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.
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