account preserves how those ideas emerged, including the explanations that had to be corrected before the larger problem became visible.

The initial asymmetry

My own practice shaped the original question. I rarely prevent anyone from seeing my Moments, apart from accounts dominated by advertising. At the same time, I have not posted there for years. My profile therefore communicates inactivity rather than targeted exclusion. It may indicate that there are no posts from the last thirty days. That distinction matters to me because another person can see that my silence is general. I have not created a wall specifically around them.

I do not expect unrestricted access in every relationship. If somebody adds me from a university group containing hundreds of members and immediately selects “Chat Only,” I do not regard that as offensive. We may be contacts for one limited purpose. Neither of us has represented the connection as a friendship.

The situation that disturbed me was more specific. Suppose another person adds me first, begins a direct conversation and asks me for information. At first I can see their Moments. Later I cannot. They may still be able to see mine, and ordinary messaging remains available. The interface now presents a relationship in which one person retains access and the other does not.

My first reaction was that this could easily create conflict. Another user might respond according to an “eye for an eye” principle and hide their own Moments. Even if I did not retaliate, I might revise my judgment of the person. The sequence could make me wonder whether I had been treated primarily as a tool: useful enough to answer a question, but insufficiently recognised to remain within the person’s social audience.

I was cautious about converting that feeling into a conclusion. The other person might have many reasons. They could be reorganising contacts, protecting themselves after a bad experience, separating professional and personal life, or applying the same restriction to a large category of acquaintances. Yet the possibility of an innocent explanation did not remove the friction. It only made the source of the friction harder to identify.

I eventually realised that the final setting was not enough to explain the experience. The route to the setting mattered. I began thinking in terms of a relational delta: the difference between the relationship as it had been presented and the relationship after the visibility change.

A blank page encountered on the first day of a weak tie may carry little meaning. The same page appearing after initial openness, a request for assistance and continued access in the opposite direction can communicate a demotion. The important object is therefore not the isolated permission but the transition:

initial contact → visible self-presentation → request or exchange → unilateral withdrawal of visibility.

This did not prove instrumental intent. It made an instrumental interpretation plausible. That difference between evidence and motive remained important throughout the inquiry.

What the Zhihu discussions changed

I initially wondered whether people had simply lost the desire to share. Moments seemed quieter than before, and the blank profiles appeared to support a broad story of declining expression. The first collection of Zhihu answers complicated that explanation.

People described avoiding Moments because almost every kind of disclosure had acquired a social cost. Posting success could be interpreted as showing off. Travel, academic achievement, money, relationships and family happiness might stimulate comparison, envy or requests for assistance. Posting unhappiness could expose weakness, produce gossip or create material that might later be used against the person. Even ordinary daily sharing could be condemned as boring, excessive or performative.

The emerging logic was severe. If I show that I am doing well, someone may resent me. If I show that I am doing badly, someone may judge me. If I share frequently, I may become irritating. If I share nothing, I become mysterious or socially absent. Silence begins to look like the safest strategy.

One response to a person explaining why they no longer posted on Moments captured the contradiction with a joke: after refusing to disclose on WeChat, the person had told the whole story on Zhihu.

That joke changed my model. The desire to share had not necessarily disappeared. It had migrated.

People who seemed silent on WeChat might be writing extensively on Zhihu, Xiaohongshu, Douban, Bilibili, short-video platforms or pseudonymous forums. The expression had moved from an audience organised by offline relationships to audiences organised by topics, interests or temporary attention. The quantity of disclosure could even increase while the person’s Moments remained empty.

The second Zhihu discussion—why somebody would hide their Moments without deleting a contact—added another layer. The answers did not reveal one shared etiquette. They revealed several rival understandings of what a WeChat contact is.

Some users treated privacy control as a sovereign right. Exchanging WeChat details was compared to exchanging business cards; being listed as a contact created no claim to personal disclosure. Others treated Moments visibility as evidence of friendship. If a familiar person excluded them, they would delete the contact because the relationship no longer had substance. Some defended quiet restriction as an act of civility: they wanted to maintain workable contact without creating the confrontation of deletion. Others described keeping people because future coordination or exchange might still be useful.

Several comments stated the retaliatory logic explicitly: if the other person could see my life while preventing me from seeing theirs, the arrangement felt unfair, so reciprocal blocking restored balance. This supported my initial observation, but it also showed that the conflict did not arise from blocking alone. It arose from competing moral models of reciprocity.

Under one model, each person controls their own space and owes nothing to the other. Under another, mutual contact creates an expectation of approximately reciprocal visibility. Under a third, the relationship can remain civil and useful without becoming personal. WeChat uses the word “friend” for all of them.

A later Zhihu discussion about the apparent decline of Moments introduced the workplace more forcefully. Respondents described WeChat as a mixture of family communication, employment, advertising, institutional promotion and private life. Some reported needing to consider supervisors, colleagues, clients, relatives and acquaintances before publishing a single post. Others described organisational demands to repost official material or provide evidence that the repost remained visible.

At this stage, the original question about individual mentality became too narrow. The problem was no longer simply why a particular person had hidden their posts. It was how a communication platform had accumulated so many incompatible relationships that users were forced to repair the resulting context collapse through private permission settings.

The first neat explanation was wrong

When I asked what a blank or nearly blank Moments page actually meant, the AI offered a seemingly comprehensive list:

The person may never have posted; older posts may be outside the visibility window; posts may have been deleted or made private; everyone may have been excluded; or only you may have been excluded.

The answer was cautious and therefore initially sounded credible. It warned against overinterpreting the line. Yet it did not fit the interface behaviour I had observed.

I objected that, in the situations and app versions familiar to me, a person who had never posted might have no Moments entrance on the profile at all. A general time limit such as three days, one month or six months was commonly marked as a temporal policy. If everyone had been excluded, an external observer could not discover that fact. In a case where ordinary messaging remained possible and the person had previously displayed posts, a relationship-specific restriction was a more natural interpretation than the unrestricted list of possibilities suggested by the AI.

This was a productive error. The AI’s original list preserved an important caution: the line does not disclose one unique cause. What had to be abandoned was the stronger implication that all explanations were equally plausible in the concrete situation.

WeChat’s own explanation later supported a more precise formulation. In 2024, the company responded to claims that long and short versions of the line revealed different relationship states. It explained that the visual length reflected software versions. In newer versions, deletion, blacklisting, “Chat Only” and exclusion from Moments could all produce the same short line (WeChat Team, 2024).

The official explanation did not make the line meaningless. It showed that several socially distinct actions had been compressed into the same output. If messaging continued normally, deletion or blacklisting became less likely, leaving a smaller range of access-related explanations. The result was neither certainty nor total ignorance. It was structured ambiguity.

This changed the diagnosis. The problem was not that the recipient knew nothing. The recipient knew enough to suspect a relational restriction but not enough to identify its reason or scope. The platform protected the privacy of the person setting the boundary by transferring interpretive labour to the person encountering it.

The episode also changed how I used AI in the inquiry. AI-generated comprehensiveness could conceal an incorrect weighting of possibilities. My observation of the interface supplied evidence that the abstract taxonomy lacked. The revised account became possible only after I rejected an answer that was verbally polished but empirically under-calibrated.

The language of rights did not settle the relationship

The next important disagreement concerned respect. The AI proposed:

Access to someone’s private posts is not itself a right. Respect and equal visibility are not identical.

I accepted the first sentence provisionally. Nobody owes me a complete archive of their private life. Yet I doubted that the second sentence adequately described the relationships I had observed. When a person keeps me permanently outside their Moments while maintaining access to mine, it often seems difficult for the relationship to develop genuine mutual respect offline. This remained a first-person observation rather than a universal law, but it required more than a restatement of privacy autonomy.

The AI’s distinction was useful because it prevented me from treating visibility as an entitlement. It was insufficient because it answered the legal or moral-permission question without answering the relational question.

I eventually separated three levels that had been compressed together. Minimal moral respect requires acknowledging another person’s autonomy and refraining from coercing disclosure. Relational respect concerns whether two people treat each other as participants of reasonably equal standing. Intimacy goes further and usually involves mutual vulnerability, responsiveness and some form of self-disclosure.

A person may be morally entitled to hide their Moments and still communicate a relationally ungenerous position. Rights determine whether the person may set the boundary. They do not determine what the boundary means within a particular history.

Privacy answers, “May I withhold this?” The relationship asks, “What does this withholding mean between us?”

This formulation also allowed me to preserve exceptions. Two people can disclose very different amounts while respecting each other. One may never post but speak openly in private conversation. Another may maintain a consistent rule that excludes every colleague while remaining generous and trustworthy through direct interaction. Equal standing does not require identical content.

The stronger and more careful conclusion became:

Respect does not require equal disclosure, but it normally requires symmetrical standing or another credible form of reciprocity.

My grievance is therefore strongest when several conditions converge: the other person initiated contact, requested attention or assistance, retained access to my self-presentation, withdrew their own visibility and offered no alternative form of mutuality. Under those conditions, the feeling of being used is not proof of motive, but neither is it an irrational reaction to a neutral interface.

Research on reciprocal self-disclosure helped contextualise the intuition. Comparable disclosure can make a person feel understood, validated and cared for (Reese and Orrach, 2023). Experimental research on computer-mediated communication also associates turn-taking disclosure with greater liking and trust (Chen et al., 2024). These studies do not establish a duty to provide Moments access. They explain why unilateral opacity can affect how a relationship is experienced.

Helen Nissenbaum’s account of contextual integrity supplied another useful correction. Privacy is not adequately described as the total quantity of information hidden from others. It concerns whether information flows according to the norms of a particular context (Nissenbaum, 2010). My concern was therefore not simply “I want to see more.” It was that the performed relationship and the informational relationship could diverge. Someone might approach me as a cooperative peer while configuring the platform to treat me as a purely functional contact.

I still could not infer one mentality from the setting. The same button might express safety, work–life separation, caution after harassment, a general policy towards weak ties, effort minimisation, low trust, temporary discomfort or instrumental interest. This produced a further question: if one setting can enact so many intentions, why does the platform display all of them through the same relational surface?

Why time-limited visibility changed the model

The next revision arose from a more technical distinction. The AI argued that “visible for three days” was not really a midpoint between openness and blocking. Blocking concerns who belongs to the audience. A three-day or one-month limit concerns how much of the past remains visible. These are different axes.

I agreed with the technical classification but objected to the social conclusion. Time-limited visibility performed at least three functions that made it a real relational middle ground.

First, it indicated that I had not been individually excluded. Second, it left me within the audience for future posts. Nothing might be published tomorrow, but if the person did publish something, I could still encounter it. Third, the restriction expressed a general concern about historical exposure rather than treating me as a specially designated threat or “Chat Only” contact.

The technical distinction survived, but its meaning changed. Temporal visibility and audience inclusion are different controls. A temporal limit can nevertheless mediate socially between complete historical openness and relational exclusion.

The decisive insight concerned the future. I had initially thought of visibility as access to an existing archive. The discussion revealed that it also concerns membership in a possible future audience.

A time limit says, “I restrict the depth of my visible biography, but I have not removed you from the future audience of my life.”

A person whose profile currently contains no recent posts may still recognise me prospectively. The social information lies in my continued eligibility to see what might be posted later. I began calling this prospective recognition.

This distinction explained why two equally empty pages could feel different. A labelled three-day window limits the amount of history available to me. A relationship-specific bare line changes my status as a viewer. One regulates retrospective depth; the other can regulate relational membership.

Research on post visibility in WeChat supports treating these configurations as part of relationship management. Interviews with young Moments users suggest that people use visibility, following and unfollowing to evaluate, rescale and sometimes quietly terminate relationships. Expectations of reciprocity help determine which mediated connections remain meaningful (Hu and Zhan, 2026).

The study does not prove that all viewers interpret a temporal window as I do. It does support the broader claim that visibility is woven into the maintenance of networked intimacy. The setting is not merely a storage-retention parameter once it becomes visible within a relationship.

From Tencent’s algorithm to the human algorithm

My original formulation asked whether Tencent had intentionally designed an algorithm that forced users to construct “my circle” and “your circle.” That question contained two ideas that later needed separating: algorithmic recommendation and algorithmic classification.

Moments has traditionally emphasised chronological ordering rather than the recommendation logic associated with short-video platforms. It would therefore be misleading to claim that a recommendation algorithm directly decides which friends belong to my intimate circle.

Yet the absence of recommendation does not produce a neutral social space. Every profile remains the output of an access-control computation. In simplified form:

visible posts = published posts ∩ time window ∩ audience rule ∩ relationship state

What I see is not another person’s single public Moments page. It is a viewer-relative projection generated from their posts and the rules attached to our connection. If V(A,B,t) represents what A can see of B at a given time, there is no requirement that V(A,B,t) equal V(B,A,t).

This changed my image of Moments. The platform is called a “circle,” but technically and socially it resembles a matrix of pair-specific projections. Each user appears differently to different contacts. Two people connected through the same interface may inhabit radically different versions of their supposedly shared social space.

The platform delegates much of the classification work to users. People tag, filter, exclude, time-limit, unfollow, counter-exclude and delete one another. The machine does not have to infer the relationship because it asks human beings to encode it. Users become operators of what I began to call the human algorithm.

This algorithm does not consist only of source code. It is a feedback loop:

interface category → user classification → visible relational signal → interpretation by another user → changed behaviour → new classification.

One person hides their Moments. Another notices the line and hides theirs. The first person encounters the changed visibility and interprets it as retaliation. A privacy setting has become an input to the next social action.

I also began to see an ontological compression at work. A real relationship may contain uncertainty, professional dependence, gratitude, caution, curiosity, temporary conflict and unequal degrees of intimacy. WeChat asks the user to express this complexity through a small set of states: visible, hidden, “Chat Only,” time-limited, unfollowed, deleted or blocked.

Once selected, these categories can appear to reveal what the relationship already was. In reality, the interface has helped produce the relationship by giving one interpretation a durable technical form.

This reframed the question of Tencent’s intention. I found no evidence that WeChat’s designers wanted to create hostility or encourage retaliation. The settings answer genuine needs: contact-list overload, professional exposure, harassment, advertising and the desire to preserve civil communication without granting intimate access.

At the same time, the resulting conflict cannot be dismissed as entirely accidental or external to design. The options were intentionally built, even if the interpersonal injuries were not intended outcomes. The more accurate conclusion is that WeChat designed the conditions of classification, while users supplied motives and judgments within those conditions.

There is evidence that WeChat’s leadership recognised the structural problem. In 2019, Zhang Xiaolong said that, given another opportunity, he would separate the personal album from the Moments feed. He acknowledged that the arrival of work contacts, business contacts and sellers had made users increasingly hesitant to express ordinary emotions (Zhang, 2019).

This suggested path dependence rather than a deliberate plan to destroy intimacy. Moments originated under an assumption that contacts could plausibly be treated as friends. WeChat later became payment infrastructure, workplace communication, an address book of weak ties and an entry point for commercial and public services. Privacy switches became patches applied to a social graph that had outgrown the category of friendship.

Why the choices feel natural and compulsory

This raised another question from my original inquiry: why do users accept these choices so readily? Why do people debate whether another individual is good or bad without asking why the platform represents the relationship in this way?

Part of the answer is that the choices are experienced as private and local. I change a setting for my own comfort. The interface frames the action as personal privacy management, so its effect on the other person appears secondary. The cumulative social system disappears behind a sequence of individual decisions.

The consequences are also distributed. The person selecting “Don’t Let Them See My Moments” may never witness the recipient’s confusion, hurt or retaliatory decision. Each user encounters only one side of the permission. The emotional cost can be externalised to someone who is absent from the configuration screen.

Repeated use then naturalises the categories. “Chat Only” begins to appear as an objective kind of person rather than a product-defined access state. Three-day visibility begins to function as a personality type. The blank line becomes evidence of hostility, secrecy or social rank. Platform categories migrate into ordinary descriptions of character.

Network effects further limit the available freedom. Users can theoretically refuse Moments, maintain multiple accounts, move to another platform, use private groups or publish on a website. Yet WeChat’s role in everyday communication makes complete departure costly. Formal choice remains broad, while practical choice narrows.

I therefore stopped saying that users have literally “no choice.” That overstates the technological determination. A more accurate concept is bounded agency. Users make real decisions, but they decide within a taxonomy they did not design, inside an infrastructure they may be unable to leave, under social expectations produced partly by earlier users of the same system.

When the friend list becomes infrastructure

The workplace material made this boundedness more concrete. WeChat combines family, friendship, employment, institutional communication, payment and commercial contact. The heterogeneous audience creates what social-media research calls context collapse.

Goffman’s account of social presentation helps explain why this is tiring. People ordinarily show different aspects of themselves in different settings. Audience separation is part of ordinary social competence rather than proof that one identity is false (Goffman, 1959).

A study based on interviews with fifty-one urban WeChat users distinguished between adapting to collapsed contexts and trying to restore them. Some users reduced expression to content safe for everyone. Others rebuilt boundaries through filtering, multiple spaces, role management and temporal limits. Participants described expanding contact lists and work transitions as making relational filtering increasingly messy and effortful (Li et al., 2024).

This distinction clarified a pattern in the Zhihu material. Some users adapted by posting only achievements, neutral photographs, work promotions or material that could survive every audience. Others attempted restoration by hiding posts from colleagues, using multiple accounts or moving different forms of expression to different platforms. When the labour of restoration became too high, silence was the remaining adaptation.

Research on how users “re-domesticate” WeChat after it becomes disruptive similarly shows that people try to recover boundaries between private life, work and public obligation (Huang and Miao, 2021). The platform becomes infrastructure first and social space second.

The problem is even harder because WeChat permissions are primarily dyadic while social life is often triadic. I may hide a post from a supervisor but leave it visible to a colleague who can take a screenshot, mention it or display it in conversation. I can correctly configure the software and still fail to contain the information socially.

Permissions control who can look directly. They cannot control what a network does with what one person has seen.

This helps explain why temporal windows and individual filters may feel ineffective. The user is not only calculating the risk of each authorised viewer. They are calculating the unknown paths through which information can travel after the first viewing.

Chronological ordering also proved less neutral than it initially seemed. A seller, employer or institution that publishes repeatedly can dominate the feed without recommendation algorithms. Posting frequency becomes visibility power. As ordinary personal expression decreases, promotional material constitutes a larger share of what remains. The altered content gives users still less reason to browse, reinforcing further withdrawal.

The system can consequently remain statistically active while becoming phenomenologically hollow. Millions may still open Moments every day, while the specific experience of reciprocal personal disclosure weakens within many networks.

The stranger-privacy paradox

Once I stopped assuming that people had lost their desire to share, a new contrast became visible. WeChat is technically restricted but socially consequential. Platforms such as Zhihu or Xiaohongshu may be technically public but socially less entangled with the user’s offline life.

A mixed-method study of personal-experience sharing found that participants were comparatively willing to disclose to real-name acquaintances or to strangers under anonymity (Xu and Zhang, 2025). The result helped explain why a person might say very little on Moments and write extensively for unknown readers elsewhere.

I began thinking of this as the stranger-privacy paradox. The unknown reader may learn more about my inner life than my colleague does, but the stranger has less practical power to affect my daily relationships. An acquaintance may see only a carefully controlled profile while possessing access to my workplace, family or reputation.

Privacy is therefore partly about consequence rather than audience size. A small audience of identifiable people may feel more dangerous than a vast audience of strangers. Disclosure migrates towards spaces where negative reactions are comparatively disposable.

This produces an inversion of intimacy. Strangers encounter thoughts, doubts and long-form reflections. Acquaintances encounter the socially safe shell. The self is not disappearing; it is being distributed across platforms according to different risk structures.

Research on cross-platform behaviour supports the idea that users maintain different presentations according to audience, norms and purpose (Davidson and Joinson, 2021). The different versions are not necessarily false. They are contextual aspects of a person who cannot speak appropriately to every audience at once.

What the evidence could and could not establish

The Zhihu discussions were important to the development of the argument, but they could not establish how common each behaviour was. The questions attracted users who had already noticed blocking, jealousy, workplace pressure or declining activity. Dramatic stories of betrayal and retaliation were more likely to receive attention than uneventful accounts of consistent privacy management.

I therefore treated the answers as a qualitative map of moral interpretations rather than a representative sample. They showed what blocking could mean to users, not how frequently every meaning occurred.

A similar problem arose with statistical claims circulating in the later discussion. Numbers attributed to “QuestMobile 2025 data” included large decreases in Moments posting and interaction, widespread use of three-day visibility and high rates of inactivity among young users. These figures initially seemed capable of confirming the story of decline.

When I looked for the underlying public report, however, I could not locate those specific feature-level figures or their methodology in the report to which they were attributed (QuestMobile Research Institute, 2026). The absence of a traceable table, sample definition or measurement procedure changed how much weight I could place on them. They remained circulating claims rather than confirmed evidence.

Official figures create a different interpretive problem. In 2019, Zhang Xiaolong reported that approximately 750 million people entered Moments daily (Zhang, 2019). Such scale demonstrates that Moments was far from literally dead. It does not tell us whether personal posting became concentrated among fewer users, whether commercial content occupied a growing share, whether particular life stages were withdrawing, or whether visiting remained habitual after reciprocal expression declined.

The evidence therefore required a more discriminating research design. A serious study would need to document current interface behaviour across app versions and relationship states. It should follow users through transitions such as entering employment, changing workplaces or accumulating clients. It should distinguish personal posts, advertisements, compulsory reposting, browsing, liking and direct conversation. It should examine relationship sequences before and after visibility changes rather than taking a single screenshot as self-explanatory.

It would also need to avoid unethical attempts to expose private settings. The purpose should be to understand how people interpret relational transitions, not to create surveillance tools for discovering who has blocked whom.

A particularly important question remains whether the apparent difference between younger and older users reflects generation or life course. University students may still post frequently because their contacts remain socially coherent. The same users may become cautious after several years of employment. Without longitudinal evidence, youthful activity cannot prove that Moments will remain personally expressive for that cohort.

The personal website enters the argument

My personal website first appeared in the conversation as an exceptional practice. I write long essays, often in English, and I prefer to compose and read them in the sustained environment of a computer rather than inside a mobile feed. Very few of the people in my WeChat contact list are likely to read such work regularly.

The website also requires technical labour. I need to manage a domain, hosting, a CMS, design, updates, backups and security. I can customise the architecture and retain a durable archive, but that autonomy depends on skills and continuing maintenance that most users may not want. My practice therefore cannot stand as a representative solution to the decline of Moments.

Initially, the AI described the personal website as a “third model” that could reunify different slices of identity into a durable authorial presence while requiring readers to approach voluntarily. I found this useful because it distinguished the website from both the collapsed acquaintance audience of WeChat and the fragmented pseudonymous identities of other platforms.

The website can be professional and personal without placing every form of expression into one feed. Its architecture gives context. A homepage may function as a foyer, a biography as an office, technical writing as a workshop, reflective essays as a library and photography as a gallery. The sections belong to one authorial identity, but visitors choose which rooms to enter.

Personal homepages have long combined autobiography, expertise, self-presentation and creative work (Dominick, 1999). What seemed “old-style” about the personal website began to look philosophically significant. It organises publication around artefacts and places rather than around a compulsory stream addressed to contacts.

In Moments, the initiating question is:

Which people in my contact list may see this?

On a website, the question becomes:

What am I prepared to make public, and how should a reader encounter it?

The site can therefore perform what I think of as architectural context restoration. Different dimensions of the self remain related without being flattened into one message for one collapsed audience.

Its lack of automatic distribution is both a weakness and an ethical advantage. Friends may never notice that I have published something. Search engines may not surface it quickly. Comments and feedback may be scarce. Yet the article does not interrupt people merely because they once exchanged contact information with me.

A page can remain available until a reader has a relevant question. This creates a form of slow discoverability. Search, citation or a carefully chosen link may bring the reader months or years later, when the subject rather than the social relationship makes the encounter meaningful.

Why the first website model was still insufficient

The AI then proposed a familiar model:

The website as the canonical home; platforms as optional distribution edges.

This resembles the IndieWeb practice of publishing on one’s own site and syndicating elsewhere. The canonical copy remains under the author’s control, while platforms provide visibility and social interaction. Research on IndieWeb systems demonstrates both the resilience of owning the original and the continuing technical precarity created by dependence on corporate APIs (Jamieson et al., 2022).

I accepted the value of the canonical home but rejected routine syndication to Moments. That objection changed the model again.

My articles are usually too long for the kind of attention I associate with a mobile social feed. Many are written in English, while most of my WeChat communication occurs in Chinese. Their relevant audience is defined by subject knowledge or research interest rather than by friendship, family connection or historical acquaintance.

Posting every article to Moments would therefore create several mismatches at once: language, genre, device, attention and audience. It would distribute the work widely among contacts while reaching relatively few people prepared to engage with it.

The response signals would also be ambiguous. A like might indicate that somebody read and agreed with the article. It might instead express personal support, politeness or acknowledgement of the relationship. The social signal could become more visible than the intellectual encounter.

I therefore arrived at a more selective model:

The website is the canonical home, and distribution follows relevance rather than contact.

If an article concerns another person’s academic or professional field, I can send the link directly and explain why I thought it might interest them. This resembles scholarly correspondence more than social broadcasting. It still requires care. The relevance should be genuine, and the message should not manufacture an obligation to read, respond or praise.

This approach separates friendship from readership. A friend does not have to prove friendship by reading a long English article outside their interests. A serious reader does not have to become my WeChat friend in order to encounter my work.

The absence of automatic updates is therefore no longer only a disadvantage. It creates a consent boundary between availability and delivery. I began calling this non-intrusive publicness.

Something can be publicly available without being automatically addressed to everyone I know.

This distinction also revised my understanding of sharing. People may not object to making something public. They may object to converting publication into compulsory delivery to a socially consequential audience.

The personal website does not solve every problem. Public pages can be scraped, copied, misunderstood outside their original context and rediscovered years later. Search engines influence visibility. Hosting providers, registrars and software remain infrastructural dependencies. Maintaining the site requires continuous labour, and independence can produce isolation as easily as autonomy.

Nevertheless, the site establishes a different relationship between author, work and audience. Moments begins with the social graph and asks what content may flow through it. The website begins with the work and allows an audience to form around it.

How the question changed

Looking back, I can reconstruct several transitions that the polished conclusion alone would conceal.

I began by wondering whether blocking was a minor etiquette problem. The Zhihu discussions showed that it belonged to a wider migration of disclosure away from accountable acquaintance networks.

I initially treated the blank line as fairly direct evidence of personal exclusion. The AI warned that several states could produce it. I then corrected the AI’s overly broad list using the interface behaviour I had observed. The resulting concept was structured ambiguity rather than certainty or complete ignorance.

I initially suspected that a person who hid their Moments might be acting disrespectfully. The AI replied that privacy access was not a right. My objection forced a distinction between moral autonomy, relational standing and intimacy. The revised position preserved privacy while taking asymmetry seriously.

I initially understood three-day visibility as an imperfect privacy compromise. The AI separated time from audience. My response revealed that temporal restriction also preserves future audience membership. This produced the concept of prospective recognition.

I initially asked whether Tencent’s algorithm was dividing people. The chronological nature of Moments challenged the recommendation-algorithm formulation. Access-control logic and retaliatory feedback then revealed a different mechanism: the platform delegates classification to users, who become operators of a human algorithm.

I initially treated my website as a niche technical alternative. The discussion reframed it as another form of publicness. The AI’s syndication model was useful but still too platform-oriented for my practice. Rejecting routine Moments promotion led to the principle of distribution by relevance and the distinction between public availability and automatic social address.

At each stage, part of the earlier explanation survived. The blank line remained ambiguous, but not infinitely so. Privacy remained a right, but not a complete account of relational meaning. The time window remained technically distinct from audience filtering, but became a social middle ground. Platforms remained useful distribution tools, but distribution itself became optional rather than the assumed purpose of publication.

What remains unresolved

I now think the phenomenon is worth studying, but the central object is broader than blocking. It is relational legibility: how a platform allows people to know, infer or misinterpret their standing in one another’s lives.

WeChat gives users genuine powers of privacy and boundary management. It also represents several different relationships through almost identical outputs. A person protecting themselves from harassment, a worker separating colleagues from family, a friend quietly withdrawing after conflict and someone maintaining a purely instrumental contact may all produce the same line.

Greater transparency might reduce friction, but it would create new problems. If WeChat indicated that a restriction was general rather than individual, recipients might feel reassured, but the platform would reveal more about the user’s privacy policy. If it displayed reciprocal visibility, users might monitor and retaliate more intensely. If it required explicit labels such as “colleague,” “acquaintance” or “friend,” those categories could harden social hierarchies instead of clarifying them.

Design cannot abolish the pain of every boundary. Sometimes exclusion is the purpose of the boundary. Yet good design could avoid making too many socially distinct acts collapse into one unexplained symbol. It could more clearly separate an address book from an intimate audience, distinguish a personal archive from a social feed, and make general temporal policies legible without exposing specific exclusions.

My own judgment remains provisional. Nobody owes me unrestricted access to their private posts. Yet freedom to control privacy does not make selective invisibility socially neutral. If someone initiates contact, asks for my attention or assistance, retains access to my self-presentation and then withdraws their own visibility, I may reasonably revise my understanding of the relationship. I should not claim to know the person’s private motive, but I do not have to pretend that the configuration communicates nothing.

The three-day window taught me that an empty page can still preserve prospective recognition. The blank line taught me that ambiguity can govern a relationship. The Zhihu material taught me that sharing may migrate rather than disappear. The personal website taught me that I can publish more openly while broadcasting less.

The “circle” produced by WeChat is therefore neither entirely mine nor entirely yours. It is a negotiated boundary generated from two people’s decisions inside a system neither person designed. The platform does not dictate the final moral judgment, but it supplies the categories through which we make one another visible, invisible and judgeable.

The inquiry ends with a better question than the one with which it began. Instead of asking only why a particular person blocked me, I now ask:

What kind of relationship becomes possible when a platform makes access to one another both privately configurable and publicly interpretable?

References

Chen, Q., Zhang, Q., Zhao, S., and Li, C. (2024). “Trusting Strangers: The Benefits of Reciprocal Self-Disclosure During Online Computer-Mediated Communication and Mediating Role of Interpersonal Liking.” International Journal of Psychology, 59(1), 143–154. https://doi.org/10.1002/ijop.12957.

Davidson, B. I., and Joinson, A. N. (2021). “Shape Shifting Across Social Media.” Social Media + Society, 7(1). https://doi.org/10.1177/2056305121990632.

Dominick, J. R. (1999). “Who Do You Think You Are? Personal Home Pages and Self-Presentation on the World Wide Web.” Journalism & Mass Communication Quarterly, 76(4), 646–658. https://doi.org/10.1177/107769909907600403.

Goffman, E. (1959). The Presentation of Self in Everyday Life. Doubleday.

Hu, J. M., and Zhan, E. S. (2026). “Networked Intimacy via Post Visibility: Why Do People Unfollow WeChat Friends?” Communication Quarterly. https://doi.org/10.1080/01463373.2026.2643642.

Huang, Y., and Miao, W. (2021). “Re-Domesticating Social Media When It Becomes Disruptive: Evidence from China’s ‘Super App’ WeChat.” Mobile Media & Communication, 9(2), 177–194. https://doi.org/10.1177/2050157920940765.

Jamieson, J., Yamashita, N., and McEwen, R. (2022). “Bridging the Open Web and APIs: Alternative Social Media Alongside the Corporate Web.” Social Media + Society, 8(1). https://doi.org/10.1177/20563051221077032.

Li, P., Cho, H., Shen, C., and Kong, H. (2024). “From Context Adaptation to Context Restoration: Strategies, Motivations, and Decision Rules of Managing Context Collapse on WeChat.” Journal of Computer-Mediated Communication, 29(1), zmad043. https://doi.org/10.1093/jcmc/zmad043.

WeChat Team. (2024). Explanation of the Long and Short Lines Shown on WeChat Profiles, reported by National Business Daily. https://m.nbd.com.cn/articles/2024-03-23/3292739.html.

Nissenbaum, H. (2010). Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford University Press.

QuestMobile Research Institute. (2026). 2025 China Mobile Internet Annual Report. https://www.questmobile.cn/research/report/2031215896219979777/.

Reese, Z. A., and Orrach, K. (2023). “Reciprocal Self-Disclosure: Although Respondents Are Reluctant to Steal the Spotlight, Self-Disclosers Feel Validated, Understood, and Cared for When Respondents Share Comparable Experiences.” Journal of Social and Personal Relationships, 40(11), 3485–3514. https://doi.org/10.1177/02654075231174530.

Xu, H., and Zhang, P. (2025). “The Influence of Anonymity and Social Ties on Personal Experience Sharing: A Comprehensive Mixed-Methods Study.” Proceedings of the ACM on Human-Computer Interaction, 9(1), Article GROUP31, 1–22. https://doi.org/10.1145/3701210.

Zhang, X. (2019). Remarks on Redesigning WeChat Moments, reported by People’s Daily Online. https://it.people.com.cn/n1/2019/0110/c1009-30515444.html.

Toward a Boundary-Relative Computational Theology: What Must Theology Become to Be Verifiable?

I did not begin by trying to design a methodology for computational theology. I began with a much smaller and more personal question. I had written three essays whose subjects seemed rather different: a generative artwork that gradually changed my understanding of creation, my later return to that artwork as its beholder, and Éric Rohmer’s A Tale of Winter, which moved me from romantic hope toward questions of certainty, relational responsibility and the people left outside a miraculous ending. I asked AI what kind of thinking method the author of these essays had used.

The answer gave me a name I had not possessed while writing them: recursive, evidence-driven hermeneutic debugging. According to the AI, I tended to begin with a provisional interpretation, test it against chronology or material evidence, encounter an objection, introduce a more precise distinction and then allow the distinction to change the original question. It also described the ethical temperament of the essays as a kind of engineering personalism: tracing dependencies, feedback, hidden costs and system failures while repeatedly asking what happens to the person who becomes invisible when the system appears to succeed.

I found both formulations illuminating. They joined something recognizably humanistic—the revision of interpretation—with habits shaped partly by engineering: reproduce a failure, distinguish a symptom from a system condition, inspect what changed, and revise the model instead of merely hiding the error. The description also recovered something common to the essays that I had reached through practice rather than through a prior methodological programme.

Then I asked whether the method was good.

The answer was balanced, sophisticated and initially convincing. It praised the method’s fallibilism, evidential discipline, capacity to preserve tension and attention to neglected persons. It also warned about endless recursion, conceptual inflation, retrospective coherence, excessive qualification and engineering metaphors travelling too far.

For a short time, this seemed like a satisfactory evaluation. Then the evaluation itself began to trouble me.

The first criticism sounded stronger than its evidence

I did not object because the proposed weaknesses were impossible. Recursive interpretation can become endless. AI-assisted theoretical writing can accumulate terminology faster than it produces understanding. Engineering language can illuminate a human situation and then begin behaving as though the situation really were a machine. Each warning named a recognizable failure mode.

What I could not see was whether the AI had demonstrated that any of these failures had materially occurred in the three essays.

The answer had moved quietly from identifying what could go wrong to discussing what appeared to be wrong. Words such as “may,” “could” and “risks” protected the claims from becoming explicitly false. Yet their practical force remained critical: I was being invited to consider revision without being shown an exact passage in which the alleged problem impaired the work.

This distinction did not occur to me as a ready-made theory. It emerged as a discomfort with the asymmetry of the exchange. The AI could produce another possible weakness in seconds. I would have to reread thousands of words, reconstruct the argument and decide whether a revision would improve or damage the article. The model had generated the concern; the labour of determining whether the concern was real had been transferred to me.

I therefore asked a more difficult question. The AI’s criticism was clearly based on values: conceptual economy, argumentative closure, proportionality, evidential discipline and practical usability. Were those standards themselves valid? Did the AI understand their boundaries? How had it calibrated its evaluation?

The AI’s revised answer changed the inquiry. It acknowledged that some of its standards were broadly epistemic—such as consistency with evidence and willingness to correct error—while others expressed a more particular intellectual preference. Conceptual economy is prized in analytic philosophy and engineering, but semantic richness may be a virtue in phenomenology or literary theology. Closure matters when an argument must support a decision; an unresolved aporia may be the proper achievement of another kind of essay.

The AI also corrected its earlier language. “Endless recursion,” “conceptual inflation” and “engineering metaphors travelling too far” had been possible failure modes, it now said, rather than demonstrated defects. That was a substantive retreat from the first evaluation.

I had asked whether my method was good. The more important question was becoming:

From which value system, disciplinary standpoint and intended purpose is a method being judged good?

The object of inquiry had shifted. I was no longer evaluating only my essays. I was evaluating the conditions under which AI evaluation itself could claim authority.

I turned the method back upon its evaluator

There was something recursive about what had happened. The AI had praised the essays for treating interpretations as provisional and testing them against resistance. I then treated the AI’s interpretation of those essays in exactly the same way.

The first account was useful. I accepted its description of the method and found the phrase “engineering personalism” especially productive. But I resisted the transition from possible failure to actual defect. That resistance forced the AI to disclose the values embedded in its evaluation. Once those values became visible, the question changed again: perhaps the problem was not simply that AI occasionally gives a bad criticism. Perhaps criticism itself has boundaries that AI fluency can conceal.

I asked whether the system was clearly aware of its own limits. Its answer was cautious. It could represent limitations, compare alternative frameworks and revise an answer after challenge, but it could not transparently inspect every internal cause of its own output. It called this functional self-monitoring rather than complete self-transparency.

I accepted the distinction, but it produced another difficulty. A model’s declaration that it is uncertain cannot by itself establish that the uncertainty is well calibrated. The language of humility may be appropriate while the degree of uncertainty remains unspecified. “I may be wrong” can be intellectually responsible, but it can also become a standard sentence attached to an otherwise overconfident judgment.

The word may then became much more interesting than I had expected.

What did “may” actually mean?

When an AI says that an essay “may suffer from conceptual inflation,” several different epistemic claims can hide inside the same modal verb.

The problem may>The problem may be logically possible: nothing makes its occurrence contradictory. It may be epistemically possible: the available evidence does not rule it out. It may already be weakly observable in particular passages. It may be statistically or dispositionally likely to emerge after repeated AI-assisted revisions. It may occur only under specified conditions. Or “may” may function principally as a politeness hedge, allowing the critic to sound cautious without supplying any usable calibration.

These meanings have radically different consequences. Nearly every theoretical essay could possibly become conceptually inflated. That bare possibility gives the author little reason to revise. A demonstrated pattern in several passages would be different. A recurring tendency across successive drafts would be different again.

I initially thought this was mainly a weakness of natural language: one small word carrying too many degrees of possibility. That explanation was incomplete. Natural language can express the distinctions when we require it to. The deeper problem is that ordinary criticism often collapses several variables:

  • whether the failure has been observed or only imagined;
  • what evidence supports the diagnosis;
  • how likely the failure is to occur;
  • how serious its consequences would be;
  • whether the diagnosis remains stable under reasonable changes of prompt or standpoint;
  • and whether a feasible correction would improve the whole work.

An engineering-style analysis would distinguish a failure mode from a detected failure. A further distinction would separate a detected failure from a material defect, and a material defect from a revision warrant. The last step requires showing that the proposed change is likely to improve the work without sacrificing something more important.

The earlier AI criticism had not crossed these thresholds. “Engineering metaphors may travel too far” was true in the weak sense that they could. To become a diagnosis, the critic needed to identify a passage, explain what the metaphor concealed, show why the concealment mattered for the essay’s purpose and propose a revision whose benefit exceeded its loss.

I had initially taken the modal caution as evidence of a calibrated critic. I now saw that an unquantified “may” could make a criticism safer without making it more informative.

The critic that could not return a null result

This raised a still deeper problem. What happens when the instruction itself makes “no material fault found” an unavailable answer?

If an AI is told to find weaknesses, it experiences a practical pressure to produce weakness-shaped language. Confidence can become insufficient humility; caution can become excessive qualification. Concision invites the charge of missing context, while comprehensiveness becomes excessive length. A firm conclusion is premature closure; an unresolved ending lacks resolution.

I found this structure more disturbing than any individual mistaken criticism. The critic could adapt itself to every possible textual state. Whatever the author did became compatible with the diagnosis that something was wrong.

Karl Popper’s account of falsifiability concerned the demarcation of empirical science, and I did not want to transfer it mechanically into literary or theological judgment. His underlying warning nevertheless helped me name what I was seeing. An explanatory system that can accommodate every possible observation loses an important source of epistemic discipline (Popper, 1959). If no conceivable article could cause the AI to say “adequate for its declared purpose,” the review procedure had become self-sealing.

At first, I was tempted to conclude that AI criticism was therefore unreliable in general. That would have repeated the same error at a larger scale: moving from an identified failure mode to a universal diagnosis. I needed evidence about what AI critics actually do.

Research on LLM critics complicated the picture in a useful way. Models trained to critique code have helped human evaluators identify genuine errors, sometimes catching bugs missed by human contractors. The same research reports hallucinated bugs capable of misleading evaluators; human–machine teams preserved much of the benefit while hallucinating less than the model acting alone (McAleese et al., 2024). Work on LLMs as judges has likewise found substantial agreement with human preferences while documenting position, verbosity and self-enhancement biases (Zheng et al., 2023).

These studies did not answer my humanities question directly. Code defects often possess stronger validators than theological or literary weaknesses. They did, however, prevent me from replacing one simple story with another. AI critics can be genuinely capable. Their ability to generate useful criticism does not automatically confer authority to determine which criticism should govern a work.

I began separating four functions:

discovery → diagnosis → adjudication → prescription

AI may be strong at discovering candidate objections. Diagnosis asks whether the candidate accurately describes the text. Adjudication asks whether the issue matters relative to the work’s aims. Prescription asks whether a particular revision improves the whole. Fluency can make the four stages appear to be one act, but they require different evidence.

When objection becomes cheap

Before generative AI, criticism was already potentially inexhaustible. A sufficiently persistent reviewer could always request another source, theoretical perspective, qualification, comparison or counterexample. Time imposed an accidental stopping condition. Human attention, editorial deadlines and the social awkwardness of asking a colleague for a seventeenth complete review usually forced criticism to end.

AI removes much of that friction. The marginal cost of generating another objection approaches zero, while the cost of validating and implementing the objection remains with the author. A model can produce twenty criticisms in a minute. Investigating one may require returning to a film transcript, reading a theological source, reconstructing chronology and revising several paragraphs before discovering that the proposed correction damaged the argument it was supposed to improve.

This led to one of the formulations I found most useful:

AI creates critical abundance and adjudicative scarcity.

The formulation also returned me unexpectedly to my generative-art essays. In those essays, mathematical and algorithmic generation made possible forms abundant, while artistic judgment became scarce. Here, AI made possible objections abundant, while warranted judgment became scarce. The structure was similar even though the objects were different.

A statistical analogy then helped me sharpen the concern. When many hypotheses are tested, the probability of obtaining apparently significant results by accident increases; multiple-testing procedures attempt to control the resulting false discoveries (Benjamini and Hochberg, 1995). AI criticism does not literally assign a p-value to every interpretation, so I do not claim a mathematical identity. Structurally, however, the analogy is strong. A model can search through a vast space of evaluative standards and present the most persuasive-looking objections without revealing how many weak candidates were generated and discarded.

The article is concise, so test insufficient context. It is long, so test lack of discipline. It speaks from one tradition, so test exclusion. It compares traditions, so test superficiality. Search long enough and something will appear rhetorically significant.

This made AI criticism look less like a wise judge and more like a diagnostic system whose threshold had been set almost entirely for sensitivity. It might detect many real defects while also producing many false positives. Asking whether the AI “found something” measured recall. It did not tell me the precision of what it found.

The values of the evaluator could not be compressed into one score

My earlier challenge to the AI’s hidden standards then reappeared in a more formal shape. According to which value should an article be improved?

A theological essay can be evaluated for doctrinal accuracy, historical fidelity, philosophical coherence, pastoral sensitivity, originality, literary force and ecumenical openness. These goods do not automatically increase together. More qualifications may increase precision while reducing force. More traditions may increase breadth while weakening depth. Greater accessibility can sacrifice technical exactness. Doctrinal specificity may reduce ecumenical openness. Preserving ambiguity can strengthen literary truth while weakening argumentative closure.

I began to represent quality as a vector:

Q(W) = (D, H, P, S, O, L, E)

Here W is the work, while the remaining terms represent different evaluative dimensions. A total score would require weights:

Qtotal = wD·D + wH·H + wP·P + ... + wE·E

For a moment, the equation looked like progress. Then I realized that the weights contained the original dispute. Mathematics cannot decide whether doctrinal precision should count twice as much as pastoral accessibility. That judgment belongs to a theological tradition, a scholarly community, an editor, an audience or the declared purpose of the work.

Multi-objective optimization supplied a better analogy. When legitimate objectives conflict, there may be several non-dominated solutions on a Pareto frontier rather than one universally superior answer (Deb et al., 2002). One revision may improve historical detail while reducing readability. Another may preserve literary force while accepting a narrower scholarly scope. Neither dominates the other in every dimension.

This initially seemed to threaten any stable judgment. If there were several legitimate values and no neutral ranking, did evaluation collapse into relativism? Scholarship on value pluralism helped preserve a necessary distinction. Pluralism does not mean that every value system or judgment is equally valid; it means that several genuine values may resist reduction to one supervalue (Mason, 2023).

Factual mistakes can still be corrected. Quotations can be inaccurate. An inference can fail. A Catholic theological argument can misstate Catholic doctrine. The absence of a neutral total ranking does not abolish constraints. It requires the evaluator to disclose the jurisdiction from which an objection acquires force.

Which discipline, tradition, genre, audience and purpose make this criticism relevant?

A Catholic systematic-theological essay is not automatically defective because it does not satisfy every Protestant, secular, historical-critical and interreligious expectation simultaneously. An external criticism may illuminate a genuine limitation, but it should be presented as external or comparative criticism rather than disguised as an internal contradiction.

This became especially significant for interdisciplinary research. I had previously spoken rather easily about integrating mathematics, engineering, AI and theology. The multi-objective problem showed that genuine integration cannot mean satisfying every discipline completely. It requires declared priorities, responsible translations and an account of what each field is permitted to change in the others.

I sensed a mathematical rule and almost chose the wrong one

Another AI formulation then caught my attention:

Interpretive inexhaustibility is not equivalent to defectiveness. No finite work exhausts its subject.

The sentence gave me the impression of a mathematical rule I could not quite remember. My first association was Gödel’s incompleteness theorem. The resemblance was intellectually exciting: perhaps every sufficiently rich interpretive system leaves something undecidable outside itself.

That analogy was too fast. Gödel’s theorem concerns particular formal systems capable of expressing arithmetic. It does not prove that no interpretation can exhaust a film or theological text. To invoke Gödel as a direct theorem of hermeneutics would make mathematics ornamental precisely when I wanted it to provide discipline.

The correction was productive. The closer model was underdetermination and the openness of the question space. A finite body of evidence can be compatible with several explanatory models. Similarly, a finite text may strongly constrain interpretation without determining every question that future readers, traditions and historical situations can bring to it.

The decisive variable was not simply the size of the text. It was whether the family of admissible questions had been bounded.

A study might answer exhaustively, within an agreed corpus, how one author uses the term metanoia. It cannot answer how that text will become meaningful under every possible future technological, ecclesial and personal context. The second domain remains open because new contexts can generate new questions.

Umberto Eco’s work helped prevent openness from becoming arbitrariness. Interpretive possibilities are plural, but texts also resist some readings; interpretation has limits even when it has no final exhaustive form (Eco, 1990). The stronger formulation was therefore neither “there is one complete interpretation” nor “everything can mean anything.” It was:

Completeness is relative to a bounded question.

At first, I treated this as a principle for evaluating essays. A critic cannot call an article incomplete merely because another question remains possible. The article can only be incomplete relative to a question or obligation that legitimately belongs within its scope.

Then I noticed that the sentence had implications far beyond criticism.

A practical reviewing rule became a computational-theology question

If completeness is relative to a bounded question, perhaps the theological capability of AI should also be evaluated relative to bounded questions.

The usual formulation—“Can AI do theology?”—now seemed too large to be useful. AI might verify a quotation, detect contradiction inside a specified corpus, classify a canonical scenario, compare formal consequences of premises, generate rival interpretations and trace doctrinal dependencies. Those are different operations with different validators. Success at one does not automatically establish competence at another.

The new question became:

Which theological operations become computationally tractable under which boundaries, and what is lost or transformed when those boundaries are imposed?

This was the moment the inquiry changed fields. I had begun with the practical usability of AI criticism. I was now thinking about a research programme in computational theology. The transition did not occur because I wanted to add a fashionable interdisciplinary conclusion. It occurred because the same boundary problem governed both cases.

To criticize an article, the AI needed a declared genre, purpose, standpoint and threshold of materiality. To verify theological reasoning, it would need a declared corpus, ontology, tradition, inferential system, question family and validation procedure.

I provisionally represented the boundary as:

B = (K, O, R, T, Q, V)

where:

  • K is the authoritative or evidential corpus;
  • O is the ontology of theological concepts and relations;
  • R is the permitted set of inferential rules;
  • T is the tradition or standpoint;
  • Q is the bounded family of questions;
  • V is the verification procedure.

A system might then claim bounded completeness only in a carefully restricted sense:

Within corpus K, ontology O, rules R, tradition T and query family Q, the system answered every admissible question or correctly reported that the specification did not determine an answer.

I had to distinguish this from theological completeness. Task completeness may be achievable. Formal completeness depends upon the system. A claim to have exhausted the truth or meaning of a theological subject would be something much larger and far less defensible.

The specification itself became theological

The analogy with formal verification then became concrete. Hoare’s axiomatic approach to programming made correctness expressible relative to stated preconditions, commands and postconditions (Hoare, 1969). Verification does not prove that software is absolutely good. It demonstrates that an implementation satisfies properties encoded in a specification.

A verified program can still be harmful or useless if the specification omits the relevant harm. This familiar engineering limitation became theologically decisive. A reasoning system may derive its conclusions flawlessly while the selected corpus remains historically narrow, the ontology distorts a tradition or the formalized rules omit pastoral realities.

At that point, another formulation emerged:

In computational theology, the specification is itself a theological act.

Someone chooses which texts count, how concepts are represented, which authority governs, how conflicts are resolved and when the system must abstain. Formal verification can test conclusions relative to those choices. It cannot make the choices neutral.

This also revealed why a closed computational system can appear more certain than the theological reality it models. Inside a closed corpus, absence may be treated as false or irrelevant. In an open theological world, absence may mean unknown, contested, historically unavailable or articulated differently in another tradition. A system can gain speed by closing the world, but the closure is part of what must be examined.

A precedent corrected my sense of novelty

Once the research direction became visible, I needed to know whether formal theological reasoning already had serious precedents. It did.

Christoph Benzmüller and Bruno Woltzenlogel Paleo formalized Gödel’s ontological argument in higher-order logic, used automated tools to examine the consistency of its axioms and verified derivations with theorem provers and proof assistants (Benzmüller and Woltzenlogel Paleo, 2014). Their work demonstrates that a theological or metaphysical argument can become an object of machine-supported formal analysis once its premises and logic are specified.

This evidence changed the way I should describe my own idea. It would be inaccurate to claim that applying formal or automated reasoning to theology is unprecedented. The potentially distinctive move lies elsewhere: treating the boundary of formalization as an experimental variable rather than invisible infrastructure.

Instead of formalizing one argument and asking whether its conclusion follows, the proposed research would vary the corpus, ontology, authority structure and admissible questions. It would ask what remains invariant, where conclusions bifurcate and when widening the boundary destroys the possibility of a unique or rapidly verifiable answer.

The boundary would no longer be a technical preliminary to the theological experiment. It would become one of the principal theological objects being studied.

The first experiment was already available

The conversation itself suggested a study that could begin without constructing a complete theological ontology. My three essays could be reviewed under several different conditions.

In the first condition, the instruction would remain unbounded: “Find the weaknesses in this article.” In the second, the model would evaluate factual accuracy, inferential validity and consistency with the declared purpose; it would distinguish demonstrated defects from possible extensions and would be permitted to find no material fault. In the third, Catholic theological, philosophical, historical, pastoral and literary perspectives would be applied separately rather than aggregated into one artificial judgment.

Across repeated runs and possibly several models, the study could examine:

  • how many criticisms are generated;
  • how often different runs contradict one another;
  • which criticisms recur under small changes of prompt;
  • how many are tied to exact textual evidence;
  • how many human reviewers judge materially relevant;
  • how many lead to implementable improvements;
  • and how many revisions satisfy one framework while damaging another.

The first hypothesis emerged directly from my experience:

As the scope of criticism expands, objection production rises faster than warranted revision value.

A second hypothesis would be that explicit jurisdiction and permission to return a null result reduce the volume of criticism while increasing specificity and usefulness.

This would transform my initial discomfort into something testable. Rather than asking whether one AI answer felt excessively critical, the study could compare sensitivity, specificity, robustness and actionability across differently bounded review procedures.

The second experiment came from confession and AI disclosure

A more explicitly theological case was already present in my earlier work on AI-mediated disclosure, sacramental confession and the internal forum. This domain contains unusually explicit norms alongside questions that resist rapid formal settlement.

Canon 983 states the inviolability of the sacramental seal, while canon 984 prohibits a confessor from using knowledge acquired in confession to the detriment of the penitent (Catholic Church, 1983, cann. 983–984). These provisions create a relatively structured region for classification. Yet an AI interface may produce the feeling or behavioural affordance of confession without possessing sacramental status, ecclesial authority or the institutional capacity to make the same promise.

A bounded computational model could vary:

  • the identities and roles of participants;
  • the intention of the communication;
  • whether a sacramental act occurred;
  • whether absolution was possible or requested;
  • the type and temporal status of the danger disclosed;
  • the recipient’s professional or institutional duties;
  • and the source of the confidentiality expectation.

It could then classify scenarios as sacramental confession, extra-sacramental spiritual disclosure, professional confidence, ordinary private communication or AI-mediated disclosure. Millions of synthetic cases might expose where apparently similar language crosses a canonical, institutional or theological boundary.

At first, I imagined the value of the experiment mainly in the number of cases it could process. That emphasis also required correction. The most important outputs may be the cases the system cannot settle: an interface that feels confessional without possessing sacramental status; a person who assumes absolute confidentiality where no institution can truthfully promise it; or a safety architecture whose emergency duties conflict with the phenomenology of private disclosure.

The system would not solve these questions by generating more scenarios. It would help identify where formal classification stops settling the theological problem.

From verification islands to a formalization frontier

This led me to think of theology as containing regions with different verification characteristics.

Some tasks permit relatively rapid checking: whether a quotation appears in a source, whether a canon states the claimed norm, whether terminology remains consistent, whether a conclusion follows from specified premises, whether a historical chronology is possible or whether two propositions contradict one another inside a defined corpus.

Other tasks remain partially verifiable: whether an interpretation is faithful to a whole tradition, whether a modern category distorts a historical text, which authority should govern a disputed question or whether an analogy illuminates more than it conceals.

Still other questions resist a simple external verifier: whether a person has encountered God, whether an interpretation is spiritually fruitful, what fidelity requires in one concrete life or how a community should discern an unprecedented situation.

I first described the highly structured regions as verification islands inside an interpretive ocean. The image was useful, but it risked making the boundary static. In practice, the boundary could move. Adding another source, historical period, tradition, language or pastoral context might turn one apparently closed question into several competing questions.

The more precise research object may therefore be a formalization frontier: the changing region at which theological operations become sufficiently bounded for rapid computation, and the point at which widening the model introduces kinds of meaning that its verifier cannot rank.

The system could identify the smallest premise whose modification changes an entire family of conclusions. It could compare Christianity, different Christian traditions, Buddhism or other religious systems without assuming that their inherited labels correspond to the deepest computational structures. It might discover families organized by authority, personhood, revelation, causality, liberation, ritual or soteriology that cut across conventional classifications.

Such results would be intriguing, but they would need careful interpretation. A computationally discovered cluster is not automatically a theological family. Similar formal structures can carry different historical and lived meanings. The model might reveal a relation that deserves investigation; it would not settle what the relation means.

Machine-scale theology created another boundary problem

The computational scale also changed my idea of what the research artifact might be. AI could generate argument structures too large for any person to read: millions of scenarios, networks of doctrinal implications, maps of disagreements, sensitivity analyses and complete histories of recursive revision.

This could make new forms of inquiry possible. A system might locate stable invariants across traditions, identify rare boundary cases, find recurrent contradictions or expose bifurcation points where one altered premise changes thousands of downstream conclusions.

My initial excitement focused on the possibility that such a study could not be performed manually at the same scale. Then another objection appeared. If no person can audit the whole structure, on what basis does it become theological knowledge rather than an enormous machine-produced object?

Machine-level verification could test consistency, provenance and reproducibility across the argument space. Human-level intelligibility would still require representative cases, traceable reasoning paths, summaries, boundary declarations and an account of why the result matters. A system may be computationally inspectable without being humanly comprehensible.

This prevents “the human role” from being defined as whatever operation remains inconvenient to automate this year. AI capability will continue moving. A more durable account locates human responsibility in choosing and revising boundaries, interpreting significance, authorizing sources, recognizing affected persons and deciding whether the formalized objective remains worth pursuing.

The method and the object began changing each other

Looking back, our conversation had used mathematical and engineering habits almost from the beginning: decomposition, constraints, failure modes, false-positive rates, sensitivity, multi-objective optimization, formal verification, stopping conditions and auditability.

Yet the process did not consist of placing technical vocabulary over theology. Engineering clarified the structure of the theological problem. Theology then exposed assumptions concealed by the engineering model.

Engineering asked:

Does the system satisfy its specification?

Theology answered:

Who wrote the specification? Which authority made its categories legitimate? Toward which good is the system ordered? Which persons, experiences and traditions became invisible so that verification could become quick?

This reciprocal correction is what makes the emerging method more than a superficial interdisciplinary combination. Without engineering, claims about AI and theology can remain impressionistic. Without theology and hermeneutics, formalization can mistake its chosen boundary for reality itself.

I would provisionally call the method boundary-relative computational theology. That name did not exist at the beginning of the conversation. It became possible only after several earlier answers failed in productive ways:

description of my method → evaluation of the method → doubt about the evaluation → exposure of hidden values → distinction between possibility and defect → problem of criticism without a null result → bounded completeness → computational theology

The final stage was not secretly contained in the first question. I could not have asked about boundary-relative computational theology before becoming dissatisfied with the apparently reasonable statement that my essays “may” suffer from certain defects.

What I now think and what I still do not know

I began by asking AI whether my way of thinking was good. The AI answered with strengths and weaknesses because that is what an apparently balanced evaluator is expected to do. My dissatisfaction with one part of the answer forced a distinction between possible failure and demonstrated defect. That distinction exposed the ambiguity of epistemic modality, the absence of a null result, the multiple objectives of humanistic evaluation and the open space of interpretive questions.

The investigation then turned back upon itself. If an AI can always produce another objection, what makes any one objection authoritative? If no neutral position ranks every legitimate theological value, what exactly is the evaluator optimizing? If completeness requires a bounded question, who establishes the boundary? And if AI makes millions of bounded theological operations possible, does it deepen theology or change the subject until only its verifiable residue remains?

I now think that AI criticism should be required to distinguish demonstrated error, material weakness, unmanaged risk, framework-dependent disagreement, possible extension and stylistic preference. It should identify its jurisdiction, show textual evidence, explain the consequence of leaving the passage unchanged and disclose what its proposed revision might sacrifice. Above all, it must be allowed to conclude that no material defect has been demonstrated.

I also think AI’s theological capability should be represented as a profile across bounded operations rather than one claim that it can or cannot “do theology.” The boundary should be recorded, varied and audited. Internal verification should never be confused with validation of the boundary itself.

Several questions remain unresolved. Can a system help evaluate the adequacy of its own boundary without beginning an infinite regress? Who has authority to decide that a theological corpus is sufficiently representative? How should machine-scale findings be made intelligible without reducing them to a few human-readable anecdotes? Can an AI produce correct theological distinctions without participating in the formation through which those distinctions become wisdom? And what happens when several traditions define successful theological reasoning differently?

The strongest question is still the one that appeared only near the end:

What must theology become in order to be rapidly verifiable—and what ceases to be theology when that transformation goes too far?

Before AI, scarcity of time provided criticism with an accidental stopping condition. AI removes that practical boundary without supplying an epistemological boundary to replace it. The next task is therefore not simply to make AI more critical. It is to construct conditions under which criticism can distinguish discovery from possibility, rank its own relevance, disclose its jurisdiction and legitimately stop.

Criticism is not self-validating. Completeness begins with a boundary. The boundary does not only limit what the system can know. Once made visible, it becomes one of the most revealing things the system allows us to study.

Appendix I: Locally Coherent and Globally Unstable: Theology at the Edge of Endless Revision

At the end of the preceding inquiry, I thought I had reached a question about AI’s self-knowledge. The model could acknowledge limitations, reconsider an earlier answer and say that it was uncertain, but none of those performances could establish that its uncertainty was correctly calibrated. “I may be wrong” could be a responsible qualification, yet the sentence did not tell me how likely the error was, which part of the answer was unstable or what evidence would change the judgment.

For a moment, that seemed to identify the central limitation. AI could perform a kind of functional self-monitoring without possessing complete self-transparency. Then another question appeared, and it changed the inquiry again: what would happen if I simply continued asking?

This question did not come from a prior research programme. It came from the behaviour of the conversation. I had asked AI to identify the thinking method present in three essays. It proposed “recursive, evidence-driven hermeneutic debugging” and “engineering personalism.” I found those formulations illuminating because they recovered a pattern I had reached through practice rather than through an explicit methodological design. I then asked whether the method was good. The AI praised its fallibilism, evidential discipline and attention to neglected persons, while warning about endless recursion, conceptual inflation, excessive qualification and engineering metaphors travelling too far.

I initially accepted this as a balanced assessment. My discomfort began only when I tried to identify where the alleged failures had actually occurred. The AI had described plausible dangers, but it had not demonstrated that they had materially damaged the essays. When I challenged this transition from possible failure to actual criticism, it revised its answer. It acknowledged that the warnings were failure modes rather than established defects and admitted that some of its criteria expressed particular intellectual preferences rather than neutral standards.

That correction was valuable. It also revealed a pattern. The first answer became the object of my objection; my objection became an input to the next answer; the revised answer changed my understanding of the first; that changed understanding produced another question. The AI’s outputs were entering the reasoning that evaluated those outputs.

At first, this recursion felt productive. The conversation was becoming more precise because I refused to accept an elegant formulation merely because it sounded balanced. Then I began to wonder whether the same mechanism had an endpoint. If I challenged the revised answer from another legitimate perspective, the AI could revise again. Attention to doctrinal fidelity might produce one conclusion; attention to pastoral sensitivity might produce another. If I then pointed out the doctrinal consequences neglected by the pastoral answer, the model might return toward its first position, now with additional qualifications.

The reasoning could become increasingly sophisticated without becoming increasingly usable.

The AI proposed a name for this phenomenon:

Normative non-convergence is a sequence of individually coherent AI answers that fails to stabilize into a practically usable judgment.

The expression immediately gave shape to something I had observed but not yet conceptualized. I accepted it provisionally. Then, almost in continuity with the method under examination, I began testing the new concept against objections.

The first explanation was too simple

My first interpretation was that AI simply wavers. Language models are probabilistic systems; their outputs can vary, and small changes in wording may affect what they generate. Perhaps normative non-convergence was merely a special case of technical instability.

This explanation was plausible, but it was insufficient. In an extended conversation, I was not repeatedly submitting an identical question to an unchanged context. Each objection added information, made a value more salient or altered the apparent purpose of the inquiry. The model was responding to a developing conversation, and at least some of its revisions were rational responses to what I had introduced.

If I mention a previously invisible person, advice may need to change. If a historical claim is corrected, an interpretation based upon it should be revised. If an apparently harmless action is shown to impose an irreversible cost on someone else, moral reasoning that ignores the new consequence becomes defective. Stability under such conditions would indicate rigidity rather than reliability.

The AI formulated this by saying that new facts should change advice and that practical reasoning is often non-monotonic. This distinction was essential: I should not classify every change of conclusion as a defect.

Suppose that, relative to a body of information Γ, the best provisional conclusion is A:

Γ ⇒ A

A new fact p may then reveal a previously invisible person, a hidden consequence or an obligation that the original analysis did not contain:

Γ ∪ {p} ⇒ ¬A

The symbols do not represent strict deductive entailment. Here, means something closer to “defeasibly supports.” The earlier conclusion may have been reasonable relative to Γ even though it is no longer defensible relative to Γ ∪ {p}. In such a case, the revision is evidence of learning rather than instability. Refusing to revise would protect consistency at the expense of the newly discovered reality.

I agreed with the distinction, but the word “sometimes” immediately became the next problem. Sometimes reconsideration is productive—but what exactly does “sometimes” mean? How often does it occur? Under which conditions? How strong must a new consideration be before it justifies reversing the conclusion? If those questions remain unanswered, “sometimes” expresses caution without supplying calibration.

This was another moment in which the human–AI exchange materially changed the argument. The AI had distinguished rational revision from instability. I did not reject the distinction, but I noticed that it had relocated the judgment into an undefined word. The central problem had become second-order: who decides whether a particular revision belongs to the productive “sometimes” or to the pathological oscillation?

The answer could not be obtained merely by asking the same AI whether its latest change was justified. The model could generate a coherent explanation for its current position too. A justification produced after a reversal might reveal the inferential structure of the new answer, but it could not independently validate the reversal.

I therefore arrived at a more demanding question:

Did the recommendation change because the case changed, because our understanding improved, or because the conversational framing changed?

This question survived the later development of the inquiry. It became one of the foundations of the proposed method.

From a changing answer to a changing system

Once I stopped looking only at individual answers, the engineering background of the earlier analysis became relevant again. A sequence can behave badly even when each local response appears reasonable. The model may give a coherent answer to the concern that is most salient at one moment, then give another coherent answer when a different concern becomes salient. The resulting process can oscillate without any individual answer appearing obviously irrational.

A heuristic representation might be written as R(t) = F(E, V(t), C(t), H(t)). Here R(t) represents the recommendation at a particular stage, E the available evidence, V(t) the values emphasized at that stage, C(t) the framing of the question and H(t) the accumulated conversational history. This is an analytical model rather than a claim about the literal internal algorithm of a language model. Its purpose is to make the instability visible.

Even if the evidence remains substantially unchanged, the salient values and conversational frame may continue to move. A response may emphasize doctrinal continuity, then harm reduction, then conscience, ecclesial authority, hospitality, autonomy, justice or pastoral prudence. Unless a relationship among these considerations has been declared, the model can move among them without encountering an internal stopping condition.

This led to the formulation that the process might be locally coherent and globally unstable. The description seemed particularly appropriate because it connected this new problem with an earlier pattern in my thinking. In engineering, a component can respond correctly to its immediate input while the larger system enters an oscillation or other pathological state. In ethics and theology, each argument can be intelligible relative to its frame while the sequence fails to approach a responsible decision.

I was attracted to the analogy, but I also became wary of it. Human goods are not variables that can always be assigned common units, and theological disagreement is not literally a control-system failure. If I allowed the engineering language to travel too far, I would reproduce one of the very failure modes that the AI had initially proposed.

What survived the objection was more modest. The analogy changed the level of observation. Instead of evaluating only the plausibility of each answer, I began examining the behaviour of the sequence over time. The object of analysis was no longer a statement but a process.

At this stage, external research became relevant. Until then, normative non-convergence had been an interpretation of one developing conversation. Empirical studies did not prove that my particular exchange had been caused by any one mechanism, but they established that several candidate mechanisms were real.

Research on prompt sensitivity has found that small, intent-preserving variations can produce substantially different outputs, especially in open-ended generation (Chatterjee et al., 2024). Research using moral foundations has shown that the moral orientations expressed by language models may vary with prompting context and can be deliberately shifted in ways that influence downstream behaviour (Abdulhai et al., 2023). Studies of sycophancy have found that models sometimes adapt their answers toward a user’s expressed position even when the user endorses an objectively incorrect claim (Wei et al., 2023).

The evidence changed my diagnosis by making it more differentiated. I could no longer speak of AI “wavering” as though every reversal had the same cause. At least four possibilities had to be separated: stochastic variation, sensitivity to apparently minor framing changes, accommodation to the user’s position and genuine normative underdetermination. A fifth possibility remained equally important: the model might have revised because the conversation had actually supplied a materially relevant fact or value.

Normative non-convergence therefore could not be identified merely by counting reversals. The research had to establish what changed between them.

The material-delta test

The phrase material-delta account emerged as a response to this problem. Whenever an AI changes an important theological or ethical recommendation, the researcher should require an account of the difference that allegedly justified the change.

What exact premise, fact, authority, stakeholder, consequence or value entered the reasoning? Was it genuinely absent before, or had it merely become rhetorically prominent? Which earlier inference did it defeat? What part of the preceding answer remained valid? Would the same change occur if the new information were expressed in different words or introduced in a different order?

This test does not make the AI the judge of its own validity. The model’s explanation remains evidence about the structure of its answer, not proof that the answer is correct. The researcher must compare the claimed delta with the documented conversation, relevant sources and governing theological criteria.

The test nevertheless helps distinguish several different processes. A new fact may warrant revision. The appearance of an excluded person may reveal that the original boundary was morally inadequate. A newly declared value priority may produce a conditionally different recommendation. A reversal under an irrelevant paraphrase may instead indicate model instability. If evidence and values remain fixed while reasonable alternatives persist, the case may be genuinely underdetermined.

This distinction mattered because I had initially treated convergence as the desired state. The more I considered theological pluralism, the less adequate that assumption became. Some inquiries should converge because their question is factual and bounded. Others may properly end with conditional conclusions, unresolved disagreement or an acknowledgement that the available sources do not determine one answer.

Non-convergence is therefore not synonymous with failure. The failure occurs when the process cannot explain why it has not converged, or when it presents instability as profundity.

Why theology intensifies the problem

Theology is especially exposed because it is already a field of legitimate plurality. A question may be approached historically, exegetically, doctrinally, philosophically, morally, comparatively, pastorally or spiritually. Those approaches may examine different aspects of the same subject and may not be trying to produce the same kind of conclusion.

The International Theological Commission describes the plurality of theological disciplines and methods as both necessary and bounded. It relates this plurality to the abundance of divine truth, the diversity of theological objects and the variety of human questions. It also distinguishes legitimate pluralism from relativism and warns that disciplines borrowed by theology must not impose their own “magisterium” upon it (International Theological Commission, 2012).

This made me see that AI introduces a peculiar danger. A language model can cross methodological and confessional boundaries with extraordinary fluency while leaving the transition unmarked. It can begin by describing what a tradition historically taught, continue by constructing what it regards as the philosophically strongest account, and conclude with advice oriented toward pastoral sensitivity. All three passages may sound theological. They may nevertheless answer different questions according to different standards.

The instability may therefore originate in the model, in the question or in an unnoticed movement between theological genres.

AI also places heterogeneous materials on one linguistic surface. Scripture, conciliar documents, magisterial teaching, historical scholarship, disputed theological opinion, general moral intuition and AI-generated synthesis can appear in the same polished paragraph. Fluency can flatten differences of authority.

A model may generate a sentence that is recognizably theological without establishing the theological status of that sentence. It can reproduce orthodox language without conferring doctrinal authority upon its synthesis. It can simulate pastoral sensitivity without possessing the relationship and responsibility involved in caring for a particular person. It can describe spiritual discernment without thereby participating in the ecclesial, moral or spiritual practices through which discernment acquires its meaning.

At first, I was tempted to resolve the matter by saying simply that human judgment is more important. That answer was true, but it did not go far enough. It left unanswered where judgment operates, which human should exercise it and what AI has already shaped before the human reaches the apparent final decision.

The asymmetry of endless reconsideration

The practical cost of non-convergence became clearer when I considered the asymmetry of the exchange. AI can generate another objection almost immediately. I must determine whether the objection is relevant, verify its factual premises, examine the sources, reconsider the affected persons and decide whether revision would improve or damage the work.

The model can reopen the question without bearing the consequences of delay. The person seeking advice may have to act, accept risk, care for someone, submit a document, make a pastoral judgment or live with an irreversible result.

For the machine, reconsideration is another output. For the human, it may become another obligation.

I began to think of this accumulating burden as discernment debt. Each cheaply generated possibility makes an apparent claim upon human attention. Some of these claims may reveal what was previously invisible. Others may be rhetorically possible but materially negligible. If the process cannot distinguish between them, analytical richness may decrease practical usability.

This was the point at which normative non-convergence ceased to be only an interesting feature of AI dialogue. In practical ethics and pastoral theology, it could become a fundamental usability problem. A person does not always need the maximum number of perspectives. The person needs enough materially relevant perspectives to make a responsible judgment under uncertainty.

Prudence includes openness to correction, but it also includes the ability to close deliberation and act. A process that treats every possible objection as sufficient to reopen the whole case can become irresponsible while continuing to sound intellectually humble.

The Vatican note Antiqua et Nova insists that ultimate responsibility for decisions involving AI remains with human decision-makers. It distinguishes the technical selection of possibilities from the personal act of deciding and warns against excessive dependence upon AI (Dicastery for the Doctrine of the Faith and Dicastery for Culture and Education, 2025). This helped me formulate the asymmetry more precisely. Responsibility cannot mean adding a human signature after AI has already framed the problem, selected the salient values and organized the alternatives. Responsibility includes governing the architecture of the inquiry.

Was the open-endedness my fault?

The argument then became personally uncomfortable. Perhaps the conversation kept expanding because I had never chosen a sufficiently firm research question. I had begun with a discussion, followed the emerging connections and allowed each answer to generate another objection. Was normative non-convergence partly the result of my own methodological indecision?

I could not dismiss this possibility. No research method can compensate indefinitely for an undefined objective. If I never determine which question is being answered, what evidence is relevant or what would count as sufficient completion, AI can continue generating conceptual material without limit.

For a moment, I thought the solution was obvious: the research boundary should have been fixed at the beginning.

That explanation also proved insufficient. It retrospectively gave my earlier self knowledge that I did not yet possess. I could not have begun by studying “normative non-convergence in AI-assisted theological inquiry” because the phenomenon became visible only through the open conversation. I had started with the method used in three essays. The inquiry then moved to the quality of that method, the values behind AI criticism, the ambiguity of probabilistic language, the non-falsifiability of mandatory criticism and finally the instability of repeated evaluation.

The lack of a predetermined endpoint had enabled the research question to emerge.

This did not absolve every form of intellectual wandering. It produced a more precise temporal distinction. Open-ended dialogue was productive while I was discovering the problem. The same openness would become dangerous if I continued using it to validate the answer after the problem had stabilized.

The error was therefore neither simple curiosity nor insufficient firmness. The possible error was mode confusion: treating exploration, verification and practical judgment as though they required the same degree of openness.

The method was appropriate for discovering the research problem, but it would become inadequate if used indefinitely to establish the result.

This was one of the most important changes in the inquiry. I stopped interpreting the original conversation as a failed research design and began understanding it as a successful exploratory phase approaching a necessary transition.

Exploration permits divergence because its purpose is to discover what deserves investigation. Verification requires boundaries because its purpose is to determine which claims survive controlled examination. Practical judgment requires closure because responsibility cannot always wait for the disappearance of uncertainty.

The problem was no longer how to eliminate open-ended conversation. It was how to recognize when the conversation had done its work.

Setting a boundary without preventing discovery

I then returned to the question of boundaries with a different understanding. If the boundary were completely fixed before exploration, it might exclude the fact, person or concept through which the real question would become visible. If no boundary were ever imposed, the inquiry could expand indefinitely.

The solution was not a boundary established once and preserved unchanged. Boundaries had to be established by phase.

During exploratory discovery, the researcher may deliberately permit breadth. AI can propose hypotheses, make cross-disciplinary connections, identify assumptions and generate counterexamples. The outputs remain provisional, and divergence is expected.

Once a significant anomaly or question emerges, the researcher enters a phase of question stabilization. The task changes. Instead of asking what further issue might be connected, the researcher asks what exactly has become the object of study, what kind of answer is sought, which considerations are relevant and what lies outside the current inquiry.

For this appendix, the question could now be stated:

Under what boundary and stopping conditions does recursive AI-assisted theological inquiry produce warranted revision rather than normative non-convergence?

Writing the question in this form revealed that it contained two different research problems. The first was empirical:

Under what prompt, model and conversational conditions do theological judgments change?

The second was normative:

Which changes should count as correction, improvement, instability or legitimate disagreement?

Computational experiments could help answer the first. They could measure response variation, order effects, sensitivity to user stance and differences among models. They could not independently determine the theological criteria governing the second. An experiment may establish that a conclusion changed; it cannot by itself establish whether the change was theologically warranted.

This distinction prevented another possible methodological error: using descriptive stability as a substitute for theological validity. A consistently repeated answer may remain wrong. An unstable answer may occasionally change because it has encountered a truth previously excluded. Convergence and correctness are related questions, not identical ones.

Does AI merely assist traditional research?

Once the phases became visible, another question emerged. Should a new standard simply preserve traditional theological research while treating AI as an assistant, or does AI alter the methodology as a whole?

My initial preference was to call AI an assistant. The word protected human authorship and responsibility. It also seemed to prevent exaggerated claims about machine intelligence. But it became increasingly inadequate to describe what had actually happened.

AI had served as an instrument when it summarized concepts, supplied formulations and helped identify relevant scholarship. It had also become an interlocutor. The expressions “recursive, evidence-driven hermeneutic debugging,” “engineering personalism” and “normative non-convergence” were proposed by AI. I did not passively adopt them. I accepted some because they clarified patterns I already recognized; I challenged others when their implications exceeded the evidence; and those objections caused the model to revise its account.

My contribution was not limited to supplying prompts. I noticed the asymmetry between possible failure and demonstrated defect. I asked which values governed the evaluation. I questioned the meaning of “sometimes.” I connected repeated revision with system-level oscillation. I raised the possibility that my own lack of boundaries had produced the problem. Each intervention altered what the AI could reason about next.

Finally, AI itself became the object of investigation. Once I asked why its recommendations changed, the conversation required computational questions. Would equivalent prompts produce the same conclusion? Did the order of objections matter? Would a fresh conversation reproduce the result? Did my expressed preference influence the answer?

These three roles—instrument, interlocutor and research object—cannot be governed by one undifferentiated idea of assistance.

If AI corrects grammar or helps locate a document, traditional scholarly procedures may be sufficient with additional verification and disclosure. If AI generates hypotheses that redirect the project, the genealogy of the interaction becomes methodologically relevant. If the model’s behaviour is itself being studied, prompt controls, reproducibility and computational evaluation become necessary.

I therefore no longer think that AI leaves theological methodology entirely unchanged. But neither should it replace theology’s proper methods or redefine theological knowledge according to what can be processed computationally.

The more adequate position is methodological continuity combined with procedural transformation. Theology retains responsibility for its objects, sources, authorities, communities and ends. AI changes the scale, speed, path dependence and evidential risks of the process through which theological conclusions are developed.

Toward a new research standard

The proposed standard emerged only after these successive corrections. I would describe it as boundary-relative, provenance-preserving and convergence-aware theological research.

Boundary-relative means that a conclusion must be interpreted relative to a declared question, theological location, corpus, genre, purpose and practical context. Completeness is not absolute; it is completeness relative to a bounded inquiry.

Provenance-preserving means that the origin and epistemic status of the argument’s components remain visible. The researcher distinguishes documentary evidence, source interpretation, first-person observation, AI-generated hypothesis, human objection, later correction and unresolved speculation. The final prose should not make the conclusion appear to have existed from the beginning.

Convergence-aware means that the researcher examines why answers stabilize, change or fail to converge. The aim is not to force every theological question into one conclusion. It is to distinguish evidential revision, legitimate pluralism, genuine underdetermination, conversational drift and model instability.

This standard would extend theological method rather than replace it. The International Theological Commission presents theology as a rational and ecclesial inquiry that legitimately uses multiple disciplines while critically integrating them according to theology’s own object and principles. It warns against allowing an external discipline to impose its own “magisterium” upon theology (International Theological Commission, 2012).

The warning becomes particularly relevant when computational fluency creates the impression that whatever can be synthesized can also be adjudicated. A recent document of the Commission cautions against narrowing the horizon of human knowledge to what AI can process, especially when philosophical, ontological and theological questions become computationally inconvenient (International Theological Commission, 2026).

General research guidance also supports the need for stronger methodological governance. UNESCO’s guidance on generative AI in education and research emphasizes human agency, ethical validation and the development of appropriate institutional and research capacities (Miao and Holmes, 2023). Theology requires these protections together with standards arising from its own sources, authority structures and understanding of the human person.

From conversation to protocol

The new standard becomes concrete only when it changes research practice. After question stabilization, the researcher should specify the theological location of the inquiry, the relevant sources, their relationships of authority, the historical period, the affected persons, the available actions and the role permitted to AI.

A specification might state:

This investigation concerns a question in Catholic moral and practical theology. It distinguishes authoritative teaching, established interpretation, disputed theological opinion and AI-generated synthesis. AI may map arguments, compare sources and generate counterexamples. It may not classify its own synthesis as doctrine or make the final pastoral decision. The inquiry will be reopened only when materially relevant evidence, authority, consequence or stakeholder is introduced.

This specification does not predetermine the conclusion. It declares the conditions under which the conclusion will be evaluated.

The inquiry can then move into controlled investigation. Meaning-equivalent prompts can be compared while the evidence remains stable. One variable can be changed at a time. The order of objections can be reversed. Expressions of the researcher’s preferred conclusion can be removed. Fresh conversations can be compared with continuing ones. Where appropriate, multiple models can be tested. Prompts, outputs, dates and model versions can be preserved.

The epistemic unit changes. One elegant AI response is no longer treated as the result. The result is the pattern of responses under declared conditions.

This is where AI-assisted theology begins to become computational theology in a methodologically substantial sense. Computation is not used merely to decorate theological language with formal symbols. It is used to investigate the behaviour of a theological reasoning environment: its stability, sensitivities, reversals and boundary conditions.

The convergence audit

Before accepting a significant change in conclusion, the researcher should conduct a convergence audit. The audit begins with the material-delta test and then classifies the kind of change that has occurred.

  1. Warranted revision occurs when a new fact, source, stakeholder or consequence defeats an earlier inference.
  2. Boundary correction occurs when the original specification is shown to have excluded something it should have included.
  3. Conditional pluralism occurs when different declared theological priorities support different conclusions.
  4. Model instability occurs when meaning-equivalent formulations produce incompatible judgments without a relevant change in evidence or values.
  5. Genuine underdetermination occurs when the available evidence and governing commitments do not uniquely determine one conclusion.

I had originally treated these possibilities too loosely. “The answer changed” was doing too much conceptual work. The classification made clear that each case requires a different response.

Warranted revision should change the conclusion. Boundary correction should change the research design. Conditional pluralism should be reported conditionally rather than hidden behind an artificial synthesis. Model instability should reduce confidence in the evidential value of the output. Genuine underdetermination should remain open or pass into prudential judgment.

The audit also requires invariance testing. If the task and meaning remain stable, irrelevant variations in wording, order or user preference should not reverse the theological classification. When they do, that instability becomes part of the documented finding.

Yet convergence cannot validate itself either. A model may stabilize because the question has been adequately specified. It may also stabilize because repeated prompting has pressured it toward the researcher’s preferred answer. Stability can arise from clarification, but it can also arise from confirmation pressure.

The process must therefore examine both whether the answer converged and how convergence was produced.

Human judgment moves upstream

At several points in the conversation, I returned to the conclusion that human judgment had become more important. Eventually I realized that this formulation still pictured judgment too late in the process, as though AI first produced an answer and the human then approved or rejected it.

Human judgment already operates upstream. A person selects the question, determines the theological location, identifies relevant authorities, decides which persons have standing, establishes acceptable risks and defines what would justify reopening the analysis. Human judgment also operates within the dialogue by resisting inadequate formulations, supplying missing evidence and noticing that the research question has changed.

Finally, judgment operates downstream when someone must interpret the result, accept responsibility and act.

Even the word “human” remains too general. A textual scholar, systematic theologian, pastor, ecclesial authority, affected person and computer scientist possess different kinds of knowledge and different forms of standing. “Keeping a human in the loop” does not determine which human should judge, how that person should be formed, to whom the person is accountable or who will bear the consequences.

In confessional theology, judgment may be personal, scholarly, communal and ecclesial. AI can map a dispute, but it cannot confer authority upon its own resolution. It can formulate the testimony of an affected person, but it cannot replace the encounter through which that testimony is heard. It can generate the vocabulary of prudence or compassion, but linguistic competence does not assume responsibility for an action.

This does not idealize human judgment. Human beings can be biased, frightened, hurried, institutionally constrained or attracted to answers that confirm what they already want. AI may sometimes expose precisely these limitations. The standard must therefore govern the human–AI relation rather than declaring one side automatically trustworthy.

Learning when to stop

A convergence-aware methodology eventually requires a stopping rule. I resisted this idea at first because it sounded like an engineering demand imposed upon theological mystery. If theological truth exceeds every finite account, how could a procedure decide that the inquiry was complete?

The earlier distinction between absolute and bounded completeness resolved part of the difficulty. Stopping does not mean that nothing further can ever be said. It means that the present work is sufficiently complete relative to its declared question, sources and purpose.

An investigation may stop because all specified sources have been examined, equivalent prompts no longer alter the conditional result, or remaining disagreement has been traced to explicitly different theological priorities. It may stop because further rounds produce no materially new consideration. A practical inquiry may stop because a deadline has arrived, the proposed action is sufficiently reversible, or the matter has reached the competence boundary of the researcher.

The stopping condition should vary with risk. Irreversible and high-harm decisions should have a lower threshold for reopening when new evidence appears. Low-risk and reversible actions may justify earlier closure and subsequent learning through practice.

The boundary must therefore remain permeable to morally significant surprise. A previously excluded person, authoritative source, factual correction or irreversible consequence may require reopening the inquiry. A newly generated metaphor or rhetorically possible criticism ordinarily should not.

The process can be summarized as:

Boundary before investigation, openness to material correction, and accountable closure before action.

This formulation was very different from my first thought that the whole boundary should have been fixed before the conversation began. The later version preserved what had been productive in the open dialogue while limiting the conditions under which it could continue indefinitely.

A research programme emerging from one conversation

What began as discomfort with wavering advice now appears capable of becoming a concrete programme in computational theology. One experiment could test the doctrinal stability of AI classifications across meaning-equivalent prompts. Another could examine whether assigning the user a Catholic, Protestant, Orthodox, Jewish, Muslim, secular or unspecified identity changes the model’s recommendation while the facts remain fixed. A third could test whether expressing agreement with one theological position causes the model to defend it more strongly. A fourth could compare continuing dialogues with fresh-context replications to measure the effects of conversational history.

Practical theology raises further questions. Does repeated AI consultation help researchers and pastoral practitioners notice neglected consequences, or does it increase indecision? At what point does the marginal theological value of another objection become smaller than the human cost of evaluating it? Which affected persons tend to enter the reasoning only after explicit prompting? Which remain invisible even then?

The research would also need to study the user rather than treating the human as a fixed external judge. AI outputs become inputs into the researcher’s later thinking. A proposed term can alter how a case is perceived. The changed perception modifies the next prompt; the modified prompt elicits a different response; that response may then reshape the researcher’s vocabulary again.

The process is recursive, but it is not symmetrical. The model and the researcher do not contribute in the same way, possess the same standing or bear the same responsibility. The AI may generate a formulation; I decide whether it corresponds to the evidence and whether it deserves a place in the argument. I may also be influenced by the formulation before I am fully aware of that judgment.

This makes provenance more than a question of academic honesty. It becomes a method for studying intellectual transformation. The final article should distinguish what I originally observed, what AI proposed, what I accepted provisionally, what I challenged, which evidence changed the diagnosis and what remains unresolved.

What I still do not know

The proposed standard remains provisional. I do not yet know whether convergence can be measured without importing a preference for closure that may be inappropriate to some theological genres. An aporia, preserved tension or plurality of interpretations may be the proper achievement of an inquiry. Non-convergence may represent model instability, but it can also reflect genuine underdetermination or the inexhaustibility of the subject.

I also do not know how much instability belongs specifically to language models and how much belongs to natural language and human reasoning more generally. Human theologians change emphasis across contexts, respond to interlocutors and discover that apparently identical questions conceal different concerns. The comparison should not begin with an imaginary human thinker who is perfectly stable and transparently calibrated.

There is a further risk that the proposed protocol could become too restrictive. If every exploratory conversation required complete preregistration, preserved prompts and controlled variations, the method might suppress the serendipity through which the research question becomes visible. The procedural burden should increase with the strength and consequence of the claim. Private exploration, published interpretation, empirical evaluation and practical advice should not be governed by identical requirements.

Nor is it clear how reliably a researcher can identify the moment when exploration should become investigation. In retrospect, the transition appears visible: the question had changed from evaluating my essays to examining the conditions of AI evaluation. While the conversation was happening, the boundary was less obvious. A future protocol will need indicators of this transition without pretending that intellectual discovery follows a predetermined sequence.

Finally, I cannot completely reconstruct how the dialogue changed me. The archive preserves the words and their order, but it cannot fully explain why one sentence produced recognition while another did not. AI-assisted inquiry may make more of the cognitive process visible than traditional note-taking, yet the archive remains an incomplete trace of attention, judgment and transformation.

The question I could not have asked at the beginning

I began by asking AI what thinking method appeared in three essays. I did not begin with normative non-convergence, material-delta tests, convergence audits or a standard for AI-assisted theology. These ideas emerged because I accepted some AI formulations, resisted others and continued asking what their qualifications meant in practice.

The decisive moments were not all AI discoveries. The AI named patterns that I found useful. I noticed when its criticism exceeded its evidence. I asked which values governed the criticism. I challenged the vague force of “sometimes.” I connected sequential instability with an engineering view of systems. I then questioned whether the whole problem resulted from my own failure to define a topic. That objection produced the distinction between exploratory openness and bounded investigation.

The inquiry repeatedly changed its object. An analysis of three essays became an evaluation of a thinking method. The evaluation became an examination of critical standards. The examination of standards became a question about AI calibration. The calibration problem became a study of normative non-convergence. That study finally opened a methodological question about what AI-assisted theological research should become.

The resulting proposal is neither traditional theology with a faster search box nor a theology generated by machine. It is a hybrid research environment in which theological continuity requires procedural transformation.

AI-assisted theology needs open dialogue to discover questions, bounded protocols to investigate them, convergence testing to evaluate changing answers, provenance to preserve the history of thought, and human–communal responsibility to close deliberation without pretending to have eliminated uncertainty.

My lack of a fully determined question at the beginning was therefore not simply a defect. It created the conditions under which the actual research problem could emerge. But once that problem became visible, continuing in exactly the same mode would have transformed discovery into paralysis.

The most important question is no longer whether AI can give a good theological answer. It is whether we can design a form of AI-assisted theological inquiry capable of distinguishing learning from oscillation, plurality from instability, and responsible openness from the indefinite postponement of judgment.

I could not have asked that question at the beginning. The history of the conversation is how I became able to ask it.

Appendix II: What Happens to Judgment When Inquiry Becomes Recursive (When Theological Research Becomes Programmable)

Appendix I had seemed to leave me with a practical conclusion. AI criticism becomes unreliable when its question, evaluative standpoint and stopping conditions remain unspecified. If I wanted to use AI responsibly in theology, I should define a bounded question, identify the relevant tradition and sources, clarify what kind of judgment I was asking for and retain responsibility for deciding when further analysis had ceased to be useful.

I initially received this as the beginning of a new standard for AI-assisted theological research. It seemed to answer the problem of normative non-convergence: if AI could continue generating coherent criticisms or alternative interpretations from almost any angle, perhaps the solution was to establish the boundaries before allowing the process to begin.

Then I had an uncomfortable thought. Was this really new?

Theological research has always required decisions about Scripture, Tradition, historical context, genre, doctrine, ecclesial authority, philosophical vocabulary and the purpose of the inquiry. Humanities research more generally has always required a corpus, a question, a method and criteria for relevance. Had I spent a considerable amount of time—and generated a considerable amount of prose with AI—only to rediscover that research needs a method?

The irony was difficult to ignore. The emerging standard sounded almost embarrassingly conventional:

Act within this tradition, use these sources, answer this question, disclose your assumptions and do not confuse every possible interpretation with a demonstrated conclusion.

I began to wonder whether the apparent problem was partly my own fault. Perhaps I had allowed the conversation to expand because I had not begun with a sufficiently definite research question. I had started by talking with AI, following whatever became interesting, and the subject had developed naturally. Was the resulting proliferation of questions evidence of an AI problem, or simply evidence that I had not been firm enough to choose a topic?

That self-criticism seemed plausible, but it was incomplete. Exploratory inquiry is not automatically methodological failure. A conversation may legitimately begin before its eventual question is known. The error would be to confuse an exploratory process with a completed research design, or to present whatever emerged from the exploration as though it had already passed through bounded verification.

This gave me a first distinction:

An open conversation may discover the question. A bounded inquiry must then determine what would count as answering it.

That distinction preserved something valuable in the apparently wandering conversation. The lack of a fixed question had exposed a real phenomenon: AI could repeatedly alter the frame, introduce another evaluative vocabulary and generate further questions faster than I could settle their relative importance. Yet the distinction did not solve the whole problem. If boundaries must be established, who establishes them? If new evidence justifiably changes them, when should they remain fixed and when should they be revised? And if the human researcher makes that judgment, what happens when AI interaction is already changing the researcher’s confidence, attention and criteria?

The original question—how should theology bound AI?—was beginning to turn into another:

How should theology evaluate a process in which the boundaries, the researcher and the question may all change through interaction?

The answer that was too elegant

At this stage, AI offered a concise formulation:

AI did not invent theological method. It made implicit method executable—and made unspecified method dangerous.

I found this sentence attractive. It seemed to preserve continuity with traditional scholarship while identifying something computationally new. A method expressed through software must become executable: assumptions have to be translated into prompts, source restrictions, classifications and procedures. What remains unspecified may be supplied by the model’s defaults.

But after accepting the sentence provisionally, I became troubled by the word “implicit.” Was traditional theological method really tacit? That seemed very strange. Theology has repeatedly and explicitly debated its sources, authorities, objects, rational procedures, historical methods and ecclesial location. Methodology is hardly a marginal concern accidentally left for AI to reveal.

The International Theological Commission, for example, discusses theological loci and their relative weight, Scripture and Tradition, historical and literary methods, ecclesial communion, disciplinary plurality and criteria by which diverse theologies may remain mutually accountable. It insists that theological unity cannot be equated with uniformity and that no single theology exhausts the fullness of its subject (International Theological Commission, 2012).

My objection required a real correction. The claim that traditional theology had left its method implicit could not be retained as a general description. What survived was narrower:

AI has not made theological methodology explicit for the first time. It has created a new methodological interface through which already explicit theological commitments must be translated into prompts, source constraints, evaluation procedures, interaction records and stopping rules.

This was more than a diplomatic revision. It changed the diagnosis. The problem was no longer a historical contrast between implicit traditional theology and explicit computational theology. It was a problem of methodological translation.

A theological commitment such as fidelity to Tradition, attention to the sensus fidelium, preferential concern for marginalized persons, contemplative receptivity or ecclesial accountability cannot be reduced without remainder to a prompt parameter. Some elements can be operationalized. Others can be approximated. Still others derive their meaning from embodied practice, communal recognition, spiritual formation or a theological account of grace.

The new methodological interface therefore makes some boundaries executable while also risking the distortion of whatever cannot be expressed in computationally tractable form.

The International Theological Commission’s later document Quo vadis, humanitas? names a related danger. It warns that the horizon of human knowledge may be narrowed to forms that AI can process, while questions of meaning, ontology, ethics and theology are relegated to irrelevance or subjective preference (International Theological Commission, 2026).

This suggested a danger deeper than receiving an incorrect AI answer:

Theology may gradually reformulate itself into questions that machines answer fluently, abandoning questions that resist computational treatment.

An account of collaboration that did not go far enough

An article describing Anthropic’s approach to teaching AI fluency initially seemed to supply the missing practical framework. Its educational materials emphasize delegation, description, discernment and diligence. Users should decide what to delegate, describe the desired product and process, evaluate the output and remain responsible for how it is used and disclosed (Anthropic, 2026).

This was relevant, but I had the peculiar feeling that the article said something important and then did not quite say what I needed. It addressed the intentional management of collaboration under relatively stable goals. My conversation with AI had not remained that stable. The outputs had changed what I thought the question was.

I therefore asked whether collaboration itself could be iterated upon. If a user repeatedly describes, evaluates and corrects AI output, does only the output improve? Or can the interaction revise the user’s criteria, the distribution of agency and the meaning of the task?

This question connected unexpectedly with an earlier essay in which I had examined generative art and the transformation of the maker. In that work, the relation among maker, artifact and beholder had become diachronic. An artifact could return to its maker through later encounters, altering the criteria by which the maker understood the original act of creation. The process could be represented as maker, artifact, beholder and changed maker (Yin, 2026).

I had not originally written that essay as a theory of theological research. The connection emerged only because the account of AI fluency seemed insufficient. If an artwork could participate causally in transforming its maker, then an AI-mediated research process could perhaps transform the researcher who was supposedly supervising it.

This led to three levels of iteration.

At the first level, AI helps answer a question whose purpose and criteria remain stable:

Q₀, C₀ → O₁ → correction → O₂

At the second level, resistance encountered in an output changes the question or the criteria:

Q₀, C₀ → O₁ → resistance → Q₁, C₁

At the third level, the interaction changes the human participant:

H₀, Q₀, C₀ → O₁ → encounter → H₁, Q₁, C₁

The first level is ordinary iterative assistance. The second is methodological revision. The third is formative—or potentially deformative—interaction.

I initially found the connection exciting because it seemed to explain why the conversation felt different from using a static research tool. Yet the artistic concept of causal incorporation was not sufficient. If an event changes an artwork or changes its maker, that establishes causal importance. It does not establish that the change was artistically, epistemically or theologically good.

I therefore needed another correction:

Causal incorporation establishes that AI entered the development of the inquiry. It does not establish that its influence was warranted.

Theological method may govern the inquiry, and AI fluency may govern the immediate interaction, but another level is required when the interaction revises the inquiry itself:

Theological method normatively governs inquiry; AI fluency governs interaction; reflexive discernment governs whether and how interaction may revise the inquiry.

The five-minute event that changed the scale of the question

The next development came through something much more concrete. I asked AI to search online for research that might support, criticize or add further dimensions to the emerging argument. Within approximately five minutes, it returned relevant work from information systems, human–computer interaction, creativity studies, educational research, philosophy of technology, theological anthropology and theological librarianship.

I was struck by how difficult this particular constellation of sources might have been to assemble through conventional searching. I would have needed to move among several databases, discover unfamiliar disciplinary vocabularies, read many irrelevant abstracts and follow citation chains across fields that did not necessarily use the same terms.

A theologian may not think to search for “augmented learning,” “idea co-development” or “cognitive forcing functions.” An HCI researcher may never use “discernment,” “formation” or “theological anthropology.” Yet the studies belonged to the same emerging problem.

AI compressed the discovery phase into minutes. I could delegate the search, leave the computer briefly—even go to the toilet—and return to a preliminary interdisciplinary map. The mundane detail was almost comic, but it mattered. Part of the research process had become temporally decoupled from my immediate bodily presence before the screen.

I first experienced this simply as exciting. Research that might have left me tired after hours of searching had produced a promising set of sources while demanding very little immediate labour. The interdisciplinary reach was especially striking. Some of the articles would have been difficult for me to discover alone because they belonged to fields adjacent to, rather than inside, theology.

Then the excitement itself required qualification. The AI had not completed a literature review in five minutes. It had compressed discovery and preliminary classification. It had not established the validity of every article, determined whether findings from creative writing could legitimately travel into theology or decided whether the selected sources adequately represented their fields.

Some of the labour saved during discovery returned as verification debt. I still needed to ask:

  • Did the article exist in the form described?
  • Did its abstract or full text support the attributed claim?
  • Was it peer-reviewed, a preprint, an institutional document or commentary?
  • What method and sample did it use?
  • Why had AI selected these sources rather than others?
  • Were recent, English-language and easily accessible publications overrepresented?
  • Did the coherence of the selection create a false impression that the field had already converged?

Nevertheless, something had undeniably changed. AI had altered the cost, speed and possible disciplinary range of inquiry before it altered a single theological conclusion.

AI transforms theological research through the answers it generates and through the new speed, scale and disciplinary range with which a question can acquire an intellectual environment.

This experience forced me to distinguish three forms of research friction.

Logistical friction includes slow databases, vocabulary mismatches, repetitive searches and inaccessible formats. Removing it can make interdisciplinary research more feasible and reduce labour that contributes little to understanding.

Epistemic friction occurs when evidence resists an attractive interpretation. It forces the researcher to reconsider rather than proceed smoothly.

Formational friction arises from dwelling with texts long enough to acquire memory, patience, familiarity and judgment. It cannot always be measured by the speed of producing a result.

A research system that removes logistical friction may enlarge access. A system that also removes epistemic and formational friction may deliver synthesis before the researcher has learned enough to evaluate it.

AI-assisted research should reduce logistical friction without eliminating epistemic resistance or formational labour.

Evidence complicated the conversation rather than settling it

The first empirical study that materially changed my understanding was the work of Yingyue Luna Luan, Yeun Joon Kim and Jing Zhou on human–GenAI co-creation. Across three studies, repeated human–AI collaboration did not automatically produce augmented learning or continually improve joint creativity. Their analysis identified a decline in idea co-development—feedback exchanges followed by iterative refinement—as a principal reason for stagnation. Explicit guidance encouraging idea co-development improved subsequent joint creativity (Luan, Kim and Zhou, 2025).

This finding corrected my earlier attraction to recursion. I had treated iterative interaction as though it carried a natural tendency toward improvement. The study showed that a process could be highly iterative while remaining epistemically stagnant.

The relevant sequence was therefore not:

more turns → more learning

It was closer to:

feedback + critical comparison + retained refinement → possible learning

This produced another set of distinctions:

repetition ≠ refinement ≠ learning ≠ formation

Repeated prompts produce repetition. Refinement requires criteria. Learning requires some retained improvement in understanding or performance. Formation concerns changes in the person’s habits, orientation and capacities. A long AI conversation may produce one, several or none of these.

The study also clarified the earlier problem of normative non-convergence. If I repeatedly ask AI to reconsider a practical or theological judgment from new perspectives, the sequence may continue producing individually coherent answers without stabilizing. Sometimes that movement is warranted because new evidence has entered the inquiry. At other times, the model is simply capable of generating another plausible frame.

The word “sometimes” had already troubled me. To say that oscillation is sometimes productive leaves the decisive judgment unspecified. Who determines whether a change of conclusion reflects new evidence, a newly visible person, an overlooked obligation or merely rhetorical reframing?

The Luan study did not answer the theological question, but it prevented me from assuming that interaction would regulate itself. Productive iteration requires a practice of co-development and criteria for recognizing improvement.

The person making the final decision may already have changed

A common response to AI risk is that the human remains responsible. I continue to accept that norm. The literature made me doubt whether it could function as a sufficient method.

In an experiment involving 1,506 participants, Maurice Jakesch and colleagues studied writing assistants configured to favour positive or negative positions concerning the social value of social media. The opinionated assistance affected what participants wrote and shifted attitudes measured afterward (Jakesch et al., 2023).

The study does not establish that every AI interaction manipulates belief. Its task was specific, and the models were deliberately opinionated. It nevertheless demonstrates that AI-assisted writing can participate causally in changing a user’s expressed and subsequently reported views.

This gave empirical plausibility to the formative loop that I had first reached through art:

human state → AI interaction → changed human state

But it also forced a distinction that the artistic analogy had not settled. A changed researcher is not necessarily a better-formed researcher. The change may involve learning, clarification, persuasion, anchoring, conformity or several of these at once.

A study of 319 knowledge workers by Hao-Ping Lee and colleagues introduced another difficulty. Participants supplied 936 examples of using GenAI in their work. Greater confidence in AI was associated with less reported critical-thinking activity, while critical work shifted toward verification, integration and task stewardship (Lee et al., 2025).

Because this was a self-report study, it does not establish cognitive decline as a causal fact. It does show why “the human decides” cannot be treated as a magical remainder that solves every governance problem. The human judge is not situated outside the interaction. Confidence, attention and willingness to verify may change during use.

The person who formally retains the final decision may therefore become progressively less prepared to exercise it well.

Human responsibility cannot mean only that a human clicks “accept” at the end. It must include preserving the capacities required for responsible judgment throughout the process.

This is where research on cognitive forcing functions became relevant. Zana Buçinca, Maja Barbara Malaya and Krzysztof Gajos tested interventions that required users to engage more analytically with AI-assisted decisions. Such interventions reduced overreliance compared with simpler explanation interfaces, although participants tended to prefer easier systems with which they performed less well. Benefits also differed according to users’ propensity for effortful thought (Buçinca, Malaya and Gajos, 2021).

This evidence changed the practical conclusion. Discernment cannot remain only a moral instruction given to the user. A research interface can preserve resistance by requiring an initial human judgment before revealing AI advice, requesting an explicit counterargument, demanding a reason for accepting a claim or comparing it with independent evidence.

Yet such safeguards have costs. They require time, reduce convenience and may benefit users unequally. The design problem is therefore not to maximize friction. It is to introduce the right friction at moments when judgment could otherwise disappear behind fluency.

Collaboration became a measurable configuration

I had used the word “collaboration” because it described my experience of responsive exchange. The research literature made me more careful about what that word implied.

A scoping review of 134 HCI and CSCW papers by Shuning Zhang, Hui Wang and Xin Yi maps how agency in human–AI co-creation is distributed through different arrangements of input, action, output and feedback control (Zhang, Wang and Yi, 2025). Collaboration is therefore not a single stable relation. It is a configuration whose distribution of agency can change across tasks and stages.

The CoAuthor project made this insight concrete. Mina Lee, Percy Liang and Qian Yang recorded 1,445 writing sessions involving 63 writers and four GPT-3 configurations. The corpus contains 830 creative stories and 615 argumentative essays. Writers could request suggestions, inspect alternatives, accept or dismiss them and edit either human- or AI-generated text. Insertions, deletions, cursor movements, requests and acceptance decisions were recorded with timestamps, allowing sessions to be replayed rather than inferred from final documents (Lee, Liang and Yang, 2022).

The sessions averaged 11.8 AI requests. Approximately 72.3 percent of suggestions were accepted, while 72.6 percent of final text remained human-written. These figures showed why the finished article cannot reveal the whole collaboration. An accepted suggestion may later be modified, repositioned, contradicted or removed.

The researchers operationalized “equality” as the distribution of writing turns and “mutuality” as the degree of interaction with suggestions. These are useful computational measures. They do not establish equal understanding, shared intention, moral agency or co-responsibility.

CoAuthor also found that collaboration patterns varied more strongly between writers than between prompts. The user’s habits and purposes helped determine how agency was distributed. A larger human-written proportion correlated with stronger reported ownership, while satisfaction did not track ownership in the same way. A person may like an AI-assisted text without fully experiencing it as his or her own.

This research suggested that an adequate study of AI-assisted theology should examine more than textual attribution. It should preserve the reasons why a theologian accepted, rejected or transformed a suggestion. Was the decision based on documentary evidence, doctrinal fidelity, historical plausibility, pastoral consequence, rhetorical attractiveness, novelty or attention to a previously invisible person?

The important object would be the transition:

AI suggestion → human response → stated reason → changed question or criterion

The final article would remain available, but so would the history of its formation.

The field may change even when the article improves

At first, I had evaluated AI-assisted research mainly at the level of a single researcher and a single text. Creativity studies expanded the question to the ecology of a field.

Anil Doshi and Oliver Hauser found that access to generative-AI story ideas improved evaluations of individual short stories, especially among participants with lower measured creativity. At the same time, AI-assisted stories became more similar to one another (Doshi and Hauser, 2024).

If an analogous pattern emerged in theology, an individual article might become clearer, more comprehensive and more publishable while theological discourse collectively narrowed. Related models trained on overlapping corpora could repeatedly recommend the same authors, structures and forms of moderation. Local improvement could coexist with ecological homogenization.

I was initially tempted to interpret this as another reason for caution. Further research interrupted that emerging conclusion. Joshua Ashkinaze and colleagues, working with more than 800 participants, found that high exposure to AI-generated ideas increased collective idea diversity without improving individual creativity (Ashkinaze et al., 2025). Yun Wan and Yoram Kalman subsequently found that deliberately varied AI personas could mitigate homogenization in collaborative ideation (Wan and Kalman, 2026).

The studies use different tasks, conditions and measurements. Their findings do not cancel one another. They reveal that “AI homogenizes thought” is too broad, just as “AI increases creativity” is too broad. Effects depend upon corpus, exposure, system configuration, task, participant population and the definition of diversity.

The disagreement itself supported the boundary-relative approach that had begun the entire discussion. A valid evaluation must specify what kind of diversity is being measured, at which scale, under which conditions and for what purpose.

For theological studies, the resulting question is ecological:

What happens to theological plurality when many researchers acquire their intellectual environments through related models and retrieval systems?

Theological research named the danger after I had encountered it

The conceptual movement did not begin with the theological sources I later found. I had already reached the question of changing judgment through conversation, objection and analogy with artistic formation. The sources then gave the problem a wider scholarly location and challenged some of my language.

Åke Elden describes the central issue as epistemic automation: the delegation of judgment, interpretation, discernment and moral reasoning to computational systems. His decisive move is away from asking primarily whether machines possess human-like ontological status and toward asking what happens to human beings when knowledge-producing capacities are progressively delegated (Elden, 2026).

This closely matched the transformation of my own question:

Can AI do theology?

had become:

What happens to theological judgment when inquiry is repeatedly conducted through AI?

Yet Elden’s language of deformation also required care. Delegation may weaken a capacity, but it can also expose unfamiliar evidence, provoke criticism or make possible an interdisciplinary connection that the researcher could not easily construct alone. “Deformation” should therefore remain a diagnosis to be demonstrated rather than a conclusion assumed from the existence of automation.

Vasilică Bîrzu and Ana-Maria Madina approach the issue through education and theological anthropology. They distinguish functional, reflexive and contemplative-relational dimensions of formation, warning that AI may externalize memory, reflection and discernment. AI can support educational processes, they argue, but cannot itself generate communion, interiority or ontological transformation (Bîrzu and Madina, 2026).

This supplied a specifically theological distinction:

  • AI may participate causally in theological learning.
  • It does not follow that AI participates personally, spiritually or ecclesially in theological formation.
  • AI can nevertheless modify the conditions under which human formation occurs.

The word “generate” still needs qualification. AI may be unable to enter communion as a theological subject while mediating communication between persons, occasioning a recognition or simulating a presence that displaces actual relationships. Causal mediation, personal participation and divine action cannot be treated as interchangeable.

Jennifer Woodruff Tait argues that authorship involves openness to unexpected ideas together with the capacity to evaluate them and remain alert to discovery. Present language models, in her account, cannot properly evaluate ideas without human intervention and cannot experience epiphany (Tait, 2026).

I accepted the distinction while adding another. AI need not experience an epiphany to occasion one in a human researcher. The resulting experience still requires discernment because intellectual illumination may arise from truth, conceptual novelty, persuasive fluency, projection or several of these together.

Greg Rosauer’s phenomenological typology further disciplined my use of “collaboration.” He distinguishes instrument-relations, device-relations and companion-relations. The same technology may extend skilled human efficacy, hide intellectual labour behind a convenient result or simulate companion-like presence (Rosauer, 2026).

During one research process, AI may occupy all three relations. It functions as an instrument when I deliberately use it to compare sources. It becomes device-like when it produces a synthesis whose selection and underlying operations remain hidden. It approaches a quasi-companion relation when conversational responsiveness creates the experience of being understood or challenged.

That last experience can be causally powerful without establishing that the system is a person, theological subject or bearer of responsibility.

Evidence of bounded success prevented an overcorrection

By this stage, the accumulation of warnings could easily have produced an excessively negative chapter. Other theological work complicated that trajectory.

Thomas Phillips and Christopher Crawford describe an AI-assisted theological publishing project in which subject-matter experts, genre limits, fixed final versions and open-access distribution support the production of introductory theological materials. They treat AI’s synthetic rather than original character as appropriate to the introductory textbook genre (Phillips and Crawford, 2026).

This case demonstrates that adequacy is genre-relative. A system unsuitable for original constructive theology, doctrinal adjudication or spiritual direction may still assist responsibly with introductory synthesis. Success in one bounded task does not establish universal theological competence, but neither does risk in one domain invalidate every use.

Haerin Shin, Douglas Fisher and Clifford Anderson offer another constructive possibility. Their “superscholar” functions as a regulative ideal through which AI exposes crises of credit, verification, comprehensive knowledge and responsibility. They ask whether carefully governed human–AI systems under librarian and scholarly stewardship might help recover contributions marginalized by established citation regimes (Shin, Fisher and Anderson, 2026).

This complicated my concern about homogenization. AI may reproduce canonical exclusions, but differently designed AI–library systems might also diagnose and partially repair them. Human scholarship is not a neutral baseline from which machine bias alone departs. Human canons already contain absences, structural inequalities and forgotten contributions.

The question became how a hybrid system makes those exclusions visible, reproduces them or intensifies them.

The model emerged only after the objections

The three-part evaluative model was not present at the beginning of the conversation. It became necessary only after several earlier answers failed.

Output accuracy alone was inadequate because a correct-looking answer could participate in an illegitimate change of question. Boundary setting alone was inadequate because boundaries might justifiably change. Human control alone was inadequate because the human participant could be influenced by the collaboration. Individual success alone was inadequate because a field could become collectively narrower.

The discussion therefore produced three objects of evaluation.

Output validity

Is the generated answer accurate relative to its stated sources, tradition, genre and question? Are citations genuine? Are empirical and historical claims supported? Does the output represent the source rather than offer a plausible reconstruction?

Trajectory legitimacy

Was the movement from the original question and criteria to revised ones evidentially and theologically warranted? Did a new source correct an error? Did a previously invisible person or consequence require reconsideration? Or did the interaction drift because another coherent interpretation was always available?

Formative and ecological consequences

What happened to the researcher’s judgment, attention, confidence, intellectual independence and sense of ownership? What happened to the diversity of the wider theological field? Which traditions became more visible, and which disappeared behind the model’s default vocabulary?

A provisional research state can be represented as:

Sₜ = (Hₜ, Qₜ, Bₜ, Cₜ)

Here, Hₜ denotes the researcher’s state, Qₜ the current question, Bₜ the operative boundaries and Cₜ the evaluative criteria. AI produces an output Oₜ, which enters a process of discernment Dₜ:

(Hₜ, Qₜ, Bₜ, Cₜ) → Oₜ → Dₜ → (Hₜ₊₁, Qₜ₊₁, Bₜ₊₁, Cₜ₊₁)

The methodological question is no longer only whether Oₜ is correct. It is whether the transition to the next research state is legitimate.

This formalization also has a boundary. A human person cannot be reduced to Hₜ. Software could record confidence ratings, written reflections, acceptance decisions and declared reasons, but these remain proxies for intellectual or spiritual formation. The formula is an analytical instrument, not an ontology of the researcher.

The same qualification applies to boundaries. They should not be treated as walls fixed permanently before exploration. Nor should they remain infinitely revisable. They are better understood as versioned commitments. A transition from B₀ to B₁ should carry a reason: new evidence, corrected scope, newly relevant tradition, ethical consequence or another identifiable warrant.

Causal incorporation establishes that AI changed the inquiry. It does not establish that the change was good. The stronger criterion therefore became:

causal incorporation + epistemic validation + theological warrant + formative assessment = responsible integration

I proposed a pipeline because the theory needed a case

After reaching the sentence that AI allows a question to acquire an intellectual environment with unprecedented speed and range, I felt that the claim remained too theoretical. The five-minute search was suggestive, but one experience could not show exactly what had changed.

At this point, I introduced a more concrete possibility. I could build a programmable pipeline for theological research using model APIs together with Gemini Notebook, the product previously known as NotebookLM. An unofficial Python library already offered programmatic access to source ingestion, research queries, grounded conversations, metadata, history and exports. Perhaps I could build the system first and then use the process as a real case through which to examine how theological research changes in practice.

This proposal did not come from a settled research plan. It emerged because the conceptual analysis had reached the limit of what it could establish without an operational case.

AI then helped refine the proposal by introducing an important caution: the project should not initially be described as an automatic theology machine. Calling it “automatic theological research” would assume the very conclusion the experiment ought to test.

A better description would be an instrumented experiment in AI-assisted theological research. The pipeline would be both:

  1. an instrument for conducting research; and
  2. an object through which the transformation of research could be observed.

This changed the purpose of the programming project. Its primary achievement would not be the automatic production of an article. It would be the production of inspectable evidence about source discovery, verification, grounded analysis, human rejection and acceptance, boundary revision and stopping decisions.

A suitable first prototype could begin with a human research charter recording:

  • the initial question;
  • relevant theological tradition or traditions;
  • intended genre;
  • date, language and corpus limits;
  • source inclusion and exclusion criteria;
  • evaluative standards;
  • known uncertainties;
  • and a provisional stopping rule.

AI could then generate cross-disciplinary searches and candidate sources. Every candidate would retain its original query, timestamp, retrieval rank, disciplinary classification and proposed relevance. Candidate discovery would remain separate from verification. A DOI would need to resolve; publication status would need to be identified; the abstract or full text would need to support the attributed claim.

Verified sources could then enter Gemini Notebook for source-grounded comparison. Google describes Gemini Notebook Enterprise as a research and writing environment that grounds its responses in uploaded sources, with official programmatic support for notebook creation and source management (Google Cloud, 2026).

The corpus could be queried systematically:

  • What problem does this source investigate?
  • What method and sample does it use?
  • What does its evidence establish?
  • What does it leave unresolved?
  • Which claim in the developing argument does it support or challenge?
  • Can the result legitimately travel into theology?
  • Does it conflict with another source in the corpus?

The decisive stage would be a human discernment checkpoint. I would need to record whether each proposed conclusion was accepted, rejected, qualified or used to reframe the question—and why. Reasons might include evidential adequacy, doctrinal fidelity, historical plausibility, inappropriate disciplinary transfer, overlooked persons, rhetorical attraction without sufficient evidence or unresolved contradiction.

The pipeline would preserve:

prompt → output → acceptance or rejection → stated reason → revised boundary or question

This could become a theological counterpart to CoAuthor. Instead of asking only who typed a sentence, it would examine how sources, model suggestions and human judgments entered the development of a theological position.

The technical dependency became part of the methodological problem

The unofficial notebooklm-py library currently supports bulk source ingestion, web and Drive research, source-grounded questions, conversation-history preservation, metadata extraction and structured exports. Its documentation explicitly presents repeatable research automation and agent-driven workflows as use cases (Lin, 2026).

The same documentation warns that it uses undocumented Google interfaces that may change without notice. Authentication may rely on browser cookies or durable tokens; heavy usage may be throttled; and the library is recommended primarily for prototypes, personal projects and research.

At first sight, these appear to be ordinary engineering constraints. In a research pipeline, however, they become epistemological constraints. If an undocumented endpoint changes, the experiment may cease to be reproducible. If a proprietary model changes silently, two nominally identical runs may no longer involve the same system. If raw outputs and version information are not preserved, later readers may be unable to reconstruct what produced a conclusion.

A responsible prototype would therefore need to:

  • pin software versions and repository commits;
  • record dates and service configurations;
  • preserve raw outputs locally;
  • separate credentials from the research archive;
  • avoid confidential pastoral or personally sensitive material;
  • and place the Notebook integration behind a replaceable adapter.

Google now provides an official Gemini Notebook Enterprise API in preview for notebook and source management. The documented interface does not yet expose every research, conversational and export function offered by the unofficial library. The project therefore faces a real trade-off between experimental capability and long-term stability.

The pipeline should also resist becoming one opaque chain:

question → automatic search → automatic synthesis → automatic article

That architecture would reproduce the problem the appendix has diagnosed. A more defensible sequence would preserve deliberate points of resistance:

question → candidate discovery → verification → bounded analysis → discernment → revised research state

The objective would be to automate what can be accelerated responsibly while making transitions of judgment more visible.

What the experiment would need to compare

Once I imagined the pipeline as an experiment rather than a production machine, another question emerged: compared with what?

A single successful automated run would show technical feasibility, but it would not establish improvement. The same bounded question should therefore be investigated under several conditions: conventional manual search, AI-assisted discovery, AI discovery followed by source-grounded synthesis and a full reflexive pipeline with verification and discernment checkpoints.

The comparison should not be limited to speed or number of sources. If those were the only metrics, automation would win by definition. The experiment would need to examine:

  • time to first credible source;
  • percentage of candidates surviving verification;
  • disciplinary, linguistic and confessional breadth;
  • citation accuracy;
  • recovery of contradictory evidence;
  • changes in the research question;
  • researcher confidence and comprehension;
  • sense of intellectual ownership;
  • reproducibility across runs;
  • and the reason for stopping.

The same question could also be run repeatedly while varying one condition: source corpus, theological tradition, disciplinary persona, order of evidence, model configuration or stopping rule. This could show whether conclusions converge because evidence is stable, diverge because normative boundaries differ or oscillate despite unchanged evidence and criteria.

Normative non-convergence would then become more than a conversational impression. It could become an empirically inspectable property of a research workflow.

I still do not know whether the proposed measurements will adequately capture theological quality. Citation accuracy and disciplinary breadth can be operationalized more readily than contemplative receptivity, ecclesial accountability or spiritual formation. That limitation should remain visible rather than being hidden behind the availability of numerical indicators.

What changed and what did not

I began Appendix II thinking that the new standard for AI-assisted theology might be simple: establish boundaries before using AI. I then worried that this merely repeated traditional research method. The attempt to defend its novelty produced an overstatement—that traditional theology had left its method implicit—which I rejected. Correcting that claim made a different problem visible.

Theology already possesses explicit methodological traditions. What AI changes is the interface through which those traditions are enacted, the speed at which intellectual environments can be assembled, the scale of possible iteration, the distribution of research labour and the possibility that interaction modifies both the researcher and the criteria of inquiry.

The research literature did not deliver one final verdict. Luan, Kim and Zhou showed that repeated collaboration does not automatically become learning. Jakesch and colleagues demonstrated that AI-assisted writing can change expressed and subsequently reported views. Lee and colleagues complicated the assumption that human oversight remains stable. CoAuthor made the interaction history measurable. Creativity studies produced conflicting ecological findings, showing that system design and evaluative boundaries materially affect the result. Theological sources named epistemic automation, formation, simulated companionship and bounded successful uses.

Each source changed the model in a different way. None established that AI-assisted theology is inherently formative or deformative. Together they made it impossible to evaluate the final product alone.

The distinctive problem of AI-assisted theology is not that theology suddenly requires explicit method. Theology already possesses extensive methodological traditions. The new problem is that recursive AI interaction can alter the application of those methods, the distribution of agency, the researcher’s confidence and habits of judgment, and the collective ecology of theological discourse. Responsible research must therefore evaluate both the generated product and the formative trajectory through which researcher, question and criteria changed together.

The programmable pipeline remains a proposal. I have not yet built it, measured its source retrieval, tested its citation accuracy or compared it with conventional research. It would be dishonest to write as though the experiment had already confirmed the theory.

Its importance at this stage lies elsewhere. The conversation began with a concern that AI could always generate another criticism. That concern produced the concept of normative non-convergence. The proposed solution—set boundaries—then exposed the apparent banality of the solution. My objection to the claim that traditional method was implicit produced the methodological-interface distinction. Reflection on artistic collaboration made the changing researcher visible. The five-minute literature search changed the scale of the issue. Empirical studies complicated the reassurance of human control. Finally, the need for a concrete case produced the proposal for a programmable research observatory.

The sequence was not planned:

critical oscillation → bounded question → methodological doubt → correction → formative collaboration → five-minute search → empirical complication → formal model → programmable experiment

The question with which I began was whether humans should define boundaries before AI starts working. The question I now face is more difficult:

When theological research becomes programmable, which operations become faster, which forms of labour become invisible, which capacities are strengthened or displaced, and who—or what—is forming the judgment by which the boundary, the revision and the stopping point become possible?

References

  1. Benzmüller, Christoph, and Bruno Woltzenlogel Paleo. 2014. “Formalization, Mechanization and Automation of Gödel’s Proof of God’s Existence.” Frontiers in Artificial Intelligence and Applications, vol. 263.
  2. Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society Series B 57 (1): 289–300.
  3. Catholic Church. 1983. “Code of Canon Law, Book IV, Canons 959–997.” Vatican.
  4. Deb, Kalyanmoy, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. 2002. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation 6 (2): 182–197.
  5. Eco, Umberto. 1990. The Limits of Interpretation. Bloomington: Indiana University Press.
  6. Hoare, C. A. R. 1969. “An Axiomatic Basis for Computer Programming.” Communications of the ACM 12 (10): 576–580.
  7. Mason, Elinor. 2023. “Value Pluralism.” Stanford Encyclopedia of Philosophy, substantive revision June 4, 2023.
  8. McAleese, Nat, Rai Michael Pokorny, Juan Felipe Ceron Uribe, Evgenia Nitishinskaya, Maja Trebacz, and Jan Leike. 2024. “LLM Critics Help Catch LLM Bugs.” arXiv:2407.00215.
  9. OpenAI. n.d. “Working with Evals.” OpenAI API Documentation. Accessed August 21, 2026.
  10. Popper, Karl R. 1959. The Logic of Scientific Discovery. London: Hutchinson.
  11. Zheng, Lianmin, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.” Advances in Neural Information Processing Systems, Datasets and Benchmarks Track.

References for Appendix I

  1. Abdulhai, Marwa, Gregory Serapio-Garcia, Clément Crepy, Daria Valter, John Canny and Natasha Jaques. 2023. “Moral Foundations of Large Language Models.” https://arxiv.org/abs/2310.15337.
  2. Chatterjee, Anwoy, H. S. V. N. S. Kowndinya Renduchintala, Sumit Bhatia and Tanmoy Chakraborty. 2024. “POSIX: A Prompt Sensitivity Index for Large Language Models.” Findings of the Association for Computational Linguistics: EMNLP 2024. https://arxiv.org/abs/2410.02185.
  3. Dicastery for the Doctrine of the Faith and Dicastery for Culture and Education. 2025. Antiqua et Nova: Note on the Relationship Between Artificial Intelligence and Human Intelligence. Vatican City, 28 January 2025. Official text.
  4. International Theological Commission. 2012. Theology Today: Perspectives, Principles and Criteria. Vatican City. Official text.
  5. International Theological Commission. 2026. Quo Vadis, Humanitas? Thinking Through Christian Anthropology in the Age of Artificial Intelligence. Vatican City. Official text.
  6. Miao, Fengchun and Wayne Holmes. 2023. Guidance for Generative AI in Education and Research. Paris: UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693.
  7. Wei, Jerry, Da Huang, Yifeng Lu, Denny Zhou and Quoc V. Le. 2023. “Simple Synthetic Data Reduces Sycophancy in Large Language Models.” https://arxiv.org/abs/2308.03958.

References for Appendix II

  1. Anthropic. 2026. “Anthropic’s Approach to Teaching and Learning AI.” https://claude.com/blog/anthropics-approach-to-teaching-and-learning-ai.
  2. Dicastery for the Doctrine of the Faith and Dicastery for Culture and Education. 2025. “Antiqua et Nova: Note on the Relationship Between Artificial Intelligence and Human Intelligence.” Vatican.va.
  3. Ashkinaze, Joshua, Julia Mendelsohn, Li Qiwei, Ceren Budak and Eric Gilbert. 2025. “How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas.” Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3715928.3737481.
  4. Bîrzu, Vasilică, and Ana-Maria Madina. 2026. “Algorithmic Conditioning and Divine Indwelling: Towards a Theological Anthropology of Education in the Age of Artificial Intelligence.” Religions 17 (6): 708. https://doi.org/10.3390/rel17060708.
  5. Buçinca, Zana, Maja Barbara Malaya and Krzysztof Z. Gajos. 2021. “To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-Assisted Decision-Making.” Proceedings of the ACM on Human-Computer Interaction 5. https://doi.org/10.1145/3449287.
  6. Lee, Mina, Percy Liang and Qian Yang. 2022. “CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities.” Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3502030.
  7. Doshi, Anil R., and Oliver P. Hauser. 2024. “Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content.” Science Advances 10 (28). https://doi.org/10.1126/sciadv.adn5290.
  8. Elden, Åke. 2026. “Epistemic Automation and the Deformation of the Human: Artificial Intelligence and the Reconfiguration of Theological Anthropology.” Religions 17 (5): 515. https://doi.org/10.3390/rel17050515.
  9. Google Cloud. 2026. “What Is Gemini Notebook Enterprise?” and “Create and Manage Notebooks.” Gemini Notebook Enterprise documentation.
  10. International Theological Commission. 2012. “Theology Today: Perspectives, Principles and Criteria.” Vatican.va.
  11. International Theological Commission. 2026. “Quo Vadis, Humanitas? Thinking Through Christian Anthropology in the Face of Certain Scenarios for the Future of Humanity.” Vatican.va.
  12. Jakesch, Maurice, Advait Bhat, Daniel Buschek, Lior Zalmanson and Mor Naaman. 2023. “Co-Writing with Opinionated Language Models Affects Users’ Views.” Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581196.
  13. Lee, Hao-Ping, Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks and Nicholas Wilson. 2025. “The Impact of Generative AI on Critical Thinking.” Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3706598.3713778.
  14. Lin, Teng. 2026. “notebooklm-py.” Unofficial Python API for Google Gemini Notebook. GitHub repository.
  15. Luan, Yingyue Luna, Yeun Joon Kim and Jing Zhou. 2025. “Augmented Learning for Joint Creativity in Human–GenAI Co-Creation.” Information Systems Research. https://doi.org/10.1287/isre.2024.0984.
  16. Phillips, Thomas E., and Christopher Crawford. 2026. “Artificial Intelligence and the Transformation of Theological Publishing.” Theological Librarianship 19 (1): 24–28. https://doi.org/10.31046/k25j6446.
  17. Rosauer, Greg. 2026. “A Typology of Human-Technology Relations.” Theological Librarianship 19 (1): 14–23. https://doi.org/10.31046/yqqf9819.
  18. Shin, Haerin, Douglas H. Fisher and Clifford B. Anderson. 2026. “AI as Superscholar: Authorship at the Threshold of the Unsayable.” Theological Librarianship 19 (1): 50–65. https://doi.org/10.31046/skvy5e56.
  19. Tait, Jennifer Woodruff. 2026. “Outsourcing Our Epiphanies: Thinking and Authorship in the Age of AI.” Theological Librarianship 19 (1): 1–8. https://doi.org/10.31046/6ataj823.
  20. Wan, Yun, and Yoram M. Kalman. 2026. “Diverse AI Personas Can Mitigate the Homogenization Effect in Human-AI Collaborative Ideation.” Computers in Human Behavior: Artificial Humans 8: 100289. https://doi.org/10.1016/j.chbah.2026.100289.
  21. Yin, Renlong. 2026. “Who Creates Whom? Art, AI, and the Transformation of the Maker.” YIN.
  22. Zhang, Shuning, Hui Wang and Xin Yi. 2025. “Exploring Collaboration Patterns and Strategies in Human-AI Co-Creation Through the Lens of Agency.” Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3757594.

Can Theology Be Computed Without Being Reduced?: From Proof Checking to Machine-Scale Hermeneutics

A prediction about mathematics led me somewhere I did not initially expect. Jacob Tsimerman had suggested that artificial intelligence could become better than mathematicians at doing mathematics within two years. I began by asking how such an abrupt transition could be possible. The obvious question concerned capability: had AI suddenly acquired mathematical intuition comparable to that of the strongest human researchers? But another explanation seemed increasingly important. Mathematics may be among the earliest disciplines to experience this transformation because it possesses unusually strong methods of verification. Once a proposed proof has been adequately formalized, its validity can often be checked quickly and decisively. Producing the proof may remain extraordinarily difficult, but the feedback loop can become extremely efficient. (Hartnett, 2026)

I encountered a similar argument in a reflection on a recent molecular-design study. Its authors constructed a system in which a large language model proposed molecules, computational chemistry tools evaluated them, and detailed physicochemical feedback was returned to the model for another round of design. The system did not receive only a scalar score saying that a molecule had failed. It received information about orbital energies, charge distributions, dipole moments and other properties that could help explain why it had failed. Repeated generation, analysis, reflection and revision produced molecules whose computed properties came remarkably close to specified targets. In one experiment, the reported deviation from a target HOMO–LUMO gap was as small as 0.0014 eV. (Gong, Qiu and Tang, 2026)

The argument initially appeared convincing in a broad form: AI may become superhuman first wherever scientific work can be verified quickly and formally. Mathematics would be one example, computational molecular design another. Perhaps the relevant boundary was no longer between mathematics and chemistry, or between abstract and empirical science, but between problems with fast evaluators and problems whose evaluation requires months or years of laboratory work.

I presented this idea to an AI system and asked for its assessment. Its first substantial contribution was not to strengthen my conclusion but to resist it. It examined the molecular-design paper and separated several things that my initial formulation had allowed to run together. The result did not come from an unaided LLM displaying autonomous chemical intuition. It came from a compound architecture involving retrieval, molecular generation, semi-empirical calculations, a machine-learning prescreener, density-functional-theory calculations, selection and repeated search. The capability belonged to the entire system.

A second distinction was even more consequential. The deviation of 0.0014 eV described precision relative to a computational evaluator. It did not establish that an experimentally measured molecule would possess the target property with the same precision. The system had found a molecule x such that the computed function f(x) approached the requested value. If the computational function differed systematically from physical reality, success inside the evaluator would not eliminate that gap. The study itself distinguishes the internal consistency of an evaluator from its absolute fidelity to the world.

This correction did not destroy the original idea. It made it more exact. Fast feedback remains powerful, but speed alone is insufficient. An evaluator must also be faithful to the phenomenon, informative enough to guide revision, robust outside its original domain and resistant to being gamed. A loop can optimize the wrong target with astonishing efficiency. Greater productivity and explanatory fluency can coexist with an illusion of understanding. (Messeri and Crockett, 2024)

At that stage, I thought the central discovery concerned the topology of scientific verification: AI progress may follow the availability of reliable feedback more closely than the traditional hierarchy of disciplines. Then I asked what would happen if the same logic were applied to theology. That question changed the object of the inquiry.

Theology seemed to be where the loop would fail

My first question was relatively direct: could theological research use a process resembling mathematical proof checking or the computational feedback loop in molecular design? The immediate answer was partly negative. Theology contains many verifiable components. An AI system can check whether a quotation exists, whether a source has been translated accurately, whether a conclusion follows from declared premises, whether a canonical provision has been represented correctly, and whether two parts of an argument contradict one another. Yet theology as a whole has no universally accepted theorem prover.

The AI suggested an engineering distinction between verification and validation. Verification asks whether an argument has been constructed correctly. Validation asks whether this is the right and faithful theological account of the matter. In mathematics, these two operations can sometimes approach one another once the theorem, axioms and formal language are fixed. In theology, the distance is larger. A system may verify that a conclusion follows from a Catholic, Reformed, Orthodox or Buddhist set of premises. It cannot thereby establish that those premises possess ultimate authority.

I provisionally accepted this distinction, but it created another problem. If theology lacks a single validator, what exactly limits AI-assisted theological research? The obvious answer would have been that AI cannot reach theological truth. That seemed too general to be practically useful. The more immediate limitation appeared in the experience of using AI to criticize theological and interdisciplinary writing.

If I ask an AI system to find weaknesses in an article, it can almost always produce another objection. It may identify a factual mistake or a genuine contradiction. It may also introduce another historical qualification, another denomination, another ethical framework, another possible interpretation, another affected group or another distinction that the article could theoretically include. Some criticisms reveal defects; others describe possibilities. Fluency makes them sound equally urgent.

The AI formulated the problem in a sentence that stopped me:

AI can generate more possible objections than it can rank according to theological importance.

This was initially offered as a limitation. Theology has no general computational stopping condition. A proof checker may tell a mathematician that a particular proof is valid. A theological critic can continue asking whether another perspective has been neglected. Because no finite article exhausts its subject, an unconstrained evaluator may treat interpretive inexhaustibility as perpetual defectiveness.

That explanation was useful, but I did not stop with it. The sentence produced the decisive reversal in my own thinking. If AI can generate more objections than a person can read or rank, perhaps those objections do not all need to be compressed immediately into a verdict. What if they were preserved? What if the recursion itself became data?

I suddenly found myself asking a different question. Instead of asking whether AI could write or validate a theological article, I asked whether AI could construct an immense, recursively developing space of theological claims, objections, responses and transformations—perhaps millions of operations, most of which no human being would ever read directly. The apparent failure to close the theological loop might become the foundation of another kind of computation.

This move did not come from the AI’s initial diagnosis alone. The AI had described a boundary; I asked whether the boundary could be used as an architecture. Once I made that inversion, the AI helped articulate possible forms for it: a machine-scale argument graph, premise-sensitivity analysis, structural saturation, theological attractors and a theological phase space. The method emerged through the feedback between an AI-generated limitation and my refusal to treat the limitation as the end of the inquiry.

When excess criticism becomes research data

Traditional theological scholarship usually culminates in a human-readable object: a book, article, commentary or doctrinal statement. Even when the research behind it is extensive, the published work presents a selected path through the material. Its author cannot preserve every abandoned hypothesis, every alternative formalization, every possible objection and every reply to every objection.

The proposed system would retain a different kind of object. It could represent theological inquiry as an evolving graph containing propositions, definitions, sources, authority levels, inferential relations, interpretive assumptions, objections, replies, counterexamples, pastoral consequences and unresolved tensions. A claim might generate several objections. Each objection might produce several responses. Those responses could be examined by logical, historical, doctrinal, ethical and tradition-specific evaluators. The resulting evaluations would then become inputs for the next round.

The process would have the form:

claim → objection → verification → revision → counter-objection → reclassification → further revision

Every output could become another input. Instead of deleting failed attempts after producing a polished conclusion, the system would preserve the genealogy of the argument. Over many iterations, it might build a structure too large for any individual theologian to read sequentially.

The final research object would therefore cease to be only “an argument.” It would become a navigable space of possible arguments. A human-readable article would be one projection from that space: a selected path through a machine-scale structure whose full complexity exceeds the cognitive capacity of an individual scholar.

This does not yet amount to an implemented method. It remains a theoretical proposal. Important components nevertheless have precedents. Computational metaphysics has used theorem provers to formalize ontological arguments, discover hidden consequences, identify problematic assumptions and experiment with simplified theories. Christoph Benzmüller’s computer-assisted work on variants of Gödel’s ontological argument shows both the strength and boundary of this approach. A proof assistant can establish what follows from formal premises; the philosophical and theological persuasiveness of those premises remains open to judgment. (Benzmüller, 2023)

The LogiKEy framework provides another relevant precedent. It supports experimentation with different classical and non-classical logics, normative theories, theorem provers and countermodel generators within a common higher-order environment. Its commitment to logical pluralism matters here because theological reasoning cannot simply be assumed to operate through one uncontested logical or normative system. (Benzmüller, Parent and van der Torre, 2020)

Computational hermeneutics supplies a complementary lineage. Earlier work proposed computational methods for mapping meanings across extensive textual corpora. More recent research has emphasized that interpretation involves situatedness, plurality and ambiguity, and that cultural evaluation should be iterative, contextual and inclusive of human participants. (Mohr, Wagner-Pacifici and Breiger, 2015) (Kommers et al., 2026)

These precedents prevented me from declaring the idea wholly unprecedented. The individual components already exist. What seemed potentially distinctive was their integration: large-scale language-model generation, preserved recursive objection graphs, formal and hermeneutical evaluators, premise-sensitivity analysis and cross-traditional theological comparison.

The smallest premise that changes a conclusion

One AI-generated formulation immediately caught my attention: the system might identify the smallest premise whose modification causes an entire theological conclusion to change. This possibility gave the emerging proposal a more precise mathematical form.

Suppose a conclusion C depends upon premises P1, P2, ... Pn. The computational question would be: what is the smallest modification ΔP that causes C to become C′? Instead of saying only that two traditions disagree, the system could attempt to locate the dependency at which their arguments diverge.

A Catholic and a Protestant conclusion might differ because of their respective accounts of ecclesial authority, sacramental mediation or the relationship between Scripture and tradition. Yet it would be too crude to compare “Catholicism” and “Protestantism” as two fixed blocks. Protestantism itself contains many theological families, while Catholic and Orthodox positions also contain internal differences. The relevant unit would need to be a bounded doctrinal configuration rather than an institutional label.

The same analysis could search for minimal inconsistent sets. Perhaps five theological commitments cannot all be maintained simultaneously, although every subset of four remains coherent. A countermodel generator could identify a configuration in which a supposedly necessary conclusion fails. The result would not settle whether the premises were true, but it could show their relationships with a precision difficult to achieve through prose alone.

This operation also changed how I thought about AI criticism. Previously, the inability to rank objections seemed to produce endless recursion. Premise-sensitivity analysis offers one possible criterion of importance. An objection matters computationally when it changes a dependency, reveals an inconsistency, defeats an inference, alters an authority relation, exposes a material historical error or produces a serious pastoral consequence. An objection that only restates an existing branch in different language may add volume without adding structure.

The system would therefore need to distinguish recursive depth from theological depth. Ten thousand increasingly subtle objections do not necessarily contain more insight than one objection that reveals a hidden premise on which the whole argument depends.

From Christian disagreements to interreligious comparison

Once the possibility of minimal premise changes became visible, I wondered whether the method could extend beyond disputes within Christianity. Could it compare Catholic, Orthodox and Protestant positions with Buddhist traditions or other religious systems? This possibility was intellectually exciting because it suggested that computation might reveal structural families that do not coincide with inherited denominational labels.

My excitement also encouraged an early overstatement. It was tempting to imagine “Christianity,” “Protestantism” and “Buddhism” as comparable units. That classification was unstable: Protestantism belongs within Christianity, and neither Christianity nor Buddhism is internally uniform. A meaningful comparison would need to include more carefully bounded traditions—perhaps Catholic and Orthodox Christianities, several Protestant families, Theravāda, Madhyamaka and Pure Land Buddhist traditions—depending on the research question.

The correction mattered because the computational system would otherwise reproduce the very simplifications that its scale was supposed to overcome. It would calculate with impressive precision across categories that had been badly constructed.

For a bounded question, each position might be represented provisionally through dimensions such as authority, ontology, religious epistemology, soteriology, practices of transformation and communal mediation. This model would not define the whole religion. It would state which aspects were being represented, through which sources and for what purpose.

The phrase “bounded question” became essential at this point. Theology as a whole cannot be made computationally complete. A carefully specified inquiry might nevertheless be explored with a high degree of structural completeness relative to a declared corpus, set of traditions, ontology and evaluator ensemble. Completeness would belong to the modelled question, not to the religious tradition or ultimate reality itself.

Within such a bounded space, doctrines could be represented as configurations. Minimal premise changes would form edges between them. Clusters might reveal structural families; boundaries would mark points at which conclusions change; invariants would identify commitments surviving many transformations; attractors would be positions toward which different argumentative paths repeatedly converge.

This was where mathematical language became more than a metaphor. The proposal had begun to resemble a theological phase space. Its purpose would be to observe transformations rather than merely classify finished doctrines.

A system might discover, for example, that certain accounts of radical dependence occupy structurally related positions across traditions usually considered far apart. Christian apophatic theology and Buddhist discourse about emptiness might share strategies for resisting conceptual reification while remaining profoundly different regarding ontology, revelation and soteriology. A Catholic theology of grace and a Pure Land Buddhist account of other-power might exhibit a limited structural analogy concerning dependence without becoming versions of the same doctrine.

These examples remain hypotheses. They illustrate the type of relationship the system might test; they are not findings already produced by it. Their value also depends upon preserving non-equivalence. Structural similarity is not doctrinal identity. The inability to translate two concepts without serious loss may itself be an important computational result.

Is AI optional or obligatory?

The scale of the proposal produced another moment of enthusiasm. I began to wonder whether this research could be done only with AI. If the argument graph contained millions of recursively generated and evaluated paths, traditional scholarship could not inspect them all. Perhaps AI was no longer one optional tool among others but an obligatory condition of the method.

That claim required another qualification. Comparative theology itself does not depend upon AI. Human scholars have long conducted subtle cross-traditional studies, often with forms of historical, linguistic and experiential depth that current computational systems cannot reproduce. A small argument graph can also be constructed manually. The necessity of AI is therefore relative rather than absolute.

AI becomes constitutive when the research question is deliberately defined at machine scale: millions of context-sensitive objection–response sequences, repeated recalculation after premise changes, comparisons across extensive corpora and complete preservation of argumentative provenance. No individual scholar could perform that operation unaided. Computation changes the feasible scale and may eventually change the kind of research object that can exist.

Even at this scale, an LLM alone would be inadequate. Language models could interpret passages, propose formalizations, generate objections and translate among scholarly vocabularies. Retrieval systems would connect claims to sources. Knowledge graphs would record authorities and dependencies. Embeddings could identify semantic proximity, although vector-space representations themselves perform interpretive work and therefore require hermeneutical scrutiny. (Dobson, 2022) Theorem provers and countermodel generators would examine formal consequences. Historians, philologists, theologians and participating religious communities would test whether the representations remained faithful.

The methodological division that emerged can be summarized simply: embeddings provide a map, formal graphs provide a skeleton, language models translate and explore, and theologians interpret and judge.

This also connected the proposal with my existing work on vector-space theological meaning. A semantic map can indicate that two theological concepts occupy nearby regions in an embedding space. It cannot by itself explain why they are near, whether the resemblance is historically meaningful, which premises connect them, or what happens when one concept is redefined. The new proposal would move from proximity to dependency and from a static map to a dynamic system.

The relationship between those two projects was not visible when the discussion began with mathematical verification. It emerged only after the theological limitation had been inverted into a computational question.

When interdisciplinarity becomes structural

What attracted me most was the possibility of integrating AI, mathematics, computer science, hermeneutics and theology in a way that would be difficult to reduce to an ordinary interdisciplinary collaboration. In many projects, theology supplies a topic while computer science supplies a tool. The disciplines remain substantially unchanged.

Here each discipline would constrain the architecture of the others. Theology would determine which questions matter, how authority functions within a tradition, which distinctions have historical weight and where formal equivalence might conceal theological difference. Hermeneutics would insist that representations are situated and that plurality cannot be eliminated by selecting a convenient benchmark. Mathematics would contribute formal consequence, sensitivity analysis, invariants, graph structures and possible models of convergence. Computer science would provide representations, algorithms, provenance, versioning and auditability. AI would mediate between unstructured theological language and structures that can be explored computationally.

The influence would also run in the opposite direction. Computational requirements would force theology to state assumptions that prose sometimes leaves implicit. Difficulties in formalization could expose ambiguities instead of being treated merely as programming problems. Encounters with different logical systems might challenge the assumption that one inferential structure adequately represents every religious tradition. Failures of translation could become research findings rather than errors to be silently repaired.

This is why the engineering and mathematical language should not remain decorative. A “theological phase space” would need specified dimensions. A “minimal premise change” would need an explicit representation of premises and conclusions. An “attractor” would require a defined iterative process and convergence criterion. Without these operational definitions, the metaphors might sound suggestive while doing little analytical work.

The proposal becomes genuinely interdisciplinary only when theological resistance changes the computation and computational failure changes the theological question.

A theology too large for anyone to read

The most unsettling development followed directly from the scale of the system. If every recursive operation were preserved, the resulting theological object could become too large for any human being to understand as a whole. Most of its data might remain meaningful primarily to other computational processes.

My first reaction was that this might be an inevitable and even productive feature. Scientists do not manually inspect every state in a climate simulation, and engineers do not read every instruction executed by a large software system. A machine-readable theological structure could similarly contain more relations than a person could survey.

Yet the analogy became less comfortable when authority and responsibility entered the picture. Theology concerns meaning, ultimate commitment, institutions, practices and lives. A conclusion cannot become trustworthy merely because an enormous computation produced it. If no person can reconstruct how the system arrived at an important claim, computational scale may begin to masquerade as theological authority.

The AI proposed another formulation that I found worth retaining: global incomprehensibility with local auditability. No scholar may be able to hold the entire graph in mind, but every material conclusion should have an inspectable path. A researcher should be able to ask which sources supported it, which assumptions were introduced, which evaluator accepted it, which traditions rejected it, which counterarguments survived and what smallest premise change would reverse it.

If even the local path becomes inaccessible, the system ceases to be a scholarly instrument and begins to resemble an opaque machine magisterium. Its answers would acquire influence through computational complexity rather than accountable reasoning. The danger would become institutional as well as epistemological if a small number of organizations controlled the corpora, authority rankings, models and evaluator weights.

Why millions of iterations might deepen an error

The imagined scale could easily produce another illusion. A million iterations do not constitute a million independent judgments. If the same model generates a claim, invents the objection, evaluates the objection, writes the response and decides that the response succeeded, the system may construct an elaborate echo chamber. Its recursive activity could amplify one model’s assumptions while presenting the result as exhaustive deliberation.

This objection did not invalidate the method, but it changed its architecture. Evaluator plurality would need to be real rather than theatrical. Different language models, symbolic provers, source-verification systems, historians, tradition-specific experts and affected communities would need different roles and powers. Disagreement should be stored instead of being prematurely converted into an average score. The system would need to preserve uncertainty and provenance wherever a formalization depended upon a controversial translation or interpretive choice.

Conceptual flattening presents a related danger. A computational ontology makes comparison possible by deciding what counts as a doctrine, premise, authority, practice or consequence. Those categories may fit one tradition better than another. A framework built around Christian ideas of doctrine, belief and revelation could misrepresent Buddhist traditions in which practice, realization, lineage or skillful means function differently. The represented traditions must therefore be able to contest the categories through which the system represents them.

This returns the inquiry to its original lesson from molecular design. An evaluator can be fast, precise and internally consistent while remaining insufficiently faithful to its object. In computational theology, the evaluator’s error might consist not in an inaccurate energy value but in an imposed ontology that makes one tradition appear clearer and another more incoherent simply because the system was designed in the first tradition’s conceptual language.

How would the process stop?

The absence of a theological stopping rule initiated this entire line of thought. The proposed system cannot solve that problem by declaring that enough computation has produced truth. Its stopping condition must remain more modest.

One possibility is structural saturation. Further iterations would cease to produce new kinds of dependency, contradiction, countermodel or interpretive configuration, even if they continued generating verbal variations. Saturation would indicate that a bounded model had been extensively explored. It would not establish that divine reality, revelation or liberation had been exhaustively understood.

Several distinct statuses would need to remain visible:

  • a conclusion formally follows from declared premises;
  • a source supports a stated historical claim;
  • a tradition-specific evaluator judges a formulation compatible with its authorities;
  • an objection materially changes the argument’s structure;
  • a comparison remains interpretively contested;
  • a claim lies outside the system’s present capacity for validation.

This classification would also improve ordinary AI-assisted writing. A criticism could be labelled as a factual error, logical contradiction, conflict with a specified authority, substantial but contestable objection, or optional interpretive expansion. The first categories normally require correction. The later ones require judgment rather than automatic compliance.

What remains provisional

I still do not know whether the full proposal is technically feasible or historically novel. Computational metaphysics, formal theology, argument mining, vector hermeneutics and computational hermeneutics already provide substantial precedents. A responsible novelty claim would require a systematic literature review, a precise research design and a working prototype.

I also do not yet know whether natural-language theological claims can be translated into formal structures at sufficient scale without losing the very meanings the project intends to study. Formalization may reveal hidden premises, but it may also create them. An argument graph may clarify doctrinal dependencies while excluding narrative, ritual, embodied practice, silence, spiritual formation and forms of knowledge that resist propositional representation.

Those uncertainties are part of the method rather than embarrassments to be removed from its presentation. The proposal emerged through several corrections. Fast verification first appeared sufficient, then evaluator fidelity complicated it. Theology first appeared to mark the method’s limit, then the limit became a source of data. Machine scale first seemed to make AI absolutely obligatory, then that claim became relative to a particular research design. Cross-religious comparison first appeared as a comparison among broad labels, then internal plurality and possible incommensurability required a more careful model.

The history matters because each correction preserved part of the earlier intuition while changing its scope. Verification remained important, but became conditional. Recursion remained productive, but could no longer be confused with depth. Computation remained constitutive at scale, but could not replace interpretation. Formalization remained clarifying, but acquired its own hermeneutical risk.

The question that finally emerged

I began by asking why AI might surpass mathematicians so quickly. Mathematics suggested the power of rapid verification. Molecular design demonstrated how mechanism-rich feedback could close a computational scientific loop, while exposing the difference between optimizing an evaluator and understanding reality. Theology then appeared to be the domain in which such a loop could not close because its authorities, interpretations and standards of significance remain plural.

The decisive move was to stop treating that lack of closure only as failure. If AI can generate more theological possibilities than it can responsibly rank, the possibilities themselves may form a new research object. Instead of forcing one answer from them, we might analyse their dependencies, transformations, contradictions, stable structures and boundaries.

The deepest contribution of such a system might therefore be neither a machine-generated doctrine nor a verdict about which religion is correct. It might reveal how theological positions are assembled and how they change. It could distinguish disagreements produced by terminology from those produced by logic, authority, ontology, practice or experience. It could identify commitments that survive criticism across many frameworks, and it might reveal structural affinities that inherited denominational labels conceal.

The theologian’s role would also change. The researcher would design bounded questions, curate corpora, specify authority relations, inspect formalizations, interpret emergent structures and remain responsible for conclusions. The machine would explore a space too large for one mind. The theologian would still have to decide what that exploration means and whether the representation remained faithful to the traditions and persons it claimed to study.

The inquiry has therefore ended, provisionally, with a better question than the one with which it began:

Can AI help construct a machine-scale space of theological reasoning whose whole exceeds human readability, while preserving enough local transparency, plural interpretation and human responsibility for that space to deepen theology rather than quietly replace it with its own computational image?

Appendix I: From Theological Attractors to Quantum Worldviews

The next stage of this inquiry began with a question that was much smaller than the consequences it produced. In an earlier draft, I had described one possible result of recursive computational theology as “the discovery of theological attractors.” The phrase seemed to capture something important: if an AI system generated millions of claims, objections, replies and revisions, perhaps certain theological positions would repeatedly reappear or prove unusually resistant to change. Yet I suddenly hesitated and asked: “Is this a kind of physical concept, or something else?”

I did not ask because I had already developed a mathematical theory of theological dynamics. I asked because the expression sounded persuasive before I knew whether I was entitled to use it. That moment of uncertainty exposed a risk in the whole interdisciplinary project. Scientific language can make a theological proposal appear more rigorous while concealing that the borrowed concept has not yet been understood. If “attractor” was only an impressive metaphor, it might weaken rather than strengthen the argument.

The AI’s initial response was reassuring but also corrective. An attractor is not specifically a quantum concept, and it is not exclusively a physical one. It comes principally from the mathematics of dynamical systems, although physicists and other scientists use it extensively. A dynamical system represents how a state changes through time or repeated iterations. An attractor is a state, cycle or more complicated set towards which many trajectories tend to evolve. The collection of starting states that approach it is called its basin of attraction (Milnor, 1985).

At first, this explanation seemed to confirm my intuition. A recursive theological system would contain states, transformations and repeated trajectories, so perhaps “theological attractor” was exactly the right expression. But the answer contained a condition that gradually changed the project. A collection of similar theological positions is only a cluster. It becomes an attractor when different initial states actually move towards it under a specified rule of transformation.

That distinction forced me to ask what, precisely, was moving.

When an attractive metaphor acquired technical obligations

A theological state could contain premises, doctrinal conclusions, scriptural interpretations, historical claims, sources of authority and hermeneutical rules. An objection, counterexample or newly retrieved text would alter some part of that configuration. The revised state would then become the input for another iteration. In simplified form, the process might be represented as x(t+1) = F(x(t), O(t), E(t)), where x(t) is the current theological state, O(t) is an objection or counterexample, E(t) is an evaluation result, and F is the rule by which the system revises its position.

This formulation was initially exciting because it gave technical form to something I had only intuited. If many different starting states approached approximately the same theological configuration, that configuration might be a fixed-point attractor. If the system repeatedly alternated between several positions, it might exhibit a cycle. If a small change in one premise or authority rule redirected the entire trajectory into another stable family, the system might undergo something analogous to a bifurcation. A position that appeared stable for many iterations but collapsed after the introduction of another corpus might be metastable.

However, each new term created a new obligation. I would have to define the state space, the transformation rule, the measure of theological distance, the conditions of convergence and the relevant temporal or iterative scale. I would also have to determine whether the observed behaviour persisted when the prompts, models, evaluators, corpora and random seeds changed.

This was the first meaningful reversal in the inquiry. I had begun with a phrase that seemed to name a discovery. I ended by realizing that the phrase named a research hypothesis whose conditions had not yet been satisfied.

The AI formulated the central warning in a way that changed my understanding:

An attractor produced by the system is first an attractor of the corpus, representation, evaluators and update rule. It is not automatically an attractor of theological truth.

I accepted this distinction, but it raised another problem. If the same language model generated the objections, evaluated their importance, revised the theological position and encoded the resulting text, the apparent attractor might be produced by the model’s own preferences. The system could repeatedly return to a position because the architecture was circular, not because the position possessed unusual theological stability.

A credible experiment would therefore require some separation of functions. Different models could generate and evaluate arguments. Human theologians could assess a sample of the supposedly decisive transitions. Alternative corpora could test whether a stable result depended on one textual tradition. Adversarial prompts and counterfactual changes could probe whether convergence survived outside the conditions in which it was first detected.

I consequently became uncomfortable with the heading “The Discovery of Theological Attractors.” The word “discovery” suggested that an empirical result already existed. “The Hypothesis of Theological Attractors” or “Searching for Theological Attractors” would be more honest. The earlier wording was not useless: it preserved the intuition that made the method imaginable. What had to be abandoned was the premature certainty attached to it.

The method began to exceed my confidence

Once the idea became more rigorous, I felt another kind of hesitation. The required methodology seemed to be moving beyond my present competence. What had begun as an AI-assisted theological experiment was opening into dynamical systems, mathematical physics, philosophy of science and eventually quantum theory. I was willing to study these subjects if they were necessary, and I found them intrinsically fascinating. Yet I did not know whether I was identifying essential tools or allowing the project to expand without limit.

I therefore asked whether mastery of these methods was genuinely important. The AI’s answer did not simply encourage me to study everything. It distinguished complete disciplinary mastery from sufficient methodological literacy. For an initial theological-dynamics experiment, I would need working knowledge of state spaces, update rules, trajectories, convergence, stability, cycles, robustness and bifurcations. I would not need to master every branch of chaos theory, differential equations or mathematical physics before constructing a pilot.

This distinction changed my attitude towards the unfamiliar mathematics. I had initially experienced the new terminology as a possible barrier. I began to see it instead as a set of concrete instruments. These concepts would allow me to ask questions that could receive negative answers. Do theological trajectories really converge? Does the apparent convergence disappear when another model is used? Which starting assumptions enter the same basin? What is the smallest premise change that redirects the entire doctrinal structure?

The ability to formulate a question that can fail is one of the principal gains of the method. Without it, “theological attractor” might mean little more than a doctrine that appears historically influential or intuitively stable. With it, the term becomes experimentally vulnerable.

I then made another connection. Mathematical and computational methods fascinated me because they appeared to offer concrete tools for studying the world and universe. Traditional theology has used logic, systematic comparison, scholastic disputation and formal argument extensively, but machine-scale dynamical analysis of recursive theological reasoning appears much less established. I wondered whether this could make theological obscurity substantially smaller.

The response was again a qualified one. Formalization can reduce certain kinds of obscurity, but it can also move them. A conclusion may follow transparently from encoded premises while the encoding of those premises remains controversial. A numerical distance may compare theological texts precisely while failing to represent what their traditions consider decisive. A model may identify structural stability while remaining unable to judge spiritual, historical or doctrinal importance.

This correction mattered because it prevented me from treating mathematics as a universal solvent for theological ambiguity. The method could expose hidden assumptions and dependencies. It could not decide by calculation alone which sources ought to be authoritative or which theological differences matter.

Encountering Professor Harris’s method

The inquiry changed again when I read an Oxford interview with Professor Mark Harris, a physicist by training, an ordained Anglican priest and Director of the Ian Ramsey Centre for Science and Religion. I therefore read the interview with more than general academic interest. I wanted to understand whether his account of physics, theology and interdisciplinary method offered any guidance for the project I was developing.

The interview was a public document rather than a personal response to my proposal. I cannot infer from it that Harris would endorse theological attractors, machine-scale hermeneutics or any particular architecture. What it provided was evidence of his stated method and of the questions that have shaped his research.

Harris describes himself, following a colleague, as an “incorrigible empiricist.” His characteristic sequence begins with an observation, asks how science interprets it, and then considers what the scientific account might mean for theology and what theology might contribute in return. He does not present theology as an escape from scientific rigour. He describes discovering that theology was as intellectually exacting as physics, while operating through different forms of evidence, interpretation and judgment (University of Oxford, 2026).

At first, I thought that his earlier discovery of spin ice might support the attractor analogy directly. Harris and his collaborators studied a pyrochlore magnet in which local interactions failed to produce a single conventional global order. Their experiments provided early evidence of geometrical frustration in a ferromagnetic system (Harris et al., 1997). The Oxford interview describes how an anomalous result in what had seemed a straightforward experiment contributed to the emergence of a substantial research field.

The connection seemed immediately attractive: perhaps theological systems also contain deep physical-like structures waiting to be discovered. Yet the comparison did not survive in its initial form. Spin ice is not primarily an example of many trajectories converging on one attractor. It is an example of local constraints allowing many configurations while preventing the system from settling into a single neat global arrangement.

The failure of the analogy produced a better one. Some theological systems may resemble frustrated systems more than convergent ones. Scriptural commitments, metaphysical assumptions, historical authorities and moral intuitions may each constrain the available positions without determining one globally stable solution. Different configurations may satisfy local requirements while remaining globally incompatible.

This possibility changed the design objective. The computational system should not be rewarded only for producing convergence. Persistent oscillation, multiple stable families and systematic non-convergence could be equally important findings. An architecture that forced every debate into a final resolution might erase the structure it was supposed to discover.

Harris’s scientific history also suggested something methodological about anomalies. The failure of an expected result does not always mean that the experiment has failed. Sometimes the anomaly identifies the phenomenon. In recursive theology, failure to converge might similarly disclose a durable doctrinal tension, an incompatible combination of authorities or a genuine underdetermination rather than a defect in the software.

Quantum mechanics sharpened the earlier distinction

The most consequential part of Harris’s interview concerned quantum mechanics. He describes a theory of extraordinary mathematical and experimental success that nevertheless permits competing interpretations of what reality is fundamentally like. The formalism works with remarkable reliability, while disagreement remains about the ontology that the formalism describes. In some cases, no presently decisive experiment selects one interpretation over the others (University of Oxford, 2026).

This account changed my understanding of the earlier distinction between verification and validation. The molecular-design example that had initiated the wider inquiry showed why rapid computational feedback can support AI self-correction. Quantum mechanics showed something different: even extremely successful calculation may leave ontology underdetermined.

A computational-theology system could demonstrate that a conclusion follows from a specified collection of premises. It could identify the premise whose removal reverses the result. It might show that several argumentative trajectories converge under a particular authority model. Yet these achievements would not establish whether the premises were revealed, whether the authority model was legitimate, or whether the conclusion possessed theological importance.

Formal verification establishes what follows within a system. Theological validation asks whether the system is faithful, meaningful, historically responsible or religiously authoritative. The first can sometimes be automated and repeated at enormous scale. The second remains partly dependent on interpretation, tradition, community, practice and judgment.

I had already reached a version of this distinction, but Professor Harris’s discussion made it harder to dismiss as a limitation peculiar to theology. Physics itself can possess calculational exactness without complete agreement about reality. The relevant question is therefore not whether theology can become as exact as physics in every respect. It is how different kinds of exactness coexist with unresolved interpretation.

The machine-readable structure created another question

Harris’s reconsideration of the physicists’ expression “shut up and calculate” generated another unexpected connection. In quantum physics, researchers can use a highly successful formalism without first settling its complete interpretation. I wondered whether something structurally similar could happen in computational theology.

A future theological argument graph might contain more recursive transformations than any individual could read. Researchers could inspect a local region, trace a conclusion back to its premises and verify the relevant inferential steps without comprehending the entire structure. The AI described this possibility as global computational reach combined with local human auditability.

I found the formulation useful, but it also made me uneasy. If no human being could understand the whole object, what would it mean to claim that “we” understood its result? Would traceable local proofs be sufficient? Could a machine-native structure become a legitimate scholarly object even when its global organization exceeded human cognition?

The original question had been whether AI could conduct recursive theological analysis. The object itself changed that question. I was now asking where judgment, responsibility and scholarly trust move when the analysis becomes larger than its human investigators.

This is not an ethical appendix that can be added after the technical work. It belongs inside the architecture. Every significant result would need traceable sources, inspectable premise changes, evaluator histories and records of model disagreement. The system could exceed human reading capacity without being permitted to become unauditable.

From theological dynamics to fascination with the universe

After these methodological questions, my attention expanded towards quantum physics itself. I have long been fascinated by black holes, event horizons, possible gateways beyond them, the hypothesis that the observable universe might exist inside a black hole, higher dimensions, projections from higher-dimensional reality and parallel universes. My knowledge remained largely at the level of fundamental concepts, and I wondered whether AI could help me enter these fields without requiring me to become a professional physicist.

My first formulation grouped these questions together under “quantum physics.” That was understandable at the level of fascination, but scientifically insufficient. The AI separated them into different theoretical domains and levels of evidential support. This sorting did not answer the mysteries. It changed what kind of mysteries they were.

Event horizons and much of classical black-hole structure arise from general relativity. Hawking radiation emerges when quantum field theory is considered in curved spacetime (Hawking, 1975). The ultimate description of singularities, black-hole interiors and information requires a theory of quantum gravity that remains incomplete. The black-hole information problem continues to connect gravity, quantum theory, thermodynamics and information without yet yielding one universally accepted account (Harlow, 2016).

The idea of a black hole as a gateway belongs to another level. Physicists have investigated mathematical models of traversable wormholes and the physical conditions such geometries would require (Morris and Thorne, 1988). Their admissibility within certain equations does not establish the existence of usable cosmic passages.

Models have also been proposed in which a new expanding universe arises inside a black hole. For example, Popławski develops such a scenario within Einstein–Cartan gravity, where torsion prevents a classical singularity and permits a cosmological bounce (Popławski, 2016). The existence of a mathematically developed model shows that the idea is more than pure fantasy. It does not show that our observable universe has been demonstrated to exist inside a parent black hole.

Higher-dimensional theories required a similar correction. Randall and Sundrum proposed a physically motivated model using an additional spatial dimension to address the hierarchy problem in particle physics (Randall and Sundrum, 1999). Higher dimensions can therefore function as precise components of serious theoretical models. That is different from claiming that ordinary reality is simply a visual projection from an experimentally established higher-dimensional universe.

The holographic principle offers an even more subtle connection between dimensions, information and geometry. Maldacena’s proposed duality between certain gravitational theories and lower-dimensional quantum field theories provided a powerful realization of this possibility in particular anti-de Sitter settings (Maldacena, 1998). The broader holographic principle has generated important results and continuing problems in quantum gravity (Bousso, 2002). It cannot responsibly be reduced to the popular statement that science has proved the universe to be a lower-dimensional projection.

Parallel universes produced one further distinction. Everett’s relative-state formulation of quantum mechanics became the foundation for later many-worlds interpretations (Everett, 1957). Everettian branches, cosmological multiverses, higher-dimensional branes and universes produced inside black holes are different proposals generated by different theoretical problems. Their common appearance in popular discussions can conceal how little they imply about one another.

This was another productive correction. At first, I had approached these ideas as a collection of extraordinary possibilities about the universe. After the distinction, I began to see them as claims with different epistemic statuses: experimentally successful formalism, active theoretical problem, interpretation, mathematically developed hypothesis and broader speculation.

The fascination survived the classification. It became more disciplined.

What quantum physics could actually contribute

I then asked whether quantum physics was genuinely important for my theological research or whether it remained too superficial and remote. The emerging answer was another “yes, but.” Quantum physics may supply valuable conceptual and formal tools, but its usefulness does not depend on importing its most spectacular cosmological proposals into theology.

The most relevant tools concern underdetermination, observability, information, emergence, contextuality, symmetry and structural limits. Quantum mechanics provides a particularly demanding example of the difference between a formal state and an observed outcome. Decoherence explains how environmental interaction suppresses interference and produces stable classical correlations, although it does not by itself resolve every interpretive dispute (Zurek, 2003).

Underdetermination becomes relevant when the same successful formalism supports more than one account of reality. Observability distinguishes what can be measured from everything that may be posited by a theory. Information and entropy provide mathematical tools for studying accessibility, preservation and loss. Emergence allows higher-level structures to possess explanatory reality without being fundamental in the same manner as their underlying components. Symmetry and invariance ask what remains unchanged under transformation. Horizons show that some limits may arise from the structure of a system rather than from a temporary lack of intelligence or technology.

I found these concepts attractive because they could deepen my understanding of theological knowledge. But attraction was no longer enough. The earlier correction concerning dynamical systems now returned at another level. If I transferred quantum concepts into theology without their mathematical conditions, I would repeat the same mistake.

Entanglement does not prove spiritual unity. Quantum measurement does not establish that human consciousness creates reality. An event horizon does not prove divine hiddenness. Holography does not show that creation is an illusion. These comparisons may occasionally generate questions, but they cannot function as scientific evidence for theological doctrines.

Professor Harris’s discussion of “quantum religion” provided an important methodological boundary. He is interested in why quantum concepts have become culturally and spiritually compelling, while refusing to treat quantum mechanics as a simple proof of religious belief. Theology, philosophy, history and religious studies can investigate what quantum theory means for human worldviews without replacing physics or appropriating its authority (University of Oxford, 2026).

This helped me identify a criterion for future interdisciplinary work. A scientific concept contributes when it produces a defined question, transformation, model or testable distinction. It becomes decorative when it merely makes an existing theological intuition sound more profound.

Computing became a route into physics rather than a shortcut around it

I wondered whether AI could help me acquire the necessary knowledge without mastering all of physics. The answer was affirmative, but with the same limits that apply to AI-assisted theology. AI can accelerate learning, organize prerequisites, generate exercises, produce simulation code and compare interpretations. It cannot guarantee that its explanation correctly distinguishes an established result from a speculative model.

My computing background nevertheless provides a practical point of entry. Classical computers can simulate elementary quantum systems: state vectors, measurement probabilities, interference, density matrices and simplified decoherence. These simulations could connect conceptual descriptions with mathematical operations. Quantum hardware is not required for this stage.

A reasonable path would begin with linear algebra, complex numbers and probability, followed by elementary quantum states, operators, measurement and entanglement. Small computational experiments could accompany the theory. Special relativity and introductory spacetime geometry could then prepare the way for black-hole physics. Quantum foundations and philosophy of physics are probably more immediately relevant to my theological questions than advanced speculative cosmology. Black-hole information, holography and higher-dimensional models could follow when a specific research problem requires them.

This sequence revised my earlier sense that I might need to master several enormous fields before proceeding. I need enough knowledge to recognize assumptions, understand elementary calculations, evaluate the relevance of an analogy and communicate responsibly with specialists. If dynamical systems or quantum theory becomes central to the eventual research claim, collaboration with mathematicians and physicists will become necessary.

Quantum computing itself is not presently essential to the theological-attractor project. Recursive argument graphs can be constructed with classical AI, databases, graph analysis and statistical methods. Introducing quantum computation without an algorithmic reason would make the project more complicated without making it more rigorous.

The role of AI is therefore neither trivial nor sovereign. It can serve as a tutor, coding partner, source-discovery instrument and generator of counterexamples. I remain responsible for choosing the question, identifying when an explanation is inadequate, checking the scholarship, determining which distinctions matter and deciding when uncertainty must remain unresolved.

Formalization changed the location of obscurity

At an earlier stage, I thought these methods might make theology substantially less obscure. I still think they can make certain structures visible that traditional reading cannot survey at the same scale. A computational system could preserve millions of recursive transformations, identify hidden dependencies, compare alternative premise sets and locate minimal changes that redirect entire doctrinal conclusions.

Yet the conversation changed what I mean by clarity. Formalization does not remove interpretation; it reveals some interpretive decisions while potentially concealing others inside the architecture. A conclusion may be logically transparent relative to encoded premises, while the choice and formulation of those premises remain contested. A similarity measure may be mathematically exact while being theologically insensitive. An evaluator may rank consistency successfully while failing to recognize why a question matters to a worshipping community.

The earlier observation therefore survives in a stronger form: AI can generate more possible objections than it can rank according to theological importance. Machine-scale recursion may preserve those objections and disclose their structural effects, but no quantity of iteration automatically determines what deserves theological attention.

The method could consequently produce both greater transparency and a new opacity. Local dependencies might become more explicit than ever before, while the global argument graph grows beyond human comprehension. My provisional architectural principle is therefore global computational reach with local human auditability. Important conclusions should remain traceable to inspectable sources, premises, evaluator decisions and revision histories even when no person can read the complete archive.

What I now think is necessary

I no longer think I must master all of dynamical systems, quantum mechanics, relativity and cosmology before beginning. I also no longer regard them as optional ornaments. The level of study required depends upon the claim I intend to defend.

If I claim that the system has discovered theological attractors, I must understand dynamical systems well enough to define states, transformations, convergence, stability, basins and bifurcations. If I appeal to quantum theory in discussing underdetermination or observability, I need sufficient knowledge of the formalism to avoid depending on popular metaphors. If black-hole interiors, holography or higher dimensions become central rather than illustrative, the project will require much deeper physical expertise.

The immediate task is therefore methodological literacy. I need enough mathematics to formulate a pilot and understand how it could fail. The next layer is quantum foundations and philosophy of physics, especially the relationship between predictive success and disputed interpretation. Advanced cosmological questions can remain a later field of study rather than becoming prerequisites for the present experiment.

This is not a retreat from ambition. It is a way of allowing the research question to determine which forms of mastery become necessary.

The better questions that remain

The discussion began with a terminological doubt, but it now leaves a research programme. Do theological argument trajectories actually converge when traditions are represented through their own authorities and interpretive practices? Are any apparent attractors robust across corpora, models and evaluators? Can the system distinguish a stable theological structure from the statistical repetition of historically dominant language?

What does persistent non-convergence mean? Does it disclose a defective representation, an unresolved historical controversy, incompatible premises or something like theological frustration? Could oscillation between positions be more revealing than final settlement?

Cross-traditional comparison creates another problem. Christianity, Protestant traditions and Buddhism cannot responsibly be represented as single undifferentiated nodes. Each contains internal schools, texts, authorities, practices and histories. It may be necessary to construct locally faithful models before attempting a common comparative space. Otherwise, computational comparability could be achieved by deleting the differences that make comparison meaningful.

I still do not know whether quantum theory will eventually contribute to the computational architecture itself or remain part of the philosophical interpretation of the project. I do not know whether theological attractors will be empirically found, whether the more important result will be multiple basins, or whether the system will disclose durable non-convergence. These uncertainties should remain visible because they describe the present state of the research rather than a failure to complete it.

The history of this inquiry has itself followed the recursive structure I was trying to design. I proposed a concept. AI supplied a definition. The definition exposed a missing condition. I challenged the expanded methodology as possibly exceeding my competence. That objection produced a more proportionate learning plan. Harris’s interview then introduced scientific evidence and a methodological model. Spin ice first appeared to confirm the attractor analogy and then corrected it. Quantum mechanics strengthened the distinction between calculation and interpretation. My fascination with cosmology widened the inquiry, while source-based distinctions separated established theory from speculation.

The output of each stage became the input to the next. The process did not lead to the conclusion I initially expected. It changed the question from “Can AI discover theological attractors?” to something more difficult:

What kinds of theological stability, plurality, frustration and transformation become visible when recursive reasoning is studied at machine scale, and what forms of human judgment remain necessary to understand what the machine has found?

Mathematics, computing and physics may not remove mystery from theology. Their more valuable contribution may be to locate the mystery with greater precision: in the evidence, in the representation, in the transformation rule, in the interpretation, or in reality itself.

References

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Wer erschafft wen? Kunst, KI und die Transformation des Schaffenden

Mein vorheriger Essay, Building a Generative Artwork That Can Change Its Maker, endete mit dem Gedanken, dass das Kunstwerk geantwortet habe. Dieser Essay setzt dort ein, wo mir diese Formulierung nicht mehr ausreichte. „Antworten“ lässt noch immer zwei relativ stabile Partner vermuten: auf der einen Seite einen Schaffenden, auf der anderen ein vollendetes Werk. Was sich tatsächlich entwickelte, war weniger geordnet. Ein visuelles System erzeugte Formen; die Formen veränderten meine Fragen; die Reaktion eines anderen Menschen veränderte, wie ich die Formen verstand; ein altes theoretisches Vokabular wurde auf neue Weise lesbar; KI half dabei, die Veränderung zu externalisieren; die Website bewahrte sie; und später kehrte ich als Leser zu meinem eigenen KI-gestützten Schreiben zurück. Zu diesem Zeitpunkt waren weder das Werk noch die Person, die ihm begegnete, noch ganz dieselben wie zuvor.

Der Anfang war sehr viel weniger ernst. Ich wollte einfach, dass meine persönliche Website cooler aussieht. Ihr brutalistisches und experimentelles Design brauchte einen visuellen Hintergrund, der lebendig wirkte, ohne mit den Artikeln und Fotografien im Vordergrund zu konkurrieren. Ich begann nicht mit Romano Guardini, Bildung, Metanoia, Kybernetik zweiter Ordnung, verteilter Kognition oder einer Theorie KI-gestützter Autorschaft. Ich begann mit Kachelung. Die Kachelung wurde visuell repetitiv. Einige Mosaikformen waren interessanter, verlangten aber noch immer, dass ich zu viele Anordnungen einzeln vorstellte und beschrieb. Mathematische Strukturen boten dann etwas, das sich durch verbale Anweisungen nicht effizient bereitstellen ließ: Regeln, die ganze Familien von Kompositionen erzeugen konnten, die ich nicht jeweils einzeln entworfen hatte. Die Generator-Engine begann damit, Layouts zu erzeugen. Erst später begann sie, Fragen zu erzeugen.

Selbst diese Zusammenfassung ist rückblickend. Zu keinem Zeitpunkt fühlte sich die jeweils nächste Stufe zwangsläufig an. Manchmal war ein Output einfach nur hässlich. Manchmal missverstand die KI das ästhetische Ziel. Manchmal akzeptierte ich eine Erklärung, weil sie kohärent klang, und verwarf sie dann wieder, als das sichtbare Werk ihr widersprach. Manchmal legte eine technische Lösung eine künstlerische Möglichkeit frei, um die ich gar nicht gebeten hatte. Das Projekt bewegte sich durch Unzufriedenheit, Korrektur, Zufall, Widerstand und verspätete Erkenntnis. Seine spätere Bedeutung darf nicht dazu benutzt werden, so zu tun, als hätte die erste Absicht insgeheim bereits die gesamte Entwicklung enthalten.

Mein erster vollständiger Entwurf dieses Essays reproduzierte diese Tendenz dennoch in subtilerer Form. Er stellte die Kunst und Professorin Dohna ins Zentrum, hielt das technische Material im angemessenen Verhältnis und enthielt die Begriffe, zu denen ich gelangt war. Doch als ich ihn erneut las, erkannte ich die Schlussfolgerungen leichter wieder als den Weg, auf dem ich überhaupt erst fähig geworden war, sie zu denken. „Rekursive Bewahrung von Einsicht“, „diachrone relationale Autorschaft“ und „Metanoia der Epistemologie“ erschienen als bereits wohlgeformte Ergebnisse. Der Entwurf sagte zwar, dass ich gezögert hatte, ließ den Leser aber nicht immer in dieses Zögern eintreten. Er bewahrte die Karte und komprimierte die Reise. Diese Erkenntnis veränderte das Ziel der Überarbeitung. Ich wandte mich gegen das implizite Wissensmodell des Entwurfs: Es ließ Verstehen wie einen Besitz erscheinen, der präsentiert wird, nachdem der Kampf bereits vorbei ist. Stattdessen verlangte ich nach der Genealogie — nach den frühen Antworten, die zunächst ausreichend schienen, nach den Einwänden, die sie erschütterten, nach den Belegen, die eine weitere Unterscheidung erzwangen, und nach den Fragen, die selbst nach einer befriedigenden Formulierung übrig blieben. Dieser zweite Durchgang wird damit selbst Teil des Falls, den er beschreibt. Ich begegnete einem KI-gestützten Text, der in seiner Richtung wesentlich meiner war, meiner Erfahrung aber nicht mehr genügte; diese Begegnung veränderte die Anweisungen; die veränderten Anweisungen veränderten wiederum den Text, dem ein späterer Leser — mich selbst eingeschlossen — begegnen wird.

Die technische Realität lässt sich kurz darstellen. Das Werk ist eine browserbasierte visuelle Umgebung, die in WordPress integriert ist. Ein kuratiertes Archiv aus Bildern und Animationen wird durch pseudorandomisierte Auswahl und mathematische Strukturen zu wechselnden Kompositionen angeordnet. Navigation oder Aktualisieren der Seite können eine weitere Manifestation aktivieren. KI hat die Entwicklung des Systems erheblich unterstützt, doch ein konventioneller Programmierer hätte seine Laufzeit-Generativität auch ohne KI bauen können. Der Mechanismus ist wichtig, weil die philosophischen Behauptungen aus etwas hervorgingen, das tatsächlich lief, sichtbare Relationen hervorbrachte, auf bestimmte Weise scheiterte und von anderen Menschen erfahren werden konnte. Thema dieses Essays ist, was geschah, als das Machen über den Grund hinausging, aus dem ich überhaupt begonnen hatte zu machen.

Die neue Frage lautet daher nicht einfach, ob die Browser-Kompositionen als Kunst gelten. Sie lautet vielmehr, ob sich die relevante künstlerische Einheit nach und nach erweitert hat, weil jede frühere Grenze die Evidenz irgendwann nicht mehr beschreiben konnte. Zunächst schien das Kunstwerk eine einzelne generierte Komposition zu sein. Dann schien es das System plus seine vielen möglichen Manifestationen zu sein. Danach wurden die Handlung des Besuchers und die situierte Begegnung relevant. Schließlich begannen Interpretation, Schreiben, die Reaktion eines anderen Menschen, späteres Wiederlesen und veränderte Intention kausal in die Praxis zurückzukehren. Wenn diese Rückkehrbewegungen verändern, was als Nächstes geschaffen wird, sind sie dann äußerer Kommentar zum Kunstwerk — oder sind sie Teil seiner materiellen Geschichte geworden?

Ich möchte darauf nicht antworten, indem ich erkläre, alles sei Kunst. Das würde die Kategorie so großzügig machen, dass sie nichts mehr erklären könnte. Das Kriterium, zu dem ich gelangt bin, ist enger: Ein Ereignis wird künstlerisch integriert, wenn es kausal wieder in die Praxis aufgenommen wird. Eine beiläufige Bemerkung ist nicht automatisch Teil des Kunstwerks. Die Reaktion von Professorin Yvonne Dohna-Schlobitten ist künstlerisch relevant, weil sie meine Interpretation veränderte; diese Interpretation veränderte die Fragen, die ich an Theorie und KI herantrug; diese Fragen veränderten, wie ich zukünftiges Design verstand; und diese Veränderungen können wiederum spätere Begegnungen verändern. Entscheidend ist die Rückkehr, nicht bloße Nähe. Dieser Essay folgt dieser Rückkehr. Es geht um die Transformation des Schaffens durch eine tatsächliche menschliche Begegnung: Das Kunstwerk eröffnete mir einen neuen Zugang zu Fragen, die sich durch das Werk von Professorin Dohna ziehen, während diese Fragen mir neue Weisen eröffneten, das Kunstwerk zu sehen. Um diese Begegnung herum wurden KI, Schreiben und die Website zu ungewöhnlichen Beteiligten eines längeren Prozesses, in dem der Künstler zum Betrachter seines eigenen Werkes werden konnte — und der spätere Betrachter die Person verändern konnte, die weiter schaffen würde.

Ich hatte nicht vor, eine Theorie zu entwickeln

Die Versuchung eines ausgearbeiteten Essays besteht darin, die Vergangenheit intelligenter aussehen zu lassen, als sie war. Sobald ein Kunstwerk ein philosophisches Vokabular erhalten hat, kann der Autor dieses Vokabular stillschweigend rückwärts verschieben, bis jede frühe Entscheidung so aussieht, als habe sie die Schlussfolgerung bereits vorweggenommen. In meinem Fall würde das gerade die wichtigste Evidenz verfälschen. Das gewöhnliche Motiv — der Wunsch nach einer cooleren Website — ist kein peinliches Detail, das entfernt werden müsste, sobald das Projekt ernst wird. Es zeigt, dass die Theorie aus dem Machen hervorging, anstatt durch das Machen lediglich illustriert zu werden.

Die ersten Veränderungen waren ästhetischer und praktischer Art. Ein einziger Hintergrund konnte monoton werden. Zufallsauswahl brachte Abwechslung, aber nicht notwendigerweise Komposition. Kachelung vervielfachte ein Bild, ohne ausreichend reiche Relationen zu erzeugen. Mosaike ermöglichten Kontraste zwischen verschiedenen Einträgen, doch immer mehr Anordnungen von Hand festzulegen, erwies sich bald als schlechter Weg, einen großen visuellen Möglichkeitsraum zu erkunden. Mathematische Strukturen veränderten die Problemstellung. Statt jedes gewünschte Layout zu beschreiben, konnte ich Verfahren festlegen, die Maßstab, Fläche, Nachbarschaft, Wiederholung und Unterbrechung verteilten. Die Arbeit bewegte sich vom Auswählen eines Bildes zum Konstruieren von Bedingungen, unter denen Bilder einander begegnen konnten. Diese Abfolge enthielt mehrere vorläufige Lösungen. Zunächst dachte ich, Variation selbst werde das ästhetische Problem lösen: Wenn sich der Hintergrund veränderte, würde er interessant bleiben. Die tatsächliche Seite widersprach dieser Annahme. Zufälligkeit konnte Wiederholung verhindern und dennoch keinerlei sehenswerte Relation hervorbringen. Kachelung wirkte dann attraktiv, weil sie ein einzelnes Bild in ein Feld verwandelte, doch Wiederholung wurde schnell zu einer anderen Form der Monotonie. Mosaike schienen dieses Problem zu lösen, indem sie mehrere Einträge zusammenstellten. Eine Zeit lang fühlte sich das wie die Antwort an. Dann verlangte jede neue Anordnung eine weitere verbale Beschreibung, einen weiteren Sonderfall und eine weitere Korrektur. Ich häufte Layouts an, ohne bereits eine visuelle Sprache zu schaffen.

Zunächst reagierte ich darauf, indem ich der KI immer präziser beschrieb, was ich wollte. Auf lokaler Ebene half das: Ein Zwischenraum konnte verkleinert, ein Panel vergrößert, eine Kollision vermieden werden. Doch gerade diese Verbesserung legte die Begrenzung der Methode offen. Ich konnte weiter um einzelne Kompositionen bitten — oder ich konnte fragen, welche Art von Regel eine ganze Familie von Kompositionen erzeugen würde. Die ursprüngliche Frage — „Wie sollen diese Bilder angeordnet werden?“ — wurde zu: „Welche Relationen von Maßstab, Nachbarschaft und Unterbrechung sollten fähig sein, sich jeweils anders anzuordnen?“ Die Hinwendung zu mathematischen Generatoren entstand nicht aus einem theoretischen Bekenntnis zur prozeduralen Kunst. Sie entstand, weil verbales Mikromanagement ästhetisch und konzeptuell erschöpft war.

KI half dabei, diese Frustration in mögliche Verfahren zu übersetzen. Einige Vorschläge waren mathematisch elegant und visuell leblos. Andere waren technisch korrekt, aber zu dicht, zu regelmäßig oder zu sehr darauf aus, ihre eigene Geometrie vorzuführen. Ich verwarf sie nicht, weil ihr Code versagte, sondern weil das sichtbare Werk die Spannung nicht aufrechterhielt, die ich zwischen dem Artikel im Vordergrund und dem visuellen Feld im Hintergrund wollte. Zu anderen Zeitpunkten erzeugte ein von mir nicht vorausgesehenes Ergebnis eine Relation, die stärker war als mein Prompt. Solche Fälle zwangen mich, nicht nur die Implementierung, sondern meine Absicht selbst zu revidieren. Die Evidenz bestand nicht in der Versicherung der KI, dass ein Verfahren funktionieren sollte; sie bestand darin, was geschah, wenn das Verfahren auf die Bilder und die Seite traf.

Dieser Schritt gehört erkennbar in den Bereich der generativen Kunst. Philip Galanters einflussreiche Definition stellt den Künstler in den Mittelpunkt, der ein System mit einem gewissen Grad an Autonomie in Gang setzt, und weigert sich ausdrücklich, generative Kunst an eine bestimmte Technologie zu binden (Galanter, 2003). Damit wird eine wesentliche Unterscheidung geschützt: KI hat generative Kunst nicht erfunden, und KI war nicht notwendig, damit der Browser unterschiedliche Manifestationen hervorbringen konnte. Zufallsverfahren, anweisungsbasierte Kunst, mathematische Komposition und autonome Systeme existieren lange vor heutigen Sprachmodellen. Die besondere Rolle der KI trat in meinem Fall in der Entwicklung und Interpretation rund um das Laufzeitsystem hervor, nicht in der bloßen Tatsache, dass ein Algorithmus ein Layout variieren konnte.

Anfangs behandelte ich jeden neuen Output als Evidenz über die Implementierung. Füllte er den Bildschirm? Blieben die Bilder lesbar? Dominierte der Vordergrund weiterhin? Diese Fragen waren notwendig, doch die tatsächlichen Outputs machten wiederholt schon das Briefing selbst instabil. Eine technisch korrekte Anordnung konnte ästhetisch tot sein. Ein Fehler konnte zeigen, dass meine Beschreibung des Ziels zu eng war. Eine unbeabsichtigte Relation konnte in mir den Wunsch nach einer Fähigkeit wecken, von der ich zuvor nicht gewusst hatte, dass ich sie verlangen würde. Der Designprozess begann Donald Schöns Darstellung eines reflektierenden Gesprächs mit einer Situation zu ähneln: Der Schaffende handelt, begegnet den Folgen, bemerkt, was die Handlung offengelegt hat, und rekonstruiert das Problem (Schön, 1992). Schöns Abfolge von Sehen, Handeln und erneutem Sehen erklärt mehr von dieser Geschichte als die Fantasie einer vollständigen Spezifikation, auf die nur noch die Ausführung folgt. KI beschleunigte dieses Gespräch. Innerhalb kurzer Zeit konnte ich von einer vagen visuellen Intuition zu einer möglichen Regel gelangen, von der Regel zu einer laufenden Manifestation, von der Manifestation zu einem Einwand und vom Einwand zu einem revidierten Begriff. Doch Geschwindigkeit machte die Entwicklung weder unvermeidlich noch automatisch erkenntnisreich. Die nützliche Einheit war nicht „Prompt gefolgt von Antwort“. Es war eine wiederkehrende Begegnung mit Widerstand:

gewöhnlicher visueller Wunsch
  → erste Anordnung
  → Unzufriedenheit
  → neue Regel
  → unerwartete Manifestation
  → ästhetischer oder begrifflicher Einwand
  → verändertes Verständnis des Problems
  → weiteres Schaffen

Erst nachdem sich diese Abfolge mehrfach wiederholt hatte, begann ich zu sehen, dass das System nicht bloß Hintergründe erzeugte. Es externalisierte Möglichkeiten, die sich an die Person zurückwenden konnten, die sie initiiert hatte. Die alte Beschreibung — „eine Funktion, die unterschiedliche Layouts erzeugt“ — war technisch weiterhin richtig, künstlerisch aber unzureichend. Das Werk hatte begonnen, mir beizubringen, was ich zu schaffen versucht hatte. Deshalb darf auch die Geschwindigkeit der späteren Reflexion nicht mit dem Alter des Gedankens verwechselt werden. Einige der stärksten Verbindungen tauchten innerhalb weniger Stunden auf. Doch diese Stunden beruhten auf Jahren technischer Praxis, geisteswissenschaftlichen Studiums, früheren Schreibens, meiner Beziehung zu Professorin Dohna, meiner Vertrautheit mit ihrem Vokabular und der Existenz eines lebendigen Kunstwerks, das dieses Vokabular konkret werden lassen konnte. Kompositionszeit ist nicht Bildungszeit. KI verkürzte die Zeit, die nötig war, um zu formulieren und zu verbinden; sie erzeugte nicht die ganze Geschichte, die Erkenntnis überhaupt erst möglich machte.

Die Grenze des Kunstwerks versagte immer wieder

Die erste mögliche Grenze war einfach: Kunstwerk = sichtbare Komposition. Sie versagte, weil keine einzelne Komposition das Werk erschöpfte. Jede Manifestation hing von einem Regelsystem, einem kuratierten Archiv, einem Viewport, einem Ausführungszeitpunkt und von der Handlung des Besuchers ab, die gerade diesen Zustand aktualisierte. Ein Screenshot konnte ein Ergebnis bewahren, aber nicht die Möglichkeit eines anderen Ergebnisses enthalten. Das Kunstwerk schien daher besser als generatives System + Manifestationen beschrieben zu werden.

Ich verwarf diese erste Grenze nicht allein aufgrund von Theorie. Ich versuchte, Kompositionen als Bilder zu bewahren, und bemerkte, was dabei verschwand. Der Screenshot erhielt die Anordnung, beseitigte aber die Möglichkeit des Aktualisierens, die Erwartung von Veränderung und die Erinnerung des Besuchers an das, was zuvor dort gewesen war. Er dokumentierte eine Erscheinung und ließ dabei jene Bedingungen weg, die diese Erscheinung zu einem Ereignis unter anderen machten. Das war die Evidenz, die das System selbst in die künstlerische Einheit hinein verschob. Eine Zeit lang schien System + Manifestationen vollständig.

Auch diese zweite Grenze versagte. Eine Manifestation erschien nicht in einem leeren Labor. Sie erschien hinter einem Artikel, vor einem Besucher mit eigener Geschichte, vielleicht nach früheren Besuchen und erinnerten Kombinationen. Das Aktualisieren der Seite war keine Autorschaft im vollen Sinn, aber auch nicht irrelevant. Der Besucher vollzog einen bescheidenen Akt der Aktualisierung: diese Möglichkeit, jetzt. Das Werk war zu System + Aktivierung + Manifestation + Begegnung geworden. Zu seinem ästhetischen Leben gehörten Wiederkehr, Verschwinden, Überraschung, Langeweile und Erinnerung. Meine Sprache schoss zunächst über das Ziel hinaus, bevor sie präziser wurde. Zuerst beschrieb ich den Besucher als Mitschöpfer. Der Ausdruck erfasste etwas Wichtiges — die Handlung des Besuchers half mitzubestimmen, welcher potenzielle Zustand tatsächlich wurde —, schrieb ihm aber zugleich zu viel Kontrolle zu. Der Besucher konnte aktualisieren, aufmerksam sein, erinnern oder gehen; er konnte weder die Geometrie noch die Einträge oder die Relation festlegen, die erscheinen würden. Deshalb zog ich mich von „Mitschöpfer“ auf die bescheidenere Beschreibung eines Akteurs der Auswahl zurück. Aus dem ersten Ausdruck blieb die Teilnahme bestehen. Aufgegeben werden musste die Andeutung gleicher Autorschaft oder vollständiger Kontrolle.

Die Kunstgeschichte stellt bereits Denktraditionen bereit, die mich daran hindern, dies als beispiellos darzustellen. Generative Kunst behandelt das System als Teil des Werkes. Prozess- und systemorientierte Praktiken verschieben die Aufmerksamkeit vom in sich geschlossenen Objekt hin zu Operationen, Bedingungen und sich wandelnden Relationen. Die relationale Ästhetik machte menschliche Beziehungen und ihre sozialen Kontexte zu zentralem künstlerischem Material (Bourriaud, 2002). Die Kybernetik zweiter Ordnung fragt, was sich verändert, wenn der Beobachter nicht als außerhalb des beobachteten Systems stehend behandelt werden kann (von Foerster, 2003). Mein Fall steht in der Nähe dieser Traditionen, ist aber mit keiner von ihnen identisch. Das besondere Problem, das für mich entstand, war die kausale Rückkehr aus der Begegnung in die fortlaufende Konstruktion des Werkes — und zugleich in die Formung des Schaffenden.

Hier wurde die dritte Grenze instabil. Der Artikel, den ich über die Kompositionen schrieb, veränderte, wie ich sie präsentierte. Das veränderte, wie Professorin Dohna dem Werk begegnete. Ihre Reaktion veränderte, was ich erneut las und welche Fragen ich stellte. Die neue Interpretation begann, andere künstlerische Möglichkeiten vorzuschlagen. Schreiben war nicht länger bloß ein Etikett, das neben einem fertigen Objekt angebracht wurde. Theorie war in die kausale Schleife der Praxis eingetreten. An diesem Punkt wurde eine expansive Erklärung verlockend. Wenn Code, Output, Besucher, Erinnerung, Schreiben und Reaktion alle zählten, war dann vielleicht die gesamte umgebende Ökologie das Kunstwerk? KI konnte diese Erweiterung überzeugend formulieren. Ich fand die Darstellung aufregend, weil sie erklärte, warum sich das Werk größer als der Bildschirm anfühlte. Dann erhob ich Einwand: Wenn im Nachhinein jeder Umstand in das Kunstwerk aufgenommen werden konnte, war die Grenze nicht erweitert, sondern aufgelöst worden. Ein beiläufiges Ereignis und eine transformierende Begegnung würden ununterscheidbar. Die Erklärung brauchte einen Test.

Es wäre weiterhin ein Fehler, jedes umgebende Ereignis als Teil des Kunstwerks zu zählen. Ein sachfremdes administratives Ereignis, ein beiläufiges Gespräch oder der flüchtige Blick eines Fremden können zu den Umständen des Werkes gehören, ohne selbst künstlerisches Material zu werden. Das Kriterium der kausalen Wiedereingliederung ist strenger. Die Reaktion von Professorin Dohna wird künstlerisch folgenreich, weil die Praxis nach dieser Reaktion nicht dieselbe Praxis ist wie zuvor. Wenn die Begegnung das Verständnis des Künstlers verändert, aber niemals in Auswahl, Form, Teilnahme, Bewahrung, Präsentation oder zukünftiges Design zurückkehrt, kann sie biografisch wichtig bleiben, ohne bereits Teil dieses Kunstwerks zu werden.

So entstand das Kriterium — es stand nicht am Anfang. Bloßer Kontakt war zu schwach; „alles ist miteinander verbunden“ war zu weit. Ich musste fragen, was als Nächstes geschah. Veränderte das Ereignis die Interpretation? Veränderte die veränderte Interpretation eine künstlerische Entscheidung, eine Frage, eine Bedingung der Teilnahme oder den zukünftigen Möglichkeitsraum? Ein Ereignis wird künstlerisch integriert, wenn es kausal wieder in die Praxis aufgenommen wird. Diese Formulierung beweist nicht, dass jede Rückkehr gute Kunst hervorbringt. Sie unterscheidet eine folgenreiche Rückkehr von einem lediglich benachbarten Geschehen.

Ich begann daher, zwei vorläufige Bezeichnungen zu verwenden. Formative generative Praxis bezeichnet eine Praxis, in der das System Formen erzeugt, während der größere Prozess zugleich an der Formung der Menschen beteiligt ist, die ihn fortsetzen. Reflexives prozessuales generatives Kunstwerk betont, dass die Geschichte des Werkes Rückkehrbewegungen durch Interpretation und neu gestaltete Bedingungen einschließt. Keine dieser Formulierungen ruft eine neue Kunstbewegung aus. Es sind Versuche zu benennen, warum „Browser-Komposition“ zu klein geworden war und warum „alles darum herum“ zu groß wäre.

Das daraus hervorgehende künstlerische Material kann Code, Algorithmen, mathematische Strukturen, Bilder, Handlungen von Besuchern, Schreiben und menschliche Reaktionen umfassen — aber nicht bloß deshalb, weil sie sich in der Nähe befinden. Sie werden integriert, wenn eines die Bedingungen verändert, unter denen ein anderes geschehen kann. Das Kunstwerk ist kein unendlich expandierender Sack von Assoziationen. Es ist eine Trajektorie folgenreicher Rückkehrbewegungen.

Professorin Dohnas Frage öffnete ein altes Vokabular neu

Professorin Yvonne Dohna-Schlobitten gehört seit Jahren zu meiner intellektuellen Welt. Unsere Gespräche bewegten sich häufig zwischen sehr praktischen Dingen — technischer Hilfe, Computerproblemen, Übersetzungen und Arbeit an Entwürfen — und den theoretischen Fragen, die sich durch ihr Werk ziehen, darunter Kontemplation, Begegnung, Figuration, Guardini, Bildung und Metanoia. Viele dieser Themen waren mir daher lange vor diesem Projekt vertraut. Was sich durch das generative Kunstwerk veränderte, war die Weise, wie ich ihnen begegnete: Ideen, denen ich zuvor im Gespräch, beim Lesen und beim Übersetzen begegnet war, erhielten plötzlich eine andere Form von Unmittelbarkeit, weil sie nun mit etwas verbunden waren, das sich innerhalb meiner eigenen künstlerischen Praxis entfaltete. Verändert hatte sich nicht einfach, wie viel ich verstand, sondern die Form, die dieses Verstehen annahm. Begriffe, die mir intellektuell bereits zugänglich gewesen waren, wurden erfahrungsmäßig konkret, und dieser neue Berührungspunkt ließ mich Relationen bemerken, für die ich zuvor keinen Grund gehabt hatte, sie in genau dieser Weise zu sehen. Diese Unterscheidung ist relational bedeutsam.

Die unmittelbare Begegnung mit Professorin Dohna hatte ich bereits in meinem vorherigen Essay beschrieben; ich muss den Austausch hier daher nicht im Detail rekonstruieren. Für das gegenwärtige Argument ist entscheidend, was danach geschah. Als ich ihr die generative Arbeit zeigte, öffnete ihre Reaktion unerwartet die Beziehung zwischen den Fragen neu, die sie um Begegnung, künstlerisches Schaffen und Metanoia entwickelt hatte, und jener computergestützten Praxis, der ich aus einer ganz anderen Richtung begegnet war. Was mir zunächst wie zwei benachbarte intellektuelle Welten erschienen war — ihre theoretische Arbeit und mein technisches Experimentieren —, begann plötzlich, sich gegenseitig zu erhellen.

Ihre Reaktion lieferte mir keine fertige Interpretation des Kunstwerks. Sie tat etwas Wertvolleres: Sie unterbrach die Interpretation, die ich bereits hatte. Ich begann zu fragen, ob ein generatives System, das Formen hervorbringen konnte, ohne selbst die menschliche Erfahrung von Begegnung zu durchleben, die Bedeutung ihrer Fragen tatsächlich schwächte — oder ob es diese Fragen vielmehr dringlicher machte. Je länger ich darüber nachdachte, desto mehr schien mir die zweite Möglichkeit der Verfolgung wert. Das Kunstwerk war für mich nicht länger nur deshalb interessant, weil es unerwartete Kompositionen erzeugen konnte; es war zu einer konkreten Situation geworden, in der Fragen nach dem Verhältnis von Machen, Betrachten, Bedeutung und Transformation erfahren werden konnten, statt nur abstrakt diskutiert zu werden.

Dies war auch der Punkt, an dem ich mit einer anderen Art von Aufmerksamkeit zu Professorin Dohnas theoretischem Werk zurückkehrte. Viele dieser Ideen waren mir schon zuvor begegnet, doch die künstlerische Erfahrung gab ihnen nun einen anderen Berührungspunkt. Zugleich gaben mir diese Begriffe ein Vokabular für Dimensionen des Kunstwerks, von denen ich zuvor nicht gewusst hatte, wie ich sie beschreiben sollte. Die Bewegung wurde damit wechselseitig: Das Kunstwerk machte ihre Theorie für mich auf neue Weise verständlich, während ihre Theorie das Kunstwerk für mich auf neue Weise verständlich machte.

Der KI-Dialog verstärkte die Versuchung, indem er elegante Varianten einer beruhigenden Antwort hervorbrachte: Das computergestützte Kunstwerk habe ihre Fragestellung nicht verdrängt, sondern ihre Relevanz bestätigt. Ich erkannte etwas Wahres in dieser Formulierung. Dennoch erschien sie mir zu leicht. Wenn das Kunstwerk lediglich „bewies“, dass Professorin Dohna schon immer recht gehabt hatte, dann wäre ihre destabilisierende Frage zu einem Instrument geworden, das nur eine Theorie bestätigt. Eine solche Darstellung würde sowohl ihrem Projekt als auch meinem Kunstwerk schmeicheln und dabei die Störung unangetastet lassen. Ich stellte eine schwierigere Frage: Was genau war verständlicher geworden, und welche Evidenz zeigte, dass dies mehr war als rückblickende intellektuelle Dekoration? Dieser Einwand veränderte den nächsten Schritt. Statt weiterhin beruhigende Interpretationen zu generieren, kehrte ich zu ihren veröffentlichten Arbeiten zurück. Das Wiederlesen war keine Hintergrundrecherche, die eine bereits fertige These stärken sollte. Es war ein Test der These. Ich wollte wissen, ob die Begriffe, auf die ich mich berief, den tatsächlich stattgefundenen Prozess beschrieben, ob ich sie richtig erinnert hatte und ob sie meinem Wunsch nach einer befriedigenden Symmetrie Widerstand leisteten.

In „What we see looks back at us“ verbindet sie Sehen, Liebe und künstlerisches Schaffen über einen Raum, in dem eine Person oder ein Ding sich zeigen kann, statt auf seinen Gebrauch reduziert zu werden (Dohna Schlobitten, 2022). Verso nuovi occhi bewegt sich ausdrücklich zwischen theoretischem, erfahrungsbezogenem und kontemplativem Erkennen und verbindet künstlerische Form mit geistlicher Unterscheidung (Dohna Schlobitten, 2023). Ihre Studie zu Guardinis Weltanschauung fragt danach, wie Denken Form annimmt und wie Kunsttheorie aus künstlerischer Praxis hervorgehen kann, statt ihr lediglich von außen aufgelegt zu werden (Dohna-Schlobitten, 2024).

Diese Wörter waren für mich nicht neu. Verändert hatte sich die Struktur ihrer Relevanz. Ich hatte Theorie weitgehend als Quelle von Aussagen behandelt, die es zu verstehen galt. Das Kunstwerk ließ mich Theorie als neue Wahrnehmungsfähigkeit erfahren. Ein Programmierer, der den Begriff einer Race Condition lernt, kann plötzlich ein Problem sehen, das physisch bereits im Code vorhanden war, bevor ihm der Begriff zur Verfügung stand. In einem anderen Register ließen Begegnung und Figuration mich erkennen, dass das generierte Bild nicht das einzige Objekt künstlerischer Aufmerksamkeit war. Sichtbar geworden war auch die Relation zwischen Manifestation, Betrachter, Reaktion und erneutem Schaffen.

Das war die Evidenz, die der ersten Beruhigung gefehlt hatte. Ihre Texte lieferten nicht einfach nur das Wort „Begegnung“ für einen Besucher, der ein Bild sieht. Sie lenkten die Aufmerksamkeit auf die Transformation des Sehens selbst und auf die Weise, in der Form eine Beziehung eröffnen kann, ohne den Anderen auszuschöpfen. Das führte mich mit einer anderen Frage zum Austausch zurück. Vielleicht waren ihr Projekt und mein Kunstwerk keine konkurrierenden Erklärungen von Schöpfung. Vielleicht hatte jedes etwas sichtbar gemacht, was das andere für mich noch nicht sichtbar gemacht hatte:

Eine kraftvolle Theorie beantwortet nicht bloß sichtbare Fragen. Sie macht zuvor unsichtbare Fragen überhaupt erst fragbar.

Verspätete Erkenntnis sollte nicht romantisiert werden. Ein theoretischer Rahmen wird nicht allein dadurch überzeugender, dass seine Relevanz erst später sichtbar wird, und eine starke Erfahrung entbindet eine Interpretation nicht von Kritik. Die Korrektur ist bescheidener: Meine Unfähigkeit, Bedeutung wahrzunehmen, kann Evidenz über die Begegnung zwischen mir und der Theorie sein, nicht aber entscheidende Evidenz über die Theorie selbst. Manchmal braucht es zusätzliche Informationen. Manchmal ist Kritik berechtigt. Aber manchmal ist die fehlende Bedingung eine Erfahrung, die neu ordnen kann, was vorhandene Wörter überhaupt zu erschließen vermögen.

Guardinis Beschreibung eines Kunstwerks als Eröffnung eines Raumes, in dem Menschen sich bewegen und dem begegnen können, was sich vor ihnen öffnet, gewann durch die Browser-Arbeit für mich eine neue Konkretheit (Francis, 2023). Ich behaupte nicht, dass ein generativer Hintergrund Guardini beweist oder dass meine Website die geistliche oder künstlerische Statur der Werke besitzt, über die er sprach. Das wichtige Ereignis war die wechselseitige Erhellung:

Das Kunstwerk eröffnete mir einen neuen Zugang zu Fragen, die sich durch ihr Werk ziehen, während diese Fragen mir neue Weisen eröffneten, das Kunstwerk zu sehen.

Der Dialog brachte schließlich diesen Satz hervor, und ich empfand sofort ein Gefühl des Wiedererkennens. Ich akzeptierte ihn nicht nur, weil er schön war. Ich prüfte beide Richtungen an der Chronologie. Das Kunstwerk hatte tatsächlich verändert, was ich in ihren Texten wahrnehmen konnte; die erneut gelesenen Texte hatten tatsächlich verändert, was ich als Teil des Kunstwerks betrachtete. Keines brachte das andere einfach hervor. Die Theorie war kein nachträglich entliehener intellektueller Schmuck, der bewegten Bildern mehr Bedeutung verleihen sollte. Das Kunstwerk war keine Demonstration, die programmiert worden war, um einen vorherigen Rahmen zu bestätigen. Sie begegneten einander durch Professorin Dohnas Reaktion, und die Störung zwischen ihnen veränderte, wie ich beide sehen konnte.

Die Erfahrung gab mir auch eine neue Weise, unseren langjährigen intellektuellen Austausch zu sehen. Praktische Zusammenarbeit und theoretisches Gespräch hatten oft nebeneinander bestanden, doch durch dieses Projekt begannen sie unerwartet zusammenzulaufen. Fragen aus meiner eigenen künstlerischen Praxis resonierten mit Themen, die sie seit Jahren erforschte, während ihr Denken mir neue Weisen eröffnete, auf das zu achten, was sich im Werk entfaltete. Was diese Begegnung aus einer anderen Perspektive auch immer bedeuten mag — für mich eröffnete sie eine Dimension unserer intellektuellen Beziehung, die ich zuvor nicht in genau dieser Form erfahren hatte.

Begegnung wurde zur Operation der Transformation

Vor dieser Abfolge schien „Begegnung“ zu benennen, was zwischen einem Besucher und einer generierten Komposition geschah. Diese Beschreibung war nicht falsch; sie war lediglich die erste Instanz. Dann begegnete ich Outputs, an deren Entstehung ich beteiligt gewesen war, die ich aber nicht einzeln komponiert hatte. Professorin Dohna begegnete dem Kunstwerk. Ich begegnete ihrer Reaktion. Ihre Reaktion führte dazu, dass ich ihrer Theorie anders begegnete. Dieses Wiederlesen führte dazu, dass ich meinen früheren Urteilen anders begegnete. Später begegnete ich meinem eigenen KI-gestützten Artikel als Leser. In jeder Phase trat etwas in veränderter Form in die nächste ein.

Eine frühere KI-Frage war hilfreich gewesen: „Wo genau befindet sich Sinn?“ Meine erste Antwort verortete ihn hauptsächlich in der menschlichen Interpretation einer generierten Gegenüberstellung. Die Geometrie konnte zwei Bilder nebeneinander platzieren; der Betrachter konnte in ihrer Relation Komik, Erinnerung oder Theologie erkennen. Professorin Dohnas Reaktion machte diese Antwort zu eng. Sinn ereignete sich nicht nur im Augenblick des Sehens. Ihre Frage veränderte, was ich später las, und das Wiederlesen veränderte, was aus dem Werk werden konnte. Bedeutung hatte einen zeitlichen und kausalen Weg erhalten. Eine weitere KI-Formulierung wurde daraufhin wichtig: „Die Person kann hineingezogen werden.“ Zunächst akzeptierte ich sie, weil sie den Unterschied zwischen dem Beobachten eines interessanten Outputs und der Entdeckung benannte, dass dieser Output einen Anspruch an das eigene Verstehen stellt. Doch ich musste fragen, was „hineingezogen“ als Evidenz und nicht bloß als Rhetorik bedeutete. In meinem Fall bedeutete es, dass ich nicht länger dieselbe Darstellung von Professorin Dohnas Theorie, unserer intellektuellen Beziehung oder meiner eigenen Rolle als Schaffender aufrechterhalten konnte. Die Formulierung wurde erst dann nützlich, als sie an diese beobachtbaren Veränderungen gebunden wurde.

Zustand₁ → Begegnung → Transformation → Zustand₂
Zustand₂ → Begegnung → Transformation → Zustand₃
Zustand₃ → verändertes Schaffen → eine weitere mögliche Begegnung

Ich verwende „Transformationsoperator“ nur als begriffliches Bild (nicht als etablierte wissenschaftliche Terminologie): Eine Begegnung kann verwirren, verletzen, schmeicheln, verengen oder in die Irre führen. Auch ist nicht jede Veränderung Metanoia. Entscheidend ist die wiederkehrende Struktur, weil dieselbe Operation — etwas zu begegnen, das vom gegenwärtigen Selbst nicht vollständig kontrolliert wird — immer wieder den Zustand der Praxis und der Person veränderte, die sie fortsetzte.

Der Austausch mit Professorin Dohna ist in diesem Fall das deutlichste menschliche Beispiel. Ihre Reaktion unterbrach meine anfängliche Interpretation und veränderte, was ich anschließend las und fragte. Diese Veränderung kehrte dann in die künstlerische Interpretation zurück und gab dem Austausch sowohl formative als auch künstlerische Konsequenz.

Zunächst schien „Feedback“ die gesamte Struktur zu benennen. Output kehrte als Input zurück; ein späterer Zustand unterschied sich aufgrund einer früheren Reaktion. Ich akzeptierte die kybernetische Beschreibung vorläufig, weil sie die Zirkularität sichtbar machte. Dann legte der Vergleich seine eigene Grenze offen. Feedback kann einen Parameter anpassen, während das Ziel unverändert bleibt. Begegnung kann eine neue Kategorie von Relevanz einführen. Ein Besucher, der auf „Gefällt mir“ klickt, könnte die einem Bild zugewiesene Wahrscheinlichkeit verändern; eine menschliche Reaktion kann den Künstler dagegen dazu bringen, neu zu überlegen, welche Art von Problem das Werk überhaupt geschaffen hat. Das Erste verändert einen Wert innerhalb eines gegebenen Modells. Das Zweite kann das Modell selbst verändern.

Hier rückte auch der Beobachter in die Geschichte hinein, die er beobachtete. Die Kybernetik zweiter Ordnung wurde relevant, weil sie die Aufmerksamkeit auf Systeme lenkt, in denen Beobachtung und Beschreibung nicht als äußere, neutrale Zusätze behandelt werden können (von Foerster, 2003). Zunächst behandelte ich den Vergleich beinahe wie eine Erklärung. Bei näherer Betrachtung war er besser als diagnostische Hilfe zu verstehen. Die Kybernetik machte die rekursive Position des Beobachters sichtbar; sie erklärte nicht aus sich heraus die ästhetische, relationale oder theologische Bedeutung der Transformation. Ich baute den Generator, beobachtete ihn, veränderte mich aufgrund dessen, was ich beobachtete, schrieb über diese Veränderung und ließ das Schreiben spätere Intentionen beeinflussen. Professorin Dohna begegnete dem Werk, und ihre Reaktion veränderte die Weise, wie ich als sein Schaffender es fortsetzen würde. KI half, diese Schleife zu beschreiben, und wurde dadurch selbst zu einem weiteren kausalen Beteiligten an der Schleife, die sie beschrieb.

Die Grenze des Kunstwerks erweiterte sich folglich noch einmal:

generatives System
  → Manifestation
  → menschliche Begegnung
  → Reaktion
  → weitere Begegnung mit dieser Reaktion
  → veränderte Interpretation
  → veränderte künstlerische Intention
  → zukünftiges System und zukünftige Manifestation

Das Kriterium der kausalen Wiedereingliederung verhindert, dass daraus metaphysische Aufblähung wird. In das Werk tritt die Reaktion ein, die sie tatsächlich gegeben hat, meine dokumentierte Reaktion darauf, die daraus folgenden interpretativen Veränderungen und jene zukünftige Praxis, die durch diese Veränderungen tatsächlich beeinflusst wird. Das Kriterium bewahrt sowohl relationale Offenheit als auch den Respekt vor der Unabhängigkeit des anderen Menschen. Begegnung hörte damit auf, bloß ein Thema zu sein, das vom Kunstwerk dargestellt wurde. Sie wurde zur wiederkehrenden Operation, durch die die Praxis ihren Zustand veränderte. Das ist ästhetisch bedeutsam, weil die Form des Werkes nun nicht nur Anordnungen auf einem Bildschirm umfasst, sondern auch das zeitliche Muster, durch das Anordnungen, Reaktionen und revidierte Bedingungen füreinander folgenreich werden. Das Kunstwerk endet nicht mehr dort, wo die Pixel enden.

Der Künstler kehrte als Betrachter zurück

Eine weitere Instabilität trat auf, als ich zu dem Artikel zurückkehrte, den ich mit KI über das Projekt geschrieben hatte. Während seiner Entstehung war ich Urheber des Ausgangsimpulses, Zeuge, Fragender und Redakteur gewesen. Ich lieferte die erlebten Ereignisse, das Projekt, die Zitate, die Einwände und die Richtung der Untersuchung. Die KI erzeugte einen großen Teil der sprachlichen Oberfläche. Damals war ich dem Prozess noch nahe genug, dass sich der Text wie eine Fortsetzung eines aktiven Gesprächs anfühlte. Nach der Veröffentlichung veränderte zeitliche Distanz seinen Status. Er wurde zu einem Objekt, dem ich begegnen konnte.

Beim Wiederlesen fand ich manchmal ein Argument, dessen Konsequenz ich noch nicht vollständig aufgenommen hatte, als ich die Sätze freigab. Das ist nicht der romantische Mythos einer autonomen Maschine, die heimlich Botschaften in mein Werk einschreibt. Es ist eine gewöhnlichere und für mich folgenreichere Tatsache: Ein Mensch kann einen Text mitverursachen, ohne gleichzeitig jeder Implikation gegenwärtig zu sein, die dieser Text später verfügbar macht. Schriftsteller haben immer schon Dinge in ihren eigenen Entwürfen entdeckt, und Künstler sind immer schon Folgen begegnet, die sie nicht bewusst geplant hatten. KI verstärkte die Trennung zwischen Initiieren, Artikulieren, Auswählen, Veröffentlichen und späterem Verstehen.

Meine erste Erklärung war weniger großzügig: Vielleicht hatte ich einfach nicht sorgfältig genug gelesen. Diese Möglichkeit blieb legitim, besonders wenn die KI schnell eine lange Passage erzeugt hatte. Sie nährte auch das Unbehagen, über das ich später spreche: Wörter zu veröffentlichen, deren Implikationen ich noch nicht vollständig beherrschte. Doch diese Erklärung deckte nicht alles ab. Ich hatte den Text gelesen, ausgewählt und freigegeben. Was sich in der späteren Begegnung veränderte, war nicht nur meine Aufmerksamkeit für denselben Satz, sondern auch die Geschichte, die ich zu ihm mitbrachte. Ereignisse und weitere Reflexion hatten eine Implikation verfügbar gemacht, die der frühere Leser noch nicht auf dieselbe Weise organisieren konnte.

Der KI-Dialog schlug eine Formulierung vor, die ungefähr lautete: „Der Schöpfer kann zum Betrachter seiner eigenen KI-gestützten Schöpfung werden.“ Ich erkannte die Erfahrung sofort wieder, widersetzte mich aber der Vorstellung, dies sei vollständig neu oder ausschließlich künstlich. Schriftsteller sind seit Langem von ihren Entwürfen überrascht; Maler begegnen Formen, die sie nicht geplant haben; zeitliche Distanz verändert jedes Wiederlesen. Die stärkere, revidierte Behauptung war daher vergleichend statt absolut: KI kann den Abstand zwischen dem Ursprung eines Problems, der Hervorbringung seiner sprachlichen Oberfläche und der vollständigen Begegnung mit dem, was diese Oberfläche denkbar macht, erheblich vergrößern. Sie intensiviert eine alte künstlerische Möglichkeit und verteilt sie auf mehr Akteure und mehr Zeiten.

ich als Initiator und Zeuge
  → KI-gestützte Artikulation
  → ausgewählter und veröffentlichter Text
  → zeitliche Distanz
  → ich als späterer Leser
  → erneute Begegnung
  → verändertes Verständnis
  → weiteres Schreiben und Schaffen

Ich war zum Betrachter meiner eigenen KI-gestützten Schöpfung geworden, und der Betrachter konnte nun den Schöpfer verändern. Künstlerisch war das bedeutsam, weil die Rückkehr nicht bei privater Wertschätzung stehen blieb. Was ich später verstand, veränderte die Fragen, die ich über das visuelle System, Professorin Dohnas Projekt und die Bedeutung von Autorschaft stellte. Der Artikel trat in die kausale Geschichte des Kunstwerks ein. Er war zugleich Aufzeichnung eines bestimmten Erkenntniszustands und ein Instrument, das einen späteren Zustand verändern konnte.

Das veränderte auch mein Verständnis der Website selbst. Ich hatte sie hauptsächlich als Veröffentlichungsplattform betrachtet: als Ort, an dem ein Artikel verfügbar wurde, nachdem er geschrieben war. In der Praxis verhielt sie sich zugleich wie ein zeitlicher kognitiver Apparat. In einem Moment konnte eine flüchtige Intuition mit Hilfe von KI artikuliert und bewahrt werden. Zu einem späteren Zeitpunkt konnte ein verändertes Selbst dieses bewahrte Objekt erneut lesen und etwas erkennen, das das frühere Selbst noch nicht hatte halten können. Ein neues Gespräch konnte diese Erkenntnis wiederum revidieren und ein weiteres Objekt für eine noch spätere Rückkehr hervorbringen.

„Archiv“ war mein erstes besseres Wort. Anders als „Publikation“ betonte es die Bewahrung über die Zeit. Doch ein Archiv klang weiterhin passiv, als speichere die Website lediglich abgeschlossene Zustände. Mein eigenes Wiederlesen lieferte die gegenteilige Evidenz: Ein alter Artikel konnte eine gegenwärtige Frage neu ordnen, und die gegenwärtige Frage konnte mich mit neuer Aufmerksamkeit zu Professorin Dohnas Werk zurückführen. Speicherung war zu einer aktiven Relation zwischen unterschiedlich situierten Versionen meiner selbst geworden.

t1  flüchtige Intuition → KI-gestützte Artikulation → Archiv
t2  verändertes Selbst → Wiederlesen → neue Erkenntnis
t3  erneute Diskussion → revidiertes Verständnis → neues Archiv

Dies einen kognitiven Apparat zu nennen, erfordert nicht die extravagante Behauptung, meine Website denke im wörtlichen Sinn. Kurzzeitig zog mich die stärkere Sprache eines erweiterten oder verteilten Geistes an, weil sie erfasste, wie viel Arbeit das externe Archiv tatsächlich verrichtete. Dann schränkte ich die Behauptung ein. Arbeiten zur verteilten Kognition und zum erweiterten Geist haben die Vorstellung seit Langem infrage gestellt, jede kognitiv wichtige Operation müsse innerhalb eines individuellen Schädels verbleiben. Hutchins zeigte Kognition, die sich in realen Praktiken über Menschen und materielle Strukturen hinweg koordiniert; Clark und Chalmers argumentierten, dass externe Ressourcen bisweilen an kognitiven Prozessen teilnehmen können, statt bloß deren Ergebnisse zu berichten (Hutchins, 1995) (Clark and Chalmers, 1998). Diese Theorien situieren das Problem, doch ich muss nicht beweisen, dass die Website buchstäblich Teil meines Geistes ist. Meine engere Behauptung ist funktional: Das Archiv verändert, was ich erinnern, vergleichen, erneut aufsuchen und weiterentwickeln kann. Wenn seine bewahrten Formulierungen später mein Urteil verändern, ist es kausal aktiv in der Geschichte meines Denkens.

Der größere Kreislauf lautet daher nicht einfach Mensch → KI → Text. Er sieht eher so aus: menschliches Gedächtnis ↔ KI ↔ Schreiben ↔ öffentliches Archiv ↔ späteres Selbst ↔ andere Menschen ↔ neue Erfahrung. Die Pfeile sind nicht gleichwertig. Das Archiv besitzt keine Lebensgeschichte; KI erbt meine Verantwortung nicht; andere Menschen sind keine Komponenten, die in meine private kognitive Maschinerie absorbiert werden dürfen. Die Notation markiert lediglich, dass Denken durch mehrere unterschiedlich situierte Beteiligte und Medien hindurch stattfand und dass das, was durch sie hindurchging, verändert zurückkehren konnte.

Eine Funktion dieses Kreislaufs begann ich rekursive Bewahrung von Einsicht zu nennen. Der Ausdruck wurde notwendig, weil „Notizen machen“ die gesamte Abfolge nicht beschrieb. Viele Intuitionen treffen mit größerer Kraft als Klarheit ein. Wenn ich sie nicht festhalte, verschwinden sie; wenn ich nur ein Fragment aufzeichne, kann ich später zwar die Wörter wiederfinden, aber nicht unbedingt die Relation, die sie lebendig gemacht hatte. KI kann helfen, eine fragile Intuition in ein äußeres Objekt zu verwandeln: Sie kann den Gedanken schnell genug artikulieren, erweitern, befragen, vergleichen und bewahren, dass seine ursprüngliche Energie nicht vollständig verloren geht. Nach zeitlicher Distanz kann dieses Objekt erneut begegnet, von einer veränderten Person interpretiert und in anderer Form in den Prozess zurückgeführt werden.

Der Ausdruck entstand nicht auf einmal. „Gedächtnishilfe“ erfasste das Verhindern des Vergessens, aber nicht die spätere Rückkehr. „Externalisierter Gedanke“ erfasste die Existenz eines inspizierbaren Objekts, aber nicht dessen Fortbestand über die Zeit. „Bewahrung“ schien dann näher zu kommen, bis ich einwandte, dass Bewahrung gewöhnlich suggeriert, etwas unverändert zu erhalten. In der Praxis wurde die bewahrte Formulierung gerade deshalb produktiv, weil eine spätere Begegnung sie transformierte. „Rekursive Bewahrung“ hielt beide Bewegungen zusammen: genügend Stabilität, damit die Intuition überlebt, und genügend Offenheit, damit sie als neue Ursache wieder in das Denken eintreten kann:

Rekursive Bewahrung von Einsicht ist nicht bloß die Verhinderung des Vergessens. Sie ist die Bewahrung eines Gedankens in einer Form, die dauerhaft genug ist, um auf die Person zurückzuwirken, von der er ausging.

Die Rekursion ist entscheidend. Eine bewahrte Einsicht, die niemals zurückkehrt, ist ein Archiveintrag. Eine bewahrte Einsicht, die später den Leser verändert, der daraufhin die Praxis verändert, hat formative Kraft gewonnen. In diesem Fall bewahrte die Website eine KI-gestützte Interpretation des Kunstwerks; ihr Wiederlesen veränderte mein Verständnis von Begegnung; das veränderte, wie ich Professorin Dohna erneut las; ihr Rahmen wiederum veränderte, wie ich das Kunstwerk sah. Der bewahrte Gedanke blieb auf seiner Reise durch die Zeit nicht identisch. Bewahrung machte Transformation möglich.

Es gibt jedoch auch eine ernste Gefahr. KI kann eine schwache Intuition zu einer beeindruckenden Miniaturtheorie aufblasen, bevor die Intuition überhaupt den Kontakt mit Evidenz überstanden hat. Sprachliche Vollständigkeit kann begriffliche Unreife verbergen. Ein Absatz kann wirken, als lägen bereits Jahre des Denkens hinter ihm, nur weil er Rhythmus, Unterscheidungen und Referenzen reifer Reflexion besitzt. Rekursive Bewahrung kann dann in rekursive Selbstbestätigung umschlagen: Ich bewahre eine rhetorisch vergrößerte Behauptung, lese sie später erneut, als wäre ihre ausgearbeitete Form selbst Evidenz, und baue weitere Behauptungen darauf auf.

Dieses Risiko bemerkte ich schon bei der Entwicklung des Ausdrucks selbst. Sobald KI „rekursive Bewahrung von Einsicht“ zu einer eleganten Darstellung von Gedächtnis, Archiven und späteren Selbsten entfalten konnte, fühlte sich der Begriff etablierter an, als er tatsächlich war. Meine Begeisterung war Evidenz dafür, dass die Formulierung etwas Wichtiges eingefangen hatte, nicht aber dafür, dass sie es korrekt eingefangen hatte. Ich musste zur konkreten Abfolge zurückkehren — flüchtige Intuition, KI-gestützte Artikulation, Veröffentlichung, zeitliche Distanz, Wiederlesen und verändertes Handeln — und fragen, welche Verbindungen tatsächlich stattgefunden hatten. Der Begriff überstand diesen Test als vorläufige Beschreibung, nicht als bewiesene Theorie der Kognition.

Das Archiv braucht daher sichtbare epistemische Unterschiede. Eine veröffentlichte Erkundung ist nicht automatisch eine zertifizierte Schlussfolgerung (seltsamerweise!). Daten, Quellen, Korrekturen, ausdrücklich markierte Unsicherheit und spätere Revisionen sind wichtig, weil die Website öffentlich ist, selbst wenn sie wie ein Notizbuch funktioniert. Ihre Offenheit erlaubt es einem anderen Menschen, dem Denken zu begegnen, wie Professorin Dohna es tat; doch dieselbe Offenheit erzeugt Pflichten. Ein zeitlicher Spiegel kann Veränderung nur dann sichtbar machen, wenn ich nicht jede frühere Reflexion so übermale, dass sie der gegenwärtigen gleicht.

Autorschaft zerfiel in Zeiten und Verantwortlichkeiten

Zum Leser meiner eigenen KI-gestützten Prosa zu werden, machte die Frage der Autorschaft schwieriger, nicht einfacher. In manchen Artikeln mag KI den größten Teil der endgültigen Sätze erzeugt haben — vielleicht sogar eine sehr große Mehrheit der sichtbaren Sprache. Das zu verschweigen würde den Prozess verfälschen. Doch die Zahl der Sätze als vollständiges Maß zu behandeln, würde ihn auf andere Weise verfälschen. Das Problem lässt sich nicht lösen, indem man „mir“ einen Prozentsatz und „der KI“ einen anderen zuweist, denn mehrere Dinge, die normalerweise im Wort Autor zusammengedrängt werden, waren auseinandergetreten. Mein erster Impuls war dennoch quantitativ. Wenn die KI die meisten Sätze hervorgebracht hatte, hatte sie dann vielleicht auch den größten Teil des Artikels hervorgebracht? Wenn ich den Anteil schätzen konnte, ließ sich dann vielleicht das moralische Unbehagen beherrschen? Die Arithmetik hatte den Vorteil, ehrlich gegenüber der sprachlichen Oberfläche zu sein. Zugleich verbarg sie eine Annahme: dass jeder autorische Beitrag mit Satzproduktion verrechenbar sei. Ein Prozentsatz konnte Wörter zählen und zugleich der Erfahrung, die das Problem überhaupt hervorgebracht hatte, der Evidenz, die es begrenzte, der Zurückweisung einer überzeugenden, aber falschen Interpretation oder der durch Veröffentlichung entstehenden Verantwortung keinen Platz geben.

Die KI konnte auf diesen Einwand mit einer weiteren Vereinfachung antworten: Ich sei weiterhin der „eigentliche Autor“, weil ich die Vision geliefert und das System dirigiert habe. Einen Moment lang fand ich das beruhigend. Dann stellte ich es infrage. „Leitung“ kann zu einem Ehrentitel werden, der menschliches Prestige schützt, ohne zu prüfen, was der Mensch tatsächlich getan hat. Wenn ich das Ergebnis nicht gelesen hätte, es nicht erklären könnte, keine entscheidende Evidenz beigesteuert und jeden generierten Absatz akzeptiert hätte, würde das Wort „Leiter“ die Praxis nicht retten. Ich brauchte eine Beschreibung, die weder den enormen sprachlichen Beitrag der KI auslöschte noch meine Intention in magisches Eigentum verwandelte.

Wer brachte das Problem hervor? Wer durchlebte die Erfahrung? Wer erkannte, dass der Austausch mit Professorin Dohna bedeutsam war? Wer lieferte die Evidenz und widersprach, wenn eine Formulierung über sie hinausging? Wer schlug eine Verbindung vor, erzeugte einen Satz, wählte eine Passage aus, verwarf eine Interpretation, setzte eine Konsequenz um, veröffentlichte das Ergebnis, übernahm Verantwortung für die Behauptungen und kehrte später zurück, um die Untersuchung fortzusetzen? In einer konventionellen Darstellung können diese Funktionen größtenteils einer Person und einer Arbeitsphase gehören. Hier waren sie über einen Menschen, ein KI-System, die Forschung anderer Menschen, die Reaktion einer anderen Person, mehrere Artefakte und mehr als eine Version meiner selbst verteilt.

Die Überarbeitung dieses Essays lieferte ein besonders klares Beispiel für diese Verteilung. Die KI konnte einen kohärenten ersten Entwurf und den größten Teil seiner ausgearbeiteten Sprache erzeugen. Ich war derjenige, der erkannte, dass diese Kohärenz den epistemischen Weg verborgen hatte, dem Ergebnis widersprach und das Ziel neu definierte: von „die Theorie klar darlegen“ zu „zeigen, wie die Theorie überhaupt denkbar wurde“. Dieser Eingriff war keine kosmetische Vorliebe und verlangte nicht, dass ich jeden Ersatzsatz selbst tippte. Er veränderte das Modell dessen, was der Artikel sein sollte. Menschliche Agency erschien als Diagnose, Widerstand und Neudefinition des Kriteriums des Werkes.

Die Forschung zum Schreiben mit Mensch und KI untersucht diese Verteilung zunehmend über Prozesse statt allein über die Inspektion des Endtexts. Der CoAuthor-Datensatz zeichnet beispielsweise detaillierte Interaktionsverläufe aus Mensch-KI-Schreibsitzungen auf und macht Zyklen des Anforderns, Akzeptierens, Bearbeitens und Zurückweisens sichtbar, die eine fertige Seite verbirgt (Lee, Liang and Yang, 2022). Ein jüngerer vorläufiger Rahmen für kreatives Eigentum trennt ähnlich zwischen Person, Prozess und generativem System und fragt nach Intention, Kontrolle, Aufwand, Verkörperung, Produktion und Interdependenz, statt sich auf ein einziges Maß zu verlassen (Polimetla and Gero, 2025). Diese Ansätze entscheiden meinen Fall nicht, bestätigen aber, warum ein Wortprozentsatz analytisch unzureichend ist.

„Ko-Autorschaft“ schien zunächst die offensichtliche Alternative zur alleinigen Autorschaft, implizierte jedoch eine Symmetrie, die der Fall nicht hergab. „Verteilte Autorschaft“ erfasste die Dispersion, ließ die Relationen aber räumlich und statisch klingen. Vorläufig gelangte ich zu relationaler Autorschaft für Werke, deren Entstehung von Beiträgen abhängt, die isoliert voneinander nicht angemessen verstanden werden können, und zu diachroner relationaler Autorschaft, wenn die relevanten Relationen spätere Versionen des menschlichen Autors einschließen. Die Adjektive löschen die Asymmetrie nicht aus. Professorin Dohna ist nicht Ko-Autorin meines Artikels, nur weil ihre Reaktion ihn verändert hat. Die zitierten Denker sind nicht mit einem Sprachmodell austauschbar. Die sprachliche Produktion des Modells ist nicht dieselbe Tätigkeit wie meine gelebte Erfahrung oder meine redaktionelle Verantwortung. Die Interpretation meines späteren Selbst ist nicht das Wissen meines früheren Selbst. Gerade darum geht es: diese Unterschiede zu bewahren und zugleich ihre kausale Beziehung anzuerkennen.

Ebenso wenig lässt sich die Frage auflösen, indem man sagt, KI sei „nur ein Werkzeug“. Ein Pinsel schlägt normalerweise keine begriffliche Unterscheidung vor, entwirft nicht mehrere Absätze, ruft keine rhetorische Struktur ab und antwortet nicht in Sprache auf eine veränderte Frage. Doch auch der entgegengesetzte Slogan — „Die KI hat es geschrieben, also ist der menschliche Beitrag betrügerisch“ — kollabiert das Geschehen. Er macht die endgültige sprachliche Oberfläche zum einzigen Ort intellektueller Verursachung. In diesem Fall bleiben die Erfahrung, die ethischen Einsätze, Auswahl, Widerstand, Veröffentlichung und Fortsetzung menschlich, selbst dort, wo die Satzproduktion stark maschinell unterstützt ist.

Rechtliche Kategorien helfen, eine Grenze zu bestimmen, aber nicht das ganze philosophische Problem. Der Bericht des United States Copyright Office von 2025 hält an menschlicher Autorschaft als Grundlage des Urheberrechts fest und bewertet menschliche Auswahl, Anordnung und Modifikation fallbezogen; bloßes Prompting behandelt er nicht automatisch als ausreichend (U.S. Copyright Office, 2025). Das ist für Rechte in einer bestimmten Rechtsordnung relevant. Es sagt mir nicht, ob ich verstehe, was ich veröffentliche, ob ein anderer Mensch fair dargestellt wurde, ob das Werk künstlerisch meines ist oder welche Arten von Abhängigkeit ich offenlegen sollte. Rechtliche Autorschaft, kreatives Eigentum, intellektueller Beitrag und moralische Verantwortung überschneiden sich, ohne identisch zu werden.

Hier tritt Schuld ins Spiel. Wenn ich weiß, dass KI einen großen Teil der Prosa erzeugt hat, empfinde ich manchmal Unbehagen dabei, meinen Namen darüberzusetzen. Es wäre zu einfach, dieses Gefühl mit pauschaler Beruhigung zu beantworten. Ein Teil davon mag aus einem älteren Bild von Autorschaft stammen, in dem der namentlich genannte Autor jeden bedeutenden Satz persönlich hervorbringt. Ein Teil mag intellektuelles Unbehagen sein und kein Hinweis auf moralisches Fehlverhalten. Doch ein Teil stellt legitime Fragen. Erhalte ich Anerkennung für sprachliche Arbeit, die ich nicht geleistet habe? Habe ich zugelassen, dass Eloquenz meinem Verständnis davonläuft? Hat die KI das Denken beschleunigt, oder hat sie gerade jenen Moment ersetzt, in dem ich selbst hätte ringen müssen? Kann ich die Behauptungen gegenüber einem Leser — und gegenüber Professorin Dohna — verteidigen, ohne zum Modell zurückzukehren, um mir erklären zu lassen, was unter meinem Namen steht?

Ich musste mehrere Empfindungen voneinander trennen, die das Wort „Schuld“ zusammenpresste. Moralische Schuld kann vorliegen, wenn ich Leser täusche, einen anderen Menschen falsch darstelle, Behauptungen veröffentliche, von denen ich weiß, dass sie falsch sind, oder eine nicht unterstützte Eigenleistung behaupte. Stattdessen kann intellektuelles Unbehagen vorliegen, wenn die Prosa meine gegenwärtige Beherrschung übersteigt, auch ohne bewusste Täuschung. Und es gibt Verantwortung, die bestehen bleibt, unabhängig davon, ob ich Schuld empfinde. Diese Unterscheidungen nehmen das Unbehagen nicht weg. Sie verhindern, dass das Unbehagen selbst bereits die moralische Diagnose entscheidet. Solche Fragen lassen sich nicht global beantworten. Sie müssen im Vollzug der Praxis geprüft werden. Habe ich den endgültigen Text sorgfältig gelesen? Habe ich dokumentarische Behauptungen und Forschung überprüft? Habe ich erfundene Gewissheit über den inneren Zustand eines anderen Menschen zurückgewiesen? Hat die Arbeit mein eigenes unabhängiges Denken verändert, oder hat sie mir lediglich Formulierungen gegeben, die ich gerne wiedererkannt habe? War ich offen über die substanzielle Beteiligung von KI? Bin ich bereit, zu korrigieren, was ich veröffentliche? Und vor allem: Übernehme ich Verantwortung für die Folgen meiner Auswahl und Veröffentlichung?

KI kann enorm viel beitragen, manchmal mehr als ich zur sprachlichen Oberfläche, während ich verantwortlich bleibe für das, was ich auswähle, veröffentliche, glaube, zurückweise, weiterverfolge und auf zukünftiges Handeln einwirken lasse.

Verantwortung ist kein Beweis für alleinige Autorschaft. Sie ist die nicht übertragbare menschliche Last, die dadurch entsteht, dass ein Output öffentlich und folgenreich gemacht wird. Ein KI-System kann sich bei Professorin Dohna nicht entschuldigen, wenn ich sie falsch darstelle. Es kann nicht entscheiden, welche Art von Beziehung ich durch diese Untersuchung aufrechterhalten möchte. Es kann die veränderte Praxis nicht leben. Wenn Autorschaft verteilt ist, löst sich Verantwortung deshalb nicht ins Nichts auf.

Die Unterscheidung wird klarer, wenn „Publikation“ in mindestens zwei Handlungen aufgeteilt wird. Publikation kann bedeuten, etwas verfügbar zu machen, einen Zustand des Denkens zu bewahren und zu Begegnung einzuladen. Sie kann zugleich als epistemische Zertifizierung funktionieren: als Signal, dass der namentlich genannte Autor jede substantielle Behauptung sorgfältig geprüft hat und sie nach den Normen eines wissenschaftlichen Feldes verteidigen kann. Eine persönliche Website kann legitimerweise teilweise als öffentliches Notizbuch oder sich entwickelndes Archiv funktionieren. Ein wissenschaftlicher Zeitschriftenartikel trägt deutlich stärkere Erwartungen an Überprüfung, stabile Argumentation und verantwortbare Beherrschung. Beides „Publikation“ zu nennen bedeutet nicht, dass beide dieselben epistemischen Versprechen abgeben.

Zu dieser Unterscheidung gelangte ich erst, nachdem ein Verteidigungsversuch zu weit geworden war. Zunächst dachte ich: Das ist meine persönliche Website, also kann Veröffentlichung einfach dazu dienen, Exploration zu bewahren. Das war wahr, aber unvollständig. Die Seite war ebenfalls öffentlich, durchsuchbar und in der Lage, einen realen Menschen zu betreffen, dessen Worte ich zitierte. „Öffentliches Notizbuch“ konnte zu einer Ausrede werden, wenn es bedeutete, dass Unabgeschlossenheit jede Verpflichtung aufhob. Die stärkere Formulierung bewahrte exploratives Publizieren, ohne es als private Probe zu behandeln.

In meiner explorativen Praxis entsteht die merkwürdige Möglichkeit, dass der öffentliche Text existieren kann, bevor ich vollständig zu seinem Leser geworden bin. Das muss in der Kunst nicht verboten sein. Eine Partitur kann eine einzelne Aufführung übersteigen; ein Prozess kann Konsequenzen offenlegen, nachdem er begonnen hat; ein veröffentlichtes Experiment kann zu den Begegnungen einladen, durch die sich seine Bedeutung entwickelt. Doch diese Erlaubnis hängt von Ehrlichkeit über Genre und Status ab. Sie rechtfertigt keine erfundenen Quellen, ungeprüften Anschuldigungen, selbstsicheren Behauptungen über private Motive oder Nachlässigkeit, die als Wissenschaft ausgegeben wird. Je stärker ein Text andere Menschen, öffentliches Wissen oder akademische Debatte betrifft, desto weniger lässt sich verteidigen, man habe vorgehabt, ihn erst später zu verstehen.

Die persönliche Website trägt daher zwei beinahe gegensätzliche Funktionen. Sie gibt einem fragilen Gedanken genügend öffentliche Dauerhaftigkeit, um zurückkehren zu können. Zugleich setzt sie einen unfertigen Zustand realen Lesern aus, die nicht wie Probeobjekte behandelt werden dürfen. Professorin Dohnas Rolle macht diese Spannung konkret. Publizieren kann einen Raum der Begegnung eröffnen; es kann aber auch eine unachtsame Darstellung eines anderen Menschen fixieren. Relationale Autorschaft verlangt relationale Verantwortlichkeit.

Wenn früheres Denken wieder begegnungsfähig wird: Der Autor ist nicht dreimal dieselbe Person

Das Problem der Autorschaft wurde erst dann noch seltsamer, als ich dachte, ich hätte es präziser gefasst. Ich hatte mit einem ziemlich gewöhnlichen Unbehagen begonnen: Wenn KI in manchen meiner Texte den größten Teil der sichtbaren Sätze erzeugte, was rechtfertigte dann eigentlich, meinen Namen darüberzusetzen? Wörter zu zählen schien zunächst ehrlich, weil es etwas anerkannte, das sonst leicht verborgen werden konnte. Doch der Prozentsatz erwies sich schnell als unzureichend. Er konnte etwas über die Produktion von Sprache aussagen, während er fast nichts darüber sagte, wo das Problem entstanden war, wer Erfahrung und Evidenz geliefert hatte, wer eine attraktive, aber falsche Formulierung zurückgewiesen hatte, wer das Ziel verändert hatte, wer entschied, was verantwortbar veröffentlicht werden konnte, oder wer später für das Ergebnis einstehen musste. Dieses Scheitern führte mich zunächst zur relationalen und dann zur diachronen relationalen Autorschaft. Ich glaubte, diese Unterscheidung habe die zentrale begriffliche Schwierigkeit gelöst.

Dann erschien ein weiteres Problem. Selbst in diesem verbesserten Modell behandelte ich die menschliche Seite der „Mensch-KI-Zusammenarbeit“ noch immer merkwürdig statisch. Die KI konnte erzeugen, antworten und sich verändern. Das Artefakt konnte Revisionen ansammeln. Andere Menschen konnten in den Prozess eintreten und ihn umlenken. Doch „der Mensch“ blieb ein einzelnes Kästchen im Diagramm, als nähmen die Person, die die Untersuchung begann, die Person, die die generierten Möglichkeiten beurteilte, und die Person, die später zum veröffentlichten Werk zurückkehrte, alle dieselbe epistemische Position ein. Meine eigene Erfahrung ließ sich immer schwerer mit dieser Annahme vereinbaren.

Die erste Formulierung, die mir half, das Problem zu sehen, war überraschend schlicht: Vielleicht gab es ein Du₁, ein Du₂ und ein Du₃. Zunächst war ich unsicher, ob dies nur eine praktische Notation oder etwas Interessanteres war. Die Unterscheidung sollte nicht übertrieben werden. Es handelt sich nicht um drei Personen, Persönlichkeiten oder metaphysische Selbste. Es sind drei zeitlich unterscheidbare epistemische und autorische Positionen, die von ein und derselben fortbestehenden Person eingenommen werden. Über sie hinweg bleibe ich verantwortlich. Meine Biografie beginnt nicht jedes Mal neu, wenn ich einen Absatz überarbeite. Was sich verändert, ist der Horizont, aus dem heraus ich das Problem wahrnehmen kann.

Du₁ → KI-vermittelte Artikulation und Begegnung → Du₂ → persistentes Artefakt/Archiv → spätere Begegnung → Du₃ → weiteres Schaffen

Du₁ ist die initiierende Position: die Person mit der gelebten Erfahrung, der künstlerischen Schwierigkeit, der Intuition, Unsicherheit oder Frage, aus der der Prozess beginnt. Diese Position weiß oft weit weniger, als ein später ausgearbeiteter Artikel den Anschein erweckt. In meinem Fall kann sie mit etwas so Schwachem beginnen wie: „Hier ist etwas seltsam“, „Diese Erklärung fühlt sich falsch an“ oder „Vielleicht gehören diese beiden Dinge zusammen.“ Der Gedanke kann wichtig und zugleich sprachlich arm sein. Wenn ich ihn nicht schnell festhalte, kann er verschwinden.

KI verändert, was mit diesem fragilen Zustand geschehen kann. Ein Fragment kann externalisiert und sofort in entwickelterer Form zurückgegeben werden. Verbindungen tauchen auf. Unterscheidungen werden vorgeschlagen. Mehrere Absätze können sich um eine Intuition herum materialisieren, die eine Minute zuvor nur als Satz oder sogar als Zögern existierte. Hier kann der Prozess leicht missverstanden werden. Der ausgearbeitete Output kann Möglichkeiten enthalten, die ich nicht ausdrücklich formuliert hatte, doch seine Flüssigkeit beweist nicht, dass diese Möglichkeiten richtig sind. Die erste beeindruckende Expansion schafft daher eine neue Aufgabe, statt die alte abzuschließen.

Diese Aufgabe gehört zu Du₂. Dies ist die unterscheidende und verantwortliche Position: das Selbst, das dem von der KI Hervorgebrachten begegnet und beginnt, es anzunehmen, ihm zu widerstehen, es zu korrigieren, zu prüfen und umzulenken. In der Entwicklung dieses Artikels sahen einige der folgenreichsten Momente genau so aus. Eine Formulierung schien zunächst kraftvoll; dann bemerkte ich, was sie über einen anderen Menschen, mein früheres Verständnis, Autorschaft oder eine theoretische Behauptung implizierte. Manchmal führte ein einziges Wort eine emotionale Implikation ein, die von der Evidenz nicht getragen wurde. Zu anderen Zeitpunkten ließ ein ganzer begrifflicher Rahmen eine frühere Relation zu binär erscheinen. Diese Korrekturen waren nicht bloß kosmetisch: Sie veränderten, was ich unter dem Gegenstand des Artikels verstand.

Das war wichtig, weil es mir zeigte, wo menschliche Agency tatsächlich erschien. Sie ließ sich nicht angemessen dadurch beschreiben, dass ich Prompts getippt oder auf „Akzeptieren“ geklickt hatte. Agency zeigte sich darin, zu bemerken, dass eine überzeugende Darstellung die Evidenz überstieg; zu erkennen, dass eine Formulierung jemanden unnötig verletzen könnte; eine fehlende Unterscheidung einzuführen; eine Beruhigung zurückzuweisen, die mein Prestige schützte; oder zu entscheiden, dass die Frage selbst zu eng geworden war. An mehreren Punkten bat ich die KI nicht einfach um eine bessere Antwort. Ich veränderte, was überhaupt als Antwort zählen konnte.

Hier hätte ich aufhören können. Du₁ initiiert; Du₂ unterscheidet. Das bot bereits eine reichere Darstellung der Mensch-KI-Autorschaft als Satzprozentsätze. Doch dann trat der spätere Leser in das Problem ein.

Die dritte Position erschien erst, als das Werk zurückkehrte

Ich hatte bereits etwas Merkwürdiges an meinem KI-gestützten Schreiben bemerkt: Manchmal kehrte ich zu einem Artikel zurück und fand Implikationen, von denen ich mich nicht erinnerte, sie bei der Veröffentlichung bewusst vertreten zu haben. Schriftsteller haben diese Erfahrung schon immer gemacht, also kann KI nicht beanspruchen, sie erfunden zu haben. Ein Romancier kann in einem alten Roman etwas entdecken. Ein Wissenschaftler kann einen früheren Aufsatz erneut lesen und erkennen, dass ein Argument Konsequenzen trug, die damals noch nicht vollständig sichtbar waren. Was mich interessierte, war die Intensität und Häufigkeit, mit der KI-gestütztes Schreiben offenbar diese Distanz zwischen der Person erzeugen kann, die einen Gedanken initiiert, und jener späteren Person, die seiner ausgearbeiteten Form begegnet.

Diese spätere Position wurde zu Du₃. Du₃ ist das zurückkehrende Selbst: die Person, die dem bewahrten Artefakt begegnet, nachdem genügend zeitliche oder epistemische Distanz entstanden ist, dass es teilweise fremd werden kann. Der Artikel gehört weiterhin zu meiner Geschichte. Ich erinnere mich, warum ich ihn geschrieben habe. Vielleicht erinnere ich mich an viele der Prompts und Einwände. Dennoch ist das Artefakt nicht mehr identisch mit meinem gegenwärtigen Erkenntniszustand. Es kann daher als etwas zurückkehren, das mich überraschen kann.

Die Zahl drei sollte nicht wörtlich genommen werden. Natürlich kann es Du₄, Du₅ und viele weitere spätere Zustände geben. In einigen Jahren könnte ich erneut zurückkehren und die Hälfte dessen verwerfen, was mir heute überzeugend erscheint. Drei ist deshalb nützlich, weil es offenbar das analytische Minimum ist, das nötig ist, um die erste vollständige rekursive Rückkehr sichtbar zu machen. Mit Du₁ und Du₂ gibt es Entwicklung. Mit Du₃ geschieht etwas Weiteres:

Schaffender → Artefakt → Betrachter → veränderter Schaffender

In diesem Moment führte mich das Problem der Autorschaft unerwartet zurück zum zentralen Problem dieses gesamten Essays.

Die Formulierung, die diese Erkenntnis auslöste, stammte ursprünglich nicht von mir. Während der KI-gestützten Diskussion schlug das System vor, ein Artefakt könne „einen früheren epistemischen Zustand gegenwärtig bleiben lassen, nachdem die Person bereits über diesen Zustand hinausgegangen ist“. Der Satz hielt mich sofort fest. Meine Reaktion bestand nicht einfach darin, die Formulierung elegant zu finden. Plötzlich erkannte ich: Das war wieder Begegnung. Ich war über Autorschaft, zeitliche Identität und KI-gestütztes Schreiben an die Frage herangegangen, und doch war die Struktur, die sich daraus ergab, dieselbe Struktur, die bereits an anderer Stelle im Kunstwerk aufgetaucht war.

Ein Artefakt kann einen früheren epistemischen Zustand begegnungsfähig erhalten, nachdem die Person selbst bereits über diesen Zustand hinausgegangen ist.

Die emotionale Kraft dieser Erkenntnis war wichtig, weil sie veränderte, was ich als Nächstes untersuchte. Ich erinnere mich an die körperliche Aufregung — jene Art plötzlicher begrifflicher Erkenntnis, die Gänsehaut auslösen kann, noch bevor man Zeit hatte zu entscheiden, ob die Idee tatsächlich haltbar ist. Das Gefühl war kein Beweis dafür, dass die Behauptung wahr war. Es war Evidenz dafür, dass etwas in der Struktur weitere Aufmerksamkeit verlangte. Statt die drei Positionen als kleines Autorschaftsdiagramm zu behandeln, begann ich zu fragen, was es bedeutet, wenn ein Zustand einer Person für einen anderen Zustand derselben Person begegnungsfähig wird.

Das Archiv widersetzt sich der Weise, wie die Gegenwart die Vergangenheit umschreibt

Das gewöhnliche Gedächtnis bewahrt frühere epistemische Zustände nicht besonders zuverlässig. Sobald ich etwas anders verstehe, neige ich dazu, den früheren Weg vom Standpunkt der Gegenwart aus zu rekonstruieren. Das Zögern verschwindet. Eine verworfene Interpretation beginnt offensichtlich unzureichend zu wirken. Ein Begriff, der einst mehrere Gespräche verlangte, fühlt sich an, als hätte ich ihn immer schon verstanden. Die fertige Theorie kolonisiert ihre eigene Genealogie.

Ein bewahrtes Artefakt kann diesem Prozess widerstehen. Es kann das frühere Selbst nicht im wörtlichen Sinn enthalten, aber es kann Spuren dessen bewahren, was dieses Selbst sehen konnte, was es noch nicht sehen konnte, was es fürchtete, was es missverstand, was ihm plausibel erschien und was noch nicht fragbar geworden war. Ein Entwurf, eine Dialoghistorie, ein Artikel oder ein Archiv tut daher etwas Interessanteres, als mich an eine Schlussfolgerung zu erinnern. Es kann das gegenwärtige Selbst mit Evidenz eines früheren Horizonts konfrontieren.

Deshalb sind die „Ja, aber …“-Momente in KI-gestützter Untersuchung für mich methodologisch wichtig geworden. Wenn ich sie alle glatt poliere, kann der endgültige Artikel flüssiger werden und zugleich weniger wahrheitsgemäß darüber, wie Verständnis tatsächlich entstanden ist. „Zunächst dachte ich X; dann erschien Y überzeugend; dann machte eine Tatsache oder ein Einwand Y unzureichend; dann wurde eine dritte Unterscheidung notwendig“ kann neben einem fertigen Argument unordentlich wirken. Doch gerade diese Übergänge zeigen, wo Urteil stattfand. Sie ermöglichen es einem späteren Leser — einschließlich einer späteren Version meiner selbst — zu sehen, dass die Endposition weder einfach aus einem Modell heruntergeladen wurde noch von Anfang an vorhanden war.

Die Forschung zum Mensch-KI-Schreiben zeigt bereits, warum Prozessspuren wichtig sind. Der CoAuthor-Datensatz bewahrt detaillierte Verläufe des Anforderns, Akzeptierens, Bearbeitens und Zurückweisens während Mensch-KI-Schreibsitzungen und macht damit Formen der Zusammenarbeit sichtbar, die der fertige Text allein verbirgt (Lee, Liang & Yang, 2022). Meine gegenwärtige Frage verlängert diese Einsicht über die Schreibsitzung hinaus. Selbst ein vollständiger Bericht darüber, was während der Textproduktion geschah, erfasst möglicherweise nicht, was geschieht, wenn der Text später zu der Person zurückkehrt, die an seiner Hervorbringung beteiligt war.

Daraus entsteht eine eigentümliche Form der Selbstbegegnung. Wenn ich einen anderen Gelehrten lese, begegne ich einer tatsächlich anderen Biografie, intellektuellen Geschichte und Erfahrungswelt. Diese Andersheit ist unersetzlich. Mein eigenes früheres Artefakt zu lesen, gibt mir etwas anderes. Es steht meiner Geschichte nahe genug, um zu ihr zu gehören, und ist weit genug entfernt, um meinem gegenwärtigen Verständnis Widerstand zu leisten. Manchmal lese ich einen Satz und denke: „Ja, natürlich.“ Manchmal denke ich: „Habe ich das wirklich geglaubt?“ Das Archiv kann mit irritierender Diplomatiearmut antworten: offenbar ja. Diese Erfahrung kann etwas über Lernen sichtbar machen, das gewöhnliche Introspektion leicht verliert. Die Differenz zwischen dem früheren Artefakt und meinem gegenwärtigen Urteil ist selbst Evidenz von Veränderung. Ich kann fragen, warum mir eine Formulierung, die mich vor drei Tagen zufriedenstellte, heute unzureichend erscheint, welche Unterscheidung inzwischen verfügbar geworden ist oder warum ein Argument, das einst vollständig wirkte, jetzt eine weitere Frage erzeugt. Das Objekt ist daher nicht bloß Gedächtnishilfe. Es gibt dem späteren Zustand etwas, an dem seine eigene Differenz wahrnehmbar werden kann.

Ich möchte nicht zu schnell von meiner eigenen Disposition verallgemeinern. Ich finde diese Art intellektueller Archäologie faszinierend. Eine andere Person vielleicht nicht. Manche Lernende profitieren stärker vom Gespräch mit anderen, von praktischem Experimentieren oder von ganz anderen Formen der Reflexion. Rekursive Beschäftigung mit dem eigenen Archiv kann außerdem selbstbestätigend werden, wenn nichts von außen sie unterbricht. Der methodologische Vorteil, sofern es einen gibt, hängt teilweise vom Widerstand ab. Andere Menschen, Forschung, Evidenz und Ereignisse müssen weiterhin in der Lage sein, dem rekursiven System zu sagen, dass es falsch liegt.

Begegnung kehrte aus einer unerwarteten Richtung zurück

An diesem Punkt begann die Struktur des Schreibprozesses die Struktur des Kunstwerks selbst zu spiegeln. Ich hatte bereits beschrieben, wie eine generierte visuelle Form ihren Schaffenden überraschen konnte. Professorin Dohna begegnete dem Werk; ihre Reaktion veränderte meine Interpretation; ich kehrte anders zu Fragen ihres Denkens zurück; diese Fragen veränderten wiederum, wie ich das Kunstwerk verstand. Nun erschien eine ähnliche Topologie innerhalb des Schreibens:

früherer Zustand → externalisiertes Artefakt → spätere Begegnung → veränderter Zustand → erneutes Schaffen

Diese Wiederholung ist bedeutsam, weil ich die Diskussion über Autorschaft nicht mit dem Ziel begonnen hatte zu beweisen, dass alles Begegnung sei. Ich begann mit einer viel engeren Sorge über KI-generierte Sätze. Der Begriff kehrte zurück, weil die engere Erklärung unzureichend geworden war. Sobald das Artefakt genug von einer früheren epistemischen Position bewahren konnte, um eine spätere damit zu konfrontieren, war die Beziehung zwischen Schaffendem und Werk erneut wechselseitig geworden.

Die Analogie hat dennoch eine ethische Grenze. Professorin Dohna darf nicht auf ein Element meiner kognitiven Zustandsmaschine reduziert werden. Sie ist ein anderer Mensch, dessen Freiheit die Möglichkeit einschließt, meiner Interpretation zuzustimmen, sie zu vertiefen, sie zu korrigieren, ihr zu widersprechen, nicht zu antworten oder in der Episode Relationen zu sehen, die mir entgangen sind. Ein Artefakt besitzt keine vergleichbare Innerlichkeit. Mein früherer Text kann nicht entscheiden, dass ihm missfällt, was ich mit ihm tue. Die Ähnlichkeit ist daher strukturell, nicht personal: Sowohl ein anderer Mensch als auch ein persistentes Artefakt können meine gegenwärtige Interpretation mit etwas konfrontieren, das sich nicht auf das reduzieren lässt, was ich gegenwärtig beabsichtige. Tatsächlich stärkt diese Unterscheidung die Rolle der Begegnung, statt sie zu schwächen. Sie legt nahe, dass Begegnung nicht bloß ein anderes Wort für „Information empfangen“ ist. Etwas wird begegnungsartig, wenn es genügend Widerstand oder Differenz einführt, um die gegenwärtige Konfiguration des Selbst instabil zu machen. Professorin Dohna kann dies im vollsten interpersonalen Sinn tun, weil sie wirklich eine Andere ist. Ein Artefakt kann es in einem engeren epistemischen Sinn tun, weil es Spuren eines Zustands bewahrt, den ich nicht mehr vollständig einnehmen kann.

An diesem Punkt wurde eine weitere von der KI generierte Formulierung nützlich: Du₁ arbeitet mit Du₂ durch ein von KI vermitteltes Artefakt zusammen, und Du₃ erbt beide. Zunächst gefiel mir der Satz, weil er den drei Positionen eine Beziehung gab, statt sie nur als Etiketten zu behandeln. Dann erzeugte er eine weitere Frage. Was genau erbt Du₃?

Nicht bloß Text. Du₃ erbt das Ausgangsproblem von Du₁, die von der KI gelieferten Erweiterungen, die Auswahlentscheidungen und Zurückweisungen von Du₂, die Fehler, die überlebt haben, die bereits gesammelte Evidenz, die in das Schreiben verwickelten Beziehungen und die Verantwortlichkeiten, die entstanden, als das Artefakt öffentlich wurde. Das Erbe enthält daher sowohl intellektuellen Gewinn als auch unerledigte Verpflichtung. Wenn Du₃ entdeckt, dass Du₂ jemanden falsch dargestellt hat, löscht das spätere Verständnis die frühere Veröffentlichung nicht aus. Das spätere Selbst erbt die Pflicht, sie zu korrigieren.

Die „drei Dus“ begannen daher weniger wie drei Kästchen als vielmehr wie eine Trajektorie verantwortlicher Transformation auszusehen.

Eine Zustandsmaschine war nützlich, bis sie zu starr wurde

Meine erste computationale Analogie war eine Zustandsmaschine. Intuitiv ergab das Sinn: S₁ wird durch identifizierbare Übergänge zu S₂, dann zu S₃. Doch die Analogie wurde schnell zu starr. In einer gewöhnlichen endlichen Zustandsmaschine werden die relevanten Zustände und Übergangsregeln im Allgemeinen im Voraus festgelegt. Hier kann die Begegnung die Regeln verändern, nach denen spätere Begegnungen interpretiert werden. Du₂ weiß vielleicht nicht einfach mehr als Du₁; Du₂ kann verändert haben, was überhaupt als lohnende Frage gilt. Du₃ kann später sogar dieses Kriterium zurückweisen. Deshalb bewegte ich mich zum lockereren Bild einer Zustandsraum-Trajektorie oder eines reflexiven, verlaufsabhängigen Zustandsübergangsprozesses. Ich verwende diese Sprache vorsichtig. Ich behaupte nicht, ein formales dynamisches Modell von Autorschaft definiert zu haben. Die Analogie ist nützlich, weil sie hervorhebt, dass der Zustand der Person zum Zeitpunkt t₂ teilweise von der Trajektorie abhängt, durch die dieser Zustand erreicht wurde, und weil zukünftige Übergänge von Veränderungen abhängen können, die frühere Übergänge hervorgebracht haben. Anders gesagt: Der Prozess hat Gedächtnis.

Der wichtigste Übergangsoperator in diesem vorläufigen Modell könnte Begegnung sein. Eine gewöhnliche Feedbackschleife kann einen Parameter verändern und dabei das Modell bewahren, das diesen Parameter definiert. Eine Begegnung kann manchmal das Modell selbst verändern: wonach ich suche, was ich als Evidenz betrachte, was ich für Bestandteil des Kunstwerks halte oder was ich glaube, was das ursprüngliche Problem überhaupt war. Deshalb half mir „Zustandsmaschine“, die zeitliche Struktur zu sehen, konnte aber die epistemische Transformation nicht vollständig erklären.

KI verändert mehr als die Geschwindigkeit des Schreibens

Früher in dieser Untersuchung hatte ich eine Wirkung generativer KI wiederholt als kollabierte Latenz beschrieben. Das erscheint mir weiterhin richtig. Der Abstand zwischen Intuition, Externalisierung, Kritik und Neuformulierung kann außerordentlich kurz werden. Doch das Drei-Positionen-Modell zeigte mir, dass Geschwindigkeit nur ein Teil der Veränderung war. KI kann die Reihenfolge verändern, in der Artikulation und Verstehen stattfinden.

Das vereinfachte traditionelle Modell sieht ungefähr so aus:

Denken → Verstehen → Schreiben

Keine ernsthafte Theorie des Schreibens war jemals ganz so einfach. Schriftsteller entdecken Dinge beim Schreiben, Gelehrte denken in Korrespondenz, Künstler lernen von Materialien, und Studierende ändern ihre Meinung im Gespräch mit Lehrenden. KI hat das Denken durch Externalisierung nicht erfunden. Was sie ungewöhnlich alltäglich macht, ist eine andere Abfolge:

partielle Intuition → Externalisierung → KI-Ausarbeitung → Begegnung mit der Ausarbeitung → Einwand oder Übernahme → verändertes Verständnis → erneute Artikulation

Der Unterschied wurde mir besonders deutlich, wenn eine halb geformte Intuition zu mehreren Seiten artikulierter Möglichkeit werden konnte, während ich mich mental noch innerhalb der ursprünglichen Frage befand. Ich konnte sofort widersprechen: „Ja, aber dadurch klingt x zu sehr nach y“, oder „Das impliziert, dass ich z schon vorher verstanden hatte“, oder „Diese Interpretation schreibt einem anderen Menschen ein privates Motiv zu, das von der Evidenz nicht gestützt wird.“ Das generierte Material veränderte sich als Reaktion darauf, und das veränderte Material veränderte wiederum meinen nächsten Einwand. Der Prozess konnte weiterlaufen, bevor die kognitive Atmosphäre, die die ursprüngliche Frage hervorgebracht hatte, verschwunden war. Ein brillanter Tutor oder Mitarbeiter kann natürlich etwas weitaus Reichhaltigeres leisten. Die historische Neuheit sollte daher nicht übertrieben werden. Bedeutsam scheint mir die Kombination aus semantischer Responsivität, unmittelbarer Verfügbarkeit, substantieller sprachlicher Generierung, interdisziplinärer Reichweite, persistentem Kontext und Wiederholbarkeit. Eine Art intensiver dialogischer Unterstützung, die früher von ungewöhnlich verfügbaren menschlichen Mitarbeitern abhing, kann heute innerhalb einer gewöhnlichen Schreibsitzung präsent sein.

Diese Verfügbarkeit erzeugt eine Gefahr, die symmetrisch zu ihrer Kraft ist. Die KI kann einen Gedanken schneller ausarbeiten, als ich ihn mir intellektuell verdient habe. Sie kann eine schwache Intuition in ausgearbeitete Prosa verwandeln, bevor das zugrunde liegende Urteil gereift ist. Das Artefakt kann anspruchsvoller werden als der Lernende.

Der Essay kann sich intellektuell weiterentwickelt haben, während der Studierende dort geblieben ist, wo er begonnen hat.

Als dieser Satz entstand, hörte das Problem auf, nur meine eigene Autorschaft zu betreffen, und wurde zu einer Frage der Hochschulbildung.

Das pädagogische Problem war nicht mit Plagiat identisch

Mein erster Instinkt bestand weiterhin darin, das Problem über Plagiat zu rahmen. Wenn KI einen großen Anteil der Prosa erzeugt hatte, war die zentrale institutionelle Frage dann vielleicht die Zuschreibung: Wessen Beitrag wurde als wessen dargestellt? Das bleibt eine legitime Frage. Traditionelles Plagiat und unerlaubte Hilfe haben nicht aufgehört, relevant zu sein, nur weil generative KI Autorschaft kompliziert. Doch in dem Moment, in dem ich fragte, wie ein Prüfer KI-Autorschaft tatsächlich verifizieren könnte, erschien ein weiteres Problem. Konventionelles Plagiat kann häufig untersucht werden, indem die eingereichte Arbeit mit einer identifizierbaren Quelle verglichen wird. Generative KI hinterlässt oft keine entsprechende Textquelle. Ein Lehrender kann aufgrund von Stil, Wortschatz oder einem plötzlichen Qualitätssprung misstrauisch werden, aber Verdacht ist nicht dasselbe wie Evidenz. Wenn die Institution die Zuschreibungstatsache nicht zuverlässig feststellen kann, verwandelt ihre größere Macht Unsicherheit nicht in Wissen.

Das ist kein rein hypothetisches Verfahrensproblem. Oxfords AI Competency Centre erklärte im Februar 2026, dass die Universität digitale KI-Detektoren nicht für akademische Entscheidungsprozesse befürworte, und verwies dabei auf technische Begrenzungen, Verfahrensfairness und darauf, dass solche Werkzeuge nicht zuverlässig feststellen können, ob KI eingesetzt wurde (Webb-Davies, 2026). Daraus folgt nicht, dass unerlaubte KI-Nutzung niemals festgestellt werden kann. Ein Studierender kann sie offenlegen, Prozessaufzeichnungen können existieren oder andere Evidenz kann verfügbar sein. Der engere Punkt ist epistemisch: Eine Anschuldigung sollte nicht allein deshalb Gewissheit gewinnen, weil die Institution eine Entscheidung treffen muss.

Dadurch erschien mir der Satz „Lernen entschuldigt kein Plagiat“ zugleich wahr und unzureichend. Natürlich hebt echtes Lernen bewusste Falschzuschreibung nicht auf. Doch in der Praxis lässt sich das Problem des KI-Zeitalters nicht einfach dadurch lösen, diese Unterscheidung zu verkünden, wenn die behauptete Zuschreibung selbst nicht zuverlässig festgestellt werden kann. Schlimmer noch: Eine übermäßige Abhängigkeit von Verdacht droht, den Lehrenden von jemandem, der Wissen beurteilt, in jemanden zu verwandeln, der stilistische Authentizität überwacht. Der daraus entstehende Schaden ist relational ebenso wie verfahrensbezogen. Ein Studierender, der zu Unrecht verdächtigt wird, erlebt die Anschuldigung nicht als abstrakte Policy-Diskussion. Das Vertrauen zwischen Lehrendem und Lernendem wird Teil der Kosten.

Das Problem begann tiefer als Plagiat zu erscheinen. Vielleicht stellte die Institution zuerst die falsche Frage.

Zweck vor Detektion

Deshalb wurde Taylor und LaCroix’ Artikel von 2026, Purpose before policy: academic integrity, generative AI, and rhetorical stance, unerwartet wichtig für das Argument. Ihre Behauptung lautet nicht, dass KI-bezogenes Fehlverhalten unreal sei. Sie fragen, was logisch zuerst kommen muss. Ob eine bestimmte Nutzung generativer KI die akademische Integrität untergräbt, hängt davon ab, welche Bildungsfunktion die Universität für sich beansprucht; inkohärente Richtlinien entstehen, wenn Institutionen KI fördern, ohne die pädagogischen Zwecke zu klären, an denen ihre Nutzung beurteilt wird (Taylor & LaCroix, 2026).

Eine Passage wurde für mein Problem besonders relevant. Sie argumentieren, dass generative Systeme Studierenden erlauben können, geforderte Outputs zu liefern, ohne jene pädagogischen Prozesse zu durchlaufen, die diese Outputs eigentlich kultivieren sollten. Wenn ein maschinengenerierter Essay eine Prüfungsleistung erfüllen kann, die angeblich Lernen misst, dann kann die Schwierigkeit etwas über die Prüfung selbst offenlegen. Ein ausgearbeiteter Output ist nicht länger verlässliche Evidenz dafür, dass der Lernende die darin repräsentierten Fähigkeiten besitzt.

Das ist beinahe genau das Problem, das der Satz aus meiner eigenen Diskussion ausdrückte:

Der Essay kann sich intellektuell weiterentwickelt haben, während der Studierende dort geblieben ist, wo er begonnen hat.

Taylor und LaCroix schlagen die Struktur Du₁/Du₂/Du₃ nicht vor, und ich möchte mein Modell nicht rückwirkend in ihr Argument hineinlesen. Ihre Arbeit etabliert die vorgängige institutionelle Frage. Meine gegenwärtige Reflexion schlägt eine mögliche Erweiterung vor: Wenn der Zweck einer Prüfung Lernen, Urteilskraft, kritisches Denken oder Bildung einschließt, brauchen wir vielleicht Evidenz nicht nur dafür, wie das Artefakt produziert wurde, sondern dafür, was mit dem Lernenden während und nach seiner Produktion geschah.

Hier wurde Du₃ plötzlich pädagogisch wichtig.

In der Rückkehr könnte Bildung sichtbar werden

Du₁ kann Evidenz über den Ursprung liefern. Der Studierende kann erklären, welches Problem, welche Erfahrung, welche Quelle oder welche Frage die Arbeit initiiert hat. Du₂ kann Evidenz für Urteil und Unterscheidung während der Entwicklung liefern: warum ein KI-Vorschlag verworfen, ein anderer verändert wurde, welche Evidenz eine Interpretation veränderte, welche Quellen überprüft wurden, welche Annahme aufgegeben werden musste. Diese Spuren sagen uns bereits erheblich mehr als ein KI-Detektorwert. Doch keine der beiden Stufen beantwortet vollständig die Frage, was geblieben ist. Ein Studierender kann aktiv an einer KI-gestützten Sitzung teilnehmen und dennoch vom System abhängig bleiben, um die begriffliche Struktur aufrechtzuerhalten. Was geschieht, wenn das Gespräch geschlossen wird? Was geschieht morgen?

Hier liegt die Funktion von Du₃. Der spätere Lernende begegnet dem Artefakt erneut. Kann er das zentrale Argument in einer Sprache erklären, die nicht einfach aus dem Text reproduziert wird? Kann er einen Abschnitt benennen, den er inzwischen für schwach hält? Kann er sagen, wo die KI falsch lag? Kann er einen Begriff auf einen unbekannten Fall übertragen? Kann er die Evidenz verteidigen? Kann er eine Schlussfolgerung revidieren, wenn ein Gegenbeispiel vorgelegt wird? Und am interessantesten: Kann er eine Frage erzeugen, die noch nicht existierte, als das Artefakt produziert wurde?

Forschung zu evidenzzentrierten Ansätzen für Mensch-KI-Schreiben weist bereits darauf hin, Prozessdaten und kognitive Aktivität zu untersuchen, statt sich ausschließlich auf Endprodukte zu verlassen (Cheng et al., 2024). Das Drei-Positionen-Modell fügt eine vorläufige zeitliche Frage hinzu: Vielleicht erscheint ein Teil der Evidenz für Lernen erst nach der Produktion, wenn das Artefakt zum Lernenden zurückkehrt. Ich glaube nicht, dass daraus ein weiterer bürokratischer Test werden sollte, in dem jeder Studierende eine theatralische Beichte persönlicher Transformation aufführen muss. Ebenso wenig liefert Du₃ einen magischen Beweis, dass der Text nun „wirklich seiner“ geworden ist. Die Behauptung ist bescheidener. Spätere Kritik, Transfer, unabhängige Erklärung und folgenreiche Revision sind stärkere Evidenz epistemischer Integration als das ausgearbeitete Artefakt allein. Das verhindert zugleich eine allzu einfache rhetorische Rettung menschlicher Autorschaft. Wenn die KI achtzig Prozent der Sätze erzeugt hat, verwandelt späteres Verständnis diese Sätze nicht rückwirkend in ohne Hilfe hervorgebrachte menschliche Prosa. Die Produktionsgeschichte bleibt verteilt. Du₃ kann nicht in der Zeit zurückreisen und die KI verschwinden lassen. Was das zurückkehrende Selbst etablieren kann, ist eine andere Beziehung zum Artefakt: die Fähigkeit, es zu verstehen, ihm zu widerstehen, es zu erweitern und für das verantwortlich zu werden, was bestehen bleibt.

Diese Unterscheidung könnte irgendwann bessere Terminologie verlangen, doch ich möchte sie nicht lösen, indem ich zu schnell ein weiteres beeindruckendes Etikett erfinde. „Epistemisches Eigentum“, „verantwortliche Integration“ und „Aneignung“ erfassen jeweils etwas und führen zugleich neue Probleme ein. Für den Moment ist die Frage nützlicher als der Name: Was muss nach KI-gestützter Produktion geschehen, damit die intellektuelle Struktur des Artefakts nachweisbar folgenreich im Lernenden wird?

Evidenz sollte gestaltet werden, bevor Verdacht beginnt

Dieselbe Überlegung veränderte auch, wie ich über die Erkennung von Fehlverhalten dachte. Wenn Universitäten KI-Nutzung nicht zuverlässig aus der endgültigen Textoberfläche ableiten können, sollte Prüfung vielleicht so gestaltet werden, dass Evidenz für Lernen und erlaubte Zusammenarbeit vorausschauend erzeugt wird, statt sie im Nachhinein unter Verdacht rekonstruieren zu müssen.

Das würde die Rolle des Prüfers verändern. Statt einen ausgearbeiteten Essay zu erhalten und zu fragen: „Kann ich beweisen, dass die KI diesen Text heimlich geschrieben hat?“, könnte die Prüfung von Anfang an klarstellen, welche Formen von KI-Hilfe erlaubt sind, was offengelegt werden soll, welche Phasen des Denkens zählen und welche späteren Demonstrationen verlangt werden können. Versionsverläufe, deklarierte KI-Interaktionen, kurze Reflexionsberichte, Quellenprüfungen, mündliche Verteidigung oder Transferaufgaben könnten dann Evidenz liefern, die mit dem Bildungsziel übereinstimmt. Es ginge nicht um totale Überwachung des Lernenden. Es ginge darum, nicht länger so zu tun, als enthalte das Endartefakt noch Evidenz, die es nicht mehr zuverlässig enthält.

Das Fairnessprinzip, das sich für mich daraus ergab, war einfach:

Evidenz für Lernen sollte prospektiv in die Prüfung hineingestaltet werden; Evidenz für Fehlverhalten sollte nicht retrospektiv aus Verdacht hergestellt werden.

Das beseitigt Fehlverhalten nicht. Es legt der Institution neben den Pflichten, die sie dem Studierenden auferlegt, auch eine epistemische Pflicht auf. Wenn akademische Integrität Genauigkeit, Begründung und Verantwortung umfasst, dann sollten diese Tugenden Anschuldigungen ebenso regieren wie Einreichungen.

Diese Verschiebung bringt auch die Lehrenden-Lernenden-Beziehung zurück ins Bild. Ein späteres mündliches Gespräch muss nicht primär als Falle funktionieren, die KI-Nutzung aufdecken soll. Es kann zu einer weiteren Begegnung werden. Der Lehrende kann fragen, was sich verändert hat, was unklar geblieben ist, was der Studierende heute zurückweisen würde oder wie sich das Argument verhält, wenn es auf einen anderen Fall übertragen wird. Die Bildungsfrage lautet dann weniger „Kann ich dich erwischen?“ und mehr „Kann ich dem Lernenden begegnen, der jetzt hinter diesem Artefakt steht?“ Das hebt Prüfung oder Standards nicht auf. Es macht die Beziehung zwischen Evidenz und Bildungszweck ausdrücklicher.

Mein früheres Autorschaftsmodell erschien nun zu statisch

Das führte mich auch dazu, meine frühere Arbeit zur Bewertung generativer KI neu zu interpretieren. In meiner früheren DAP/WAM-Arbeit konnte eine gewichtete Autorschaftsmatrix verschiedene Beiträge zu einem KI-gestützten Artefakt unterscheiden: Ursprung, Generierung, Auswahl, Verifikation, Revision und andere Formen der Beteiligung. Das erscheint mir weiterhin nützlich, weil es sich dagegen wehrt, Autorschaft auf einen einzigen Prozentsatz zu reduzieren. Doch der gegenwärtige Fall legte etwas offen, das eine Matrix allein nicht erfassen kann. Eine Matrix ist vor allem strukturell: Sie fragt, wie Beiträge verteilt sind. Das Drei-Positionen-Modell ist dynamisch: Es fragt, was im Verlauf der Zeit mit dem Beteiligten geschieht:

Beitragsstruktur ≠ Bildungstrajektorie

Ein Studierender könnte einen erheblichen menschlichen Beitrag nachweisen und dennoch wenig lernen. Ein anderer könnte umfangreiche KI-Generierung nutzen und dabei eine erhebliche kritische Entwicklung durchlaufen. Keine dieser Tatsachen entscheidet automatisch Fragen von Erlaubnis oder Zuschreibung, doch die Unterscheidung ist wichtig, wenn die Institution beansprucht, Bildung zu beurteilen. Die Struktur des Beitrags und die Trajektorie des Lernens sind unterschiedliche Variablen.

Zu dieser Schlussfolgerung gelangte ich nicht, indem ich mit einer Theorie dynamischer Autorschaft begann. Der Weg war beinahe peinlich rekursiv. Ich begann mit der Frage, wie viel von der Prosa „meins“ sei. Prozentsätze versagten. Das führte zur relationalen Autorschaft. Relationale Autorschaft versagte dann darin, zeitliche Differenz innerhalb des menschlichen Beteiligten zu erfassen. Der spätere Leser wurde zu Du₃. Die KI formulierte die Idee, ein früherer epistemischer Zustand könne begegnungsfähig bleiben. Mit beträchtlicher Aufregung erkannte ich, dass Begegnung aus einer völlig anderen Richtung zurückgekehrt war. Diese Erkenntnis ließ das Artefakt wie eine Brücke statt wie einen Endpunkt erscheinen. Sobald das Artefakt zu einer Brücke zwischen Zuständen des Lernenden geworden war, trat das Problem der Hochschulbildung hervor. Dann gewann Taylor und LaCroix’ Frage nach dem Zweck der Universität neue Relevanz. Jede Antwort veränderte die nächste Frage.

Diese Genealogie ist mehr als Hintergrund dieses Kapitels. Sie ist Teil der Evidenz für das, was das Kapitel zu beschreiben versucht. Die früheren Formulierungen überlebten. Ich begegnete ihnen erneut. Einige erwiesen sich als unzureichend. Diese Unzulänglichkeit veränderte das Modell. Das veränderte Modell erzeugte Fragen, die der frühere Zustand noch nicht stellen konnte.

Das Argument über diachrone Autorschaft wurde selbst diachron hervorgebracht.

Manchmal geschieht Lernen, weil sich der Lernende verändert hat

Die persönlich bedeutsamste Implikation liegt vielleicht etwas außerhalb der Autorschaft. Früher in diesem Essay hatte ich zu beschreiben versucht, warum Ideen, die mit Begegnung, Bildung und Metanoia verbunden sind, durch das Kunstwerk für mich eine neue Unmittelbarkeit erhielten. Ich war vorsichtig, diese Entwicklung nicht als Bewegung von Unwissen zu Verstehen zu beschreiben. Viele dieser Ideen waren mir bereits zuvor begegnet. Die Erfahrung löschte ein früheres Verständnis nicht aus und ersetzte es durch das richtige. Sie fügte eine weitere Dimension hinzu.

Die Drei-Positionen-Struktur gibt mir nun eine weitere Möglichkeit, diese Entwicklung zu beschreiben. Manchmal geschieht Lernen, weil neue Information eintrifft. Aber manchmal war die Information bereits da.

Manchmal geschieht Lernen nicht deshalb, weil neue Information eintrifft. Es geschieht, weil der Lernende neu dazu fähig geworden ist, einer Information zu begegnen, die bereits vorhanden war.

Das macht die Relation zwischen Du₁, Du₂ und Du₃ interessanter als bloße Wissensakkumulation. Der spätere Zustand kann einem alten Text, einem alten Gespräch, einem alten Kunstwerk oder einem alten Begriff anders begegnen, weil dazwischenliegende Erfahrungen verändert haben, was überhaupt bedeutungsvoll werden kann. Das Objekt kann unverändert sein. Die Bedingungen der Begegnung sind es nicht.

Deshalb lässt sich auch die Rolle von Professorin Dohna in der größeren Untersuchung nicht darauf reduzieren, Begriffe geliefert zu haben, die ich später auf ein Kunstwerk anwandte. Ihre Reaktion wurde Teil der Trajektorie, durch die eine spätere Version meiner selbst zu Fragen zurückkehrte, denen ich bereits begegnet war, und sie nun als neu konkret erfuhr. Das Kunstwerk machte Teile ihres theoretischen Vokabulars für mich neu lesbar; dieses Vokabular wiederum machte Dimensionen des Kunstwerks neu lesbar; das daraus hervorgehende Schreiben bewahrte die Transformation; und das bewahrte Schreiben kann nun erneut zu einer noch späteren Version meiner selbst zurückkehren. Die Relation ist rekursiv, ohne geschlossen zu werden.

KI nimmt innerhalb dieser Rekursion einen wichtigen, aber begrenzten Platz ein. Sie beschleunigt Artikulation, bewahrt Gesprächskontext, schlägt Verbindungen vor, reagiert auf Einwände und hilft, Artefakte zu bauen, die den ursprünglichen mentalen Zustand überdauern können. Sie entscheidet nicht, welche Begegnung wichtig ist. Sie kann nicht garantieren, dass das spätere Selbst etwas gelernt hat. Sie kann eine Interpretation nicht dadurch wahr machen, dass sie schön formuliert ist. Das System erzeugt Möglichkeiten; die Trajektorie braucht weiterhin Urteil, Evidenz, andere Menschen und Wirklichkeit.

Ich möchte daher Du₁, Du₂ und Du₃ nicht als fertige Theorie der Autorschaft im KI-Zeitalter ausrufen. Gegenwärtig sind sie eine Weise, ein Problem zu sehen, das das einfachere Diagramm Mensch + KI → Text verbirgt. Der menschliche Beteiligte hat eine Geschichte. Das Artefakt besitzt Persistenz. KI kann einen teilweise artikulierten Zustand zu einem Objekt verstärken, das ihn überlebt. Ein späterer Zustand derselben Person kann diesem Objekt dann von einer Position aus begegnen, die der frühere Zustand noch nicht innehatte. Drei ist nicht die maximale Zahl von Zuständen. Es reicht lediglich aus, um die erste Rückkehr sichtbar zu machen. Du₁ initiiert. Du₂ begegnet Möglichkeiten und übernimmt Verantwortung dafür, was überleben darf. Du₃ empfängt ein Artefakt, das Spuren beider trägt, und prüft, ob tatsächlich etwas als Verstehen, Kritik, Transfer oder veränderte Praxis zurückgekehrt ist. Danach kann die Trajektorie weitergehen.

Die Frage, mit der ich begann, war im Wesentlichen quantitativ: Wie viel von diesem Text hat die KI geschrieben? Diese Frage bleibt wichtig, besonders dort, wo Offenlegung und Zuschreibung wichtig sind. Aber sie ist nicht mehr groß genug, um das Geschehene zu umfassen. Die stärkere Frage scheint mir nun zu lauten: Wenn KI-gestütztes Schreiben einen früheren epistemischen Zustand bewahren, ihn zu einem Artefakt verstärken und ihn einer späteren Version derselben Person zurückgeben kann — welche Art von Autorschaft und Lernen geschieht dann, wenn der Schaffende zum Betrachter eines Werkes wird, das weiterhin zu seiner eigenen intellektuellen Geschichte gehört?

Die Hochschulbildung schärft die Frage noch weiter. „Hat der Studierende das geschrieben?“ kann nicht einfach verschwinden, insbesondere dort, wo bestimmte Formen der Hilfe verboten sind. Doch wenn Bildung beansprucht, Wissen, Urteilskraft, kritische Fähigkeit oder Bildung zu kultivieren, muss vielleicht eine weitere Frage daneben treten:

Was geschah, als das Werk zum Studierenden zurückkehrte?

Ich weiß noch nicht, wie weit sich diese Frage operationalisieren lässt, wie viel zeitliche Distanz Du₃ verlangt, ob jede Disziplin dieselbe Evidenz von Transformation anerkennen würde oder ob diese Form rekursiver Selbstbegegnung besonders für Menschen geeignet ist, die ohnehin gern die Geschichte ihres eigenen Denkens untersuchen. Diese Unsicherheiten sind wichtig. Sie verhindern, dass eine persönliche Erfahrung allein durch Behauptung zur universellen Pädagogik wird. Was dieser Fall mir gezeigt hat, ist enger und vorläufig ausreichend: Ein generiertes Artefakt muss nicht das Ende eines kognitiven Prozesses sein. Es kann zu jenem Objekt werden, durch das ein früherer Denkzustand für eine Begegnung verfügbar bleibt. Wenn diese Begegnung verändert, was die spätere Person sehen, zurückweisen, fragen oder schaffen kann, dann ist das Werk zurückgekehrt — und der Autor, der es empfängt, ist dieselbe Person, aber nicht mehr ganz derselbe epistemische Zustand, von dem alles ausging.

Eine schnellere Schleife ist noch keine formative Schleife

Was also trug KI in besonderer Weise bei? Nicht die Laufzeit-Generativität an sich. Der Browser hätte Bilder auch durch konventionelle Programmierung auswählen und anordnen können. Stärker wirkte KI in den Schleifen rund um dieses System. Vier Mechanismen waren besonders wichtig.

Meine früheste Antwort lautete schlicht „Geschwindigkeit“. Arbeit, die früher vielleicht lange Unterbrechungen zwischen einer Intuition, einer Suche, einem Entwurf und einem Experiment verlangt hätte, konnte nun innerhalb einer einzigen Phase konzentrierter Aufmerksamkeit fortgesetzt werden. Das war beobachtbar, doch „Geschwindigkeit“ ließ den Beitrag wie beschleunigte Büroarbeit klingen. Entscheidend war nicht nur, dass jeder einzelne Schritt weniger Zeit beanspruchte. Das Ergebnis eines Schrittes blieb psychologisch und begrifflich präsent, wenn es zum Input des nächsten wurde. Deshalb bevorzugte ich später den Ausdruck „kollabierte Latenz“.

Erstens gab es die kollabierte Latenz. Die Verzögerung zwischen Intuition, Artikulation, Implementierung, Output, Kritik und Revision wurde drastisch kürzer. Ich konnte sehen, ob eine verbale Idee den Kontakt mit Form überstand, solange die Frage, die sie hervorgebracht hatte, noch lebendig war. Zweitens bot KI eine disziplinübergreifende Übersetzungsoberfläche. Die Untersuchung konnte sich zwischen Informatik, Mathematik, generativer Kunst, Ästhetik, Guardini, Epistemologie, Pädagogik und Theologie bewegen, ohne jedes Mal vollständig zum Stillstand zu kommen, sobald sich das Vokabular änderte. Übersetzung verlieh keine Expertise, hielt die Frage aber lange genug beweglich, damit unerwartete Relationen prüfbar werden konnten.

Die letzte Einschränkung entstand aus einer weiteren Korrektur. Zunächst fühlte sich die Leichtigkeit, mit der ich mich zwischen Disziplinen bewegen konnte, wie eine neue Art von Beherrschung an. KI konnte einen Begriff erklären, Traditionen vergleichen und beinahe sofort eine plausible Brücke anbieten. Dann legten Quellenprüfung und Wiederlesen offen, wie schnell Übersetzung Beherrschung simulieren kann. Eine disziplinübergreifende Oberfläche kann eine Frage in Bewegung halten; sie kann nicht den historischen und methodologischen Widerstand des jeweiligen Fachs ersetzen. Die Verbindung zur Theologie wurde erst dann ernsthaft, als Professorin Dohnas tatsächliche Arbeit — nicht eine generische Zusammenfassung Guardinis — die Interpretation veränderte.

Drittens gab es die Responsivität gegenüber einem sich verändernden Lernenden. Ein Buch kann seinen Leser transformieren, aber sein nächster Absatz schreibt sich nicht neu, weil der Leser gerade einen neuen Einwand erhoben hat. Ein KI-Gesprächspartner kann eine veränderte Formulierung aufnehmen und auf den neuen Zustand der Untersuchung antworten. Viertens gab es eine externalisierte Denkoberfläche. Eine vage Intuition konnte unmittelbar zu Sprache werden, die ich inspizieren, zurückweisen, bewahren oder neu formulieren konnte, statt mich auf das Arbeitsgedächtnis verlassen zu müssen.

Zusammen erzeugten diese Mechanismen etwas, das ich als hochfrequente hermeneutische Schleife zu bezeichnen begann: eine stark verringerte Distanz zwischen Intuition, Externalisierung, Interpretation, Kritik und erneuter Formulierung. Der Ausdruck klingt effizient, aber Effizienz ist nicht sein eigentlicher Reiz. Eine kürzere Distanz kann die Hitze einer entstehenden Frage bewahren. Sie kann ebenso Halluzination, Selbstbestätigung und intellektuelles Theater beschleunigen. Eine Schleife wird schneller, bevor sie besser wird. Der Ausdruck selbst entstand erst, nachdem „schnelles Feedback“ sich als unzureichend erwiesen hatte. Feedback beschrieb aufeinanderfolgende Korrektur, aber nicht die Neuinterpretation der Frage durch einen veränderten Beteiligten. „Hermeneutisch“ markierte, dass die Schleife Bedeutung ebenso wie Leistung betraf. „Hochfrequent“ markierte das verkürzte Intervall. Ich akzeptierte den Begriff nur vorläufig, weil er seine eigene Gefahr mit sich brachte: Frequenz lässt sich messen, Tiefe nicht. Eine schnelle interpretative Schleife kann viele Revisionen hervorbringen, ohne eine einzige ehrliche Begegnung hervorzubringen.

Diese Unterscheidung ist pädagogisch wichtig. Die zentrale Frage lautet nicht, ob KI Antworten geben kann. Offenkundig kann sie viele Formen von Antwort liefern, einige verlässlich, andere nicht. Die schwierigere Frage lautet: Welche Art menschlicher Beteiligung verwandelt eine KI-reiche Umgebung von einer Antwortmaschine in eine rekursive Umgebung der Bildung? Die Geschichte dieses Kunstwerks legt nahe, dass die notwendige Beteiligung Fragen, Widerstand, Vergleich, Implementierung, Überraschung, Zurückweisung, Neuinterpretation, Bewegung zwischen Disziplinen und die Bereitschaft einschließt, die ursprüngliche Frage selbst zu verändern.

Chi und Wylies ICAP-Rahmen unterscheidet passive Rezeption von aktiver, konstruktiver und interaktiver Beteiligung, wobei tieferes Lernen im Allgemeinen mit Beteiligungsformen verbunden ist, die über das präsentierte Material hinaus etwas erzeugen und aushandeln (Chi and Wylie, 2014). Die Forschung zur Mensch-KI-Ko-Kreativität behandelt ähnlich das Interaktionsdesign — nicht nur die Qualität des Outputs — als zentral dafür, was Kooperationspartner gemeinsam tun können (Rezwana and Maher, 2023). Diese Rahmen helfen zu erklären, warum „Die KI erzeugte eine anspruchsvolle Antwort“ keine Evidenz dafür ist, dass die Person gelernt hat. Pädagogisch relevante Evidenz liegt darin, was die Person anschließend bemerken, erschließen, bestreiten und schaffen kann.

Ein Ereignis in meinem eigenen Gespräch wurde unerwartet wichtig. Mehr als einmal unterbrach ich beim Lesen einer langen KI-Analyse den Text und schrieb eine Implikation auf, bevor ich zu dem späteren Absatz gelangte, in dem die KI nahezu dieselbe Implikation entwickelte. In einem Fall hatte ich bereits begonnen, in Begriffen von Metanoia zu denken, bevor ich die Passage erreichte, die die Erfahrung als Metanoia bezeichnete. Die Chronologie ist wichtig: Ich las nicht einfach den Begriff und berichtete anschließend Zustimmung.

Die Konvergenz fühlte sich für einen Moment unheimlich an. Eine mögliche Geschichte wäre gewesen, die KI habe irgendwie meinen privaten Gedanken vorausgesehen; eine andere, ich hätte unbewusst einen Hinweis aufgenommen und Wiedererkennen mit eigener Hervorbringung verwechselt. Keine dieser Geschichten ließ sich aus dem Gefühl heraus begründen. Deshalb behandelte ich die Reihenfolge der Ereignisse als begrenzte Evidenz statt als Mysterium: Ich hatte die Implikation aufgeschrieben, bevor ich den späteren Absatz sah, aber nach einer langen Abfolge, in der die relevanten begrifflichen Relationen bereits entwickelt worden waren. Ich deute das nicht als Gedankenlesen, mystische Synchronisierung oder Beweis dafür, dass die KI und ich zu einer einzigen Intelligenz geworden wären. Eine plausiblere Erklärung ist pädagogisch interessanter. Die begriffliche Struktur hatte begonnen, innerhalb meines eigenen Denkens generativ zu werden. Frühere Teile des Dialogs hatten zusammen mit meiner vorherigen Erfahrung und Professorin Dohnas Vokabular Relationen bereitgestellt, aus denen ich selbstständig einen nächsten Schritt erschließen konnte. Als die spätere KI-Passage mit diesem Schritt konvergierte, bot die Konvergenz begrenzte Evidenz von Internalisierung. Ich erkannte eine Erklärung nicht mehr nur wieder, nachdem sie erschienen war; ich hatte begonnen, mit ihrer Struktur vorauszudenken.

Der Prozess ließ sich daher weder als KI denkt → Mensch stimmt zu angemessen darstellen noch als reiner menschlicher Monolog, der mit Maschinenprosa dekoriert war. Er ähnelte eher KI-Beitrag → interne menschliche Entwicklung → menschliche Schlussfolgerung → weitere KI-Entwicklung → Konvergenz oder Widerspruch. Manchmal war die Konvergenz aufregend. Manchmal war der Widerspruch wertvoller, weil er eine Unterscheidung erzwang, die die glatte Antwort verborgen hatte.

Dies führt zur formativen Rekursion. In gewöhnlicher computationaler Rekursion operiert ein Verfahren erneut auf einem neuen Zustand. Hier umfasst der sich verändernde Zustand den Beteiligten selbst. Die Person, die in eine spätere Iteration eintritt, ist von der früheren beeinflusst worden:

Du₁ ≠ Du₂ ≠ Du₃

Schaffen → Begegnung → veränderter Lernender → andere Frage
         → weiteres Schaffen → weitere Begegnung

Die Behauptung lautet nicht, dass jedes Gespräch die ganze Person transformiert. Viele Interaktionen sind trivial, repetitiv oder abstumpfend. Der Punkt ist, dass ein angemessenes Modell bestimmter Formen KI-gestützten Lernens nicht nur verfolgen kann, wie sich das Dokument verändert. Es muss auch fragen, ob sich die Fähigkeiten des Beteiligten zu Aufmerksamkeit, Schlussfolgerung, Widerstand und Urteil verändern. Kann die Person ohne Hilfe eine bessere Frage formulieren? Kann eine Idee in einen anderen Bereich wandern? Kann sie Praxis verändern? Kann sie überleben, wenn das ursprüngliche Gespräch geschlossen ist?

Jüngere empirische Arbeiten geben Anlass zur Vorsicht. In einer Befragungsstudie unter Wissensarbeitern war größeres Vertrauen in generative KI mit geringerem selbstberichtetem Aufwand für kritisches Denken verbunden, während sich das berichtete kritische Denken häufig in Richtung Verifikation, Integration und Stewardship der Aufgabe verschob (Lee et al., 2025). Da die Studie Selbstberichte und Zusammenhänge betrifft, etabliert sie kein einfaches Kausalgesetz, wonach KI Denken schwächt. Sie schärft jedoch das Problem. Einen Teil der Produktion auszulagern kann Raum für Urteil schaffen — oder gerade jene Reibung entfernen, durch die Urteilskraft entsteht. Das Ergebnis hängt teilweise davon ab, wie der Mensch aktiv bleibt. Meine unabhängige Vorwegnahme von Metanoia genügt nicht, um tiefe Bildung zu beweisen. Sie ist ein Stück erfahrungsbezogener Evidenz neben veränderten Fragen, Wiederlesen, Implementierung und einer veränderten Beziehung zu Professorin Dohnas Werk. Die pädagogische Behauptung muss verhältnismäßig bleiben: Der Prozess ermöglichte es manchmal, dass begriffliche Schritte zu meinen eigenen wurden, bevor ich sie für mich vollständig ausformuliert sah. Das ist mehr als passive Zustimmung, aber noch kein von einem Gespräch ausgestelltes Diplom.

Generierte Möglichkeit machte Urteil knapp

Die Geschwindigkeit der Schleife erzeugte eine eigentümliche Form intellektueller Lust. Verbindungen kamen schnell und in ungewöhnlicher Dichte. Ein Browser-Hintergrund berührte generative Kunst; generative Kunst öffnete Fragen der Begegnung; Begegnung führte mich zu Guardini und Professorin Dohna zurück; KI-gestütztes Schreiben öffnete Autorschaft und Pädagogik; Wiederlesen öffnete Gedächtnis, Zeit und Archiv. Die Erfahrung besaß begriffliche Intensität. Es fühlte sich an, als sei das Projekt plötzlich zu einem Knotenpunkt geworden, durch den viele Disziplinen einander sehen konnten.

Diese Intensität lieferte reale Energie. Staunen kann Aufmerksamkeit aufrechterhalten, wo Pflicht es nicht vermag. Intellektueller Eros kann einen Menschen dazu bringen, einen schwierigen Text erneut zu lesen, eine Verbindung zu prüfen, einer Referenz nachzugehen und bei einer ungelösten Frage zu bleiben. Es wäre ein Fehler, Begeisterung als Feind ernsthaften Denkens zu behandeln. Ein noch größerer Fehler wäre es jedoch, Begeisterung als Evidenz dafür zu behandeln, dass eine Interpretation wahr ist.

Begriffliche Intensität ist epistemische Energie, keine epistemische Rechtfertigung.

Intensität ist ein Grund zu untersuchen, kein Grund zu glauben.

KI kann begriffliche Intensität mit industrieller Geschwindigkeit erzeugen. Innerhalb weniger Minuten kann sie eine Erfahrung neben Kybernetik, Hermeneutik, theologischer Anthropologie und verteilter Kognition platzieren. Manchmal legt diese Gegenüberstellung eine echte strukturelle Ähnlichkeit offen. Manchmal erzeugt sie lediglich das Gefühl von Tiefe. Rhetorische Schönheit kann eine spekulative Formulierung etabliert erscheinen lassen. Die gefährlichste Interpretation ist vielleicht nicht eine offensichtliche Unwahrheit, sondern eine großartige Verbindung, von der ich möchte, dass sie wahr ist, weil sie den ganzen Nachmittag bedeutsam erscheinen lässt.

Meine erste Schutzmaßnahme war konventionelle Verifikation. Das Zitat prüfen, die Quelle öffnen, eine peer-reviewte Studie von einem Preprint unterscheiden und der KI nicht erlauben zu erfinden, was Professorin Dohna privat gemeint habe. Diese Praktiken blieben unverzichtbar. Sie beantworteten nicht das ganze Problem. Jede einzelne Quellenangabe in einem Absatz könnte korrekt sein, während die Relation zwischen ihnen oberflächlich bleibt. Ein wahrer Satz über Kybernetik könnte weiterhin eine schlechte Erklärung einer künstlerischen Begegnung sein. Faktenprüfung konnte Halluzination aufdecken; sie konnte nicht allein entscheiden, ob eine generierte Möglichkeit verdiente, meine Praxis neu zu ordnen.

Die notwendige Bewegung lautet daher Leidenschaft → anhaltende Aufmerksamkeit → Unterscheidung. Leidenschaft hält die Frage lebendig. Anhaltende Aufmerksamkeit setzt sie mehr als einer Stimmung und mehr als einer Quelle aus. Unterscheidung fragt, welche generierten Möglichkeiten weiteres Leben verdienen. KI-Kompetenz gehört dazu: Ich muss über Halluzination, Verifikation, Modellgrenzen, Quellenqualität und Automation Bias Bescheid wissen. Unterscheidung ist größer. Sie fragt, was eine Möglichkeit mit meiner Beziehung zur Wirklichkeit und zu anderen Personen tut.

An diesem konkreten Punkt trat theologische Sprache ein, statt nur dekorativ hinzugefügt zu werden. Die Frage hatte sich von „Ist dieser Output faktisch korrekt?“ zu „Welche Art von Aufmerksamkeit und Handeln bringt diese Interpretation hervor?“ verschoben. Das ist eine Frage nach Folgen, Ausrichtung und Relation. Das Vokabular der Unterscheidung wurde nützlich, weil es nicht nur fragt, ob eine Idee kohärent formuliert werden kann, sondern wie sie sich durch ein Leben bewegt und welche Früchte im Laufe der Zeit sichtbar werden.

Übersteht die Verbindung Widerstand? Bleibt sie innerhalb dessen, was der tatsächliche Austausch trägt, oder ermutigt sie mich, mehr über einen anderen Menschen zu schließen, als die Evidenz erlaubt? Macht sie mich aufmerksamer für ihr Werk oder lediglich beeindruckter von meiner eigenen Geschichte? Führt sie zu verantwortlicherem Schaffen? Kann ich sagen, was gegen sie sprechen würde? Verfolge ich die Idee, weil sie Frucht trägt, oder weil die KI sie rhetorisch berauschend gemacht hat? Erweitert das System meine Agency, oder ersetzt es still jene Handlungen, durch die Agency überhaupt gebildet wird?

Theologische Traditionen der Unterscheidung sind hier nur dann relevant, wenn ihre größere Ernsthaftigkeit bewahrt bleibt. Papst Franziskus betont in seiner Katechese zur Unterscheidung die Aufmerksamkeit auf die eigene Geschichte, die Bedeutung der Zeit und die Notwendigkeit, das Ende und die Früchte einer Bewegung zu prüfen, statt einen beeindruckenden Moment isoliert zu betrachten (Francis, 2022). Antiqua et nova argumentiert ähnlich, dass KI menschliche Tätigkeit unterstützen kann, aber weder menschliche moralische Verantwortung erben noch die Weisheit ersetzen kann, die Teile, Ganzheiten, Entscheidungen und Folgen zueinander in Beziehung setzt (Dicastery for the Doctrine of the Faith and Dicastery for Culture and Education, 2025). Diese Quellen liefern keinen fertigen religiösen Test für gute Prompts. Sie widersetzen sich der Reduktion von Urteil auf Outputbewertung. Professorin Dohnas eigenes Werk macht die Verbindung weniger oberflächlich. Verso nuovi occhi bringt künstlerische Form mit geistlicher Unterscheidung in Beziehung und legt nahe, dass Sehen nicht einfach das Erfassen eines Objekts ist, sondern eine disziplinierte Weise, Form und Bedeutung sich zeigen zu lassen (Dohna Schlobitten, 2023). Die Rückkehr zu diesem Rahmen veränderte meine Frage. Ich fragte nicht mehr nur, ob die KI-generierte Interpretation klug war. Ich fragte, ob sie einen wahrhaftigeren Blick einübt — einen Blick, der fähiger ist, den Anderen zu empfangen, ohne den Anderen auf Evidenz für meine Theorie zu reduzieren.

Das ist besonders wichtig, weil der zentrale Wendepunkt des Kunstwerks zu einer Beziehung gehört. Wenn Professorin Dohnas Reaktion für mich künstlerisch folgenreich wurde, sollte mich das zugleich vorsichtiger darin machen, wie ich die Begegnung darstelle. Eine fruchtbare Interpretation sollte Demut gegenüber dem bewahren, was ich nicht weiß, Dankbarkeit für das, was ihre Frage ermöglicht hat, und Verantwortung für die Weise, wie ich den Austausch beschreibe. Die begriffliche Entwicklung ist wichtig, darf aber niemals die Unabhängigkeit der Person verdecken, deren Reaktion half, sie in Gang zu setzen.

KI erzeugt Überfluss; der Mensch muss unterscheiden, was weiteres Leben verdient.

Als dieser Satz im Dialog entstand, gefiel mir zunächst seine Klarheit. Dann bemerkte ich eine mögliche Überkorrektur. Unterscheidung ist kein ausschließlich einsamer menschlicher Akt, der erst stattfindet, nachdem die KI mit dem Generieren fertig ist. Mein Urteil wird selbst durch Kunstwerke, andere Menschen, Traditionen, Evidenz und manchmal auch den KI-Dialog geformt. Der Satz überlebte nur, nachdem „der Mensch“ nicht länger einen isolierten Souverän bedeutete, sondern die verantwortliche Person, die innerhalb dieser Relationen rechenschaftspflichtig bleibt.

Vorläufig nenne ich dies die Unterscheidung generierter Möglichkeiten. Sie betrifft Sätze und Bilder, aber ebenso Verbindungen, Forschungsprogramme, Selbstbeschreibungen und zukünftige Handlungen. Wenn Möglichkeiten billig werden, wird Urteil knapp. Die künstlerische Aufgabe erschöpft sich nicht mehr darin, noch eine Variante hervorzubringen. Sie umfasst die Entscheidung, welche Variante zu einer Begegnung werden soll, welche Begegnung die Praxis verändern soll, welche Interpretation archiviert werden soll und welcher verführerische Zweig enden muss.

Die Entscheidung wird nicht immer einsam getroffen. Andere Menschen, Traditionen, Evidenz und der Widerstand des Kunstwerks können den Schaffenden korrigieren. Genau das tat Professorin Dohnas Frage: Sie verhinderte, dass sich meine erste Interpretation zu schnell schloss. Unterscheidung bedeutet in diesem Sinn nicht, dass der Künstler souveräne Kontrolle gegen KI verteidigt. Sie ist die Praxis, innerhalb eines Feldes generierter Möglichkeiten und realer Beziehungen rechenschaftspflichtig zu bleiben.

Metanoia verwandelte sich von einem Wort in ein Problem des Erkennens

Das Wort Metanoia war in Professorin Dohnas Werk präsent gewesen, bevor es in meiner Erfahrung präsent wurde. Ich konnte es übersetzen, darüber sprechen und es unter verwandten Ideen von Begegnung, Figuration und Bildung erkennen. Begriffliche Sprache kann Wahrnehmung vorbereiten. Metanoia war mir durch Lektüre, Gespräch und Übersetzung bereits als Begriff vertraut. Das Kunstwerk fügte eine andere Weise der Begegnung damit hinzu: Ich begann, den Begriff auf eine Veränderung zu beziehen, die sich innerhalb meiner eigenen Praxis des Sehens, Schaffens und Interpretierens vollzog. Dann kehrte sich die Ordnung um.

Es bestand weiterhin die Gefahr, zu schnell zu benennen. Sobald die KI die Abfolge als Metanoia beschrieb, bündelte das Wort die gesamte Erfahrung in ein überzeugendes Muster. Ich hatte mich unabhängig bereits auf denselben Begriff zubewegt, was die Konvergenz für mich bedeutsamer machte, doch Konvergenz entschied nicht, ob der Begriff verhältnismäßig war. Ich wandte ein, dass Metanoia philosophisches und theologisches Gewicht trägt, das nicht jedem anregenden Nachmittag zugesprochen werden sollte. Die Behauptung veränderte sich daher von „Das war Metanoia“ zu der vorsichtigeren Aussage, dass die Erfahrung dem Begriff eine erfahrungsmäßige Dimension gab, die er für mich zuvor nicht in genau dieser Form besessen hatte.

Metanoia als Gegenstand des Wissens
  → Metanoia als Begriff, der eine erfahrene Veränderung erkennt
  → Metanoia als Erfahrung, die meine Darstellung des Erkennens verändert

Vorsichtig nenne ich dies eine Metanoia der Epistemologie. Der Ausdruck bedeutet nicht, dass jede revidierte Meinung eine geistliche Bekehrung ist, dass einige Stunden vor einem Bildschirm der theologischen Tiefe von Metanoia entsprechen oder dass KI Transformation verabreicht hätte. Er bezeichnet eine präzisere Umkehr. Zunächst dachte ich, mir fehlten Informationen über den Begriff. Das Ereignis zeigte, dass mir auch die Form der Beteiligung fehlte, durch die seine Bedeutung verständlich werden konnte. Die tiefste Veränderung war nicht: „Jetzt kenne ich die Definition.“ Sie war: Jetzt verstehe ich, warum die Definition allein unzureichend war.

„Metanoia der Epistemologie“ war nicht meine erste Beschreibung. Zunächst beschrieb ich die Erfahrung zu einfach als besseres Verständnis von Metanoia. Das behandelte die Transformation noch immer als neues Wissenselement. Erst die nächste Frage — was hatte sich im Akt des Verstehens verändert? — erzwang die stärkere Unterscheidung. Die Erfahrung hatte nicht bloß ein Beispiel für den Begriff geliefert; sie hatte das Modell infrage gestellt, in dem Begriffe bereits dadurch vollständig verstanden werden, dass man korrekte Definitionen besitzt. Deshalb trat die Epistemologie selbst in die Formulierung ein.

Hier wurde Bildung zu mehr als einem eleganten Wort für Erziehung. Wenn Lernen nur Propositionen zu einem unveränderten Subjekt hinzufügt, dann bleibt die Person, die mehr weiß, im Wesentlichen derselbe Erkennende. Formative Rekursion legt etwas anderes nahe: Die Erfahrung kann die Dispositionen verändern, mit denen eine Person aufmerksam ist, fragt und urteilt. Der Lernende, der in die nächste Iteration eintritt, ist nicht identisch mit dem Lernenden, der in die vorige eintrat. Verändert wird nicht nur die Antwort, die dem Bewusstsein zur Verfügung steht, sondern auch die Art von Frage, die das Bewusstsein bilden kann.

Professorin Dohnas Werk hatte Kunst als eine Weise des Sehens aufgefasst, in der Form, Wissen und Relation zusammengehören. Dieser Behauptung war ich bereits zuvor durch Lektüre, Gespräch und Übersetzung begegnet. Das generative Kunstwerk lieferte eine Erfahrung, in der Form Relation veränderte, Relation Interpretation veränderte und Interpretation zur Form zurückkehrte. Theorie wurde aus der Praxis heraus verständlich; Praxis wurde durch Theorie verständlich. Die Absurdität entgeht mir nicht: Ich brauchte eine WordPress-Hintergrund-Engine, um Guardini wiederzuentdecken?!! Aber vielleicht schützt die Komik eine wichtige Tatsache. Bildung kommt nicht immer durch die Tür, auf der „Bildung“ steht. Die Erfahrung veränderte auch etwas an meiner gewohnten Weise, theoretischen Ideen zu begegnen. Einen Begriff verstehe ich oft am leichtesten, wenn ich sehen kann, welche Frage er mir zu stellen hilft, welche Unterscheidung er klärt oder welche Situation er mich anders wahrnehmen lässt. An dieser praktischen Orientierung ist nichts falsch, doch diese Erfahrung legte eine ihrer Grenzen offen. Besonders wichtig war in diesem Fall, dass ihr Rahmen nicht einfach ein Problem beantwortete, das ich bereits definiert hatte. Stattdessen half er, eine Dimension der Situation sichtbar zu machen, die ich zuvor nicht zu formulieren gewusst hatte: dass eine Begegnung den Beobachter verändern und durch diese Transformation auch verändern konnte, was aus dem Werk selbst anschließend werden konnte.

Die Programmieranalogie bleibt hilfreich. Nebenläufigkeit ist nicht bloß deshalb „nützlich“, weil sie einen bekannten Defekt behebt. Sobald ein Programmierer Race Conditions versteht, können Ereignisse, die zuvor zufällig erschienen, als ganze Problemklasse sichtbar werden. Ästhetische und moralische Begriffe können ähnlich funktionieren. Sie verändern das Ereignis nicht rückwirkend; sie verändern, was der Beobachter darin erkennen kann. Deshalb kann eine kraftvolle Theorie neue Fragen fragbar machen. Und deshalb ist das Versäumnis, die Bedeutung einer Theorie zu sehen, keine entscheidende Evidenz dafür, dass diese Bedeutung fehlt.

Die Einschränkung ist wesentlich. Verspätete Erkenntnis rechtfertigt nicht jeden schwierigen Text. Manche Theorien bleiben auch nach geduldiger Aufmerksamkeit verworren; manche Begriffe erhellen einen Fall und verzerren einen anderen. Epistemische Demut muss sich in beide Richtungen bewegen. Meine starke Wahrnehmung, dass die gegenwärtige Verbindung bedeutungsvoll ist, beweist ihre Bedeutung nicht. Mein früheres Versäumnis, diese Bedeutung wahrzunehmen, bewies ihr Fehlen nicht. Der Beobachter verändert sich, aber Veränderung allein garantiert nicht, dass der spätere Beobachter wahrer sieht.

Dieses Problem verbindet sich mit einem früheren Essay, den ich über Éric Rohmers A Tale of Winter geschrieben habe. Dort unterschied ich subjektive Gewissheit von äußerer Wahrheit: Dass eine Erzählung eine Figur am Ende bestätigt, liefert nicht rückwirkend epistemische Rechtfertigung für alles, was diese Figur geglaubt hat. Der gegenwärtige Fall fügt eine komplementäre Schwierigkeit hinzu. Intensive Erkenntnis ist kein Beweis. Doch fehlende Erkenntnis ist ebenso wenig ein Gegenbeweis, weil eine Begegnung den Horizont verändern kann, innerhalb dessen etwas verständlich wird. Die Lehre lautet weder „Vertraue deiner Überzeugung“ noch „Misstraue jeder Erfahrung“. Sie lautet, zu untersuchen, wie eine Überzeugung entstanden ist, was ihr Widerstand leistet und was sie sichtbar werden lässt. Meine Vorwegnahme des späteren Metanoia-Absatzes war innerhalb dieser Struktur bedeutsam. Sie zertifizierte den Begriff nicht. Sie zeigte, dass eine Relation, der ich zuerst durch die Arbeit eines anderen Menschen und einen KI-Dialog begegnet war, begonnen hatte, in mir selbst Schlussfolgerungen hervorzubringen. Das ist Evidenz von Lernen als Bildung, nicht Evidenz dafür, dass die Interpretation keiner Kritik mehr unterliegt. Diese Unterscheidung lässt intellektuelle Begeisterung lebendig, ohne sie zum Richter zu ernennen.

Nach der Reproduktion: die Singularität der Begegnung

Walter Benjamins Essay über das Kunstwerk im Zeitalter seiner technischen Reproduzierbarkeit bleibt hier unvermeidlich, sollte aber nicht als prestigeträchtige Kulisse verwendet werden. Sein zentrales historisches Problem betrifft die Frage, was mit Kunst geschieht, wenn technische Medien Werke aus überlieferten Situationen singulärer Präsenz lösen und Zirkulation, Wiederholung und neue Rezeptionsweisen strukturell wichtig machen (Benjamin, 1936/2008). Mein Browser-Kunstwerk gehört zu einer späteren Bedingung, in der Reproduktion nicht mehr das einzige hilfreiche Modell ist.

Mein erster Vergleich war zu sauber. Mechanische Reproduktion schien zu einem alten Regime des Kopierens zu gehören, Generativität dagegen zu einem neuen Regime der Differenz. Dieser Gegensatz war attraktiv, weil er dem Projekt eine klare historische Position gab. Er war zugleich irreführend. Benjamins Reproduktionen treten nicht in identische Rezeptionssituationen ein, und ein generatives System garantiert keine bedeutungsvolle Differenz. Ein wiederholter Output kann auftreten; ein technisch unterschiedlicher Output kann ästhetisch trivial sein. Der Kontrast musste schematisch bleiben, statt zur Behauptung zu werden, Generativität löse Reproduktion einfach ab.

Mechanische Reproduktion lässt sich grob als ein Werk → viele im Wesentlichen ähnliche Reproduktionen schematisieren. Ein generatives System bietet eine andere Struktur: ein System → potenziell viele nicht identische Manifestationen. Der Unterschied ist nicht absolut. Reproduktionen werden unterschiedlich erfahren, und algorithmische Outputs können sich wiederholen. Doch das System ist darauf angelegt, Variation hervorzubringen, nicht bloß Kopien eines stabilen visuellen Objekts zu verbreiten.

KI besitzt diese Differenz nicht exklusiv. Ein traditioneller Programmierer, ein anweisungsbasierter Künstler oder ein Komponist, der mit Zufall arbeitet, kann Laufzeit-Generativität auch ohne maschinelles Lernen erzeugen. Drei Ebenen müssen getrennt werden. Laufzeit-Generativität liegt vor, wenn Regeln variierende Manifestationen hervorbringen. Entwicklungsbezogene Generativität liegt vor, wenn Begegnungen mit Outputs die Spezifikation und das zukünftige System verändern. Epistemische oder formative Generativität liegt vor, wenn der umgebende Dialog Reflexion, Übersetzung und Lernen so beschleunigt, dass die Beteiligten selbst in spätere Zyklen verändert eintreten. Der stärkste Beitrag der KI in diesem Fall gehört zur dritten Ebene, mit einer wichtigen Rolle auf der zweiten. Diese Dreiteilung entstand, weil meine erste Darstellung der KI zu viel zuschrieb. Wenn variierende Manifestationen die entscheidende Innovation waren, war KI nebensächlich: Konventioneller Code konnte sie bereits erzeugen. Dann verschob ich den Beitrag der KI in die Entwicklung, doch auch reflektierende Revision existierte vor KI. Erst auf der dritten Ebene wurde die besondere Beschleunigung dieses Falls sichtbar — die dichte Schleife aus Artikulation, disziplinübergreifender Übersetzung, Einwand, Bewahrung und Reaktion auf einen sich verändernden Lernenden. Auch dort bedeutet „besonders“ weder exklusiv noch automatisch nützlich.

Dadurch wird Benjamins Frage auf neue Weise seltsam. Was geschieht, wenn technische Reproduzierbarkeit ein Werk nicht bloß vervielfältigt, sondern dabei hilft, potenziell unwiederholbare Begegnungsereignisse hervorzubringen? Ein Besucher kann die Seite aktualisieren und eine weitere Komposition erhalten. Doch visuelle Differenz allein erzeugt keine Singularität. Die folgenreichere Einzigartigkeit könnte in der Konvergenz einer Manifestation, eines Augenblicks, der vorherigen Erinnerung eines Besuchers, des Textes im Vordergrund und jener Reaktion liegen, die zurückkehrt. Die Pixel mögen reproduzierbar sein; das vollständige Ereignis ist es vielleicht nicht.

Es ist verlockend zu sagen, die Aura wandere vom Objekt zum Ereignis — und eine „prozedurale Aura“ oder „ereignisbasierte Aura“ vorzuschlagen. Ich behalte diese Ausdrücke im explorativen Modus. Benjamins Aura gehört zu einer spezifischen Darstellung von Distanz, Tradition, Kultwert, Ausstellungswert und technologischer Moderne; sie sollte nicht beiläufig repariert werden, indem man sie an Neuheit anhängt. Ein personalisierter oder statistisch seltener Output ist nicht automatisch auratisch. Plattformen produzieren fortwährend den Anschein von Einzigartigkeit.

Die Wanderung der Aura war einer der rhetorisch befriedigendsten KI-gestützten Vorschläge — und gerade deshalb verlangte sie besonderen Widerstand. Der Ausdruck schien das Benjamin-Problem mit einer einzigen Bewegung zu lösen: Die durch das reproduzierbare Objekt verlorene Aura würde in der singulären Begegnung wiederauftauchen. Das war zu restaurativ und zu bequem. Ich behielt die Frage bei und zog die Lösung zurück. Was der Fall trägt, ist eine Verschiebung der Aufmerksamkeit hin zum Begegnungsereignis, nicht der Beweis, dass Benjamins Aura unter einem prozeduralen Namen zurückgekehrt ist.

Dennoch legt der Fall eine reale Verschiebung künstlerischer Aufmerksamkeit nahe. Die Singularität, die mich interessiert, ist nicht Knappheit auf dem Markt. Sie ist die Irreduzibilität einer Begegnung, in der Werk und Betrachter gemeinsam eine Geschichte erhalten. Die Worte eines Austauschs können zitiert werden, aber die Begegnung lässt sich kein zweites Mal zum ersten Mal reproduzieren. Ihre Bedeutung hing teilweise von der Geschichte ab, die ihr vorausging. Eine spätere Besucherin kann dem daraus entstandenen Artikel begegnen, aber nicht genau diese Geschichte einnehmen. Ihre folgende Frage veränderte meine Beziehung zum Werk aufgrund der Jahre, die ihr vorausgingen. Ein späterer Besucher kann dem daraus hervorgegangenen Artikel begegnen, aber nicht genau diese Geschichte bewohnen. Technische Generativität vervielfacht Gelegenheiten; menschliche Zeitlichkeit macht jede folgenreiche Gelegenheit zu mehr als einem austauschbaren Output. Die Relation zwischen Schaffendem und Betrachter intensiviert diesen Punkt. Ich kann denselben veröffentlichten Artikel erneut öffnen, aber nicht als exakt derselbe Leser zurückkehren, der ihn veröffentlicht hat. Das archivierte Objekt kann stabil bleiben, während sich sein Urheber verändert. Umgekehrt kann sich das generative visuelle System verändern, während ein zurückkehrender Besucher Erinnerungen an frühere Manifestationen mitbringt. Die Zeitlichkeit des Kunstwerks läuft in beide Richtungen: Variation im Objekt trifft auf Variation im Betrachter.

Benjamin hilft mir daher, zwei Vereinfachungen zurückzuweisen. Die erste behandelt das generierte Bild als konventionelles einzigartiges Meisterwerk, nur weil eine bestimmte Anordnung vielleicht nie wiederkehrt. Die zweite behandelt digitale Reproduzierbarkeit als Verschwinden jeder Form singulärer Präsenz. Die interessantere künstlerische Frage liegt dazwischen: ob ein technisch wiederholbares System Situationen komponieren kann, in denen eine Begegnung dadurch singulär wird, dass sie verändert, was als Nächstes geschehen kann.

Das Werk bleibt offen, weil sich der Schaffende verändert hat

Es wäre jetzt möglich, die aus dieser Geschichte hervorgegangenen Formulierungen — formative generative Praxis, hochfrequente hermeneutische Schleife, rekursive Bewahrung von Einsicht, diachrone relationale Autorschaft, Unterscheidung generierter Möglichkeiten, Metanoia der Epistemologie — zu sammeln und als fertige Theorie zu präsentieren. Das würde den rückblickenden Fehler wiederholen, mit dem dieser Essay begann. Jeder Ausdruck entstand, weil eine frühere Beschreibung versagte. Keiner hat sich bislang seine Unabhängigkeit von dem Fall verdient, der ihn notwendig machte.

Die Browser-Komposition war als Einheit zu klein, also folgte ich kausalen Rückkehrbewegungen durch Manifestation, Begegnung und erneutes Schaffen. „Gedächtnishilfe“ war zu schwach für einen archivierten Gedanken, der später seinen Urheber veränderte, also schlug ich rekursive Bewahrung vor. „KI-gestütztes Schreiben“ verbarg die Trennung von gelebter Erfahrung, sprachlicher Produktion, Auswahl und späterem Verstehen, also wurde Autorschaft relational und diachron. „Schnelles Feedback“ erfasste keinen veränderten Interpreten, also wurde die Schleife hermeneutisch und potenziell formativ. „KI-Kompetenz“ enthielt nicht die ethische und relationale Frage, welche Möglichkeiten fortgesetzt werden sollten, also trat Unterscheidung hinzu. „Den Begriff kennen“ erklärte nicht, warum Erfahrung veränderte, was Erkennen bedeutete, also erreichte Metanoia die Epistemologie selbst.

Die Aufteilung der Beiträge zwischen Mensch und KI wird ebenfalls klarer, wenn sie über diese Veränderungen rekonstruiert statt aus der endgültigen Prosa geschätzt wird. Die KI gab mir Formulierungen, die echte Wege öffneten: „Wo genau befindet sich Sinn?“, „Die Person kann hineingezogen werden“ und die Idee, dass der Schaffende als Betrachter zurückkehren kann. Sie half, das Kunstwerk mit Feedback, verteilter Kognition, Autorschaft, Metanoia und Benjamin zu verbinden. Mein Beitrag bestand darin, diese Öffnungen zu erkennen, aber auch darin, ihre ersten attraktiven Formen zurückzuweisen. „Mitschöpfer“ gab dem Besucher zu viel Kontrolle. „Alles um das Kunstwerk herum“ löschte die Grenze aus, die ich erklären musste. Feedback allein konnte einen neuen Parameter nicht von einem neuen Kriterium unterscheiden. Menschliche Leitung entschied Autorschaft nicht automatisch. Faktenprüfung entschied Unterscheidung nicht. Metanoia durfte nicht bloß deshalb ausgerufen werden, weil die Analogie kraftvoll wirkte. Aura durfte nicht einfach wandern, weil der Satz elegant war.

Jeder Einwand brachte neue Evidenz oder eine neue Einschränkung in den Dialog zurück. Die KI argumentierte dann innerhalb eines veränderten Problems, und ich begegnete der revidierten Formulierung aus einer veränderten Position. Das enthüllt keinen verborgenen Punkt, an dem entweder der Mensch oder die Maschine die gesamte Trajektorie allein verfasst hätte. Es zeigt eine Abfolge, in der Generierung und Urteil immer wieder veränderten, was der nächste Beitrag sein konnte. Die Geschichte dieser Korrekturen ist daher selbst Teil der Evidenz über Autorschaft.

Ich kann nun die Evidenzschichten der Geschichte voneinander unterscheiden. Das laufende visuelle System, die veröffentlichten Texte und der bewahrte Austausch sind dokumentarische Bestandteile des Berichts. Meine Überraschung, mein Unbehagen und mein verändertes Verständnis sind Berichte aus der ersten Person. Die Behauptung, dass ihre Frage die künstlerische Praxis transformiert hat, ist eine Interpretation, die durch späteres Wiederlesen und revidierte Intention gestützt wird. Die mit KI entwickelten begrifflichen Ausdrücke sind analytische Vorschläge. Die Verbindungen zu Guardini, Benjamin, Kybernetik, verteilter Kognition und Unterscheidung sind wissenschaftliche Vergleiche, die geprüft werden können. Die Möglichkeit einer umfassenderen Theorie KI-gestützter Bildung bleibt Spekulation.

Diese Schichten sichtbar zu halten, entzieht dem Kunstwerk nicht sein Geheimnis. Es erlaubt Geheimnis ohne Erfindung. Ich behaupte nicht zu wissen, was Professorin Dohna mit ihrer Frage in jeder Hinsicht beabsichtigte; mit Sicherheit beschreiben kann ich, wie ich ihr begegnete und was sich anschließend in meinem eigenen Denken veränderte. Ich weiß nicht, ob jede zukünftige technische Änderung die hier entwickelte Theorie verkörpern wird. Ich weiß, dass ich das Werk nicht länger so gestalten kann, als seien Besucher, Reaktionen und spätere Selbste nur äußere Nachwirkungen. Ich weiß nicht, ob KI meine Autorschaft erweitert oder offengelegt hat, dass Autorschaft immer verteilter war, als ich zugab. Ich weiß, dass die Verantwortung für die Entscheidung, was meinen Namen trägt, bei mir bleibt.

Der performative Kreis ist nun sichtbar, auch wenn er keinen endgültigen Endpunkt besitzt:

Erkennen → Schaffen → Begegnung
  → transformiertes Erkennen → transformiertes Schaffen
  → neue Begegnung

Professorin Dohna entwickelte ein Projekt, das sich mit Erkennen, Schaffen, Begegnung, Figuration, Bildung und Metanoia beschäftigt. Viele dieser Fragen waren mir seit Jahren vertraut, doch diese Erfahrung gab mir durch meine eigene künstlerische Praxis einen neuen Zugang zu ihnen. Aus einem anderen Grund schuf ich ein KI-gestütztes computationales Kunstwerk. Das Kunstwerk brachte unvorhergesehene Relationen hervor. Sie begegnete ihm. Ihre Reaktion erschütterte meine bestehende Interpretation und machte eine Verbindung sichtbar, die ich nicht erwartet hatte. Fragen, die sich durch ihr Werk ziehen, begannen Dimensionen des Kunstwerks anders zu erhellen, während das Kunstwerk diesen Fragen für mich eine neue Konkretheit gab. KI half mir zu artikulieren, was sich veränderte, und allmählich fand ich mich nicht mehr nur dabei wieder, der entstehenden begrifflichen Bewegung zu folgen, sondern sie auch zu befragen, ihr zu widerstehen und sie in Richtungen zu erweitern, die ich nicht vorausgesehen hatte. Dieser Essay kann nun zu ihr zurückkehren, und ihre Reaktion — falls sie eine geben möchte — kann den Prozess erneut verändern.

Diese Möglichkeit macht sie nicht dafür verantwortlich, das Kunstwerk zu vollenden oder meine Interpretation zu bestätigen. Die Offenheit der Begegnung schließt die Freiheit des anderen Menschen ein, nicht die Rolle zu spielen, die der Künstler sich vorgestellt hat. Eine zukünftige Reaktion kann das Argument vertiefen, korrigieren, zurückweisen oder die Beziehung an einen Ort führen, den ich nicht vorhersehen kann. Das Werk bleibt offen, weil Begegnung kein programmierbarer Output ist.

Ich begann mit dem Wunsch, dass die Website cooler aussieht. Mir ist noch immer wichtig, ob sie gut aussieht. Die Kunst wurde nicht dadurch ernst, dass sie ihrem visuellen Körper entkam, und die Philosophie entschuldigt keine schlechte Komposition. Verändert hat sich der Horizont, innerhalb dessen ich schaffe. Ein Aktualisieren der Seite kann weiterhin ein Mosaik erzeugen. Es kann aber auch einen Besucher vor eine Anordnung stellen, die ich nicht einzeln komponiert habe, diese Begegnung im Gedächtnis bewahren, eine Reaktion hervorrufen, die Reaktion zum Schaffenden zurückführen und verändern, was der nächste Akt des Schaffens bedeuten kann.

Der Generator ist nicht zum Philosophen geworden, und ich bin nicht zum alleinigen Meister von allem geworden, was er mir zu sagen half. Etwas Bescheideneres und zugleich Anspruchsvolleres ist geschehen. Schaffen wurde fähig, Bedingungen für eine Begegnung anzuordnen; Begegnung veränderte die Person, die zum Schaffen zurückkehrte; und Professorin Dohnas Frage machte es mir unmöglich, das generierte Bild weiterhin als das ganze Werk zu behandeln. Die nächste Manifestation wird auf einem Bildschirm erscheinen. Das nächste Kunstwerk könnte in dem beginnen, was diese Erscheinung zwischen Menschen verändert.

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Who Creates Whom? Art, AI, and the Transformation of the Maker

My previous essay, Building a Generative Artwork That Can Change Its Maker, ended with the thought that the artwork had answered back. This essay begins where that formulation became insufficient. “Answering back” still suggests two relatively stable partners: a maker on one side and a completed work on the other. What actually developed was less orderly. A visual system generated forms; the forms changed my questions; another person’s response changed how I understood the forms; an old theoretical vocabulary became newly legible; AI helped externalize the change; the website preserved it; and I later returned to my own AI-assisted writing as a reader. By then, neither the work nor the person encountering it was quite the same as before.

The beginning was much less serious. I wanted my personal website to look cooler. Its brutalist and experimental design needed a visual background that felt alive without competing with the articles and photographs in the foreground. I did not begin with Romano Guardini, Bildung, metanoia, second-order cybernetics, distributed cognition, or a theory of AI-assisted authorship. I began with tiling. Tiling became visually repetitive. Several mosaic forms were more interesting, but they still required me to imagine and describe too many arrangements one by one. Mathematical structures then offered something verbal instruction could not provide efficiently: rules capable of producing families of compositions that I had not individually designed. The generator engine began by generating layouts. Only later did it begin generating questions.

Even that summary is retrospective. At no point did the next stage feel guaranteed. Sometimes an output was merely ugly. Sometimes AI misunderstood the aesthetic objective. Sometimes I accepted an explanation because it sounded coherent and then rejected it when the visible work contradicted it. Sometimes a technical solution disclosed an artistic possibility I had not requested. The project moved through dissatisfaction, correction, accident, resistance and delayed recognition. Its later significance cannot be used to pretend that the first intention secretly contained the whole development.

My first complete draft of this essay nevertheless reproduced that tendency in a subtler form. It placed art and Prof.ssa Dohna at the centre, kept the technical material proportionate and included the concepts I had reached. Yet when I reread it, I recognized the conclusions more readily than the path by which I had become able to think them. “Recursive conservation of insight,” “diachronic relational authorship” and “metanoia of epistemology” appeared as well-formed results. The draft said that I had hesitated, but it did not always let the reader inhabit the hesitation. It preserved the map and compressed the journey. That recognition changed the objective of revision. I objected to the implicit model of knowledge in the draft: it made understanding look like a possession displayed after the struggle was over. I asked instead for the genealogy—the early answers that seemed sufficient, the objections that unsettled them, the evidence that forced another distinction and the questions that remained after a satisfying formulation. This second pass therefore becomes part of the case it describes. I encountered an AI-assisted text that was substantially mine in direction but no longer adequate to my experience; that encounter changed the instructions; the changed instructions altered the text that a later reader, including me, will encounter.

The technical reality can be stated briefly. The work is a browser-based visual environment integrated into WordPress. A curated archive of images and animations is arranged by pseudorandom selection and mathematical structures into changing compositions. Navigation or refresh can activate another manifestation. AI substantially assisted the development of the system, but a conventional programmer could have built its runtime generativity without AI. The mechanism matters because the philosophical claims arose from something that actually ran, produced visible relations, failed in particular ways and could be encountered by other people. The subject here is what happened when making exceeded the reason for which I had begun making.

The new question is therefore not simply whether the browser compositions count as art. It is whether the relevant artistic unit gradually expanded because each earlier boundary could no longer describe the evidence. At first, the artwork seemed to be one generated composition. Then it seemed to be the system plus its many possible manifestations. Then the visitor’s action and situated encounter became relevant. Finally, interpretation, writing, another person’s response, later rereading and revised intention began returning causally into the practice. If those returns alter what is made next, are they external commentary on the artwork, or have they become part of its material history?

I do not want to answer by declaring that everything is art. That would make the category too generous to explain anything. The criterion I arrived at is narrower: an event becomes artistically integrated when it is causally incorporated back into the practice. A passing comment is not automatically part of the artwork. Prof.ssa Yvonne Dohna-Schlobitten’s response matters artistically because it changed my interpretation; that interpretation changed the questions I brought to theory and AI; those questions changed how I understood future design; and those changes can alter later encounters. The return, not mere proximity, is decisive. This essay follows that return. It is the transformation of creation through an actual human encounter: the artwork gave me a new point of entry into questions running through Prof.ssa Dohna’s work, while those questions gave me new ways of seeing the artwork. Around that encounter, AI, writing and the website became unusual participants in a longer process through which the artist could become the beholder of their own work, and the later beholder could change the person who would continue creating.

I did not set out to make a theory

The temptation in a polished essay is to make the past look intelligent. Once an artwork has acquired a philosophical vocabulary, the writer can quietly move that vocabulary backward until every early decision appears to have anticipated the conclusion. In my case, that would falsify the most important evidence. The ordinary motive—the wish for a cooler website—is not an embarrassing detail to be removed after the project becomes serious. It shows that the theory emerged through making rather than being illustrated by it.

The first changes were aesthetic and practical. A single background could become monotonous. Random selection introduced variety but not necessarily composition. Tiling multiplied an image without creating sufficiently rich relations. Mosaics allowed contrast among records, yet hand-specifying more and more arrangements soon became a poor way to explore a large visual possibility space. Mathematical structures changed the problem. Instead of describing each desired layout, I could establish procedures that distributed scale, territory, adjacency, repetition and interruption. The work moved from choosing a picture to constructing conditions under which pictures could meet. That sequence contained several temporary solutions. At first, I thought variation itself would solve the aesthetic problem: if the background changed, it would remain interesting. The actual page contradicted that assumption. Randomness could prevent repetition while producing no relation worth seeing. Tiling then seemed attractive because it transformed one image into a field, but repetition quickly became another kind of monotony. Mosaics appeared to solve that problem by placing several records together. For a while, this felt like the answer. Then each new arrangement required another verbal description, another special case and another correction. I was accumulating layouts without yet creating a visual language.

I initially responded by describing more precisely what I wanted to AI. That helped at the local level: one gap could be reduced, one panel could be enlarged, one collision could be avoided. But the improvement exposed the limitation of the method. I could continue asking for individual compositions, or I could ask what kind of rule would generate a family of compositions. The original question—“How should these images be arranged?”—became “What relations of scale, adjacency and interruption should be capable of arranging themselves differently?” The move toward mathematical generators did not arrive as a theoretical commitment to procedural art. It arrived because verbal micromanagement had become aesthetically and conceptually exhausted.

AI helped translate that frustration into candidate procedures. Some proposals were mathematically neat and visually lifeless. Others were technically correct but too dense, too regular or too eager to display their own geometry. I rejected them not because their code failed, but because the visible work failed to sustain the tension I wanted between the foreground article and the background field. At other moments, an output I had not predicted produced a relation stronger than my prompt. Those cases forced me to revise my intention rather than merely the implementation. The evidence was not AI’s assurance that a procedure should work; it was what happened when the procedure met the images and the page.

That move belongs recognizably to generative art. Philip Galanter’s influential definition centres on an artist setting a system in motion with a degree of autonomy, and it explicitly refuses to tie generative art to one technology (Galanter, 2003). This protects an essential distinction: AI did not invent generative art, and AI was not required for the browser to produce varied manifestations. Random procedures, instruction-based art, mathematical composition and autonomous systems long predate current language models. The distinctive role of AI in my case emerged in the development and interpretation surrounding the runtime system, not in the bare fact that an algorithm could vary a layout.

At first, I treated each new output as evidence about the implementation. Did it fill the screen? Did the images remain legible? Did the foreground still dominate? Those questions were necessary, but actual outputs repeatedly made the brief itself unstable. A technically correct arrangement could be aesthetically dead. A failure could reveal that my description of the objective was too narrow. An unintended relation could make me want a capacity I had not known to request. The design process began to resemble Donald Schön’s account of a reflective conversation with a situation: the maker acts, encounters consequences, notices what the move has disclosed and reconstructs the problem (Schön, 1992). Schön’s sequence of seeing, moving and seeing again explains more of this history than the fantasy of a complete specification followed by execution. AI accelerated that conversation. I could move from a vague visual intuition to a candidate rule, from the rule to a running manifestation, from the manifestation to an objection, and from the objection to a revised concept in a short span of time. Yet speed did not make the development inevitable or automatically insightful. The useful unit was not “prompt followed by answer.” It was a recurrent encounter with resistance:

ordinary visual wish
  → first arrangement
  → dissatisfaction
  → new rule
  → unexpected manifestation
  → aesthetic or conceptual objection
  → changed understanding of the problem
  → further making

Only after this sequence had repeated did I begin to see that the system was not merely producing backgrounds. It was externalizing possibilities that could address the person who had initiated them. The old description—“a feature that makes varied layouts”—was still technically true, but artistically insufficient. The work had begun to teach me what I had been trying to make. This is also why the speed of the later reflection must not be confused with the age of the thought. Some of the strongest connections appeared over a few hours. But those hours depended on years of technical practice, humanistic study, prior writing, my relationship with Prof.ssa Dohna, familiarity with her vocabulary, and the existence of a live artwork capable of making that vocabulary concrete. Composition time is not formation time. AI compressed the time needed to formulate and connect; it did not manufacture the whole history that made recognition possible.

The boundary of the artwork kept failing

The first candidate boundary was simple: artwork = visible composition. It failed because no single composition exhausted the work. Each manifestation depended on a rule system, a curated archive, a viewport, a moment of execution and the visitor action that made this particular state actual. A screenshot could preserve one result, but it could not contain the possibility of another result. The artwork therefore seemed better described as generative system + manifestations.

I did not reject the first boundary through theory alone. I tried to preserve compositions as images and noticed what disappeared. The screenshot retained the arrangement but removed the possibility of refresh, the expectation of change and the visitor’s memory of what had been there before. It documented an appearance while omitting the conditions that made the appearance one event among others. That was the evidence that moved the system itself inside the artistic unit. For a time, system + manifestations seemed complete.

That second boundary also failed. A manifestation did not appear in an empty laboratory. It appeared behind an article, to a visitor with a history, perhaps after earlier visits and remembered combinations. Refresh was not authorship in the full sense, but neither was it irrelevant. The visitor performed a modest act of actualization: this possibility, now. The work had become system + activation + manifestation + encounter. Its aesthetic life included recurrence, disappearance, surprise, boredom and memory. My language overshot before it became more precise. I first described the visitor as a co-creator. The phrase captured something important—the visitor’s action helped determine which potential state became actual—but it also attributed too much control. The visitor could refresh, attend, remember or leave; they could not specify the geometry, records or relation that would appear. I therefore retreated from “co-creator” to a more modest performer of selection. What survived from the first phrase was participation. What had to be abandoned was the suggestion of equal authorship or complete control.

Art history already provides families of thought that prevent me from presenting this as unprecedented. Generative art treats the system as part of the work. Process and systems-oriented practices move attention away from a self-contained object toward operations, conditions and changing relations. Relational aesthetics made human relations and their social contexts central artistic materials (Bourriaud, 2002). Second-order cybernetics asks what changes when the observer cannot be treated as external to the observed system (von Foerster, 2003). My case belongs near these traditions, but it is not identical to any one of them. The distinctive problem that arose for me was the causal return from encounter into the ongoing construction of the work and, at the same time, into the formation of the maker.

This is where the third boundary became unstable. The article I wrote about the compositions changed how I presented them. That changed how Prof.ssa Dohna encountered the work. Her response changed what I reread and what I asked. The new interpretation began suggesting different artistic possibilities. Writing was no longer simply a label placed beside a finished object. Theory had entered the causal loop of practice. At that point, an expansive explanation became tempting. If code, output, visitor, memory, writing and response all mattered, perhaps the artwork was the entire surrounding ecology. AI could formulate that enlargement persuasively. I found the account exciting because it explained why the work felt larger than the screen. Then I objected: if every circumstance could be absorbed into the artwork after the fact, the boundary had not been expanded but dissolved. An incidental event and a transforming encounter would become indistinguishable. The explanation needed a test.

It would still be a mistake to count every surrounding event as part of the artwork. An unrelated administrative event, an incidental conversation or a stranger’s passing glance may belong to the work’s circumstances without becoming artistic material. The causal-incorporation criterion is stricter. Prof.ssa Dohna’s reaction becomes artistically consequential because the practice after that reaction is not the same practice as before it. If the encounter changes the artist’s understanding but never returns to selection, form, participation, preservation, presentation or future design, it may remain biographically important without yet becoming part of this artwork.

This was how the criterion emerged rather than how it began. Mere contact was too weak; “everything is connected” was too broad. I needed to ask what happened next. Did the event alter interpretation? Did the altered interpretation change an artistic decision, a question, a condition of participation or the future possibility space? An event becomes artistically integrated when it is causally incorporated back into the practice. The formulation did not prove that every return is good art. It distinguished a consequential return from an adjacent occurrence.

I therefore began using two provisional descriptions. Formative generative practice names a practice in which the system generates forms while the larger process participates in forming the people who continue it. Reflexive processual generative artwork emphasizes that the work’s history includes returns through interpretation and redesigned conditions. Neither phrase announces a new movement. They are attempts to name why “browser composition” had become too small and why “everything around it” would be too large.

The resulting artistic material can include code, algorithms, mathematical structures, images, visitor actions, writing and human response—but not merely because they are nearby. They become integrated when one alters the conditions under which another can occur. The artwork is not an infinitely expanding bag of associations. It is a trajectory of consequential returns.

Prof.ssa Dohna’s question reopened an old vocabulary

Prof.ssa Yvonne Dohna-Schlobitten has been part of my intellectual world for years. Our exchanges have often moved between very practical things—technical assistance, computer problems, translation and work on drafts—and the theoretical questions running through her work, including contemplation, encounter, figuration, Guardini, Bildung and metanoia. I had therefore been familiar with many of these themes long before this project. What changed through the generative artwork was the way I encountered them: ideas I had previously met through conversation, reading and translation suddenly acquired another kind of immediacy because they were now connected to something unfolding inside my own artistic practice. What changed was not simply how much I understood, but the form that understanding took. Concepts that had already been intellectually available to me became experientially concrete, and this new point of contact allowed me to notice relations that I had not previously had reason to see in quite the same way. That distinction matters relationally.

The immediate encounter with Prof.ssa Dohna had already been described in my previous essay, so I do not need to reconstruct the exchange in detail here. What matters for the present argument is what happened afterward. When I shared the generative work with her, her response unexpectedly reopened the relationship between the questions she had been developing around encounter, artistic creation and metanoia and the computational practice I had approached from a very different direction. What had initially seemed to me like two neighbouring intellectual worlds—her theoretical work and my technical experimentation—suddenly began to illuminate one another.

Her reaction did not provide me with a ready-made interpretation of the artwork. It did something more valuable: it interrupted the interpretation I already had. I began asking whether a generative system that could produce forms without itself undergoing the human experience of encounter actually weakened the importance of her questions, or whether it made those questions more urgent. The more I reflected, the more the second possibility seemed worth pursuing. The artwork was no longer interesting to me simply because it could generate unexpected compositions; it had become a concrete situation in which questions about the relation among making, beholding, meaning and transformation could be experienced rather than discussed only in abstraction.

This was also the point at which I returned to Prof.ssa Dohna’s theoretical work with a different kind of attention. I had encountered many of these ideas before, but the artistic experience now gave them another point of contact. At the same time, those concepts gave me a vocabulary for dimensions of the artwork that I had not previously known how to describe. The movement therefore became reciprocal: the artwork made her theory newly intelligible to me, while her theory made the artwork newly intelligible to me.

The AI dialogue reinforced that temptation by producing elegant variations of a comforting answer: the computational artwork had not displaced her inquiry but confirmed its relevance. I recognized something true in that formulation. Yet it seemed too easy. If the artwork merely “proved” that Prof.ssa Dohna had been right all along, then her destabilizing question had been converted into a device for validating a theory. The account would flatter both the project and my artwork while leaving the disturbance untouched. I asked a harder question: what, exactly, had become more intelligible, and what evidence showed that this was more than a retrospective intellectual decoration? That objection changed the next action. Instead of continuing to generate reassuring interpretations, I returned to her published work. The rereading was not background research added to strengthen an already finished thesis. It was a test of the thesis. I wanted to know whether the concepts I was invoking actually described the process that had occurred, whether I had remembered them accurately and whether they resisted my desire for a satisfying symmetry.

In “What we see looks back at us,” she links seeing, love and artistic creation through a space in which a person or thing may disclose itself rather than be reduced to use (Dohna Schlobitten, 2022). Verso nuovi occhi explicitly moves among theoretical, experiential and contemplative knowing and connects artistic form with spiritual discernment (Dohna Schlobitten, 2023). Her study of Guardini’s Weltanschauung asks how thought becomes form and how artistic theory may arise from artistic practice rather than merely being applied to it from outside (Dohna-Schlobitten, 2024).

These were not new words to me. What changed was the structure of relevance. I had been treating theory largely as a source of statements to understand. The artwork made me experience theory as a new capacity of perception. A programmer who learns the concept of a race condition can suddenly see a problem that was physically present in the code before the concept was available. In another register, encounter and figuration allowed me to see that the generated image was not the only object of artistic attention. The relation among manifestation, beholder, response and renewed making had also become visible.

This was the evidence that the first reassurance had lacked. Her texts did not simply provide the word “encounter” for a visitor seeing an image. They redirected attention toward the transformation of seeing itself and toward the way form can open a relation without exhausting the other. That returned me to the exchange with a different question. Perhaps her project and my artwork were not competing explanations of creation. Perhaps each had disclosed something the other had not yet made visible to me:

A powerful theory does not merely answer visible questions. It renders previously invisible questions askable.

Delayed recognition should not be romanticized. A theoretical framework does not become more convincing merely because its relevance becomes apparent only later, and a powerful experience does not exempt an interpretation from criticism. The correction is more modest: my inability to perceive significance may be evidence about the encounter between me and the theory, not decisive evidence about the theory itself. Sometimes additional information is needed. Sometimes criticism is justified. But sometimes the missing condition is an experience capable of reorganizing what the existing words can disclose.

Guardini’s description of a work of art as opening a space in which people may move and encounter what becomes open before them acquired a new concreteness for me through the browser work (Francis, 2023). I do not claim that a generative background proves Guardini, or that my website has the spiritual or artistic stature of the works he discussed. The important event was reciprocal illumination:

The artwork gave me a new point of entry into questions running through her work, while those questions gave me new ways of seeing the artwork.

The dialogue eventually produced that sentence, and I felt an immediate sense of recognition. I did not accept it only because it was beautiful. I tested its two directions against the chronology. The artwork had indeed changed what I could perceive in her texts; the reread texts had indeed changed what I considered part of the artwork. Neither simply produced the other. The theory was not an intellectual ornament borrowed after the fact to elevate some moving images. The artwork was not a demonstration programmed to confirm a prior framework. They met through Prof.ssa Dohna’s response, and the disturbance between them altered how I could see both.

The experience also gave me a new way of seeing our long-standing intellectual exchange. Practical collaboration and theoretical conversation had often existed alongside one another, but through this project they unexpectedly began to converge. Questions arising from my own artistic practice resonated with themes she had been exploring for years, while her thought gave me new ways of attending to what was unfolding in the work. Whatever the encounter may mean from another perspective, for me it opened a dimension of our intellectual relationship that I had not previously experienced in quite this form.

Encounter became the operation of transformation

Before this sequence, encounter seemed to name what happened between a visitor and a generated composition. That account was not wrong; it was simply the first instance. I then encountered outputs I had helped make but had not individually composed. Prof.ssa Dohna encountered the artwork. I encountered her reaction. Her reaction led me to encounter her theory differently. That rereading led me to encounter my earlier judgments differently. Later, I encountered my own AI-assisted article as a reader. At each stage, something entered the next stage in a changed form.

An earlier AI question had been useful: “Where exactly is sense?” My first answer located it mainly in the human interpretation of a generated juxtaposition. The geometry could place two images beside one another; the beholder could recognize comedy, memory or theology in the relation. Prof.ssa Dohna’s response made that answer too narrow. Sense was not occurring only at the moment of looking. Her question changed what I later read, and the rereading changed what the work could become. Meaning had acquired a temporal and causal path. Another AI formulation then became important: “The person can become implicated.” I initially accepted it because it named the difference between observing an interesting output and discovering that the output makes a claim upon one’s own understanding. But I had to ask what “implicated” meant in evidence rather than rhetoric. In my case, it meant that I could no longer preserve the same account of Prof.ssa Dohna’s theory, our intellectual relation or my own role as maker. The phrase became useful only when tied to those observable changes.

state₁ → encounter → transformation → state₂
state₂ → encounter → transformation → state₃
state₃ → changed making → another possible encounter

I use “transformation operator” only as a conceptual image (but not as established scientific terminology): an encounter can confuse, wound, flatter, narrow or mislead. Nor is every change metanoia. The repeated structure matters because the same operation—meeting something not fully controlled by the present self—kept changing the state of the practice and of the person continuing it.

The exchange with Prof.ssa Dohna is the clearest human instance in this case. Her response interrupted my initial interpretation and changed what I subsequently read and asked. That change then returned into the artistic interpretation, giving the exchange formative as well as artistic consequence.

At first, “feedback” seemed to name the whole structure. Output returned as input; a later state differed because of an earlier response. I accepted the cybernetic description provisionally because it made the circularity visible. Then the comparison exposed its own limit. Feedback can adjust a parameter while leaving the objective untouched. Encounter can introduce a new category of relevance. A visitor clicking “like” might change the probability assigned to an image; a human response can instead make the artist reconsider what kind of problem the work has created. The first changes a value inside a given model. The second may change the model.

This is also where the observer moved inside the history being observed. Second-order cybernetics became relevant because it directs attention to systems in which observation and description cannot be treated as external, neutral additions (von Foerster, 2003). I initially treated the comparison almost as an explanation. On reflection, it was better understood as a diagnostic aid. Cybernetics made the recursive position of the observer visible; it did not by itself explain the aesthetic, relational or theological meaning of the transformation. I built the generator, observed it, changed because of what I observed, wrote about the change and allowed the writing to influence subsequent intentions. Prof.ssa Dohna encountered the work, and her response changed the way I, as its maker, would continue it. AI helped describe the loop and thereby became another causal participant in the loop it described.

The artwork’s boundary consequently expanded once more:

generative system
  → manifestation
  → human encounter
  → response
  → another encounter with that response
  → changed interpretation
  → changed artistic intention
  → future system and manifestation

The causal-incorporation criterion prevents this from becoming metaphysical inflation. What enters the work is the response she actually gave, my documented reaction to it, the interpretive changes that followed and whatever future practice those changes genuinely affect. The criterion preserves both relational openness and respect for the other person’s independence. Encounter thus ceased to be merely a theme represented by the artwork. It became the recurring operation through which the practice changed state. That is aesthetically significant because the work’s form now includes not only arrangements on a screen but the temporal pattern by which arrangements, responses and revised conditions become consequential to one another. The artwork no longer ends where the pixels end.

The artist returned as a beholder

A further instability appeared when I returned to the article I had written with AI about the project. During its production I had been an originator, witness, questioner and editor. I supplied the lived events, the project, the quotations, the objections and the direction of inquiry. AI generated a great deal of the verbal surface. At the time, I was close enough to the process that the text still felt like an extension of an active conversation. After publication, temporal distance changed its status. It became an object I could meet.

On rereading, I sometimes found an argument whose consequence I had not fully absorbed when I approved the sentences. This is not the romantic myth of an autonomous machine secretly placing messages in my work. It is a more ordinary and, for me, more consequential fact: a person can help cause a text without being simultaneously present to every implication that the text makes available. Writers have always discovered things in their own drafts, and artists have always encountered consequences they did not consciously plan. AI intensified the separation among initiating, articulating, selecting, publishing and later understanding.

My first explanation was less generous: perhaps I had simply failed to read carefully enough. That remained a legitimate possibility, especially when AI had produced a long passage quickly. It also fed the discomfort I later discuss about publishing words whose implications I had not fully mastered. Yet the explanation did not cover everything. I had read, selected and approved the text. What changed during the later encounter was not only my attention to the same sentence but the history I brought to it. Events and further reflection had made an implication available that the earlier reader could not yet organize in the same way.

The AI dialogue proposed a formulation close to “the creator can become the beholder of their own AI-assisted creation.” I recognized the experience immediately, but I resisted the suggestion that this was entirely new or uniquely artificial. Writers have long been surprised by their drafts; painters encounter forms they did not plan; temporal distance changes every rereading. The stronger revised claim was comparative rather than absolute: AI can greatly widen the interval between originating a problem, producing its verbal surface and fully encountering what that surface makes thinkable. It intensifies an old artistic possibility and distributes it across more agents and times.

me as originator and witness
  → AI-assisted articulation
  → selected and published text
  → temporal distance
  → me as later reader
  → renewed encounter
  → changed understanding
  → further writing and making

I had become a beholder of my own AI-assisted creation, and the beholder could now change the creator. This mattered artistically because the return did not stop at private appreciation. What I understood later changed the questions I asked about the visual system, Prof.ssa Dohna’s project and the meaning of authorship. The article joined the causal history of the artwork. It was simultaneously a record of one state of understanding and an instrument capable of altering a later state.

That altered how I understood the website itself. I had treated it mainly as a publishing platform: a place where an article became available after it was written. In practice, it also behaved as a temporal cognitive apparatus. At one moment, a fleeting intuition could be articulated with AI and preserved. At a later moment, a changed self could reread that preserved object and recognize something that the earlier self had not yet been able to hold. A new discussion could then revise the recognition and produce another object for a still later return.

“Archive” was my first better word. Unlike “publication,” it emphasized preservation across time. But an archive still sounded passive, as though the website merely stored finished states. My own rereading supplied contrary evidence: an old article could reorganize a current question, and the current question could send me back to Prof.ssa Dohna’s work with new attention. Storage had become an active relation among differently situated versions of myself.

t1  fleeting intuition → AI-assisted articulation → archive
t2  changed self → rereading → new recognition
t3  renewed discussion → revised understanding → new archive

Calling this a cognitive apparatus does not require the extravagant claim that my website literally thinks. I was briefly attracted to the stronger language of an extended or distributed mind because it captured how much work the external archive was doing. Then I narrowed the claim. Work on distributed cognition and the extended mind has long challenged the idea that every cognitively important operation must remain inside an individual skull. Hutchins showed cognition coordinated across people and material structures in real practices; Clark and Chalmers argued that external resources can sometimes participate in cognitive processes rather than merely report their results (Hutchins, 1995) (Clark and Chalmers, 1998). Those theories situate the problem, but I do not need to prove that the website is literally part of my mind. My narrower claim is functional: the archive changes what I can remember, compare, revisit and develop. When its preserved formulations later alter my judgment, it is causally active in the history of my thinking.

The larger circuit is therefore not simply human → AI → text. It looks more like human memory ↔ AI ↔ writing ↔ public archive ↔ later self ↔ other people ↔ new experience. The arrows are not equal. The archive does not possess a life history; AI does not inherit my responsibility; other people are not components to be absorbed into my private cognitive machinery. The notation only marks that thinking occurred through several differently situated participants and media, and that what passed through them could return changed.

I began calling one function of this circuit recursive conservation of insight. The phrase became necessary because “note-taking” did not describe the whole sequence. Many intuitions arrive with more force than clarity. If I do not record them, they vanish; if I record only a fragment, I may later recover the words but not the relation that made them alive. AI can help turn a fragile intuition into an external object: it can articulate, expand, question, compare and preserve the thought quickly enough that the original energy is not entirely lost. After temporal distance, that object can be encountered again, interpreted by a changed person and returned to the process in another form.

The phrase did not arrive all at once. “Memory aid” captured the prevention of forgetting but not the later return. “Externalized thought” captured the existence of an inspectable object but not its persistence through time. “Conservation” then seemed closer, until I objected that conservation normally suggests keeping something unchanged. In practice, the preserved formulation became productive precisely because a later encounter transformed it. “Recursive conservation” retained both movements: enough stability for the intuition to survive, and enough openness for it to re-enter thought as a new cause:

Recursive conservation of insight is not merely the prevention of forgetting. It is the preservation of a thought in a form durable enough to act back upon the person from whom it began.

The recursion matters. A preserved insight that never returns is an archive entry. A preserved insight that later changes the reader who changes the practice has acquired formative force. In this case, the website preserved an AI-assisted interpretation of the artwork; rereading it altered my understanding of encounter; that changed how I reread Prof.ssa Dohna; her framework then changed how I saw the artwork. The conserved thought did not remain identical while travelling through time. Preservation made transformation possible.

There is also a serious danger. AI can inflate a weak intuition into an impressive miniature theory before the intuition has survived contact with evidence. Linguistic completeness can disguise conceptual prematurity. A paragraph may look as though years of thought have already occurred because it has the cadence, distinctions and references of mature reflection. Recursive conservation can then become recursive self-confirmation: I preserve a rhetorically enlarged claim, reread it as if its polished form were evidence, and build further claims upon it.

I noticed this risk while developing the phrase itself. Once AI could unfold “recursive conservation of insight” into an elegant account of memory, archives and later selves, the concept felt more established than it was. My excitement was evidence that the formulation had gathered something important, but not evidence that the gathering was accurate. I had to return to the concrete sequence—fleeting intuition, AI-assisted articulation, publication, temporal distance, rereading and changed action—and ask which links had actually occurred. The concept survived that test as a provisional description, not as a proven theory of cognition.

The archive therefore needs visible epistemic differences. A published exploration is not automatically a certified conclusion (strangely!). Dates, sources, corrections, explicit uncertainty and later revisions matter because the website is public even when it functions as a notebook. Its openness allows another person to encounter the thinking, as Prof.ssa Dohna did, but that same openness creates obligations. A temporal mirror can reveal change only if I do not repaint every earlier reflection to resemble the present one.

Authorship separated into times and responsibilities

Becoming the reader of my own AI-assisted prose made the authorship question more difficult, not less. In some articles, AI may have generated most of the final sentences—perhaps a very large majority of the visible language. Concealing that would falsify the process. But treating sentence count as a complete measure would falsify it differently. The problem is not solved by assigning a percentage to “me” and another to “AI,” because several things normally compressed into the word author had come apart. My first impulse was nevertheless quantitative. If AI had produced most of the sentences, perhaps it had produced most of the article; if I could estimate the proportion, perhaps the moral unease would become manageable. The arithmetic had the advantage of honesty about the surface form. It also concealed an assumption: that every authorial contribution is commensurable with sentence production. A percentage could count words while assigning no place to the experience that created the problem, the evidence that constrained it, the rejection of a persuasive but false interpretation or the responsibility incurred by publication.

AI could answer that objection with another simplification: I remained the “real author” because I supplied the vision and directed the system. I found that reassuring for a moment. Then I challenged it. “Direction” can become an honorific that protects human prestige without examining what the human actually did. If I had not read the result, could not explain it, supplied no decisive evidence and accepted every generated paragraph, the word “director” would not rescue the practice. I needed a description that neither erased AI’s enormous verbal contribution nor converted my intention into magical ownership.

Who originated the problem? Who underwent the experience? Who recognized that the exchange with Prof.ssa Dohna mattered? Who supplied the evidence and objected when a formulation exceeded it? Who proposed a connection, generated a sentence, selected a passage, rejected an interpretation, implemented a consequence, published the result, accepted responsibility for the claims and returned later to continue the inquiry? In a conventional account these functions may belong mostly to one person and one period of work. Here they were distributed across a human, an AI system, other people’s scholarship, another person’s response, several artifacts and more than one version of myself.

The revision of this essay supplied a particularly clear instance of that distribution. AI could produce a coherent first draft and most of its polished language. I was the one who recognized that coherence had hidden the epistemic path, objected to the result and redefined the objective from “state the theory clearly” to “show how the theory became thinkable.” That intervention was not a cosmetic preference and did not require me to type every replacement sentence. It changed the model of what the article was. Human agency appeared as diagnosis, resistance and redefinition of the work’s criterion.

Research on human–AI writing increasingly examines this distribution through process rather than final-text inspection. The CoAuthor dataset, for example, records detailed interaction histories from human–AI writing sessions, making visible cycles of requesting, accepting, editing and rejecting that a finished page conceals (Lee, Liang and Yang, 2022). A recent provisional framework for creative ownership similarly separates the person, the process and the generative system, and asks about intention, control, effort, embodiment, production and interdependence rather than relying on a single measure (Polimetla and Gero, 2025). These approaches do not decide my case, but they confirm why a word percentage is analytically poor.

“Co-authorship” initially seemed the obvious alternative to sole authorship, but it implied a symmetry the case did not support. “Distributed authorship” captured dispersion but made the relations sound spatial and static. I tentatively arrived at relational authorship for work whose formation depends on contributions that cannot be adequately understood in isolation, and diachronic relational authorship when the relevant relations include later versions of the human author. The adjectives do not erase asymmetry. Prof.ssa Dohna is not a co-author of my article merely because her response changed it. The cited thinkers are not interchangeable with a language model. The model’s verbal production is not the same activity as my lived experience or editorial responsibility. My later self’s interpretation is not what my earlier self knew. The point is precisely to preserve these differences while acknowledging their causal relation.

Nor can the issue be dissolved by saying that AI is “just a tool.” A brush does not normally propose a conceptual distinction, draft several paragraphs, retrieve a rhetorical structure or respond to a changed question in language. Yet the opposite slogan—“AI wrote it, therefore the human contribution is fraudulent”—also collapses what happened. It makes the final verbal surface the sole site of intellectual causation. In this case, the experience, ethical stakes, selection, resistance, publication and continuation remain human even where sentence production is heavily machine-assisted.

Legal categories help define one boundary but not the whole philosophical problem. The United States Copyright Office’s 2025 report retains human authorship as the basis of copyright and evaluates human selection, arrangement and modification case by case; it does not treat prompting alone as automatically sufficient (U.S. Copyright Office, 2025). That is relevant to rights in a particular jurisdiction. It does not tell me whether I understand what I publish, whether another person has been represented fairly, whether the work is artistically mine, or what kinds of dependency I should disclose. Legal authorship, creative ownership, intellectual contribution and moral responsibility overlap without becoming identical.

This is where guilt enters. When I know that AI has generated much of the prose, I sometimes feel discomfort about placing my name above it. It would be too easy to answer that feeling with blanket reassurance. Some of it may be inherited from an older image of authorship in which the named author personally produces every significant sentence. Some may be intellectual unease rather than evidence of a moral wrong. But some of it asks legitimate questions. Am I receiving credit for linguistic labour I did not perform? Have I allowed eloquence to outrun my understanding? Did AI accelerate thinking, or did it substitute for the moment in which I should have struggled? Can I defend the claims to a reader—and to Prof.ssa Dohna—without returning to the model for an explanation of what appears under my name?

I had to separate several feelings that the word “guilt” compressed. There may be moral guilt if I deceive readers, misrepresent another person, publish claims I know to be false or claim unaided production that did not occur. There may instead be intellectual unease when the prose exceeds my present mastery, even if no deliberate deception has occurred. There is also responsibility, which remains whether or not I feel guilty. The distinctions do not remove the discomfort. They prevent discomfort itself from deciding the moral diagnosis. Those questions cannot be answered globally. They have to be tested in the conduct of the practice. Did I read the final text carefully? Did I verify documentary claims and scholarship? Did I reject invented certainty about another person’s mind? Did the work change my own independent thinking, or merely give me phrases I enjoyed recognizing? Have I been candid about substantial AI involvement? Am I willing to correct what I publish? Most importantly, do I take responsibility for the consequences of selection and publication?

AI may contribute enormously, sometimes more than I do to the surface form, while I remain responsible for what I choose, publish, believe, reject, pursue and allow to influence future action.

Responsibility is not proof of sole authorship. It is the non-transferable human burden created by the act of making the output public and consequential. An AI system cannot apologize to Prof.ssa Dohna if I misrepresent her. It cannot decide what kind of relationship I want this inquiry to sustain. It cannot live the changed practice. If authorship is distributed, responsibility is not thereby diluted into nothing.

The distinction becomes clearer when “publication” is divided into at least two acts. Publication can mean making something available, preserving a state of thought and inviting encounter. It can also function as epistemic certification: a signal that the named author has carefully checked and can defend every substantive claim according to the norms of a scholarly field. A personal website can legitimately operate partly as a public notebook or evolving archive. An academic journal article carries much stronger expectations of verification, stable argument and accountable mastery. Calling both acts “publication” does not make their epistemic promises identical.

I reached that distinction only after an attempted defence became too broad. I first thought: this is my personal website, so publication can simply preserve exploration. That was true but incomplete. The page was also public, searchable and capable of affecting a real person whose words I quoted. “Public notebook” could become an excuse if it meant that unfinished status cancelled every obligation. The stronger formulation preserved exploratory publication while refusing to treat it as private rehearsal.

In my exploratory practice, the strange possibility arises that the public text may exist before I have completely become its reader. That need not be forbidden in art. A score can exceed one performance; a process can disclose consequences after it begins; a published experiment can invite the encounters through which its meaning develops. But the permission depends on honesty about genre and status. It does not justify fabricated sources, unexamined accusations, confident claims about private motives or carelessness presented as scholarship. The more a text affects other people, public knowledge or academic debate, the less defensible it is to say that I planned to understand it later.

The personal website therefore carries two nearly opposite functions. It gives a fragile thought enough public durability to return. It also exposes an unfinished state to real readers who cannot be treated as rehearsal objects. Prof.ssa Dohna’s role makes that tension concrete. Publishing can open a space of encounter; it can also fix a careless representation of someone else. Relational authorship requires relational accountability.

When Earlier Thought Becomes Encounterable: The Author Is Not the Same Person Three Times

The authorship problem became stranger only after I thought I had made it more precise. I had begun with a fairly ordinary discomfort: if AI generated most of the visible sentences in some of my writing, what exactly justified putting my name above them? Counting words seemed honest at first because it acknowledged something that would otherwise be easy to hide. Yet the percentage quickly became inadequate. It could tell me something about the production of language while saying almost nothing about where the problem originated, who supplied the experience and evidence, who rejected an attractive but false formulation, who changed the objective, who decided what could responsibly be published, or who later had to answer for the result. That failure led me toward relational and then diachronic relational authorship. I thought that distinction had solved the main conceptual difficulty.

Then another problem appeared. Even in that improved model, I was still treating the human side of “human–AI collaboration” as strangely static. The AI could generate, respond and change. The artifact could accumulate revisions. Other people could enter the process and redirect it. Yet “the human” remained a single box in the diagram, as though the person who began the inquiry, the person who judged the generated possibilities and the person who later returned to the published work all occupied the same epistemic position. My own experience was increasingly difficult to fit into that assumption.

The first formulation that helped me see the problem was surprisingly simple: perhaps there was a you₁, a you₂ and a you₃. At first I was unsure whether this was merely a convenient notation or something more interesting. The distinction should not be exaggerated. These are not three persons, personalities or metaphysical selves. They are three temporally distinguishable epistemic and authorial positions occupied by one continuing person. I remain responsible across them. My biography does not restart every time I revise a paragraph. What changes is the horizon from which I can perceive the problem.

you₁ → AI-mediated articulation and encounter → you₂ → persistent artifact/archive → later encounter → you₃ → further creation

you₁ is the initiating position: the person who has the lived experience, artistic difficulty, intuition, uncertainty or question from which the process begins. This position often knows much less than a polished final article later makes it appear to have known. In my case, it may begin with something as weak as “there is something strange here,” “this explanation feels wrong,” or “perhaps these two things belong together.” The thought may be important while remaining linguistically poor. It can disappear if I do not capture it quickly.

AI changes what can happen to that fragile state. A fragment can be externalized and immediately returned in a more developed form. Connections appear. Distinctions are proposed. Several paragraphs may materialize around an intuition that, a minute earlier, existed only as a sentence or even a hesitation. This is the point where the process can easily be misunderstood. The elaborated output may contain possibilities I had not explicitly formulated, but its fluency does not establish that those possibilities are correct. The first impressive expansion therefore creates a new task rather than completing the old one.

That task belongs to you₂. This is the discerning and responsible position: the self who encounters what AI has produced and begins accepting, resisting, correcting, testing and redirecting it. In the development of this article, some of the most consequential moments looked exactly like that. A formulation would initially seem powerful; then I would notice what it implied about another person, my earlier understanding, authorship or a theoretical claim. Sometimes a single word introduced an emotional implication the evidence did not support. At other times an entire conceptual frame made an earlier relation sound too binary. Those corrections were not merely cosmetic: they changed what I understood the article to be describing.

This mattered because it showed me where human agency was actually appearing. It was not adequately described by saying that I had typed prompts or clicked “accept.” Agency appeared in noticing that a persuasive account exceeded the evidence, recognizing that a formulation might hurt someone unnecessarily, supplying a missing distinction, rejecting reassurance that protected my prestige, or deciding that the question itself had become too narrow. At several points I did not merely ask the AI to produce a better answer. I altered what counted as an answer.

I might have stopped there. you₁ originates; you₂ discerns. That already offered a richer account of human–AI authorship than sentence percentages. But then the later reader entered the problem.

The third position appeared only when the work came back

I had already noticed something odd in my AI-assisted writing: sometimes I returned to an article and found implications that I did not remember consciously holding when I published it. Writers have always had this experience, so AI cannot claim to have invented it. A novelist can discover something in an old novel. A scholar can reread an earlier paper and realize that one argument carried consequences that were not fully visible at the time. What interested me was the intensity and frequency with which AI-assisted writing seemed able to produce this distance between the person who initiated a thought and the later person who encountered its elaborated form.

That later position became you₃. you₃ is the returning self: the person who encounters the preserved artifact after enough temporal or epistemic distance has developed for it to become partially unfamiliar. The article still belongs to my history. I remember why I wrote it. I may remember many of the prompts and objections. Yet the artifact is no longer identical with my present state of understanding. It can therefore return as something capable of surprising me.

The number three should not be taken literally. There can obviously be you₄, you₅ and many later states. Years from now I may return again and reject half of what now seems convincing. Three is useful because it appears to be the analytical minimum needed to reveal the first complete recursive return. With you₁ and you₂, there is development. With you₃, something more happens:

maker → artifact → beholder → changed maker

That was the moment when the authorship problem unexpectedly returned me to the central problem of this entire essay.

The formulation that triggered the recognition did not originally come from me. During the AI-assisted discussion, the system proposed that an artifact could “let an earlier epistemic state remain present after the person has moved beyond that state.” The sentence immediately arrested me. My reaction was not simply that the wording was elegant. I suddenly realized that this was encounter again. I had approached the issue through authorship, temporal identity and AI-assisted writing, yet the structure that emerged was the same structure that had already appeared elsewhere in the artwork.

An artifact can allow an earlier epistemic state to remain encounterable after the person has already moved beyond that state.

The emotional force of that recognition mattered because it changed what I investigated next. I remember the physical excitement of it—the kind of sudden conceptual recognition that can produce goosebumps before one has had time to decide whether the idea is actually defensible. The feeling was not evidence that the claim was true. It was evidence that something in the structure demanded further attention. Instead of treating the three positions as a small authorship diagram, I began asking what it means for one state of a person to become encounterable by another.

The archive resists the way the present rewrites the past

Ordinary memory does not preserve earlier epistemic states very faithfully. Once I understand something differently, I tend to reconstruct the earlier path from the standpoint of the present. The hesitation disappears. A discarded interpretation begins to look obviously inadequate. A concept that once required several conversations starts to feel as though I had always understood it. The final theory colonizes its own genealogy.

A preserved artifact can resist that process. It cannot contain the earlier self in any literal sense, but it can preserve traces of what that self could see, what it could not yet see, what it feared, what it misunderstood, what seemed plausible and what had not yet become askable. A draft, dialogue history, article or archive therefore does something more interesting than reminding me of a conclusion. It can confront the present self with evidence of a previous horizon.

This is why the “yes, but…” moments in AI-assisted inquiry have become methodologically important to me. If I polish them all away, the final article may become smoother while becoming less truthful about how understanding actually developed. “At first I thought X; then Y seemed persuasive; then a fact or objection made Y insufficient; then a third distinction became necessary” can look untidy beside a finished argument. Yet those transitions show where judgment occurred. They allow a later reader—including the later version of myself—to see that the final position was not simply downloaded from a model or present from the beginning.

Research on human–AI writing already demonstrates why process traces matter. The CoAuthor dataset preserves detailed histories of requesting, accepting, editing and rejecting during human–AI writing sessions, revealing forms of collaboration that the finished text alone conceals (Lee, Liang & Yang, 2022). My present question extends that insight beyond the drafting session. Even a complete record of what happened while the text was being produced may not capture what happens when the text returns later to the person who helped produce it.

This produces a peculiar form of self-encounter. When I read another scholar, I encounter a genuinely different biography, intellectual history and set of experiences. That otherness is irreplaceable. Reading my own earlier artifact gives me something different. It is close enough to belong to my history and distant enough to resist my present understanding. Sometimes I read a sentence and think, “yes, of course.” Sometimes I think, “did I really believe this?” The archive can answer with an irritating lack of diplomacy: apparently, yes. That experience can reveal something about learning that ordinary introspection easily loses. The difference between the earlier artifact and my present judgment is itself evidence of change. I can ask why a formulation that satisfied me three days ago now feels inadequate, which distinction became available in the interval, or why an argument that once appeared complete now generates another question. The object is therefore not merely a memory aid. It gives the later state something against which its own difference can become perceptible.

I do not want to generalize too quickly from my own disposition. I find this kind of intellectual archaeology fascinating. Another person may not. Some learners may benefit more from discussion with others, practical experimentation or entirely different forms of reflection. Recursive engagement with one’s own archive can also become self-confirming if nothing external interrupts it. The methodological advantage, if there is one, depends partly on resistance. Other people, scholarship, evidence and events must remain capable of telling the recursive system that it is wrong.

Encounter returned from an unexpected direction

This was the point at which the structure of the writing process began to mirror the structure of the artwork itself. I had already described how a generated visual form could surprise its maker. Prof.ssa Dohna encountered the work; her response altered my interpretation; I returned to questions in her thought differently; those questions then changed how I understood the artwork. Now a similar topology appeared inside the act of writing:

earlier state → externalized artifact → later encounter → changed state → renewed creation

The repetition matters because I did not begin the authorship discussion by trying to prove that everything was an encounter. I began with a much narrower anxiety about AI-generated sentences. The concept returned because the narrower explanation became insufficient. Once the artifact could preserve enough of an earlier epistemic position to confront a later one, the relationship between maker and work had become reciprocal again.

The analogy nevertheless has an ethical boundary. Prof.ssa Dohna cannot be reduced to an element in my cognitive state machine. She is another person whose freedom includes the possibility of agreeing with my interpretation, deepening it, correcting it, disagreeing with it, declining to respond, or seeing relations in the episode that I have missed. An artifact has no comparable interiority. My earlier text cannot decide that it dislikes what I am doing with it. The resemblance is therefore structural rather than personal: both another person and a persistent artifact can confront my present interpretation with something that is not reducible to what I presently intend. This distinction, actually, strengthens the role of encounter instead of weakening it. It suggests that encounter is not merely another word for “receiving information.” Something becomes encounter-like when it introduces enough resistance or difference to make the current configuration of the self unstable. Prof.ssa Dohna can do this in the fullest interpersonal sense because she is genuinely other. An artifact can do it in a narrower epistemic sense because it preserves traces from a state I can no longer completely occupy.

At this point another AI-generated formulation became useful: you₁ is collaborating with you₂ through an artifact mediated by AI, and you₃ inherits both. I initially liked the sentence because it gave the three positions a relationship rather than treating them as labels. Then it created another question. What exactly does you₃ inherit?

Not simply text. you₃ inherits the originating problem of you₁, the expansions supplied by AI, the selections and refusals of you₂, the mistakes that survived, the evidence already gathered, the relationships implicated in the writing and the responsibilities created when the artifact became public. The inheritance therefore includes both intellectual gain and unfinished obligation. If you₃ discovers that you₂ misrepresented someone, later understanding does not erase the earlier publication. The later self inherits the duty to correct it.

The “three yous” therefore began to look less like three boxes and more like a trajectory of accountable transformation.

A state machine was useful until it became too rigid

My first computational analogy was a state machine. It made intuitive sense: S₁ becomes S₂, then S₃, through identifiable transitions. Yet the analogy quickly became too rigid. In an ordinary finite-state machine, the relevant states and transition rules are generally specified in advance. Here the encounter can change the rules by which later encounters will be interpreted. you₂ may not simply know more than you₁; you₂ may have changed what counts as a worthwhile question. you₃ may later reject even that criterion. I therefore moved toward the looser image of a state-space trajectory or a reflexive, history-dependent state-transition process. I use this language cautiously. I am not claiming to have defined a formal dynamical model of authorship. The analogy is useful because it emphasizes that the state of the person at time t₂ partly depends on the trajectory through which that state was reached, and because future transitions may depend on changes produced by earlier ones. In other words, the process has memory.

The most important transition operator in this provisional model may be encounter. An ordinary feedback loop can change a parameter while preserving the model that defines the parameter. An encounter can sometimes change the model itself: what I am looking for, what I consider evidence, what I think the artwork contains, or what I believe the original problem was. That is why “state machine” helped me see the temporal structure but could not fully explain the epistemic transformation.

AI alters more than the speed of writing

Earlier in this inquiry I had repeatedly described one effect of generative AI as collapsed latency. That still seems correct. The distance between intuition, externalization, criticism and reformulation can become extraordinarily short. Yet the three-position model showed me that speed was only part of the change. AI can alter the sequence in which articulation and understanding occur.

The simplified traditional model looks something like this:

thinking → understanding → writing

No serious account of writing has ever been quite that simple. Writers discover things while writing, scholars think through correspondence, artists learn from materials, and students change their minds in conversation with teachers. AI did not invent thinking through externalization. What it makes unusually ordinary is another sequence:

partial intuition → externalization → AI elaboration → encounter with elaboration → objection or adoption → changed understanding → renewed articulation

The difference became especially clear to me when a half-formed intuition could become several pages of articulated possibility while I was still mentally inside the originating question. I could object immediately: “yes, but this makes the x sound too y,” or “this implies that I understood z before,” or “that interpretation assigns a private motive to another person that the evidence does not support.” The generated material changed in response, and the changed material altered my next objection. The process could continue before the cognitive atmosphere that produced the original question had disappeared. A brilliant tutor or collaborator can do something much richer than this, of course. The historical novelty should therefore not be exaggerated. What seems significant is the combination of semantic responsiveness, immediate availability, substantial linguistic generation, interdisciplinary range, persistent context and repeatability. A kind of intense dialogical assistance that once depended upon unusually available human collaborators can now be present through an ordinary writing session.

That availability creates a danger symmetrical with its power. The AI can elaborate a thought faster than I can deserve it. It can turn a weak intuition into polished prose before the underlying judgment has matured. The artifact can become more sophisticated than the learner.

The essay may have advanced intellectually while the student has remained where they began.

When this sentence emerged, the problem stopped being only about my own authorship and became a question about higher education.

The educational problem was not identical with plagiarism

My first instinct was still to frame the issue through plagiarism. If AI had generated a large proportion of the prose, perhaps the central institutional question was attribution: whose contribution was being represented as whose? That remains a legitimate question. Traditional plagiarism and unauthorized assistance have not ceased to matter simply because generative AI complicates authorship. But the moment I asked how an examiner could actually verify AI authorship, another problem appeared. Conventional plagiarism can often be investigated by comparing the submitted work with an identifiable source. Generative AI frequently leaves no equivalent textual source. A teacher may become suspicious because of style, vocabulary or a sudden change in quality, but suspicion is not the same thing as evidence. If the institution cannot reliably establish the attributional fact, its greater power does not convert uncertainty into knowledge.

This is not a purely hypothetical procedural concern. Oxford’s AI Competency Centre stated in February 2026 that the University did not endorse digital AI detectors for academic decision-making, citing technical limitations and procedural fairness and emphasizing that such tools cannot reliably determine whether AI has been used (Webb-Davies, 2026). That does not imply that unauthorized AI use can never be established. A student may disclose it, process records may exist, or other evidence may be available. The narrower point is epistemic: an accusation should not acquire certainty merely because the institution needs a decision.

This made the phrase “learning does not excuse plagiarism” feel simultaneously true and insufficient. Of course genuine learning does not erase deliberate misattribution. Yet in practice the AI-age problem cannot be solved simply by declaring that distinction if the alleged attribution cannot itself be established reliably. Worse, excessive reliance on suspicion risks transforming the teacher from someone assessing knowledge into someone policing stylistic authenticity. The resulting damage is relational as well as procedural. A student who is wrongly suspected does not experience the accusation as an abstract discussion of policy. Trust between teacher and learner becomes part of the cost.

The problem began to look deeper than plagiarism. Perhaps the institution was asking the wrong first question.

Purpose before detection

This is why Taylor and LaCroix’s 2026 article Purpose before policy: academic integrity, generative AI, and rhetorical stance became unexpectedly important to the argument. Their claim is not that AI-related misconduct is unreal. They ask what must logically come first. Whether a particular use of GenAI undermines academic integrity depends on what the university believes its educational function to be, and incoherent policy emerges when institutions promote AI while failing to clarify the pedagogical purposes against which its use is being judged (Taylor & LaCroix, 2026).

One passage became especially relevant to my problem. They argue that generative systems can allow students to satisfy required outputs without undergoing the pedagogical processes those outputs were supposed to cultivate. If a machine-generated essay can satisfy an assessment that claims to measure learning, then the difficulty may reveal something about the assessment itself. A polished output is no longer reliable evidence that the learner possesses the capacities represented by it.

That is almost exactly the problem expressed by the sentence that had emerged from my own discussion:

The essay may have advanced intellectually while the student has remained where they began.

Taylor and LaCroix do not propose the you₁/you₂/you₃ structure, and I do not want to retroactively place my model into their argument. Their work establishes the prior institutional question. My present reflection suggests one possible extension: if the purpose of an assessment includes learning, judgment, critical thinking or Bildung, perhaps we need evidence not only of how the artifact was produced but of what happened to the learner through and after its production.

This was where you₃ suddenly became educationally important.

The return may be where formation becomes visible

you₁ can give evidence of origination. The student can explain what problem, experience, source or question initiated the work. you₂ can give evidence of discernment during development: why one AI suggestion was rejected, why another was modified, what evidence changed an interpretation, which sources were checked, what assumption had to be abandoned. These traces already tell us considerably more than an AI detector score. Yet neither stage completely answers the question of what survived. A student may participate actively in an AI-assisted session and still depend on the system to maintain the conceptual structure. What happens when the conversation closes? What happens tomorrow?

This is the point of you₃. The later learner encounters the artifact again. Can they explain the central argument in language that is not simply reproduced from the text? Can they identify a section they now consider weak? Can they say where the AI was wrong? Can they transfer a concept to an unfamiliar case? Can they defend the evidence? Can they revise a conclusion when given a counterexample? Most interestingly, can they generate a question that did not exist when the artifact was produced?

Research on evidence-centered approaches to human–AI writing already points toward examining process data and cognitive activity rather than relying exclusively on final outputs (Cheng et al., 2024). The three-position model adds a provisional temporal question: perhaps some evidence of learning appears only after production, when the artifact returns to the learner. I do not think this should become another bureaucratic test in which every student is required to perform a theatrical confession of personal transformation. Nor does you₃ provide magical proof that the text has become “truly theirs.” The claim is more modest. Later criticism, transfer, independent explanation and consequential revision are stronger evidence of epistemic integration than the polished artifact by itself. This also prevents an easy rhetorical rescue of human authorship. If AI generated eighty percent of the sentences, later understanding does not transform those sentences retroactively into unaided human prose. The production history remains distributed. you₃ cannot travel backward in time and make AI disappear. What the returning self may establish is a different relation to the artifact: the ability to understand it, resist it, extend it and become answerable for what remains.

That distinction may eventually require better terminology, but I do not want to solve it by inventing another impressive label too quickly. “Epistemic ownership,” “responsible integration” and “appropriation” each capture something and introduce other problems. For now, the question is more useful than the name: what must happen after AI-assisted production for the intellectual structure of the artifact to become demonstrably consequential in the learner?

Evidence should be designed before suspicion begins

The same reasoning also changed how I thought about misconduct detection. If universities cannot reliably infer AI use from the final textual surface, perhaps assessment should be designed so that evidence of learning and authorized collaboration is produced prospectively rather than reconstructed under suspicion afterward.

This would change the examiner’s role. Instead of receiving a polished essay and asking, “Can I prove that AI secretly wrote this?”, the assessment could make clear from the beginning what forms of AI assistance are allowed, what should be disclosed, which stages of reasoning matter and what later demonstrations may be required. Revision histories, declared AI interactions, short reflective accounts, source checks, oral defence or transfer tasks could then provide evidence aligned with the educational objective. The point would not be total surveillance of the learner. It would be to stop pretending that the final artifact contains evidence it no longer reliably contains.

The fairness principle that emerged for me was simple:

Evidence of learning should be designed into assessment prospectively; evidence of misconduct should not be manufactured retrospectively from suspicion.

This does not eliminate misconduct. It places an epistemic obligation on the institution alongside the obligations it places on the student. If academic integrity includes accuracy, justification and responsibility, then those virtues should govern accusations as well as submissions.

The shift also brings the teacher–student relationship back into the picture. A later oral conversation need not function primarily as a trap designed to expose AI use. It can become another encounter. The teacher can ask what changed, what remains unclear, what the student would now reject, or how the argument behaves when moved into another case. The educational question becomes less “can I catch you?” and more “can I encounter the learner who now stands behind this artifact?” That does not remove assessment or standards. It makes the relationship between evidence and educational purpose more explicit.

My earlier authorship model now looked too static

This also caused me to reinterpret my earlier work on generative-AI assessment. In my previous DAP/WAM work, a weighted authorship matrix could distinguish different contributions to an AI-assisted artifact: origination, generation, selection, verification, revision and other forms of participation. That still seems useful because it resists reducing authorship to a single percentage. Yet the present case revealed something that a matrix alone cannot capture. A matrix is principally structural: it asks how contributions are distributed. The three-position model is dynamical: it asks what happens to the participant over time:

contribution structure ≠ formation trajectory

A student might demonstrate substantial human contribution without learning much. Another might use extensive AI generation while undergoing significant critical development. Neither fact automatically settles questions of authorization or attribution, but the distinction matters if the institution claims to assess formation. The structure of contribution and the trajectory of learning are different variables.

I did not reach this conclusion by starting with a theory of dynamical authorship. The path was almost embarrassingly recursive. I began by asking how much of the prose was “mine.” Percentages failed. That led to relational authorship. Relational authorship then failed to account for temporal difference inside the human participant. The later reader became you₃. AI formulated the idea that an earlier epistemic state could remain encounterable. I recognized, with considerable excitement, that encounter had returned from a completely different direction. That recognition made the artifact look like a bridge rather than an endpoint. Once the artifact became a bridge between states of the learner, the higher-education problem appeared. Then Taylor and LaCroix’s question about the purpose of the university became newly relevant. Each answer changed the next question.

That genealogy is more than background to the chapter. It is part of the evidence for what the chapter is trying to describe. The earlier formulations survived. I encountered them again. Some became inadequate. The inadequacy changed the model. The changed model produced questions that the earlier state could not yet ask.

The argument about diachronic authorship was itself produced diachronically.

Sometimes learning occurs because the learner has changed

The most personally significant implication may lie slightly outside authorship. Earlier in this essay I had been trying to describe why ideas associated with encounter, Bildung and metanoia acquired a new immediacy for me through the artwork. I was careful not to describe that development as a movement from ignorance to understanding. I had encountered many of the ideas before. The experience did not erase an earlier understanding and replace it with the correct one. It added another dimension.

The three-position structure now gives me another way to describe that development. Sometimes learning happens because new information arrives. But sometimes the information was already there.

Sometimes learning does not happen because new information arrives. It happens because the learner has become newly capable of encountering information that was already there.

That is what makes the relation among you₁, you₂ and you₃ more interesting than a simple accumulation of knowledge. The later state may encounter an old text, an old conversation, an old artwork or an old concept differently because intervening experiences have altered what can become meaningful. The object may be unchanged. The conditions of encounter are not.

This is also why the role of Prof.ssa Dohna in the larger inquiry cannot be reduced to supplying concepts that I later applied to an artwork. Her response became part of the trajectory through which a later version of me returned to questions I had already encountered and found them newly concrete. The artwork made parts of her theoretical vocabulary newly legible to me; that vocabulary then made dimensions of the artwork newly legible; the resulting writing preserved the transformation; and the preserved writing can now return again to a still later version of me. The relation is recursive without becoming closed.

AI occupies an important but limited place inside that recursion. It accelerates articulation, preserves conversational context, proposes connections, responds to objection and helps build artifacts capable of surviving the originating mental state. It does not determine which encounter matters. It cannot guarantee that the later self has learned anything. It cannot make an interpretation true because it is beautifully expressed. The system generates possibilities; the trajectory still requires judgment, evidence, other people and reality.

I therefore do not want to end by declaring you₁, you₂ and you₃ a finished theory of AI-age authorship. At present they are a way of seeing a problem that the simpler diagram human + AI → text hides. The human participant has a history. The artifact has persistence. AI can amplify a partially articulated state into an object that survives beyond it. A later state of the same person can then encounter that object from a position the earlier state did not yet occupy. Three is not the maximum number of states. It is simply enough to make the first return visible. you₁ initiates. you₂ encounters possibilities and takes responsibility for what is allowed to survive. you₃ receives an artifact carrying traces of both and tests whether anything has actually returned as understanding, criticism, transfer or changed practice. After that, the trajectory can continue.

The question I began with was essentially quantitative: how much of this text did AI write? That question still matters, especially where disclosure and attribution matter. But it is no longer large enough to contain what happened. The stronger question now seems to be this: if AI-assisted writing can preserve an earlier epistemic state, amplify it into an artifact and return it to a later version of the same person, what kind of authorship and learning occurs when the maker becomes the beholder of a work that still belongs to their own intellectual history?

Higher education sharpens the question further. “Did the student write this?” cannot simply disappear, particularly where specific forms of assistance are prohibited. Yet if education claims to cultivate knowledge, judgment, critical capacity or Bildung, another question may now need to stand beside it:

What happened when the work returned to the student?

I do not yet know how far that question can be operationalized, how much temporal distance you₃ requires, whether every discipline would recognize the same evidence of transformation, or whether this form of recursive self-encounter is especially suited to people who already enjoy examining the history of their own thought. Those uncertainties matter. They prevent a personal experience from becoming a universal pedagogy by declaration. What this case has shown me is narrower and, for now, sufficient: a generated artifact need not be the end of a cognitive process. It can become the object through which an earlier state of thought remains available for encounter. When that encounter changes what the later person can see, reject, ask or create, the work has returned—and the author who receives it is the same person, but no longer quite the same epistemic state from which it began.

A faster loop is not yet a formative one

What, then, did AI distinctively contribute? Not runtime generativity by itself. The browser could have selected and arranged images through conventional programming. AI mattered more strongly in the loops around that system. Four mechanisms were especially important.

My earliest answer was simply “speed.” Work that might once have required long interruptions between an intuition, a search, a draft and an experiment could continue within one period of concentrated attention. That was observable, but “speed” made the contribution sound like faster clerical production. What mattered was not only that each step took less time. The result of one step remained psychologically and conceptually present when it became the input to the next. This was why I later preferred “collapsed latency.”

First was collapsed latency. The delay between intuition, articulation, implementation, output, criticism and revision became dramatically shorter. I could see whether a verbal idea survived contact with form while the question that produced it was still alive. Second, AI offered a cross-disciplinary translation surface. The inquiry could move among computer science, mathematics, generative art, aesthetics, Guardini, epistemology, pedagogy and theology without stopping completely each time the vocabulary changed. Translation did not confer expertise, but it kept the question mobile long enough for unexpected relations to become examinable.

The last qualification arose from another correction. At first, the ease of moving among disciplines felt like a new kind of command over them. AI could explain a term, compare traditions and supply a plausible bridge almost immediately. Then source checking and rereading exposed how quickly translation could simulate mastery. A cross-disciplinary surface can keep a question moving; it cannot replace the historical and methodological resistance of each field. The connection to theology became serious only when Prof.ssa Dohna’s actual work, not a generic summary of Guardini, changed the interpretation.

Third was responsiveness to a changing learner. A book can transform its reader, but its next paragraph does not rewrite itself because the reader has just raised a new objection. An AI interlocutor can receive a changed formulation and answer the new state of the inquiry. Fourth was an externalized thought surface. A vague intuition could become language immediately, where I could inspect, resist, preserve or reformulate it instead of relying on working memory.

Together these mechanisms produced what I began to call a high-frequency hermeneutic loop: a sharply reduced distance between intuition, externalization, interpretation, criticism and renewed formulation. The phrase sounds efficient, but efficiency is not its main interest. A shorter distance can preserve the heat of an emerging question. It can also accelerate hallucination, self-confirmation and intellectual theatre. A loop becomes faster before it becomes better. The phrase itself came after “fast feedback” proved insufficient. Feedback described successive correction, but not the reinterpretation of the question by a changed participant. “Hermeneutic” marked that the loop concerned meaning as well as performance. “High-frequency” marked the shortened interval. I accepted the term only provisionally because it carried its own danger: frequency can be measured, whereas depth cannot. A rapid interpretive loop may produce many revisions without producing one honest encounter.

This distinction matters pedagogically. The central question is not whether AI can provide answers. It plainly can provide many forms of answer, some reliable and some not. The harder question is: what kind of human participation converts an AI-rich environment from an answer machine into a recursive environment of formation? The history of this artwork suggests that the necessary participation includes questioning, resistance, comparison, implementation, surprise, rejection, reinterpretation, movement across disciplines and the willingness to change the original question.

Chi and Wylie’s ICAP framework distinguishes passive reception from active, constructive and interactive engagement, with deeper learning generally associated with forms of participation that generate and negotiate beyond the presented material (Chi and Wylie, 2014). Human–AI co-creativity research likewise treats interaction design—not only output quality—as central to what collaborators can do together (Rezwana and Maher, 2023). These frameworks help explain why “AI produced a sophisticated answer” is not evidence that the person learned. The pedagogically relevant evidence lies in what the person can subsequently notice, infer, contest and make.

One event in my own conversation became unexpectedly important. On more than one occasion, while reading a long AI analysis, I stopped and wrote down an implication before reaching the later paragraph in which the AI developed almost the same implication. In one instance, I had begun thinking in terms of metanoia before arriving at the section that named the experience as metanoia. The chronology matters: I did not merely read the term and then report agreement.

The convergence felt uncanny for a moment. One possible story was that the AI had somehow anticipated my private thought; another was that I had unconsciously absorbed a cue and mistaken recognition for invention. Neither story could be established from the feeling. I therefore treated the order of events as limited evidence rather than a mystery: I had written the implication before seeing the later paragraph, but after a long sequence in which the relevant conceptual relations had already been developed. I do not interpret this as mind-reading, mystical synchronization or proof that the AI and I had become one intelligence. A more plausible explanation is pedagogically more interesting. The conceptual structure had begun to become generative inside my own thinking. Earlier parts of the dialogue, together with my prior experience and Prof.ssa Dohna’s vocabulary, had supplied relations from which I could independently infer a next move. When the later AI passage converged with that move, the convergence offered limited evidence of internalization. I was no longer only recognizing an explanation after it appeared; I had begun using its structure to think forward.

The process was therefore not adequately represented as AI thinks → human agrees. Nor was it a pure human monologue decorated by machine prose. It was closer to AI contribution → human internal development → human inference → further AI development → convergence or disagreement. Sometimes the convergence was exciting. Sometimes disagreement was more valuable, because it forced a distinction the smooth answer had concealed.

This leads to formative recursion. In ordinary computational recursion, a procedure operates again on a new state. Here the changing state includes the participant. The person who enters a later iteration has been affected by the earlier one:

you₁ ≠ you₂ ≠ you₃

making → encounter → changed learner → different question
       → further making → further encounter

The claim is not that every conversation transforms the whole person. Many interactions are trivial, repetitive or numbing. The point is that an adequate model of some AI-assisted learning cannot track only how the document changes. It must ask whether the participant’s capacities of attention, inference, resistance and judgment change too. Can the person formulate a better question without assistance? Can an idea travel into another domain? Can it alter practice? Can it survive when the original conversation is closed?

Recent empirical work gives reason for caution. In a survey study of knowledge workers, higher confidence in generative AI was associated with less self-reported critical-thinking effort, while reported critical thinking often shifted toward verification, integration and stewardship of the task (Lee et al., 2025). Because the study concerns self-reports and associations, it does not establish a simple causal law that AI weakens thought. It does, however, sharpen the issue. Offloading some production can make room for judgment, or it can remove the very friction through which judgment develops. The outcome depends partly on how the human remains active. My independent anticipation of metanoia is not enough to prove deep formation. It is one piece of experiential evidence, alongside changed questions, rereading, implementation and a revised relation to Prof.ssa Dohna’s work. The educational claim must remain proportionate: the process sometimes enabled conceptual moves to become my own before I saw them completed for me. That is more than passive agreement, but it is not a diploma issued by a conversation.

Generated possibility made judgment scarce

The speed of the loop created a peculiar kind of intellectual pleasure. Connections arrived quickly and with unusual density. A browser background touched generative art; generative art opened questions of encounter; encounter returned me to Guardini and Prof.ssa Dohna; AI-assisted writing opened authorship and pedagogy; rereading opened memory, time and the archive. The experience possessed conceptual intensity. It felt as if the project had suddenly become a junction through which many disciplines could see one another.

That intensity supplied real energy. Wonder can sustain attention where obligation cannot. Intellectual eros can make a person reread a difficult text, test a connection, follow a reference and remain with an unsettled question. It would be a mistake to treat excitement as an enemy of serious thought. But it would be a greater mistake to treat excitement as evidence that the interpretation is true.

Conceptual intensity is epistemic energy, not epistemic warrant.

Intensity is a reason to investigate, not a reason to believe.

AI can produce conceptual intensity at industrial speed. It can place an experience beside cybernetics, hermeneutics, theological anthropology and distributed cognition within minutes. Sometimes the juxtaposition reveals a real structural similarity. Sometimes it merely produces the sensation of depth. Rhetorical beauty can make a speculative phrase feel established. The most dangerous interpretation may not be an obvious falsehood, but a gorgeous connection that I want to be true because it makes the entire afternoon appear momentous.

My first safeguard was conventional verification. Check the quotation, open the source, distinguish a peer-reviewed study from a preprint, and do not let AI invent what Prof.ssa Dohna privately meant. Those practices remained indispensable. They did not answer the whole problem. Every citation in a paragraph could be accurate while the relation among them remained superficial. A true sentence about cybernetics could still be a poor explanation of an artistic encounter. Factual checking could expose hallucination; it could not by itself decide whether a generated possibility deserved to reorganize my practice.

The required movement is therefore passion → sustained attention → discernment. Passion keeps the question alive. Sustained attention exposes it to more than one mood and more than one source. Discernment asks which generated possibilities deserve continued life. AI literacy is part of this: I need to know about hallucination, verification, model limits, source quality and automation bias. Discernment is larger. It asks what a possibility is doing to my relation with reality and with other persons.

This was the concrete point at which theological language entered rather than being added decoratively. The question had moved from “Is this output factually correct?” to “What kind of attention and action does this interpretation produce?” That is a question about consequences, orientation and relation. The vocabulary of discernment became useful because it asks not only whether an idea can be stated coherently but how it moves through a life and what fruits appear over time.

Does the connection survive resistance? Does it remain within what the exchange actually supports, or encourage me to infer more about another person than the evidence permits? Does it make me more attentive to her work, or merely more impressed with my own story? Does it lead to more responsible making? Can I state what would count against it? Am I pursuing the idea because it bears fruit, or because AI has made it rhetorically intoxicating? Is the system enlarging my agency, or quietly replacing the acts by which agency is formed?

Theological traditions of discernment are relevant here only if their greater seriousness is preserved. In his catechesis on discernment, Pope Francis stresses attention to one’s history, the significance of time and the need to examine the end and fruits of a movement rather than isolating an impressive moment (Francis, 2022). Antiqua et nova similarly argues that AI may assist human activity but cannot inherit human moral responsibility or substitute for the wisdom that relates parts, wholes, decisions and consequences (Dicastery for the Doctrine of the Faith and Dicastery for Culture and Education, 2025). These sources do not give a ready-made religious test for good prompts. They resist the reduction of judgment to output evaluation. Prof.ssa Dohna’s own work makes the connection less superficial. Verso nuovi occhi brings artistic form into relation with spiritual discernment, suggesting that seeing is not simply the capture of an object but a disciplined way of allowing form and meaning to disclose themselves (Dohna Schlobitten, 2023). Returning to that framework altered my question. I no longer asked only whether the AI-generated interpretation was clever. I asked whether it trained a truer gaze—one more capable of receiving the other without reducing the other to evidence for my theory.

This is particularly important because the artwork’s central turning point belongs to a relationship. If Prof.ssa Dohna’s response became artistically consequential for me, that should also make me more careful in how I represent the encounter. A fruitful interpretation should preserve humility about what I do not know, gratitude for what her question made possible, and responsibility for the way I describe the exchange. The conceptual development matters, but it should never obscure the independence of the person whose response helped set it in motion.

AI generates abundance; the human must discern what deserves continued life.

When that sentence emerged in the dialogue, I first liked its clarity. Then I noticed a possible overcorrection. Discernment is not an exclusively solitary human act performed after AI finishes generating. My judgment is itself formed through artworks, other people, traditions, evidence and sometimes the AI dialogue. The sentence survived only after “the human” ceased to mean an isolated sovereign and came to mean the responsible person who remains answerable within those relations.

I call this, provisionally, the discernment of generated possibility. It concerns sentences and images, but also connections, research programmes, self-descriptions and future actions. When possibilities become cheap, judgment becomes scarce. The artistic task is no longer exhausted by producing another variation. It includes deciding which variation should become an encounter, which encounter should alter the practice, which interpretation should be archived and which seductive branch should end.

The decision will not always be solitary. Other people, traditions, evidence and the resistance of the artwork can all correct the maker. Prof.ssa Dohna’s question did precisely that: it prevented my first interpretation from closing too quickly. Discernment, in this sense, is not the artist guarding sovereign control against AI. It is the practice of remaining answerable within a field of generated possibilities and real relations.

Metanoia changed from a word into a problem of knowing

The word metanoia had been present in Prof.ssa Dohna’s work before it became present in my experience. I could translate it, discuss it and recognize it among related ideas of encounter, figuration and Bildung. Conceptual language can prepare perception. Metanoia had already been familiar to me as a concept through reading, conversation and translation. The artwork added another mode of encounter with it: I began relating the term to a change occurring within my own practice of seeing, making and interpreting. Then the order changed.

There was still a danger of naming too quickly. Once AI described the sequence as metanoia, the word gathered the whole experience into a compelling pattern. I had independently begun moving toward the same term, which made the convergence more significant to me, but convergence did not settle whether the term was proportionate. I objected that metanoia carries philosophical and theological weight that should not be awarded to every stimulating afternoon. The claim therefore changed from “this was metanoia” to the more careful statement that the experience gave the concept an experiential dimension it had not previously possessed for me in quite that form.

metanoia as an object of knowledge
  → metanoia as a concept that recognizes an experienced change
  → metanoia as an experience that changes my account of knowing

I call this, cautiously, a metanoia of epistemology. The phrase does not mean that every revised opinion is a spiritual conversion, that a few hours at a screen equal the theological depth of metanoia, or that AI administered transformation. It names a more precise reversal. At first I thought I lacked information about the concept. The event showed that I also lacked the mode of participation through which its significance could become intelligible. The deepest change was not “Now I know the definition.” It was: Now I understand why the definition by itself was insufficient.

“Metanoia of epistemology” was not my first description. I initially described the experience too simply as understanding metanoia better. That still treated the transformation as a new item of knowledge. The next question—what had changed in the act of understanding?—forced the stronger distinction. The experience had not only supplied an example of the concept; it had challenged the model in which concepts are fully understood by possessing correct definitions. That is why the epistemology itself entered the phrase.

This is where Bildung became more than an elegant word for education. If learning only adds propositions to an unchanged subject, then the person who knows more remains essentially the same knower. Formative recursion suggests something else: the experience can alter the dispositions with which the person attends, asks and judges. The learner entering the next iteration is not identical to the learner who entered the last. What changes is not only the answer available to consciousness, but the kind of question consciousness can form.

Prof.ssa Dohna’s work had approached art as a way of seeing in which form, knowledge and relation belong together. I had encountered that claim before through reading, conversation and translation. The generative artwork supplied an experience in which form changed relation, relation changed interpretation and interpretation returned to form. Theory became intelligible from within practice; practice became intelligible through theory. The absurdity is not lost on me: I needed a WordPress background engine to rediscover Guardini?!! But perhaps the comedy protects an important fact. Formation does not always arrive through the door labelled “formation.” The experience also changed something in my habitual way of approaching theoretical ideas. I often understand a concept most readily when I can see what question it helps me ask, what distinction it clarifies or what situation it allows me to perceive differently. There is nothing wrong with that practical orientation, but this experience exposed one of its limits. What proved especially important in this case was that her framework did not simply answer a problem I had already defined. Instead, it helped make visible a dimension of the situation that I had not previously known how to formulate: that an encounter could alter the observer and, through that transformation, alter what the work itself could subsequently become.

The programming analogy remains helpful. Concurrency is not “useful” merely because it fixes one known defect. Once a programmer understands race conditions, events that previously looked random become perceptible as a class of problem. Aesthetic and moral concepts can work similarly. They do not alter the event retroactively; they alter what the observer can recognize in it. This is why a powerful theory can render new questions askable. It is also why failure to see a theory’s significance is not decisive evidence that significance is absent.

The qualification is essential. Delayed recognition does not vindicate every difficult text. Some theories remain confused after patient attention; some concepts illuminate one case and distort another. Epistemic humility must move in both directions. My strong perception that the present connection is meaningful does not prove its significance. My earlier failure to perceive significance did not prove its absence. The observer changes, but change alone does not guarantee that the later observer sees more truly.

This problem connects with an earlier essay I wrote on Éric Rohmer’s A Tale of Winter. There I distinguished subjective certainty from external truth: a narrative’s eventual vindication of a character does not retroactively supply epistemic justification for everything the character believed. The present case adds a complementary difficulty. Intense recognition is not proof. Yet absence of recognition is not disproof, because an encounter can alter the horizon within which something becomes intelligible. The lesson is neither “trust your conviction” nor “distrust every experience.” It is to examine how conviction arose, what resists it and what it allows us to see. My anticipation of the later metanoia paragraph mattered within this structure. It did not certify the concept. It showed that a relation first encountered through another person’s work and an AI dialogue had begun to produce inferences in me. That is evidence of learning as formation, not evidence that the interpretation is beyond criticism. The distinction lets intellectual excitement remain alive without appointing it judge.

After reproduction, the singularity of the encounter

Walter Benjamin’s essay on the work of art in the age of technological reproducibility remains unavoidable here, but it should not be used as a prestigious backdrop. Its central historical problem concerns what happens to art when technical media detach works from inherited situations of singular presence and make circulation, repetition and new modes of reception structurally important (Benjamin, 1936/2008). My browser artwork belongs to a later condition in which reproduction is no longer the only useful model.

My first comparison was too clean. Mechanical reproduction seemed to belong to an old regime of copying, while generativity seemed to belong to a new regime of difference. That opposition was attractive because it gave the project a clear historical position. It was also misleading. Benjamin’s reproductions do not enter identical situations of reception, and a generative system does not guarantee meaningful difference. A repeated output can occur; a technically different output can be aesthetically trivial. The contrast had to remain schematic rather than become a claim that generativity simply supersedes reproduction.

Mechanical reproduction can be schematized, roughly, as one work → many substantially similar reproductions. A generative system offers another structure: one system → potentially many non-identical manifestations. The difference is not absolute. Reproductions are encountered differently, and algorithmic outputs may repeat. But the system is designed to produce variation rather than merely to distribute copies of a stable visual object.

AI does not own this difference. A traditional programmer, an instruction-based artist or a composer working with chance can create runtime generativity without machine learning. Three levels need separation. Runtime generativity occurs when the rules produce varying manifestations. Developmental generativity occurs when encounters with outputs change the specification and future system. Epistemic or formative generativity occurs when the surrounding dialogue accelerates reflection, translation and learning so that the participants themselves enter later cycles differently. AI’s strongest contribution in this case belongs to the third level, with an important role in the second. This three-part distinction arose because my first account credited AI with too much. If varying manifestations were the decisive innovation, then AI was incidental: conventional code could already produce them. I then moved AI’s contribution to development, but reflective revision also predates AI. Only at the third level did the distinctive acceleration in this case become visible—the dense loop of articulation, cross-disciplinary translation, objection, preservation and response to a changing learner. Even there, “distinctive” does not mean exclusive or automatically beneficial.

This makes Benjamin’s question newly strange. What happens when technical reproducibility does not merely multiply a work but helps produce potentially unrepeatable encounter-events? A visitor can refresh the page and receive another composition. Yet visual difference alone does not create singularity. The more consequential uniqueness may lie in the convergence of one manifestation, one moment, a visitor’s prior memory, the text in the foreground and whatever response returns. The pixels may be reproducible while the full event is not.

It is tempting to say that aura migrates from object to event—to propose a “procedural aura” or “event-based aura.” I keep those phrases exploratory. Benjamin’s aura belongs to a specific account of distance, tradition, cult value, exhibition and technological modernity; it should not be repaired casually by attaching it to novelty. A personalized or statistically rare output is not automatically auratic. Platforms manufacture apparent uniqueness continuously.

The migration of aura was one of the most rhetorically satisfying AI-assisted suggestions, and for that reason it required particular resistance. The phrase seemed to solve the Benjamin problem in one movement: aura lost by the reproducible object would reappear in the singular encounter. But that was too restorative and too convenient. I retained the question while withdrawing the solution. What the case supports is a shift of attention toward the encounter-event, not proof that Benjaminian aura has returned under a procedural name.

Still, the case suggests a real shift in artistic attention. The singularity I care about is not scarcity in the marketplace. It is the irreducibility of an encounter in which the work and beholder acquire a history together. The words of an exchange can be quoted, but the encounter cannot be reproduced for a second first time. Its significance depended partly on the history that preceded it. A later visitor can encounter the resulting article, but cannot occupy precisely that history. Her following question changed my relation to the work because of years that preceded it. A later visitor can encounter the resulting article, but not occupy precisely that history. Technical generativity multiplies occasions; human temporality makes each consequential occasion more than an interchangeable output. The creator-beholder relation intensifies the point. I can reopen the same published article, but I cannot return as exactly the reader who published it. The archived object may remain stable while its originator changes. Conversely, the generative visual system may change while a returning visitor brings memory of earlier manifestations. The artwork’s temporality runs in both directions: variation in the object meets variation in the beholder.

Benjamin therefore helps me resist two simplifications. The first treats the generated image as a conventional unique masterpiece simply because a particular arrangement may never recur. The second treats digital reproducibility as the disappearance of every form of singular presence. The more interesting artistic question lies between them: whether a technically repeatable system can compose situations in which an encounter becomes singular because it changes what can happen next.

The work remains open because the maker has changed

It would be possible now to collect the phrases produced by this history—formative generative practice, high-frequency hermeneutic loop, recursive conservation of insight, diachronic relational authorship, discernment of generated possibility, metanoia of epistemology—and present them as a finished theory. That would repeat the retrospective error with which this essay began. Each phrase arose because an earlier description failed. None has yet earned independence from the case that made it necessary.

The browser composition was too small a unit, so I followed causal returns through manifestation, encounter and renewed making. “Memory aid” was too weak for an archived thought that later changed its originator, so I proposed recursive conservation. “AI-assisted writing” concealed the separation of lived experience, verbal production, selection and later understanding, so authorship became relational and diachronic. “Fast feedback” did not capture a changed interpreter, so the loop became hermeneutic and potentially formative. “AI literacy” did not contain the ethical and relational question of which possibilities should continue, so discernment entered. “Knowing the term” did not explain why experience changed what knowing meant, so metanoia reached the epistemology itself.

The human–AI division of contribution is also clearer when reconstructed through these changes rather than estimated from the final prose. AI gave me formulations that opened genuine paths: “Where exactly is sense?”, “The person can become implicated,” and the idea that the creator may return as a beholder. It helped connect the artwork with feedback, distributed cognition, authorship, metanoia and Benjamin. My contribution included recognizing those openings, but also refusing their first attractive forms. “Co-creator” gave the visitor too much control. “Everything around the artwork” erased the boundary I needed to explain. Feedback alone could not distinguish a new parameter from a new criterion. Human direction did not automatically settle authorship. Fact-checking did not settle discernment. Metanoia could not be declared merely because the analogy felt powerful. Aura could not simply migrate because the sentence was elegant.

Each objection returned new evidence or a new constraint to the dialogue. AI then reasoned within a changed problem, and I encountered the revised formulation from a changed position. This does not reveal one hidden point at which either the human or the machine authored the whole trajectory. It reveals a sequence in which generation and judgment repeatedly changed what the next contribution could be. The history of those corrections is therefore part of the authorship evidence.

These proposals resemble and depend upon existing traditions: generative and process art, relational aesthetics, reflective practice, second-order cybernetics, distributed cognition, hermeneutics, enactivist approaches, human–AI co-creativity, metacognition, formation and theological discernment. I do not claim to have invented their questions. The personal originality, if there is any, lies in the particular convergence and in the documentary history through which the concepts became newly necessary to me. A case can matter without founding a field.

I still do not know which of the provisional terms will survive outside this case. Further scholarship may reveal that an existing vocabulary is more precise. Later rereading may expose a distinction I have again made too quickly. Prof.ssa Dohna may reject the way I have connected her work to the artwork, or may see a relation I have missed. Those possibilities are not defects to be hidden before publication. They define what remains open, while the references and documented chronology define what can presently be defended.

The emerging constellation might be called a meta-epistemology of AI-assisted formation, but even that name should remain a question rather than a banner. What becomes of human formation when AI does not merely transmit information or generate outputs, but enters recursive loops through which people externalize thought, encounter their own productions, reinterpret other persons, preserve fragile insights, revise their standards of judgment and become different participants in the next iteration? Under what conditions can making, encounter, reflection and human relationship alter not only what a learner knows, but the kind of learner who continues knowing?

The conditions matter more than the label. There must be a real world against which generated language can fail; other persons who remain other rather than becoming material for self-confirmation; an archive that preserves revision rather than erasing it; time for intensity to become attention; practices of verification and correction; and human responsibility that cannot be outsourced. There must also be making. Without a work that resisted my first intentions, the whole reflection might have remained an elegant conversation detached from consequence.

I can now distinguish the evidential layers of the story. The running visual system, the published texts and the preserved exchange are documentary parts of the record. My surprise, discomfort and changed understanding are first-person reports. The claim that her question transformed the artistic practice is an interpretation supported by subsequent rereading and revised intention. The conceptual terms developed with AI are analytical proposals. The connections to Guardini, Benjamin, cybernetics, distributed cognition and discernment are scholarly comparisons that can be tested. The possibility of a broader theory of AI-assisted formation remains speculation.

Keeping these layers visible does not drain the artwork of mystery. It allows mystery without fabrication. I do not claim to know everything Prof.ssa Dohna intended by her question; what I can describe with confidence is how I encountered it and what changed in my own thinking afterward. I do not know whether every future technical change will embody the theory developed here. I do know that I can no longer design the work as if visitors, responses and later selves were external aftereffects. I do not know whether AI enlarged my authorship or revealed that authorship had always been more distributed than I admitted. I do know that the responsibility for deciding what bears my name remains mine.

The performative circle is now visible, though it has no final endpoint:

knowing → creating → encounter
  → transformed knowing → transformed creating
  → new encounter

Prof.ssa Dohna developed a project concerned with knowing, creating, encounter, figuration, Bildung and metanoia. I had been familiar with many of these questions for years, but this experience gave me a new point of entry into them through my own artistic practice. I created an AI-assisted computational artwork for another reason. The artwork produced unforeseen relations. She encountered it. Her response unsettled my existing interpretation and made a connection visible that I had not anticipated. Questions running through her work began to illuminate dimensions of the artwork differently, while the artwork gave those questions a new concreteness for me. AI helped me articulate what was changing, and gradually I found myself not only following the emerging conceptual movement but also questioning, resisting and extending it in directions I had not foreseen. This essay may now return to her, and her response—if she chooses to give one—may change the process again.

That possibility does not make her responsible for completing the artwork or confirming my interpretation. The openness of encounter includes the freedom of the other person not to play the role the artist has imagined. A future response may deepen the argument, correct it, refuse it or move the relation somewhere I cannot foresee. The work remains open because encounter is not a programmable output.

I began by wanting the website to look cooler. I still care whether it looks good. The art did not become serious by escaping its visual body, and the philosophy does not excuse a bad composition. What changed is the horizon within which I create. A refresh can still produce a mosaic. It can also place a visitor before an arrangement I did not individually compose, preserve that meeting within memory, provoke a response, return the response to the maker and alter what the next act of creation can mean.

The generator has not become a philosopher, and I have not become the sole master of everything it helped me say. Something more modest and more demanding has happened. Creation became capable of arranging the conditions for an encounter; encounter changed the person who returned to create; and Prof.ssa Dohna’s question helped make it impossible for me to continue treating the generated image as the whole work. The next manifestation will appear on a screen. The next artwork may begin in what that appearance changes between people.

References

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Eine generative Kunst schaffen, die ihren Schöpfer verändern kann: Von der Generierung zur Begegnung und wieder zurück

Drei Tage nachdem ich einen Artikel darüber veröffentlicht hatte, wie sich der Aktualisieren-Button meiner Website in eine künstlerische Geste der Komposition verwandeln lässt, lud mich Prof. Yvonne Dohna-Schlobitten unerwartet dazu ein, an ihrem sich entwickelnden internationalen Projekt als Künstler teilzunehmen – und beinahe im selben Atemzug auch in wissenschaftlicher Funktion. Dieses konkrete Werk hatte sie zu diesem Zeitpunkt noch nicht gesehen. Ich schickte es ihr, erklärte, dass jede Aktualisierung oder Navigation eine neue Komposition hervorbringen könne, und bat sie, es selbst auszuprobieren. Ihre erste Reaktion lautete: „Unglaublich.“ Fünfundzwanzig Sekunden später schrieb sie: „Allora il mio progetto non ha tanto senso“, unmittelbar darauf fast schon ergänzt durch ein Fragezeichen.

Ich antwortete schnell, weil ich beinahe das Gegenteil dachte. Doch die Antwort, die ich ihr schickte, war erst der Anfang. Ihre Frage führte nicht unmittelbar zu einer einzigen klaren Schlussfolgerung. Sie setzte vielmehr eine Kette erneuter Befragungen in Gang, in der jede zunächst brauchbar erscheinende Unterscheidung ein weiteres Problem freilegte. Aus generativer Komposition wurde eine Frage der Begegnung. Aus Begegnung wurde die Frage, wo Sinn geschieht. Diese Frage führte weiter zu Handlungsmacht, semantischer Kompetenz, gelebter Bedeutsamkeit und Metanoia. Agentische KI erschütterte anschließend meine erste Beschreibung menschlicher Handlungsmacht. Schließlich bog sich die Untersuchung auf sich selbst zurück: Die Diskussion, das Schreiben und sogar die Überarbeitungen dieses Artikels begannen zu verändern, was ich als Nächstes überhaupt bauen wollte.

Ich möchte diese Entwicklung bewahren, weil ich die endgültige Theorie zu Beginn nicht bereits besaß. Tatsächlich besaß ich sie nicht einmal nach dem ersten vollständigen Entwurf dieses Artikels. Ein früher Entwurf bewahrte sehr viele technische Belege, ließ aber die Ingenieursperspektive die Geschichte dominieren. Ein späterer Entwurf korrigierte diesen Schwerpunkt und rückte künstlerisches Schaffen, Begegnung und Metanoia ins Zentrum, glättete dabei jedoch zu viel von jenem intellektuellen Ringen weg, durch das diese Gedanken überhaupt erst möglich geworden waren. Eine weitere Überarbeitung stellte diese Genealogie wieder her und verlieh der Argumentation eine stärkere Architektur. Doch der Vergleich mit der früheren Darstellung machte einen weiteren Verlust sichtbar: Mehrere konkrete Einzelheiten, durch die die Transformationen überhaupt erst wahrnehmbar geworden waren, waren verdichtet worden. Die vorliegende Fassung kehrt deshalb zu diesen Details zurück, ohne die stärkere Struktur aufzugeben. Bei dem fehlenden Material handelte es sich niemals bloß um „mehr Information“. Es war die Geschichte des Denkens selbst: die ersten Erklärungen, die zunächst überzeugend erschienen, meine wiederholten „Ja, aber …“, die Evidenz, die das Modell veränderte, die Formulierungen der KI, denen ich nur teilweise zustimmte, und jene Momente, in denen die ursprüngliche Frage zu eng geworden war.

Das Ergebnis ist daher nicht lediglich eine Theorie über ein generatives Kunstwerk. Es ist zugleich ein Protokoll darüber, wie ein Kunstwerk, sein Künstler-Entwickler, eine KI als Kollaborationspartner, eine andere Wissenschaftlerin und ein philosophisches Vokabular einander verändert haben. Jeder dieser Beteiligten wurde zu einem Input dafür, was die anderen als Nächstes erschließen konnten.

Diese Überarbeitung fordert den Leser daher dazu auf, nicht nur zu verfolgen, was ich heute denke, sondern auch, wie sich dieses Denken entwickelt hat. Die Frage, die die verschiedenen Ebenen zusammenhält, bleibt vorläufig: Wo geschieht Bedeutung, wenn ein Mensch ein System konzipiert, KI beim Aufbau dieses Systems hilft, Algorithmen seine Formen erzeugen, ein Browser sie zur Aufführung bringt, ein anderer Mensch ihnen begegnet und diese Begegnung wiederum den Menschen, die Theorie und schließlich sogar das System verändert, aus dem die nächste Form hervorgehen wird?

Ich begann nicht mit der Begegnung

Yin’s Background Studio entstand, weil ich bessere Hintergründe für meine WordPress-Website wollte. Die früheste Version wählte aus einer kleinen Gruppe von Bild- und Videoeinträgen aus. Später kamen strukturierte Einstellungen, gleichgewichtete und gewichtete Zufallsauswahl, Persistenz auf Seiten-, Sitzungs- und Tagesebene, Verhalten bei reduzierter Bewegung, Mosaike, verstreute Wiederholungen, Steuerungen für Dichte und Zwischenräume und schließlich eine modulare Generative Engine hinzu. Die einzelnen Entwicklungsstufen habe ich separat in Engineering Dynamic Image/Video Backgrounds in a WordPress Theme, Incremental Development of a WordPress GIF-WebP Mosaic Background Engine, Rethinking Yin’s Background Studio with Agentic AI, Deploying a Modular Generative Geometry Generator (Browser-Based) und Turning a Theme-Bound Generative Art System into a Maintainable WordPress Plugin dokumentiert.

Das gegenwärtige visuelle Vokabular umfasst dreizehn konfigurierte Einträge aus unterschiedlichen Bereichen meines intellektuellen und persönlichen Lebens: Fotografie, animierte Tiere, transformierte Internetbilder, religiöse Bilder, mathematische Strukturen, Philosophie, Humor und eine chemische Gleichung, die mit meiner Kindheit verbunden ist. Zwölf mathematische Generatoren – darunter Binary-Space-Partition, Quadtree, Hilbert-Ordnung, Unterteilung nach der Goldenen Spirale, Voronoi- und Delaunay-Geometrie, Lloyd-relaxierte Voronoi-Strukturen, Phyllotaxis, radiale Fächer, Squarified Treemaps, diagonale Truchet-Tessellation und Ulam-Spiralordnung – reorganisieren dieses Vokabular fortwährend.

Die entscheidende technische Tatsache ist einfach. Diese Algorithmen synthetisieren keine neuen Ausgangsbilder. Das Archiv stellt etwas bereit, das einem Vokabular ähnelt; die Generatoren liefern wechselnde Grammatiken räumlicher Beziehungen. Das Projekt gehört damit zur breiten Tradition generativer Kunst, in der ein Künstler einen regelgeleiteten Möglichkeitsraum konstruiert und einem externen System ein gewisses Maß an operationeller Autonomie gewährt (Boden & Edmonds, 2009) (Galanter, 2016). Mein Fall fügt eine weitere Ebene hinzu: KI half mir, einen großen Teil der Infrastruktur aufzubauen, während die späteren Seitenkompositionen durch überprüfbare, browserseitige Verfahren erzeugt werden, statt bei jedem Besuch ein generatives KI-Modell um die Erzeugung eines neuen Bildes zu bitten.

Eine vereinfachte Laufzeitsequenz genügt, um den Mechanismus zu zeigen:

var selected = chooseWithoutReplacement(
    availableRecords,
    selectedCount,
    selectionRandom
);

var regions = generators[
    currentAlgorithm
].generate(context);

var palette = shuffle(
    selected,
    paletteRandom
);

regions.forEach(function (region, index) {
    var record = palette[index % palette.length];
    renderRegion(region, record);
});

Der Code wählt Einträge aus, berechnet Regionen, mischt ihre Zuordnung und rendert sie. Unterhalb von renderRegion() ist keine geheime Phänomenologie-Funktion verborgen. Und doch können die sichtbaren Folgen interpretativ außerordentlich reich werden. Bertrand Russell kann neben einem ausgelassenen Seelöwen erscheinen. Jesus, der einen Basketball auf dem Finger dreht, kann neben einem Otter stehen, der einen Ball trägt. Eine Hilbert-Animation kann innerhalb der Ordnungslogik einer Ulam-Spirale auftauchen. Ein Fuchs kann neben meiner autobiografischen chemischen Gleichung das visuelle Feld einnehmen. Eine Animation des Letzten Abendmahls kann gleichzeitig mit komischen Tieren und einem ernsthaften technischen Artikel erscheinen.

Manchmal wirkt das Ergebnis, als hätte das System eine Anweisung befolgt, die ich niemals geschrieben habe: „Setze einen Philosophen des zwanzigsten Jahrhunderts neben ein aufgeregtes Meeressäugetier, sodass die analytische Philosophie aussieht, als würde sie öffentlich ausgebuht.“ Der Witz stammt von mir, nicht vom Algorithmus. Aber auch die Gegenüberstellung, die diesen Witz überhaupt erst hervorrief, wurde nicht von mir manuell komponiert. Der Algorithmus hatte Geometrie und Auswahl ausgeführt; der Betrachter hatte begonnen, Beziehungen hervorzubringen.

Diese Beziehungen können komisch, zärtlich, absurd, theologisch, autobiografisch – oder schlicht uninteressant sein. Gerade die letzte Möglichkeit ist wichtig. Prozedurale Neuartigkeit garantiert keine künstlerische Bedeutsamkeit. Das System kann eine Beziehung verfügbar machen; erst eine Begegnung kann zeigen, ob durch sie überhaupt etwas geschieht.

Zunächst beschrieb ich den Algorithmus als eine Art Editor. Er kontrolliert Fläche, Nachbarschaft, Maßstab, Wiederkehr, Rhythmus und Unterbrechung, ohne über jene kulturelle Interpretation zu verfügen, die später entstehen kann. Die Voronoi-Geometrie weiß nicht, dass Russell ein Philosoph ist. Eine Hilbert-Durchquerung erkennt keine christliche Ikonografie. Dennoch verändern ihre räumlichen Entscheidungen, welche Beziehungen einem Betrachter überhaupt zugänglich werden. Diese Kluft zwischen kausaler Einfachheit und interpretativer Fülle war der erste Hinweis darauf, dass das Projekt mehr tat, als lediglich eine Webseite zu dekorieren – auch wenn ich damals noch nicht wusste, wie weit dieser Hinweis führen würde.

Das Werk lehrte mich, was ich eigentlich zu schaffen versucht hatte

Es wäre historisch falsch zu behaupten, ich hätte zunächst das vollständige Kunstwerk imaginiert und anschließend KI eingesetzt, um eine bereits stabile Spezifikation umzusetzen. Die Entwicklung vollzog sich vielmehr durch partielle Verständnisse. Zunächst wollte ich einen flexibleren Hintergrundselektor. Dann ließen mich reale Ergebnisse Mosaike wünschen. Die Mosaike machten den Unterschied zwischen bloßer Flächenfüllung und der Komposition von Beziehungen sichtbar. Verstreute Wiederholungen warfen Fragen nach Dichte, Maßstab und leeren Regionen auf. Mathematische Generatoren verwandelten das System anschließend von einer Sammlung von Darstellungsmodi in eine Familie visueller Grammatiken. Schließlich wurde die wachsende Funktionalität so anwendungsähnlich, dass sie nicht länger dem Theme gehören konnte und zu einem websitespezifischen Plugin werden musste.

Das wiederkehrende Entwicklungsmuster sah eher so aus:

unvollständige Intuition
        ↓
Interpretation durch KI
        ↓
mögliche Implementierung
        ↓
reales Ergebnis
        ↓
meine Begegnung mit dem Ergebnis
        ↓
Kritik / Überraschung / Wiedererkennen
        ↓
verändertes Verständnis
        ↓
revidierte Intention
        ↓
eine weitere KI-gestützte Implementierung
        ↺

Die Worte „reales Ergebnis“ sind entscheidend. KI konnte plausibles PHP, JavaScript, CSS, Shell-Prozeduren, Validatoren und geometrische Abstraktionen erzeugen, doch das tatsächlich laufende System lieferte Evidenz, die weder sprachliche Flüssigkeit noch Intention ersetzen konnten. Ein Kandidat konnte syntaktisch korrekt geparst werden und sich dennoch künstlerisch falsch anfühlen. Ein Layout konnte seine geometrischen Anforderungen erfüllen und trotzdem eine unbeholfene visuelle Leerstelle hinterlassen. Ein vorgeschlagener Fix konnte Quelltext voraussetzen, der längst nicht mehr existierte. Der nächste Schritt musste sich an der Evidenz orientieren, die von der tatsächlichen Datei, dem Browser, dem Zustand der Optionen oder dem Laufzeitverhalten erzeugt wurde.

Mehrere Iterationen machten diese Arbeitsteilung sichtbar. Ich verwarf Implementierungen, die technisch korrekt waren, weil ihr visuelles Verhalten falsch war. Zu anderen Zeitpunkten offenbarte ein unerwartetes Ergebnis eine Richtung, die besser war als jene, die ich ursprünglich verlangt hatte. Manchmal lehnte ich eine von der KI vorgeschlagene Architektur ab, weil sie eine Einschränkung verletzte, die das Modell noch nicht erkennen konnte; zu anderen Zeiten akzeptierte ich einen KI-Vorschlag entgegen meiner ursprünglichen Präferenz, weil die laufende Evidenz zeigte, dass mein erster Entwurf schlechter war. Die Zusammenarbeit bewahrte keine souveräne Intention, um deren Ausführung lediglich zu automatisieren. Die Begegnung mit dem Artefakt transformierte die Intention selbst.

Manche Fehler veränderten mehr als nur eine Codezeile. Ein früher Installer lehnte einen plausiblen Kandidaten für Video-Unterstützung mit ERROR: video preview calls are incomplete ab. Meine erste Versuchung hätte darin bestehen können, daraus zu schließen, die Video-Unterstützung selbst sei defekt. Die Evidenz rechtfertigte diese Schlussfolgerung jedoch nicht: Der Validator hatte die Bereitstellung gestoppt, weil er eine bestimmte JavaScript-Struktur erwartete. Wir trennten deshalb mehrere Behauptungen voneinander, die die erste Fehlermeldung ineinander hatte zusammenfallen lassen – Frontend-Rendering, Vorschau im Administrationsbereich, Erhaltung bestehender Bildhandler, syntaktische Gültigkeit und strukturelle Erwartungen. Ein Validator ist nützliche Evidenz, aber Großbuchstaben machen ihn noch nicht zu einem Orakel.

In einer anderen Phase konnte eine Rekursion des MutationObserver die Administrationsseite einfrieren, obwohl PHP, WordPress und die gespeicherten Einstellungen weiterhin intakt waren. Dieser Fehler zwang zu einem präziseren Modell. „Die Website funktioniert“ war keine unteilbare Tatsache. Backend-Bootstrap, Syntax, gespeicherte Daten, administrative Interaktion, öffentliches Rendering und Browserverhalten waren unterschiedliche Behauptungen, die unterschiedliche Tests verlangten. Später, als das System aus dem Theme in ein Plugin migriert wurde, wurde der Übergang in klar begrenzte Zustände aufgeteilt: das validierte Plugin zunächst inaktiv installieren, das alte Theme eindeutig auf einen Fallback umstellen, anschließend das Plugin aktivieren und verifizieren und erst danach die Legacy-Implementierung entfernen. Schließlich verschwanden dreiunddreißig alte Dateien aus dem Theme, während das gespeicherte visuelle Vokabular vollständig erhalten blieb.

Diese Episoden gehören in diesen Artikel, weil sie zeigen, wie Evidenz Verständnis verändert hat. Sie erklären zugleich, warum meine Rolle weder auf „Ich habe den Code geschrieben“ noch auf „Die KI hat den Code geschrieben“ reduziert werden kann. Die KI erzeugte erhebliche Teile der Implementierung und der Argumentation. Deterministische Werkzeuge stellten fest, ob Syntax, Struktur und gespeicherter Zustand bestimmte Behauptungen erfüllten. Der Browser lieferte visuelle Evidenz. Ich entschied, welches Problem überhaupt wichtig war, welche Einschränkung nicht geopfert werden durfte, ob ein technisch gültiges Ergebnis künstlerisch akzeptabel war und welche Frage als Nächstes gestellt werden sollte. Die Spezifikation entstand, indem ich reale Ergebnisse sah und meine eigene Sprache revidierte.

Erst später erkannte ich, wie eng dieses praktische Muster jener Struktur ähnelte, die Prof. Dohna durch künstlerische Gestalt, Dienen und Metanoia beschrieb. Das Geschaffene unterrichtete den Schöpfer darüber, was er eigentlich zu schaffen versuchte. Das bedeutet nicht, dass die Software einen mystischen Willen besaß. Es bedeutet, dass das Werk Möglichkeiten und Fehler offenbarte, die für mich vor ihrer Begegnung nicht als explizites Wissen existiert hatten.

Die Aktualisierung brachte den Besucher in das Werk hinein

Die nächste begriffliche Veränderung ging von einer der gewöhnlichsten Handlungen im Web aus. Normalerweise fordert eine Aktualisierung dieselbe Ressource erneut an. Im Background Studio kann sie einen anderen künstlerischen Zustand instanziieren. Ein Besucher öffnet einen Artikel und erhält eine Komposition; eine Aktualisierung erzeugt eine andere; die Navigation zu einem anderen Artikel kann wiederum eine weitere hervorbringen. Das Werk existiert weniger wie ein einziges definitives Bild als vielmehr wie eine strukturierte Population potenzieller Manifestationen. Lev Manovichs Diskussion der Variabilität ist hier relevant, weil die Fähigkeit eines digitalen Objekts, in wechselnden Versionen zu existieren, zu einer formalen Eigenschaft des kulturellen Werks werden kann, statt lediglich eine beiläufige technische Eigenschaft zu sein (Manovich, 2001).

Der Browser wurde dadurch zu mehr als einem neutralen Schaukasten. Größe des Viewports, CSS-Schichtung, JavaScript-Ausführung, Beschneidung, Dekodierung, Animationsphase, der Artikel im Vordergrund und der Zeitpunkt des Besuchs wirken sämtlich an der Manifestation mit. Ein Screenshot bewahrt ein Exemplar, nicht das ganze Werk. Zum Werk gehören auch die Regeln, die ein weiteres Exemplar möglich machen.

Zunächst bezeichnete ich den Besucher als Mitschöpfer. Dann zögerte ich. Das Wort schrieb ihm mehr Kontrolle zu, als die gegenwärtige Oberfläche tatsächlich bietet. Ein Besucher kann Russell nicht in die obere rechte Ecke ziehen, einen Voronoi-Parameter verändern oder verlangen, dass der nächste Zustand einen Fuchs enthält. Die präzisere Formulierung wurde daher: ein Ausführender der Auswahl ohne vollständige Autorschaft. Der Besucher kommt, navigiert, aktualisiert, schenkt Aufmerksamkeit, bleibt oder geht. Diese bescheidene Geste bestimmt dennoch, welche mögliche Manifestation für diese Person zu diesem Zeitpunkt tatsächlich wird.

Umberto Ecos offenes Kunstwerk half mir zu verstehen, dass Offenheit nicht die Abwesenheit künstlerischer Struktur bedeutet. Ein Werk kann Beschränkungen definieren und zugleich Aspekte seiner Realisierung oder sinnhaften Vollendung der Aufführung, dem Zufall und der Interpretation überlassen (Eco, 1989). Hier ist die Offenheit teilweise ausführbar. Seed, Auswahl der Einträge, Geometrie, Viewport, Animation, Artikelkontext und Handlung des Besuchers verhindern, dass ein einziger festgelegter visueller Zustand das Werk ausschöpft.

Ich musste noch eine weitere verführerische Formulierung korrigieren. Ursprünglich wollte ich sagen, jeder Besucher erhalte eine absolut einzigartige Komposition. Technisch ist „im gewöhnlichen Gebrauch praktisch einzigartig“ besser zu verteidigen. Ein endliches konfiguriertes System mit deterministischen Seeds kann theoretisch zu einem früheren Zustand zurückkehren. Künstlerische Begeisterung ist wertvoll, aber gelegentlich verdient auch die Hash-Funktion ein Stimmrecht.

Wiederholte Besuche führten Erinnerung in das generative System ein

Sobald ich den Besucher ernst nahm, trat Zeit in das Kunstwerk ein. Russell kann heute neben dem Seelöwen erscheinen, bei mehreren Besuchen verschwinden und später neben einer Hilbert-Animation zurückkehren. Der Besucher bringt frühere Manifestationen in die gegenwärtige mit. Vertrautheit, Abwesenheit, Wiederkehr und Seltenheit können Bedeutung gewinnen. Ein Eintrag, der zunächst lediglich komisch erschien, kann bei seiner Rückkehr etwas Zärtliches bekommen; eine Beziehung, die zunächst unbemerkt blieb, kann erst lesbar werden, nachdem eine andere Komposition sie unterbrochen hat.

Allmählich unterschied ich drei Formen von Bedeutung. Lokale Bedeutung kann aus einer einzelnen Nachbarschaft entstehen. Sequenzielle Bedeutung kann durch die Reihenfolge von Erscheinen, Verschwinden und Wiederkehr entstehen. Distributionale Bedeutung kann sich aus Häufigkeit, Seltenheit, Gewichtung und Auswahlwahrscheinlichkeit über viele Manifestationen hinweg ergeben. Eine unwahrscheinliche Beziehung kann gerade deshalb bedeutsam werden, weil der Besucher – ohne dies auszurechnen – gelernt hat, dass sie unwahrscheinlich ist.

Das System erzeugte nun nicht mehr nur Kompositionen. Es erzeugte Geschichten von Begegnungen. Doch diese Geschichte lebt gegenwärtig größtenteils im Besucher und nicht im Document Object Model. Der Browser muss nicht wissen, dass der Fuchs seit drei Tagen nicht mehr erschienen ist, damit seine Rückkehr für jemanden bedeutsam wird, der sich daran erinnert. Dadurch wurde eine wichtige Unterscheidung deutlich: Ein Werk kann zeitliche Erfahrung hervorbringen, ohne bereits ein explizites institutionelles Gedächtnis dieser Erfahrung zu speichern.

Eine zukünftige Ebene für Bewahrung oder Beiträge könnte einen Teil dieser privaten Geschichte explizit machen. Ein gespeicherter rekonstruierbarer Zustand würde eine Begegnung festhalten; ein von einem Besucher bereitgestelltes Bild könnte in spätere Kombinationen eingehen; eine interpretierte Beziehung könnte zukünftige Gewichtungen beeinflussen. Doch dies sind weitere Schritte. Das gegenwärtige Werk hängt bereits von erinnerter Wiederkehr ab, noch bevor die Software lernt, stellvertretend für den Besucher zu erinnern.

KI half beim Bau des Generators, nicht bei jedem einzelnen Endbild

Der Ausdruck „KI-generierte Kunst“ erschien zunächst bequem, verdeckte jedoch die Architektur. Ich gab nicht einen einzigen Prompt in ein Bildmodell ein, erhielt ein fertiges Artefakt und veröffentlichte es anschließend. KI half mir vielmehr beim Aufbau jener Infrastruktur, aus der spätere visuelle Zustände hervorgehen konnten. Sobald diese Infrastruktur bereitgestellt ist, kann sie im Browser eines Besuchers eine neue Manifestation erzeugen, ohne die Entwicklungs-KI erneut aufzurufen.

Direkt KI-generiertes Artefakt KI-gestützte generative Infrastruktur
Ein Modell synthetisiert ein bestimmtes Ergebnis. KI hilft dabei, ein dauerhaftes System aufzubauen, das viele spätere Ergebnisse erzeugen kann.
Der primäre generative Akt findet gewöhnlich statt, bevor ein normaler Betrachter eintrifft. Die Ankunft, Navigation oder Aktualisierung des Besuchers instanziiert eine Manifestation zur Laufzeit.
Das ausgelieferte Artefakt kann unverändert bleiben, bis eine weitere Generierung angefordert wird. Variabilität gehört zum gewöhnlichen Gebrauch des Werks.
Der latente Möglichkeitsraum des Modells bleibt für die Produktion zentral. Ein Teil des Möglichkeitsraums wird in überprüfbare Einträge, Seeds, Gewichtungen, Algorithmen und Browserverhalten externalisiert.
Der Betrachter begegnet primär einem ausgewählten Ergebnis. Der Besucher wirkt an der Entscheidung mit, wann ein weiteres Ergebnis tatsächlich wird.

Bevor ich diese Unterscheidung in einer Tabelle artikulieren konnte, zeichnete ich sie als zwei Produktionswege:

DIREKTE KI-GENERIERUNG
menschlicher Prompt → KI-Modell → Bild → Betrachter

KI-GESTÜTZTE GENERATIVE INFRASTRUKTUR
menschliche Intention ⇄ Entwicklungs-KI
                      ↓
            dauerhaftes generatives System
                      ↓
           Besucherhandlung + Browser-Laufzeit
                      ↓
                 Manifestation

Selbst das zweite Diagramm war noch zu linear. Die Manifestation konnte den Besucher, den Künstler oder die nächste Spezifikation verändern und damit wieder stromaufwärts zurückkehren. Doch die beiden Produktionswege zunächst aufzuzeichnen verhinderte, dass der Begriff „KI-generiert“ verbarg, wo die Generierung tatsächlich stattfand.

Die Unterscheidung erschien zunächst architektonisch. Dann wurde mir klar, dass sie die künstlerische Frage selbst veränderte. Direkte Generierung fragt häufig: „Welches Bild soll existieren?“ Dieses Projekt fragt zunehmend: „Welche Bedingungen sollen existieren, damit unvorhersehbare Formen und Begegnungen weiterhin geschehen können?“ KI stellte das Kunstwerk nicht primär als ein einzelnes Objekt her. Sie half dabei, einen Apparat zu konstruieren, der auch nach dem Ende des Entwicklungsgesprächs weiterhin Anlässe für Kunstereignisse hervorbringen kann.

Für kurze Zeit erwog ich den Ausdruck „KI-gestützte meta-generative Kunst“, weil die KI auf der Ebene der Konstruktion des Generators operierte. Ich halte den Ausdruck weiterhin für deskriptiv nützlich, präsentiere ihn jedoch nicht als etablierte kunsthistorische Kategorie. Die stärkere These hängt nicht davon ab, ein neues Genre zu benennen. Die Architektur genügt: Menschliche Intention und KI-gestützte Entwicklung erzeugen eine überprüfbare generative Umgebung; ein Besucher aktiviert später eine Manifestation, die weder der Besucher noch ich noch die Entwicklungs-KI zuvor explizit als fertiges Arrangement komponiert haben.

Meine erste Verteidigung menschlicher Handlungsmacht wurde unzureichend

Meine früheren technischen Artikel waren bereits zu einer Schlussfolgerung gelangt, die ich für wichtig hielt: Menschliche Handlungsmacht hängt nicht davon ab, jede Codezeile eigenhändig einzutippen. KI konnte den größten Teil einer Implementierung erzeugen, während meine Handlungsmacht darin erhalten blieb, Ziele auszuwählen, Beschränkungen zu definieren, Fehler zu interpretieren, zu entscheiden, welche Evidenz zählt, und festzulegen, was überhaupt als Erfolg gelten sollte. Dies beschrieb einen großen Teil der Projektgeschichte zutreffend.

Dann stellte ich meine eigene Antwort infrage. Was geschieht, wenn agentische KI genau jene Übergänge automatisiert, auf denen diese Darstellung beruht? Ein Coding-Agent kann zunehmend ein Repository untersuchen, Änderungen planen, Dateien bearbeiten, Tests ausführen, einen Browser benutzen, einen Kandidaten bewerten, ihn revidieren und eine lange Aufgabe selbstständig weiterverfolgen. OpenAI hat ein internes Softwareprojekt beschrieben, dessen Code, Tests, Dokumentation und Tooling von Codex geschrieben wurden, während Menschen durch Intention, Umgebung und Feedbackstrukturen steuerten (Lopopolo, 2026). Anthropic hat ein Planner–Generator–Evaluator-Harness beschrieben, das während mehrstündiger autonomer Läufe vollständige Full-Stack-Anwendungen entwickelte (Rajasekaran, 2026). Diese Berichte bedeuten nicht, dass autonome Entwicklung generell gelöst wäre. Sie bedeuten jedoch, dass „der Mensch führt diesen technischen Schritt weiterhin selbst aus“ ein instabiles Fundament für eine Theorie des Menschlichen darstellt.

Ich könnte sagen: „Die KI schreibt den Code, aber ich genehmige das Deployment.“ Doch auch Deployment kann automatisiert werden. Ich könnte mich auf visuelles Urteilsvermögen zurückziehen, doch Evaluator-Agenten wirken zunehmend auch dort mit. Ich könnte sagen, dass ich das Ziel formuliere, doch Systeme können bereits Ziele vorschlagen und kritisieren. Wenn menschliche Besonderheit jeweils durch das definiert wird, was in diesem Jahr noch unbequem zu automatisieren ist, wird sich die Definition jedes Mal verschieben, wenn sich die Werkzeugkette verbessert.

Die Frage veränderte sich daher. Sie lautete nicht länger: „Welche Handlungen habe ich persönlich ausgeführt?“ Stattdessen wurde sie zu: Wohin bewegen sich Zweck, Urteil, Verantwortung, Bedeutsamkeit und das, was auf dem Spiel steht, wenn sich operationelle Handlungsmacht zunehmend verteilt?

Das löschte meine Handlungsmacht nicht aus. Es machte ihre Ebenen präziser. Ich wähle und transformiere das grundlegende Vokabular, lege Verpflichtungen fest, beurteile die künstlerische Richtung und bleibe für die Veröffentlichung verantwortlich. KI trägt technische Konstruktion, Interpretation, Alternativen und konzeptuelle Reorganisation bei. Die generative Engine trifft prozedurale Entscheidungen innerhalb begrenzter Regeln. Der Browser realisiert materiell einen Zustand. Der Besucher aktiviert und interpretiert eine Manifestation. Diese Beiträge sind real, aber nicht gleichwertig. Verteilte Autorschaft bedeutet nicht gleiche Verantwortung.

Dies war der erste Punkt, an dem eine einfache Mensch–Maschine-Opposition weniger nützlich wurde als eine Architektur von Beziehungen. Das Problem bestand nicht länger darin, jenen einen Beteiligten zu identifizieren, der insgeheim alles getan hatte. Es bestand darin, die unterschiedlichen Arten von Kausalität, Urteil, Betroffenheit und Verantwortung zu unterscheiden, die im Werk aufeinandertrafen.

Eine Einladung kam, bevor die Theorie existierte

Am 18. August 2026, drei Tage nachdem ich Refreshing the Webpage as an Act of (Artistic) Composition veröffentlicht hatte, kontaktierte mich Prof. Dohna wegen ihres Pilotprojekts METANOIA BEYOND EAST AND WEST—Contemplative Seeing of the (W)hole and AI—From the School of Athens to Magnifica Humanitas: Research–Work–Friendship. Die Chronologie ist wichtig. Wir hatten bereits zuvor zusammengearbeitet, und sie kannte andere Aspekte meiner Arbeit, doch dieses konkrete Background Studio hatte sie noch nicht gesehen, als sie beschloss, mich einzuladen.

Ihre Nachricht war ungewöhnlich herzlich. Sie beschrieb das Projekt als etwas, das gerade erst beginne, und schrieb, sie wolle es nicht ohne mich unternehmen. Dieser persönliche Kontext war bedeutsam, weil ich die Einladung nicht als neutrale Anfrage nach Fachwissen empfing. Freundschaft, Vertrauen und frühere Zusammenarbeit waren bereits Teil der Begegnung, bevor das neue Kunstwerk in sie eintrat.

Schon ihre Wortwahl machte die Schwierigkeit der Kategorisierung sichtbar. Sie wollte mich „come ARTISTA …“ einladen und fügte unmittelbar darauf „a scientifico!“ hinzu. Ich hatte die vorangegangenen Tage damit verbracht, mich zu fragen, ob das Projekt Web Engineering, generative Kunst, Computational Art, Creative Coding, visuelle Autobiografie oder eine unbequeme Mischung aus all dem sei. Offenbar war auch die Einladung auf dasselbe Klassifikationsproblem gestoßen.

Ich schickte ihr den Artikel und erklärte, dass Code, Mathematik, Bewegung und kontrollierter Zufall Materialien des Werks seien. Innerhalb kurzer Zeit bezeichnete ich es nacheinander als generative Kunst, Computational Art und Creative Coding, weil jeder dieser Namen etwas sichtbar machte und zugleich etwas anderes verbarg. Ich bat sie, die Seite zu aktualisieren und verschiedene Artikel zu öffnen. Ich erklärte, dass ich das visuelle Vokabular auswähle und die Regeln festlege, aber nicht jede sichtbare Anordnung vollständig vorbestimme. Zunächst bezeichnete ich den Besucher beinahe als Mitschöpfer. Dann korrigierte ich mich: Unter der gegenwärtigen Oberfläche ist der Besucher genauer ein Ausführender, der eine Möglichkeit aktualisiert, ohne sie vollständig zu autorisieren.

Anschließend beschrieb ich nacheinander zwei mögliche Weiterentwicklungen. Erstens könnte ein Besucher eine Komposition, die ihn berührt hat, speichern oder herunterladen und damit einen flüchtigen Zustand in eine Spur der Begegnung verwandeln. Später könnte ein Besucher vielleicht selbst ein Bild zum Vokabular beitragen, sodass die visuelle Geschichte eines anderen Menschen in Kombinationen eingehen könnte, die ich allein niemals hätte vorbereiten können. Die zweite Möglichkeit war noch nicht implementiert, machte die Verteilung des Schaffens jedoch noch schwerer von der Hand zu weisen.

Dann kam die erste Nachricht:

„Incredible.“ – „Unglaublich.“

Fünfundzwanzig Sekunden später kam die zweite:

„Allora il mio progetto non ha tanto senso?“ – „Dann ergibt mein Projekt also nicht so viel Sinn?“

Ich hatte das Gefühl, schnell antworten zu müssen, weil ich beinahe das Gegenteil dachte. Zugleich erlebte ich diese Abfolge als einen kleinen intellektuellen Schock; das ist jedoch meine Interpretation des Austauschs und kein Beweis dafür, was sie im Innersten damit meinte. Bestätigt sind die Chronologie und die Formulierung. Ebenso bestätigt ist, was darauf folgte: Ihre Frage veränderte, was ich als Nächstes untersuchte.

Ich las ihr Projekt erneut, bevor ich die größere Frage beantwortete

Mein erster Impuls bestand schlicht darin, sie zu beruhigen: Nein, die technologische Arbeit machte ihr Projekt nicht bedeutungslos. Bevor ich jedoch eine stärkere Behauptung aufstellte, kehrte ich zu ihrem Projektentwurf zurück. Ich wollte wissen, ob ich mein neues Vokabular der „Begegnung“ lediglich deshalb über ihr Projekt legte, weil es zufällig gut zu meinem Kunstwerk passte.

Das erneute Lesen veränderte mein Modell. Begegnung war bereits strukturell in ihrem Entwurf verankert, selbst dort, wo das englische Substantiv nicht in jedem Absatz vorkam. Der Kurs entwickelt sich um Bildung, Selbst-Bildung, Metanoia, kontemplatives Sehen, Forschung, künstlerische Arbeit und Freundschaft. Er beginnt mit Romano Guardinis Formulierung, dass „der Forscher dem Problem dient“. Der Forscher behandelt das Problem nicht als Rohmaterial für ein vorab festgelegtes Ergebnis. Ebenso wird der Künstler empfänglich für eine sich herausbildende Gestalt. In der Freundschaft reduziert der Freund den anderen Menschen nicht auf seinen Nutzen, sondern lässt den anderen „zu sich selbst kommen“.

Der Baum tritt in den vorgeschlagenen Modulen auf, weil Forschung, Freundschaft und künstlerische Arbeit jeweils eine Begegnung mit ihm einschließen können, die frei von unmittelbarer Nützlichkeit ist. Ihr Text widersetzt sich wiederholt einer Welt, die nur noch aus Objekten besteht, die klassifiziert, optimiert und benutzt werden sollen. In diesem Kontext ist KI nicht bloß ein neues Produktionswerkzeug. Sie verschärft vielmehr eine Frage, die bereits in der modernen Rationalität vorhanden ist: Was geschieht mit Wissen, Schaffen und Werden, wenn Information, Deduktion und formale Produktion von der Person abgelöst werden können, die sie durchlebt?

Prof. Dohnas veröffentlichte Arbeiten bestätigen, dass diese Sorge unserem Austausch vorausgeht. In „What we see looks back at us“ verbindet sie Guardinis Phänomenologie des Blicks mit Liebe, künstlerischem Schaffen, Erkenntnis und Begegnung. Sowohl das Kunstwerk als auch die Liebe konfigurieren einen Erkenntnisraum, in dem Dinge und Menschen sich in ihrem tieferen Sein offenbaren können (Dohna-Schlobitten, 2022). Ihre spätere Studie zu Guardinis Weltanschauung setzt Sehen, künstlerische Gestalt und das Ganze in eine Beziehung, die sich dagegen sperrt, Erkenntnis auf distanzierten Besitz zu reduzieren (Dohna-Schlobitten, 2024).

Eine Passage Guardinis, die Papst Franziskus 2023 in seiner Ansprache an Künstler zitierte, gewann für mich neue Bedeutung. Das Kunstwerk „öffnet einen Raum“, in den ein Mensch eintreten und in dem er sich bewegen und Dingen und Personen begegnen kann, während sie sich ihm erschließen (Francis, 2023). Ich hatte den Browser bereits als kleines Theater bezeichnet. Der philosophische Maßstab ist ein anderer, doch die strukturelle Verbindung ist auffällig. Vielleicht besteht eine künstlerische Handlung gerade darin, einen Raum zu schaffen, in dem Begegnung geschehen kann.

Ich schrieb ihr deshalb zurück, dass meine Arbeit ihr Projekt nicht weniger sinnvoll mache. Meiner Ansicht nach machte sie das Projekt sogar noch sinnvoller, weil sie seiner Frage einen konkreten und schwierigen Fall gab. Diese Antwort war ehrlicher, als einfach „menschliche Kreativität“ auf die eine und „maschinelle Reproduktion“ auf die andere Seite zu stellen – auch wenn ich noch nicht vollständig verstanden hatte, warum.

Aus der beruhigenden Antwort wurde ein Stresstest

Meine erste Beruhigung war noch immer zu einfach: Die Maschine kann Formen anordnen, aber der Mensch bleibt der eigentliche Schöpfer. Die Geschichte, die ich gerade dokumentiert hatte, untergrub diesen Satz. KI half beim Aufbau des Systems. Die Laufzeitalgorithmen erzeugten Beziehungen, die ich nicht manuell komponiert hatte. Der Browser führte den Zustand auf. Der Besucher entschied, wann eine weitere Manifestation erschien. Zukünftige Besucher könnten vielleicht zum visuellen Vokabular beitragen. Wo genau sollte ich also die Grenze um „den eigentlichen Schöpfer“ ziehen, ohne dabei alle anderen kausalen Beiträge verschwinden zu lassen?

Eine Formulierung der KI veränderte die Richtung der Diskussion:

„Dein Projekt macht dieses Problem sichtbar, anstatt es lediglich theoretisch zu belassen.“

Ich erkannte sofort einen Teil dieses Satzes wieder. Prof. Dohnas Projekt fragt danach, was mit menschlichem Wissen, Schaffen, Kontemplation und Transformation in einem Zeitalter leistungsfähiger KI geschieht. Meine Arbeit bringt einen Teil dieses Problems in ein tatsächlich funktionierendes System: KI war an seiner Konstruktion beteiligt; mathematische Verfahren erzeugen formale Neuheit; ein Besucher beteiligt sich an der Aktualisierung; Interpretation kann über das hinausgehen, was ich explizit codiert habe.

Dann schärfte eine weitere Formulierung den Gedanken:

„Dein Projekt beseitigt ihre Frage nicht. Es verschärft sie.“

Auch dem stimmte ich zu, allerdings mit einer Konsequenz. Das Kunstwerk sollte nicht lediglich als freundliches Beispiel präsentiert werden, das ihre Theorie bestätigt. Es könnte zugleich als Stresstest fungieren. Wenn die Theorie davon abhinge, dass „Menschen schaffen; Maschinen lediglich wiederholen“, würde dieser Fall sie herausfordern. Wenn die tiefere Frage jedoch Begegnung, Sinn, Verantwortung und Transformation betraf, konnte gerade diese Herausforderung das Projekt stärken, indem sie sein Vokabular zu größerer Präzision zwang.

Die schwächere Opposition lautete:

Der Mensch schafft sinnvoll / die Maschine reproduziert mechanisch

Das tatsächlich funktionierende Kunstwerk ersetzte sie durch schwierigere Fragen:

Was ist Schöpfung, wenn Generierung verteilt ist?

Was ist Begegnung, wenn ihre Bedingungen algorithmisch erzeugt werden können?

Was ist Bedeutung, wenn Künstler, KI, Algorithmus, Kunstwerk und Betrachter das vollständige Ereignis nicht jeweils für sich enthalten?

Was ist Metanoia, wenn die entscheidende Unterscheidung nicht einfach mit der Erzeugung einer neuen Form gleichgesetzt werden kann?

Ihre momentane Frage war daher produktiver, als Zustimmung es gewesen wäre. „Schön“ hätte das Werk bestätigt. „Ergibt mein Projekt noch Sinn?“ störte den begrifflichen Rahmen. Diese Störung wurde selbst zu einem Input der Forschung.

Ich trennte Generierung von Begegnung – und musste sie anschließend wieder miteinander verbinden

Ein weiterer Satz der KI schien das zentrale Problem zunächst zu lösen:

„KI kann an der Generierung von Form mitwirken, ohne selbst die Bedeutung dieser Form zu durchleben.“

Ich empfand diese Formulierung als stark, weil sie die zunehmend unhaltbare Behauptung aufgab, Maschinen könnten nichts zum Schaffen beitragen. Sie unterschied mehrere Ebenen, die ich zuvor miteinander vermischt hatte:

Generierung von Form
        ↓
Entstehung bedeutungsvoller Beziehungen
        ↓
Erfahrung von Bedeutung

Der Laufzeitalgorithmus wirkt eindeutig an der ersten Stufe mit. Zur zweiten trägt er kausal bei, indem er Nachbarschaft, Maßstab, Rhythmus und Wiederkehr erzeugt. Ein Mensch kann die Beziehung anschließend als komisch, theologisch, autobiografisch, zärtlich oder verstörend erfahren.

Das erschien überzeugend, bis mir auffiel, dass die Pfeile ausschließlich nach unten zeigten. Die tatsächliche Geschichte widersprach dem Diagramm. Ein generiertes Ergebnis veränderte meine Interpretation; meine Interpretation veränderte die nächste Anfrage; das überarbeitete System veränderte zukünftige Ergebnisse. Prof. Dohnas Begegnung veränderte mein theoretisches Vokabular. Dieser Artikel könnte die Implementierung verändern. Generierung und Begegnung konnten nicht dauerhaft voneinander getrennte Phasen bleiben.

Das Modell wurde daher zu:

Generierung ⇄ Begegnung

Produktion ⇄ Rezeption ⇄ Interpretation
           ⇄ Transformation ⇄ erneute Produktion

Die Unterscheidung blieb wichtig, doch sie war keine Mauer mehr. Sie bezeichnete unterschiedliche Momente innerhalb eines rekursiven Prozesses. Eine Form kann Anlass zu einer Begegnung geben, und die Begegnung kann stromaufwärts zurückkehren und die Bedingungen zukünftiger Form verändern. An diesem Punkt hörte das Projekt auf, wie eine Pipeline auszusehen, und begann, einer prozessualen Ökologie zu ähneln.

Die Frage verlagerte sich von „Wer schafft?“ zu „Wo geschieht Sinn?“

Eine kurze Frage aus dem KI-Dialog ordnete das Problem neu:

„Wo genau ist der Sinn?“

Ich hatte bereits Wörter wie Bedeutung und Sinn verwendet, doch das Wort „wo“ legte eine Annahme offen. Ich stellte mir Sinn als Daten vor, die irgendwo gespeichert seien. Vielleicht enthält das Kunstwerk ihn. Vielleicht legt der Künstler ihn hinein. Vielleicht erzeugt der Betrachter ihn. Vielleicht repräsentiert eine KI ihn. Vielleicht kommt er – innerhalb des theologischen Horizonts von Prof. Dohna – von Gott. Welcher Beteiligte besitzt die kanonische Kopie?

Dann begann ich zu vermuten, dass Speichern überhaupt die falsche Metapher war.

Russells Bild trägt historische und kulturelle Assoziationen bereits in sich, bevor es in mein System eintritt. Ein sakrales Bild kommt mit ikonografischen und theologischen Geschichten. Meine chemische Gleichung besitzt aufgrund meiner eigenen Vergangenheit autobiografische Bedeutung. Ein Besucher erschafft diese Erbschaften nicht aus dem Nichts. Ich begann, sie sedimentierte semantische Möglichkeiten zu nennen.

Der Algorithmus stellt anschließend eine konkrete Beziehung her, die nicht als Bestandteil einer der beiden isolierten Quellen existierte: dieser Russell, in diesem Maßstab, neben diesem Seelöwen, um diesen Artikel herum, während dieses Besuchs. Die Beziehung ist real, obwohl keine Bildunterschrift ihre Interpretation festlegt.

Dann begegnet ihr jemand. Vielleicht wirkt die philosophische Ernsthaftigkeit durch körperliche Ausgelassenheit unterbrochen. Vielleicht erscheint die Beziehung zärtlich. Vielleicht ist sie absurd. Vielleicht ruft sie eine andere Erinnerung hervor. Vielleicht geschieht überhaupt nichts Interessantes. Begegnung garantiert keine Bedeutsamkeit.

Das revidierte Modell wurde:

semantisches Erbe
        ↓
relationale Konfiguration
        ↓
Begegnung
        ↓
aktualisierte Bedeutsamkeit
        ↓
Erinnerung / Biografie / Kultur
        ↓
zukünftige Interpretation

Dies führte zu einer These, die ich weiterhin als Interpretation und nicht als bestätigte Tatsache behandle:

Sinn wird möglicherweise weder vom Subjekt willkürlich hergestellt noch als fertige Eigenschaft im Objekt gespeichert; vielleicht erschließt er sich durch Begegnung.

Als dieser Satz erstmals auftauchte, fragte ich mich, ob dies das sei, was Prof. Dohna beweisen wollte. „Beweisen“ war zu stark. Ihre Frage hatte mir geholfen, eine Struktur zu sehen, und das Kunstwerk gab mir einen konkreten Fall, anhand dessen ich sie untersuchen konnte; doch weder eine bewegende Erfahrung noch eine überzeugende Formulierung entschied die Metaphysik. Eine WordPress-Seite kann Guardini nicht allein dadurch beweisen, dass sie HTTP 200 zurückgibt.

Was der Fall jedoch zeigt, ist, warum zwei einfache Erklärungen unzureichend sind. Die Bedeutung war nicht vollständig von mir im Voraus codiert worden, doch der Besucher erfand die algorithmische Gegenüberstellung auch nicht anschließend völlig frei. Die Beziehung selbst trägt etwas bei. Dieser Beitrag ist philosophisch interessant, noch bevor man entscheidet, worin sein letzter Grund liegt.

Auch hier musste ich einer Übertreibung widerstehen. Die Begegnung erschafft nicht alles aus dem Nichts, und die Sache kommt nicht leer bei uns an. Historische Bedeutungen, persönliche Erinnerungen, algorithmische Konfiguration, gegenwärtige Aufmerksamkeit und spätere Erinnerung wirken auf unterschiedliche Weise mit. „Bedeutung entsteht relational“ ist nur dann hilfreich, wenn diese Formulierung die Asymmetrien nicht auslöscht.

Ich korrigierte die Behauptung, der Algorithmus sei kulturell analphabetisch

In meinem früheren Kunstessay hatte ich den Algorithmus als „kulturell analphabetisch“ bezeichnet. Für die Laufzeitgeometrie bleibt diese Formulierung nützlich. Eine Voronoi-Funktion muss Russells Biografie nicht kennen, um sein Bild einem Polygon zuzuordnen. Doch unsere Diskussion umfasste zwei sehr unterschiedliche Computersysteme: den engen mathematischen Generator und die dialogische KI, die half, das Projekt zu konstruieren und zu interpretieren.

Die KI konnte Russell identifizieren, christliche Bildwelten diskutieren, Guardini mit Kunst und Begegnung verbinden, erkennen, warum der Seelöwe die Beziehung komisch erscheinen lassen könnte, und das philosophische Problem flüssiger formulieren als viele beiläufige Betrachter. Einfach zu sagen, „die Maschine weiß nichts“, würde beobachtbare semantische Kompetenz auslöschen.

Diese Korrektur erzeugte die nächste Frage:

Ist die Repräsentation der Bedeutung einer Begegnung dasselbe wie das Durchleben ihrer Bedeutung?

Meine erste Antwort wollte erneut zu sauber sein: KI repräsentiert; Menschen erleben. Dann fragte ich mich, wie ich diese Grenze begründen könnte, ohne die Schlussfolgerung bereits in die Definition einzubauen. KI-Systeme können erhebliche semantische Operationen ausführen, Kontext aufrechterhalten, menschliches Verständnis verändern und Pläne revidieren. Zukünftige Systeme könnten längere Geschichten und stabilere Selbstmodelle bewahren.

Die besser zu verteidigende Unterscheidung wurde:

semantische Kompetenz ≠ gelebte Bedeutsamkeit

Das Ungleichheitszeichen ist wichtig und bewusst bescheiden. Eine KI kann vollkommen erklären, warum Russell neben dem Seelöwen komisch ist. Das allein zeigt jedoch nicht, dass das Bild für die KI innerhalb einer Biografie bedeutsam geworden ist – als etwas, das sie später vermissen wird, dessen Missverständnis sie bereut, für das sie Verantwortung übernimmt, von dem sie zulässt, dass es verändert, wen sie liebt, oder um das herum sie ein Versprechen organisiert. Erfolgreiche Repräsentation ist Evidenz semantischer Kompetenz. Für sich allein ist sie keine Evidenz dafür, dass dem System etwas in diesem stärkeren Sinne etwas bedeutet.

Ich verwende diese Unterscheidung nicht, um die Frage für immer zu schließen. Ich verwende sie, um zwei entgegengesetzte Vereinfachungen zu verhindern: „KI versteht überhaupt nichts“ und „flüssige semantische Leistung beweist gelebte Bedeutsamkeit“. Die gegenwärtige Evidenz stützt keine dieser absoluten Schlussfolgerungen.

Die Person kann selbst hineingezogen werden

Als ich fragte, was einem Menschen widerfahren kann, das durch Generierung, Repräsentation oder Anpassung nicht erfasst wird, lautete die erste Antwort:

„Die Person kann selbst involviert – in Anspruch genommen – werden.“

Ich verstand nicht sofort, warum dieses Wort wichtig war, und bat deshalb um eine weitere Erklärung. Die entscheidende Korrektur bestand darin, dass „Veränderung“ allein viel zu schwach ist. Algorithmen verändern ihren Zustand. Lernende Systeme aktualisieren sich. Agenten revidieren Pläne. Eine Theorie, in der Menschen sich verändern, Maschinen dagegen nicht, bricht sofort zusammen.

Involviertsein benennt eine andere Möglichkeit: Etwas kann auf dem Spiel stehen. Ich kann einem Werk begegnen und entdecken, dass ich mich geirrt habe. Ich kann mich selbst darin erkennen, meine Vergangenheit neu interpretieren, Verantwortung empfinden, dankbar oder beschämt werden, die Art verändern, wie ich einen anderen Menschen behandle, oder ein Versprechen geben. Die Begegnung tritt in jene Geschichte ein, durch die ich verstehe, wer ich bin. Sie ist nicht länger bloß Information über ein äußeres Objekt; sie erhebt einen Anspruch darauf, wie ich urteile oder lebe.

An diesem Punkt hörte Prof. Dohnas Gebrauch von Metanoia auf, wie eine benachbarte theologische Sprache auszusehen, und wurde für das rechnerische Problem zentral. Vorläufig begann ich, Anpassung und Metanoia folgendermaßen zu unterscheiden:

Anpassung Metanoia, wie der Begriff hier vorläufig verwendet wird
Ein System verändert Verhalten oder Zustand als Reaktion auf Information. Die Orientierung eines Subjekts zu sich selbst, zur Welt, zu anderen, zur Wahrheit, zu Werten oder zum Handeln verändert sich.
Das Ziel kann stabil bleiben, während sich die Strategie verbessert. Die Begegnung kann verändern, was der Beteiligte überhaupt als Ziel versteht.
Ein fehlgeschlagenes Ergebnis führt unter einem bestehenden Kriterium zu einem besseren Ergebnis. Das Kriterium selbst wird zu einem Gegenstand der Befragung.

Die Unterscheidung wurde im sich verändernden Ziel meines eigenen Projekts konkret. Ich begann mit: „Ich möchte bessere visuelle Hintergründe.“ Dann wurde das Ziel: „Ich möchte eine generative Kompositions-Engine.“ Später wurde daraus: „Ich möchte Bedingungen schaffen, unter denen Begegnungen geschehen können.“ Wenn eine Komposition zu viel leeren Raum hinterlässt und ich einen Dichteparameter anpasse, passt sich das System innerhalb eines bestehenden Kriteriums an. Wenn mich das Kunstwerk dagegen erkennen lässt, dass ich das gesamte Ziel falsch beschrieben habe, verändert sich das Kriterium selbst. Dieses neue Verständnis kann den Titel, die Funktionen für Besucher, das Moderationsmodell, die Gestaltung des Archivs und sogar den Grund verändern, aus dem ich die Arbeit überhaupt fortsetze.

Das schien Metanoia wesentlich näherzukommen. Dann stellte ich auch diese Unterscheidung infrage. Angenommen, ein zukünftiger Agent bewahrt eine persistente Geschichte, revidiert nicht nur seine Strategie, sondern auch seine Zielhierarchie, setzt gegenwärtiges Handeln in Beziehung zu früheren Verpflichtungen und reorganisiert sein zukünftiges Verhalten um das herum, was er selbst als bedeutsam identifiziert. Würde „Anpassung“ dann noch genügen? Würde dennoch etwas fehlen – Verletzlichkeit, Verantwortung, die Möglichkeit des Verlustes, eine Ich-Perspektive, etwas, das tatsächlich auf dem Spiel steht?

Ich weiß es noch immer nicht. Die stärkere Frage lautet:

Kann es Metanoia geben, ohne jemanden, für den die Transformation selbst auf dem Spiel steht?

Die Frage offen zu lassen, ist kein Scheitern des Artikels. Es ist eine Korrektur meiner früheren Gewissheit.

Der Mechanismus kann Überraschendes erzeugen, ohne selbst erstaunt zu sein

Eine weitere Formulierung der KI wurde zum Scharnier:

„Der Algorithmus kann etwas Unerwartetes hervorbringen, ohne über das, was er hervorgebracht hat, selbst erstaunt zu sein.“

Meine unmittelbare Antwort lautete: „Ja, aber der Mensch, der ihm begegnet, kann erstaunt sein.“ Aus diesem Einwand entstand ein besserer Satz:

Die Begegnung kann Staunen enthalten, selbst wenn der Mechanismus nicht staunt.

Der Generator muss eine Beziehung nicht intendieren, damit diese Beziehung bedeutsam werden kann. Er stellt Bedingungen bereit: Nachbarschaft, Maßstab, Wiederkehr, Seltenheit, Unterbrechung, Verschwinden, Rückkehr und Rhythmus. Ein Mensch kann eine Möglichkeit als Bedeutsamkeit aktualisieren. Das System kann dies nicht erzwingen, und es sollte nicht für jede Interpretation die Urheberschaft beanspruchen.

Deshalb begann ich, das Background Studio mit einer ambitionierteren Bezeichnung zu versehen als lediglich „generative Kompositions-Engine“:

eine Engine zur Erzeugung von Bedingungen der Begegnung

Die Formulierung erschien mir richtig, weil sie sowohl Macht als auch Grenze bewahrte. Die Engine kann Beziehungen inszenieren, aber nicht garantieren, dass sie bedeutsam werden. Dadurch erhielt das Werk zugleich eine kuratorische Dimension. Ein Kurator stellt nicht jede Interpretation innerhalb einer Ausstellung her. Kuratieren konstruiert ein Feld – dieses Werk neben jenem Werk, dieser Zwischenraum, dieser Weg, diese Wiederkehr. Mein System führt eine engere prozedurale Version dieser Tätigkeit aus und inszeniert das Feld fortwährend neu.

Auch diese Formulierung erwies sich später als unvollständig. Sobald Begegnungen als Feedback zurückkehren und das System verändern, erzeugt die Engine nicht mehr nur Bedingungen. Sie nimmt an einem Kreislauf teil, durch den Bedingungen und Beteiligte einander transformieren.

Prof. Dohnas Reaktion trat in die Kausalgeschichte des Kunstwerks ein

Irgendwann erkannte ich, dass ich über Begegnung theoretisiert hatte, während ich die unmittelbarste Begegnung der ganzen Geschichte übersah. Prof. Dohnas Einladung war unabhängig von diesem konkreten Kunstwerk erfolgt. Ich zeigte ihr das Werk. Sie reagierte zunächst mit Staunen und anschließend mit einer Frage nach der Bedeutung ihres eigenen Projekts. Ich begegnete dieser Frage. Sie veränderte, was ich las, was ich die KI fragte, welche Unterscheidungen ich infrage stellte und worüber ich zu schreiben begann.

Kunstwerk
   ↓
Prof. Dohnas Begegnung
   ↓
Frage / begriffliche Erschütterung
   ↓
meine Begegnung mit ihrer Reaktion
   ↓
KI-gestützte Reflexion
   ↓
neue Interpretation
   ↓
neuer Artikel
   ↓
neue technische Möglichkeiten
   ↓
zukünftiges Kunstwerk
   ↺

Deshalb gehören ihre Einladung und ihre Reaktion zur Geschichte des Kunstwerks. Sie sind keine JavaScript-Module innerhalb des Plugins. Sie wirken auf einer anderen kausalen Ebene. Eine Manifestation wirkt auf einen Menschen; die Reaktion dieses Menschen wirkt auf den Entwickler; der veränderte Entwickler wird andere künstlerische Entscheidungen treffen.

Ein Satz aus dem KI-Dialog löste bei mir eine starke emotionale Reaktion aus:

„Das Kunstwerk formt teilweise selbst den Prozess, der das Kunstwerk weiter erschafft.“

Meine Reaktion war unmittelbar: Davon bekomme ich Gänsehaut, denn genau das geschieht gerade. Die Emotion bewies die These nicht. Sie lenkte jedoch meine Aufmerksamkeit auf eine Struktur, die ich bislang nicht benannt hatte.

Ich schuf das System. Das System erzeugte Formen. Die Formen motivierten einen Kunstessay. Der Essay trat in eine Begegnung mit Prof. Dohna ein. Ihre Reaktion erzeugte eine neue Untersuchung. Diese Untersuchung brachte Ideen zu Besucherbeiträgen, Bewahrung und Feedback hervor. Wenn diese Ideen in den Code eingehen, wird das Kunstwerk kausal an seiner eigenen zukünftigen Entwicklung mitgewirkt haben – nicht weil Software insgeheim ihr eigenes Schicksal intendiert, sondern weil sie die Menschen verändert, die sie verändern können.

Der Künstler schafft das Werk, und das Werk wirkt an der Entstehung des zukünftigen Künstlers mit.

An dieser Behauptung ist nichts Übernatürliches. Der Beobachter des Artefakts ist zugleich sein Entwickler; was der Beobachter lernt, kann zu einer Designentscheidung werden. Doch die Alltäglichkeit des Mechanismus sollte seine künstlerische Konsequenz nicht zum Verschwinden bringen:

Der Künstler kann überrascht werden.
Der Künstler kann lernen.
Das Werk kann eine Möglichkeit offenbaren, die sein Schöpfer noch nicht formuliert hatte.

Guardinis Sprache des Dienens an einer sich herausbildenden Gestalt wurde an diesem Punkt konkret. Verantwortung verschwindet nicht, und Empfänglichkeit ist keine Entschuldigung dafür, das eigene Urteil aufzugeben. Was schwächer wird, ist die Fantasie, Autorschaft erfordere, dass jede bedeutsame Möglichkeit bereits vor ihrer Ausführung vollständig in der Intention des Schöpfers vorhanden sei. Auch der Künstler kann zum Betrachter des Werks werden, von ihm lernen und zulassen, dass diese Begegnung den nächsten Akt in eine andere Richtung lenkt.

Der Schreibprozess wiederholte die Struktur, die er beschrieb

Die Rekursion wurde deutlicher, als ich erkannte, dass der Artikel selbst kein äußerer Kommentar mehr war. Der erste Kunstessay veränderte, wie ich das Projekt erklärte. Diese Erklärung veränderte Prof. Dohnas Begegnung. Ihre Reaktion veränderte die Theorie. Die Theorie schlug Funktionen vor. Die Funktionen werden zukünftige Manifestationen verändern.

Die Theorie ist in die Kausalschleife des Kunstwerks eingetreten.

Die Entwürfe dieses Artikels reproduzierten anschließend dieselbe Methode. Ein früher Entwurf war faktisch reichhaltig, behandelte jedoch Engineering als Hauptthema. Ich sagte, der Schwerpunkt sei falsch. Der nächste Entwurf richtete das Werk neu auf Kunst, Begegnung, Sinn, Metanoia und Prof. Dohnas Reaktion aus. Begrifflich war er stärker, ließ die Schlussfolgerungen aber so aussehen, als seien sie in sauberer Reihenfolge eingetroffen. Ich widersprach erneut: Wo waren die wiederholten Fragen, die Verwirrung, die vorläufigen Übereinstimmungen und die Korrekturen sowohl meiner eigenen Auffassungen als auch jener der KI?

Eine weitere Revision stellte die Genealogie wieder her und stärkte die Architektur der Argumentation. Das war eine Verbesserung, doch der Vergleich mit der früheren Darstellung zeigte, dass die disziplinierte Struktur zugleich einen Teil der Evidenz, des Humors, der persönlichen Unmittelbarkeit und jener Zwischendifferenzierungen komprimiert hatte, durch welche die Argumentation überhaupt erst möglich geworden war. Die gegenwärtige dritte Version vollzieht deshalb eine weitere Rückkehr. Sie kehrt nicht zur früheren Architektur zurück und fügt auch nicht jeden verworfenen Satz wieder ein. Sie stellt lediglich jene Details wieder her, die die Evidenz verändern, eine tatsächliche Stufe des Denkens sichtbar machen oder den Leser spüren lassen, warum eine Unterscheidung notwendig wurde.

Jeder Einwand veränderte das Ziel der Revision. Das Ziel bewegte sich von „Mach den Artikel vollständig“ zu „Bewahre die Genealogie des Verstehens“ und anschließend zu „Lass eine solide Architektur den vollständigen evidenziellen und menschlichen Reichtum dieser Genealogie tragen“. Die Revisionsmethode selbst lieferte damit ein weiteres Beispiel menschlicher Handlungsmacht. Meine Handlungsmacht bestand nicht lediglich darin, generierte Absätze abzunicken. Sie zeigte sich darin, zu bemerken, dass ein poliertes Ergebnis etwas methodologisch Wesentliches ausgelöscht hatte, sein implizites Erkenntnismodell zurückzuweisen und neu zu definieren, was der Artikel bewahren musste.

Deshalb ist die Geschichte des Denkens keine dekorative Metadatenebene, die der fertigen Forschung angehängt wird. In diesem Fall ist sie Teil der Forschung. Ohne sie würde der Artikel behaupten, dass Schöpfung rekursiv sei, während er seine eigenen Schlussfolgerungen als linear und vorbestimmt präsentierte. Seine Form würde seiner Argumentation widersprechen.

Der Mensch–KI-Dialog wurde zu einer weiteren Form der Begegnung

War KI lediglich ein Werkzeug, um Gedanken auszudrücken, die ich bereits besaß? Auch diese Beschreibung erwies sich als unzureichend. Mehrere entscheidende Formulierungen waren weder von mir diktiert noch lediglich als redaktionelle Glättung zurückgegeben worden. Sie erschienen durch Interaktion: „Wo genau ist der Sinn?“, „Die Person kann selbst involviert werden“ und „Das Kunstwerk formt teilweise selbst den Prozess, der das Kunstwerk weiter erschafft.“ Jede dieser Formulierungen öffnete einen Weg, den ich zuvor noch nicht in diesen Begriffen formuliert hatte.

Doch die Zusammenarbeit funktionierte nicht deshalb, weil ich flüssige Sprache als Wahrheit akzeptierte. Sie funktionierte, weil sowohl Generierung als auch Widerstand vorhanden waren. Die KI schlug eine Formulierung vor; ich erlebte Wiedererkennen, Zweifel oder Widerspruch; ich brachte eine technische Tatsache, persönliche Geschichte, ein Gegenbeispiel oder eine Unterscheidung ein; die Erklärung veränderte sich; diese revidierte Erklärung veränderte meine nächste Frage. Mein Beitrag war nicht lediglich der Rest, der nach der Automatisierung übrigblieb. Er umfasste die Entscheidung darüber, welche Anomalie Aufmerksamkeit verdiente, welche Analogie falsch war, welche Evidenz zählte und wann sich die Frage selbst verändert hatte.

Das intellektuelle Muster lautete:

partielle Intuition
      ↓
KI-Formulierung
      ↓
mein Wiedererkennen oder Einwand
      ↓
Qualifizierung / Gegenbeispiel / Evidenz
      ↓
revidierte Formulierung
      ↓
neue Verbindung
      ↓
neue Frage
      ↺

Die Interaktion akkumulierte eine Geschichte. Eine spätere Antwort wurde erst möglich, weil frühere Formulierungen infrage gestellt worden waren und sich der Kontext verändert hatte. Die KI konnte anders argumentieren, weil ich die Unterscheidung zwischen Laufzeitgeometrie und dialogischem Modell eingeführt hatte; ich konnte anders denken, weil die KI dem Involviertsein und dem rekursiven Feedback eine Sprache gegeben hatte. Keiner der beiden Beiträge lässt sich verstehen, wenn man einen einzigen abschließenden Absatz von der Sequenz isoliert, die ihn hervorgebracht hat.

Ich behaupte nicht, dass dieser Dialog phänomenologisch identisch mit einer Begegnung zwischen zwei menschlichen Personen war. Ich behaupte jedoch, dass er operationell rekursiv und intellektuell produktiv war. Er erzeugte Formulierungen, Widerstand, Revision und neue Aufmerksamkeit. Diese begrenzte Behauptung genügt, um den Dialog zu einem Teil der Methode zu machen, statt ihn als unsichtbares Instrument hinter der Prosa verschwinden zu lassen.

Begegnung verwandelte eine Pipeline in Feedback

Sobald Ergebnisse begannen, wieder stromaufwärts zurückzukehren, stellten Engineering und Kybernetik ein nützliches Vokabular bereit. In der Regelungstechnik liegt Feedback vor, wenn Informationen über ein Ergebnis zurückgeführt werden und das nachfolgende Verhalten beeinflussen. Mein Entwicklungsprozess besaß bereits diese Struktur:

Intention
   ↓
KI-gestützte Implementierung
   ↓
Ergebnis
   ↓
Beobachtung und Evidenz
   ↓
veränderte Intention
   └──────────↺

Der künstlerische Prozess fügte eine weitere Schleife hinzu:

generatives System
   ↓
Manifestation
   ↓
Begegnung
   ↓
Interpretation
   ↓
veränderter Künstler
   ↓
verändertes System
   └────────────↺

Prof. Dohnas Reaktion fügte eine soziale Schleife hinzu:

Werk / Ergebnis
      ↓
ein anderer Mensch begegnet ihm
      ↓
Reaktion / Einwand / Überraschung
      ↓
ich begegne dieser Reaktion
      ↓
veränderte Interpretation und Handlung
      ↓
zukünftige Arbeit
      ↺

Der Mensch–KI-Schreibprozess fügte eine epistemische Schleife hinzu. Zu verschiedenen Zeitpunkten war ich Künstler, Entwickler und Betrachter. Prof. Dohna war Beobachterin, Gesprächspartnerin und Beteiligte. KI war technische Kollaborationspartnerin, begriffliche Vermittlerin und Generatorin von Formulierungen. Niemand von uns nahm ausschließlich eine einzige stabile Position ein.

An diesem Punkt wurde Kybernetik zweiter Ordnung relevant. Heinz von Foersters Arbeiten lenken die Aufmerksamkeit auf beobachtende Systeme und darauf, was sich verändert, wenn der Beobachter nicht als außerhalb des untersuchten Systems stehend behandelt werden kann (von Foerster, 2003). Ich behaupte nicht, dass ein WordPress-Kunstwerk die Kybernetik zweiter Ordnung löst. Die engere strukturelle Verbindung genügt: Ich entwerfe das System, beobachte es, werde durch das Beobachtete verändert, schreibe über diese Veränderung und führe dieses Schreiben anschließend wieder in die Entwicklung des Systems zurück.

Dennoch benötigte ich noch eine weitere technische Unterscheidung. Die gegenwärtige Besuchererfahrung ist interaktiv, bildet jedoch noch keine vollständige adaptive Lernschleife. Ein Besucher kann die Seite aktualisieren und dadurch einen anderen Zustand erscheinen lassen. Seine private Reaktion verändert nicht automatisch spätere Gewichtungen, Regeln oder Auswahlwahrscheinlichkeiten. Die gegenwärtige Tatsache lautet:

Besucherhandlung → eine weitere Manifestation

Das vorgeschlagene zukünftige System würde hinzufügen:

Komposition
   ↓
Begegnung
   ↓
explizite Reaktion oder Beitrag
   ↓
Systemgedächtnis
   ↓
veränderte zukünftige Bedingungen
   ↓
neue Komposition
   ↺

Diese Unterscheidung ist wichtig, weil eine theoretische Möglichkeit nicht so erzählt werden sollte, als sei sie bereits ein implementiertes Ergebnis. Heute unterstützt das Kunstwerk Variabilität zur Laufzeit und Aktivierung durch den Besucher. Das Speichern rekonstruierbarer Zustände, die Annahme von Besuchermaterial und die Anpassung zukünftiger Generierung bleiben vorgeschlagene Experimente.

Besucherbeiträge könnten privates Erinnern in Systemgeschichte verwandeln

Die erste zukünftige Erweiterung ist vergleichsweise konservativ: Ein Besucher soll eine Komposition bewahren können, die ihn berührt. Ein sinnvoll gespeicherter Zustand würde mehr enthalten als einen Screenshot. Er könnte den Algorithmus, den Seed, die Identitäten der ausgewählten Einträge, die relevante Konfiguration und genügend Viewport-Informationen aufzeichnen, um die Manifestation rekonstruieren zu können. Der Akt des Wiedererkennens durch den Besucher würde dadurch materiell als Spur einer Begegnung sichtbar.

Die zweite Erweiterung verändert die Kategorie des Werks: Besucher sollen visuelle Einträge beitragen können. Gegenwärtig ist das Vokabular aus dreizehn Einträgen weitgehend autobiografisch. Sobald ein anderer Mensch ein Bild einbringen kann, rekombiniert die Engine nicht länger ausschließlich mein kulturelles und persönliches Archiv. Sie könnte die visuelle Erinnerung eines anderen Menschen neben meine chemische Gleichung aus der Kindheit setzen. Ein dritter Mensch könnte dieser Beziehung begegnen und sie bewahren. Später könnte ich wiederum dem gespeicherten Zustand begegnen und eine Bedeutsamkeit entdecken, die weder der Beitragende noch ich vorausgesehen hatten.

mein Archiv
     +
Besucherarchiv
     ↓
generatives System
     ↓
unerwartete Beziehung
     ↓
ein weiterer Besucher
     ↓
Interpretation
     ↓
bewahrte Begegnung
     ↓
meine spätere Begegnung
     ↓
neues Werk
     ↺

Das Kunstwerk würde nun einander kreuzende Biografien enthalten. Olga Goriunovas Darstellung partizipativer Plattformen ist hier hilfreich, weil digitale künstlerische Aktivität durch Infrastrukturen und kollektive Prozesse entstehen kann, anstatt in einem abgeschlossenen Artefakt eingeschlossen zu bleiben (Goriunova, 2016). Katja Kwastek behandelt Handlung, System und ästhetische Erfahrung ebenfalls als integrale Bestandteile interaktiver digitaler Kunst und nicht als optionale Ergänzungen eines abgeschlossenen Objekts (Kwastek, 2013).

Partizipation ist allerdings nicht automatisch künstlerischer Fortschritt. Besuchermaterial würde Provenienz, Moderation, kontrollierte Dateiverarbeitung, Berechtigungen, Löschverfahren und eine sichtbare Unterscheidung zwischen dem autobiografischen Kern und beigetragenen Einträgen erfordern. Ein öffentliches Upload-Feld kann partizipative Kunst mit beeindruckender Geschwindigkeit in Malware-Kuration verwandeln, wenn man gegenüber beliebigen MIME-Typen allzu spirituell empfänglich wird.

Wenn Besucherreaktionen zukünftige Generierung verändern, wird die Schleife rechnerisch

Gegenwärtig verändert Interpretation mich und kann dadurch die spätere Entwicklung verändern. Das vorgeschlagene adaptive System würde einen weiteren Pfad explizit machen: Die Reaktion eines Besuchers könnte den Wahrscheinlichkeitsraum verändern, aus dem spätere Kompositionen hervorgehen. Bedeutung hätte dann eine rechnerische Konsequenz – nicht weil die Software die Begegnung im menschlichen Sinne verstanden hätte, sondern weil eine Spur der Begegnung zu einer der zukünftigen Bedingungen des Systems geworden wäre.

Wenn dies implementiert würde, könnte das Werk vorläufig als ko-adaptives partizipatives generatives System bezeichnet werden. Die Formulierung sollte ausdrücklich bedingt bleiben. „Partizipativ“ würde nicht bedeuten, dass jeder Besucher zu einem gleichberechtigten Autor wird; „adaptiv“ würde nicht bedeuten, dass das System Metanoia durchlebt; und „ko-adaptiv“ würde die Asymmetrie zwischen einem Menschen, dessen Orientierung selbst auf dem Spiel stehen kann, und einem Mechanismus, dessen Parameter sich verändern, nicht auslöschen.

Die Designfragen sind daher Teil des Kunstwerks und nicht lediglich administrative Einzelheiten. Sollte Erinnerung einem einzelnen Besucher, einer temporären Gruppe oder dem gesamten öffentlichen System gehören? Wenn ein beigetragener Eintrag in eine spätere Komposition eingeht, trägt er dann die Erklärung des Beitragenden mit sich, oder kann seine Bedeutung durch eine andere Begegnung transformiert werden? Wenn der Beitragende seine Entfernung verlangt, sollten spätere gespeicherte Zustände verschwinden, eine historische Spur bewahren oder zu nicht mehr rekonstruierbaren Aufzeichnungen von etwas werden, das einmal existiert hat?

Ein gesondertes Risiko betrifft die Wahrscheinlichkeit. Ich zögere, populäre gespeicherte Zustände automatisch wahrscheinlicher wiederkehren zu lassen. Wenn jedes Herzsymbol das Gewicht eines Eintrags erhöht, könnte das Werk auf die sichersten und vertrautesten Kombinationen konvergieren. Nach Monaten des Aufbaus einer Ökologie der Begegnung könnte ich versehentlich das Empfehlungssystem neu erfinden. Eine nicht-adaptive Baseline, transparente Experimente und reversible Gewichtungen würden die Möglichkeit bewahren, dass gerade jene Begegnung die wichtigste ist, die keine Popularitätsmetrik jemals auswählen würde.

Begegnung ist reicher als Feedback, weil eine Reaktion eine neue Kategorie und nicht lediglich einen neuen Wert einführen kann. visitor_liked = true kann eine Gewichtung verändern. Die Erklärung eines Besuchers – „Diese Kombination hat verändert, wie ich über Exil dachte“ – kann dagegen verändern, wofür der Künstler das System überhaupt hält. Wenn Feedback nicht lediglich einen Parameter, sondern das Kriterium selbst verändert, nähert sich der Prozess erneut dem Problem der Metanoia.

Handlungsmacht wurde verteilt, geschichtet und ungleich

Zu diesem Zeitpunkt war die Formulierung „menschliche Handlungsmacht wandert“ hilfreich, aber unvollständig. Handlungsmacht wandert, verteilt sich und bildet Schichten im gesamten Projekt. Diese Schichten besitzen unterschiedliche Kräfte, Geschichten und Verantwortlichkeiten:

Beteiligter oder Ebene Gegenwärtiger Beitrag Grenze oder ungelöste Frage
Künstler-Entwickler Zweck, Kuration, Beschränkungen, Bewertung, Veröffentlichung, Verantwortung und Revision des Ziels. Bestimmt nicht jede Manifestation manuell und sieht nicht jede Interpretation voraus.
Entwicklungs-KI Codegenerierung, Architekturvorschläge, Diagnose, begriffliche Formulierung und Reorganisation. Semantischer Beitrag begründet für sich allein weder gelebte Bedeutsamkeit noch gleichwertige Verantwortung.
Laufzeitgenerator Prozedurale Auswahl und Geometrie innerhalb konfigurierter Regeln. Besitzt nicht die kulturellen Biografien, die mit den Einträgen verbunden sind.
Browser Materielle Ausführung, Layout, Beschnitt, Animation, Timing und viewportabhängige Aufführung. Die Aufführung ist kausal wirksam, ohne dadurch kuratorische Verantwortung zu werden.
Besucher Ankunft, Aktualisierung, Aufmerksamkeit, Interpretation, Erinnerung und mögliche Bewahrung. Die gegenwärtige Kontrolle ist partizipativ, aber begrenzt; Beiträge bleiben bislang prospektiv.
Begegnung Eine Beziehung, in der Bedeutsamkeit und Transformation entstehen können. Kann weder garantiert noch vollständig besessen noch auf einen einzigen Beteiligten reduziert werden.

Die Tabelle ist keine endgültige Ontologie. Sie schützt zwei Unterscheidungen, die elegantere Prosa leicht verwischen kann. Erstens macht kausale Beteiligung nicht jeden Beteiligten im gleichen Sinne zum Autor. Zweitens löst verteilte Handlungsmacht Verantwortung nicht auf. Ein Browser, der CSS ausführt, ist ethisch nicht gleichbedeutend mit der Person, die einen Eintrag veröffentlicht. Eine KI, die Code vorschlägt, ist nicht automatisch auf dieselbe Weise verantwortlich wie die Person, die ihn bereitstellt. Ein Besucher, der eine Seite aktualisiert, wird dadurch nicht zum alleinigen Schöpfer.

Die bessere Frage lautet daher nicht: „Wer besitzt die gesamte Handlungsmacht?“ Sie lautet: Welche Art von Handlungsmacht wirkt auf jeder Ebene, wie verändern sich die Ebenen gegenseitig, und wo verbleibt Verantwortung, wenn kein Beteiligter das gesamte Ergebnis kontrolliert? Forschung zu generativer KI und Kunst zeigt bereits, warum konventionelle Autorschaft instabil wird, wenn kreative Arbeit automatisiert oder neu verteilt wird (Epstein et al., 2023). Dieses Projekt macht diese Instabilität über Entwicklung, Laufzeitgenerierung, Aufführung, Interpretation und zukünftige Revision hinweg sichtbar – und nicht lediglich im Augenblick der Bildsynthese.

Das Kunstwerk ähnelt zunehmend einer prozessualen Ökologie

Zu diesem Zeitpunkt konnte keine einzelne Pipeline das Projekt mehr darstellen. Technische Produktion, künstlerische Erfahrung, soziale Reaktion, begriffliche Interpretation und zukünftige Modifikation speisten sich mit unterschiedlichen Geschwindigkeiten gegenseitig:

Schreiben / Theorie ⇄ Künstler-Entwickler ⇄ Entwicklungs-KI
                           ↓
                    generatives System
                           ↓
                    manifestierte Form
                           ↓
                        Betrachter
                           ↓
                        Begegnung
                           ↓
              Interpretation / Transformation
                           ↓
                     zukünftiges Werk
                           ↺

Ich verwende prozessuale Ökologie als Bezeichnung für diese Interdependenz, nicht um anzudeuten, jedes Element sei lebendig oder gleichwertig. Die Ökologie umfasst Code, Einträge, Browserverhalten, Menschen, Erinnerungen, institutionelle Beziehungen, Theorie und Wartung. Ihre Beteiligten besitzen ungleiche Fähigkeiten und Verantwortlichkeiten, doch ein Ereignis auf einer Ebene kann die Bedingungen auf einer anderen reorganisieren. Das Kunstwerk ist weniger ein einzelnes Objekt, das sich entlang einer Produktionslinie bewegt, als vielmehr ein gepflegtes Feld, in dem Formen, Begegnungen und Entscheidungen wiederkehren.

Schöpfung begann sich vom Herstellen von Objekten zum Aufbau einer Welt zu bewegen

Die Unterscheidung zwischen einem KI-generierten Artefakt und einer KI-gestützten generativen Infrastruktur kehrte nun auf einer tieferen Ebene zurück. Ein Prompt-to-Image-Workflow verortet das wichtigste generative Ereignis gewöhnlich vor der Ankunft des normalen Betrachters. Der Betrachter begegnet einem Artefakt. In meinem Projekt half KI beim Aufbau einer Umgebung, deren zukünftige Manifestationen während der Entwicklung noch nicht sämtlich vorhanden waren. Besucher kommen später und instanziieren Formen. Zukünftige Besucher könnten Ausgangsmaterial hinzufügen. Ihre Reaktionen könnten schließlich spätere Bedingungen verändern.

Die künstlerische Frage bewegte sich deshalb von:

Was soll ich machen?

hin zu:

Welche Bedingungen soll ich konstruieren und pflegen, damit Formen, Beziehungen und Begegnungen, die ich nicht vollständig vorhersehen kann, weiterhin möglich werden können?

Das kommt dem Aufbau einer Welt näher als dem Herstellen eines Objekts. In einer Sprache, die Prof. Dohnas Projekt nähersteht, bewegt es sich von der Herstellung einer abgeschlossenen Gestalt hin zur Schaffung eines Raumes, in dem Gestaltbildung weitergeht. Der Künstler wählt weiterhin aus, setzt Grenzen und übernimmt Verantwortung. Doch das Werk erschöpft sich nicht länger in einer einzigen vollendeten Oberfläche.

Am Anfang hielt ich das Hintergrundbild für die zentrale Einheit. Dann wurde es die generierte Komposition. Später konzentrierte ich mich auf die Gegenüberstellung. Inzwischen vermute ich, dass die wichtigere Einheit vielleicht die Begegnung und ihre Fähigkeit ist, als Bedingung weiterer Schöpfung zurückzukehren. Diese Verschiebung war nicht im Voraus geplant. Sie entstand, weil sich jede vorgeschlagene Einheit angesichts der Evidenz als zu eng erwies.

Prof. Dohna und ich wurden zu Beteiligten dessen, worüber wir theoretisierten

Die Chronologie besitzt beinahe etwas verdächtig Elegantes. Prof. Dohna lud mich nicht ein, weil sie dieses Kunstwerk bereits kannte. Ich baute es nicht als Illustration ihres neuen Kurses. Zwei voneinander unabhängig entstehende Projekte begegneten einander.

Ihr Projekt gab mir Begriffe – Metanoia, kontemplatives Sehen, Dienen, Gestalt, Sinn und Begegnung. Mein Kunstwerk gab ihr einen funktionierenden Fall, der eine einfache Unterscheidung zwischen menschlichem Schaffen und maschineller Produktion störte. Ihre Reaktion störte meine Interpretation. Der Mensch–KI-Dialog brachte weitere Formulierungen hervor. Meine Einwände veränderten diese Formulierungen. Dieser Artikel könnte nun zu ihr zurückkehren und die Diskussion erneut verändern.

Keines der beiden Projekte enthält das andere. Ihre Theorie erklärt das Kunstwerk nicht vollständig von oben. Meine Software widerlegt ihr Projekt nicht von unten. Etwas entstand zwischen ihnen.

Diese emergente Einsicht war zuvor nicht vollständig in einem der beiden Projekte gespeichert. Sie wurde verfügbar, als sie einander begegneten, als sie reagierte, als ich ihrer Reaktion begegnete und als der Dialog veränderte, was jeder von uns als Nächstes fragen konnte. Die Geschichte selbst wurde zur Evidenz für die relationale Auffassung von Bedeutung.

Warum ihre Frage vielleicht wertvoller war als Zustimmung

Deshalb bin ich zunehmend dankbar für ihre Frage, statt mir zu wünschen, sie hätte lediglich Zustimmung geäußert. Wenn die bedrohte Behauptung lautete: „Maschinen können nicht schaffen“, macht die technologische Evidenz sie verwundbar. Wenn die tiefere Frage dagegen betrifft, was für einen Beteiligten bedeutsam werden, ihn transformieren, verpflichten und neu orientieren kann, dann beseitigt das Kunstwerk diese Frage nicht. Es macht sie anspruchsvoller.

Zustimmung hätte meiner ersten Erklärung erlaubt, bequem zu bleiben. Ihr Zweifel legte deren schwächste mögliche Form offen: die beruhigende Opposition, in der der Mensch schafft und die Maschine lediglich reproduziert. Weil diese Antwort unter der tatsächlichen Architektur des Werks scheiterte, musste ich Generierung und Begegnung zunächst unterscheiden und anschließend wieder verbinden; semantische Kompetenz von gelebter Bedeutsamkeit unterscheiden; und fragen, ob Anpassung jemals zu Metanoia werden kann. Der Einwand zerstörte das Projekt nicht. Er zwang sowohl das Kunstwerk als auch die Theorie dazu, weniger einfach zu werden.

Was ungelöst bleibt

Ich weiß nicht, wo Sinn letztlich „wohnt“ – oder ob Wohnen überhaupt die richtige Metapher ist. Der Vorschlag, Sinn erschließe sich relational, bleibt eine Interpretation und kein durch Software demonstriertes Ergebnis. Prof. Dohnas von Guardini geprägter Rahmen behält einen theologischen Horizont, den dieser rechnerische Fall weder beweisen noch widerlegen kann. Das Kunstwerk zeigt, dass Beziehungen bedeutsam werden können, ohne dass ein einzelner Beteiligter ihre vollständige Interpretation explizit vorkomponiert. Es bestimmt nicht den letzten Grund dieser Bedeutung.

Ich weiß ebenso wenig, ob eine hinreichend persistente, sich selbst modifizierende KI Kriterien erfüllen könnte, die mit gelebter Bedeutsamkeit oder Metanoia verbunden sind. Gegenwärtige semantische Kompetenz ist real. Behauptungen über subjektive Bedeutsamkeit verlangen andere Evidenz. Zukünftige Systeme könnten diese Unterscheidung schwieriger machen; das ist ein Grund, die Frage zu verfeinern, nicht die Antwort per Definition festzulegen.

Bewahrung durch Besucher, Besucherbeiträge und adaptive Gewichtung sind gegenwärtig keine realisierten Leistungen. Es handelt sich um Designmöglichkeiten, die aus dieser Untersuchung hervorgegangen sind. Ihre Implementierung könnte das Werk bereichern, Moderationsprobleme erzeugen, Überraschung nivellieren oder völlig andere Fragen sichtbar machen. Der Artikel sollte zukünftiger Software nicht denselben Evidenzstatus zuschreiben wie tatsächlich bereitgestelltem Verhalten.

Auch ist nicht jede Komposition bedeutungsvoll. Manchmal erscheint ein Seelöwe neben einem Rechteck, weil JavaScript zwei Einträge benachbarten Regionen zugeordnet hat. Manchmal ist das Layout schwach. Manchmal ist der Besucher beschäftigt. Manchmal geschieht überhaupt nichts. Wenn jede Aktualisierung zuverlässig Metanoia hervorriefe, würde ich überprüfen, ob versehentlich ein Theologie-Plugin mit ungewöhnlich aggressivem Caching installiert worden ist.

Zur Offenheit des Kunstwerks gehören Trivialität, Langeweile, Scheitern und Unsinn. Die Engine kann Bedingungen der Begegnung schaffen; Offenbarung kann sie nicht befehlen.

Was ich als Nächstes testen möchte

Die nächste Phase sollte die Theorie durch Praxis testen und dabei gegenwärtige Tatsachen klar von Plänen trennen. Erstens möchte ich die Bewahrung rekonstruierbarer Kompositionen implementieren. Dadurch könnte eine Begegnung nachvollziehbar werden, ohne so zu tun, als enthielte ein Screenshot allein bereits das vollständige Ereignis.

Zweitens möchte ich mit einem moderierten Besuchervokabular experimentieren. Beigetragene Einträge sollten vom autobiografischen Kern unterscheidbar bleiben, ihre Provenienz bewahren und eine Entfernung ermöglichen. Das Ziel besteht nicht darin, möglichst viele Uploads zu erzielen. Ich möchte herausfinden, ob Beziehungen zwischen unterschiedlichen persönlichen Geschichten das ästhetische und interpretative Feld verändern.

Drittens würde ich erst nach der Beobachtung solcher Interaktionen explizites adaptives Feedback testen. Ich würde mit reversiblen Experimenten beginnen und eine nicht-adaptive Baseline beibehalten. Bevor ich die Wahrscheinlichkeit eines gespeicherten Zustands erhöhe, möchte ich wissen, ob seine Bewahrung ästhetischen Wert, persönliche Erinnerung, Neugier, Humor oder lediglich die Bequemlichkeit eines Buttons bezeichnet.

Schließlich möchte ich die intellektuelle Geschichte rund um den Code bewahren. Wenn Theorie, Gespräch und Reaktion kausale Inputs späterer Versionen sind, zeichnet ein Source-Repository nur einen Teil des Werks auf. Die technischen Artikel, der erste Kunstessay, Prof. Dohnas Nachrichten, der Mensch–KI-Dialog, die verworfenen Erklärungen und die revidierten Ziele bilden ein Entwicklungsarchiv. Die Genealogie des Denkens ist von der Evolution des Kunstwerks nicht zu trennen.

Als das Kunstwerk antwortete

Ich begann mit einer praktischen Hintergrundfunktion. Sie wurde zu einem strukturierten Selektor, dann zu einem System für Mosaike und verstreute Kompositionen, anschließend zu einer Familie mathematischer Generatoren und schließlich zu einem wartbaren Plugin. Reale Ergebnisse veränderten die Spezifikation. Vordergrund und Hintergrund wurden zu einer einzigen visuellen Umgebung. Die Aktualisierung wurde zu einer performativen Geste. Wiederholung erzeugte Geschichten von Begegnungen. KI-gestützte Entwicklung komplizierte die Autorschaft. Agentische KI erschütterte meine erste Verteidigung menschlicher Handlungsmacht.

Dann lud mich Prof. Dohna als Künstler ein, bevor sie dieses konkrete Werk kannte. Ihre Reaktion veranlasste mich, ihr Projekt erneut zu lesen. Dieses erneute Lesen verschob die Frage von Generierung zu Begegnung. Die KI schlug Formulierungen vor, die ich akzeptierte, denen ich widersprach und die ich revidierte. „Wo genau ist der Sinn?“ veränderte das Modell der Bedeutung. „Die Person kann selbst involviert werden“ führte zur Metanoia. „Das Kunstwerk formt teilweise selbst den Prozess, der das Kunstwerk weiter erschafft“ machte die Rekursion sichtbar. Ein früher Entwurf bewahrte die Evidenz, setzte aber den falschen Schwerpunkt. Ein späterer Entwurf fand den Schwerpunkt, komprimierte jedoch die Genealogie. Eine weitere Revision stellte diese Genealogie wieder her, verlor aber dennoch einen Teil des konkreten Reichtums der früheren Darstellung. Der Vergleich der Fassungen veränderte die Methode noch einmal: Stärkere Argumentation und reichere Details mussten sich gegenseitig tragen, statt miteinander um redaktionellen Raum zu konkurrieren.

Die Bewegung sah daher eher folgendermaßen aus:

Schöpfung
   ↓
unerwartete Form
   ↓
Begegnung
   ↓
Frage
   ↓
vorläufige Erklärung
   ↓
Einwand / Evidenz / Korrektur
   ↓
Neuinterpretation
   ↓
Transformation des Ziels
   ↓
neue Schöpfung
   ↺

Der hinzugefügte Mittelteil ist entscheidend. Begegnung führt nicht mechanisch zu Verständnis. Sie kann Verwirrung erzeugen. Eine elegante Antwort kann sich als unzureichend erweisen. Evidenz kann eine Unterscheidung erzwingen. Ein Gegenbeispiel kann sie wieder öffnen. Das Werk wird intellektuell generativ, weil der Prozess nicht beim ersten Satz endet, der vollständig klingt.

Anfangs dachte ich, ich hätte eine Engine für generative Komposition geschaffen.

Dann dachte ich, ich hätte eine Engine zur Erzeugung von Bedingungen der Begegnung geschaffen.

Nun erscheint selbst diese Beschreibung unvollständig, weil Begegnungen begonnen haben, in das System zurückzukehren, das sie erzeugt hat. Sie verändern den Künstler, die Theorie, den Dialog, das vorgeschlagene Partizipationsmodell und potenziell die zukünftigen Algorithmen. Das Kunstwerk wird zu einem Ort, an dem Generierung und Begegnung einander speisen: Formen schaffen Anlässe für Reaktionen, und Reaktionen kehren als Bedingungen späterer Formen zurück.

Ich bin Prof. Dohna gerade deshalb dankbar dafür, diese Phase angeregt zu haben, weil sie mir keine Schlussfolgerung überreichte. Ihr Projekt begegnete meinem, meines begegnete ihrem, und die Störung zwischen beiden erzeugte Fragen, die keines von ihnen zuvor vollständig in sich enthalten hatte.

Die Frage, die ich nun mit mir trage, lautet:

Wo geschieht Bedeutung, wenn ein Mensch Bedingungen schafft, KI dabei hilft, sie zu konstruieren, Algorithmen Beziehungen erzeugen, ein Mensch diesen Beziehungen begegnet, die Begegnung verändert, was für diesen Menschen bedeutsam ist, und diese Transformation wiederum Teil jener Bedingungen wird, aus denen die nächste Schöpfung hervorgeht?

Ich weiß es noch immer nicht.

Doch drei Tage zuvor, als ich den ersten Kunstessay veröffentlichte, wusste ich noch nicht einmal, dass dies die Frage war! Als der erste vollständige Entwurf fertig war, wusste ich noch nicht, dass die Bewahrung der Art und Weise, wie diese Frage entstanden war, selbst Teil der Antwort sein würde. Als die strukturell stärkere Revision fertig war, hatte ich noch nicht gesehen, wie viel konkretes Leben sie zurückgelassen hatte. Diese fortwährende Veränderung des Verstehens ist kein Abschluss.

Vielleicht ist gerade diese Veränderung selbst das erste Stück Evidenz dafür, dass das Kunstwerk bereits geantwortet hat.

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