Three days after I published an article about turning the refresh button of my website into an artistic compositional gesture, Prof. Yvonne Dohna-Schlobitten unexpectedly invited me to participate in her developing international project as an artist—and, almost in the same breath, in a scientific capacity as well. She had not yet seen this particular work. I sent it to her, explained that every refresh or navigation could produce another composition, and asked her to try it. Her first response was “Incredible.” Twenty-five seconds later she wrote, “Allora il mio progetto non ha tanto senso”, followed almost immediately by a question mark.
I answered quickly because I thought almost the opposite. Yet the answer I sent was only the beginning. Her question did not lead directly to one clean conclusion. It began a chain of re-questioning in which each apparently useful distinction exposed another problem. Generative composition became a question of encounter. Encounter became a question of where sense happens. That question led to agency, semantic competence, lived significance and metanoia. Agentic AI then destabilized my first account of human agency. Finally, the inquiry curved back upon itself: the discussion, the writing and even the revisions of this article began changing what I wanted to build next.
I want to preserve that development because I did not possess the final theory when I began. In fact, I did not even possess it after the first complete draft of this article. An early draft retained a great deal of technical evidence but allowed engineering to dominate the story. A later draft corrected the centre of gravity, placing artistic creation, encounter and metanoia at the centre, but polished away too much of the intellectual struggle that had made those ideas possible. A subsequent revision restored that genealogy and gave the argument a stronger architecture. Yet comparing it with the earlier telling exposed another loss: several concrete details that made the transformations perceptible had been compressed. This version therefore returns to those details without abandoning the stronger structure. The missing material was never simply “more information.” It was the history of the thinking itself: the first explanations that seemed persuasive, my repeated “yes, but…”, the evidence that changed the model, the AI formulations I accepted only partly, and the moments when the original question became too narrow.
The result is not simply a theory about a generative artwork. It is also a record of how an artwork, its artist–developer, an AI collaborator, another scholar and a philosophical vocabulary altered one another. Each became an input to what the others could disclose next.
This revision therefore asks the reader to follow not only what I now think, but how the thought developed. The question holding the different layers together is still provisional: where does meaning happen when a human conceives a system, AI helps build it, algorithms generate its forms, a browser performs them, another person encounters them, and that encounter changes the human, the theory and eventually the system from which the next form will emerge?
I did not begin with encounter
Yin’s Background Studio began because I wanted better backgrounds on my WordPress website. The earliest version selected among a small group of image and video records. It then acquired structured settings, equal and weighted random selection, page/session/daily persistence, reduced-motion behaviour, mosaics, scattered repetition, density and gap controls, and eventually a modular Generative Engine. I documented the stages separately 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), and Turning a Theme-Bound Generative Art System into a Maintainable WordPress Plugin.
The present visual vocabulary contains thirteen configured records drawn from different parts of my intellectual and personal life: photography, animated animals, transformed Internet imagery, religious images, mathematical structures, philosophy, humour, and a chemical equation connected with my childhood. Twelve mathematical generators—including binary-space partition, quadtree, Hilbert ordering, golden spiral partitioning, Voronoi and Delaunay geometry, Lloyd-relaxed Voronoi, phyllotaxis, radial fans, squarified treemaps, diagonal Truchet tessellation and Ulam spiral ordering—continually reorganize that vocabulary.
The important technical fact is simple. These algorithms do not synthesize new source pictures. The archive supplies something like a vocabulary; the generators provide changing grammars of spatial relation. The project therefore belongs to the broad tradition of generative art in which an artist constructs a rule-governed possibility space and gives an external system some operational autonomy (Boden & Edmonds, 2009) (Galanter, 2016). My case adds another layer: AI helped me construct much of the infrastructure, while the later page compositions are produced by inspectable browser-side procedures rather than by asking a generative AI model to make a fresh picture on every visit.
A simplified runtime sequence is enough to show the mechanism:
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);
});
The code chooses records, computes regions, shuffles their assignment and renders them. There is no secret phenomenology function hidden below renderRegion(). Yet the visible consequences can become interpretively rich. Bertrand Russell may appear beside an exuberant sea lion. Jesus spinning a basketball may be placed beside an otter carrying a ball. A Hilbert animation can appear inside the ordering logic of an Ulam spiral. A fox may occupy the visual field beside my autobiographical chemical equation. A Last Supper animation can coexist with comic animals and a serious technical article.
Sometimes the result feels as though the system has followed an instruction I never wrote: “Place a twentieth-century philosopher beside an excited marine mammal so that analytic philosophy looks publicly heckled.” The joke is mine, not the algorithm’s. But the juxtaposition that invited it was not manually composed by me either. The algorithm had performed geometry and selection; the beholder had begun producing relationships.
Those relationships can be comic, tender, absurd, theological, autobiographical—or simply uninteresting. That last possibility matters. Procedural novelty does not guarantee artistic significance. The system can make a relation available; only an encounter can reveal whether anything happens through it.
At first I described the algorithm as an editor. It controls territory, adjacency, scale, recurrence, rhythm and interruption without possessing the cultural interpretation that may later arise. Voronoi geometry does not know that Russell is a philosopher. A Hilbert traversal does not recognize Christian iconography. Nevertheless, its spatial choices alter which relations become available to a viewer. This gap between causal simplicity and interpretive richness was the first clue that the project was doing more than decorating a webpage, although I did not yet know how far that clue would lead.
The work taught me what I had been trying to make
It would be historically false to say that I imagined the complete artwork and then used AI to implement an already stable specification. The development moved through partial understandings. First I wanted a more flexible background selector. Then actual outputs made me want mosaics. Mosaics exposed the difference between merely filling space and composing relations. Scattered repetitions raised questions about density, scale and empty regions. Mathematical generators then changed the system from a collection of display modes into a family of visual grammars. Finally, the growing feature became too application-like to remain owned by the theme and had to become a site-specific plugin.
The recurring development pattern was closer to this:
incomplete intuition
↓
AI interpretation
↓
candidate implementation
↓
real output
↓
my encounter with the output
↓
criticism / surprise / recognition
↓
changed understanding
↓
revised intention
↓
another AI-assisted implementation
↺
The words “real output” matter. AI could generate plausible PHP, JavaScript, CSS, shell procedures, validators and geometric abstractions, but the running system supplied evidence that neither fluency nor intention could replace. A candidate could parse correctly and still feel artistically wrong. A layout could satisfy its geometry and leave an awkward visual void. A proposed fix could assume source text that no longer existed. The next step had to follow the evidence produced by the actual file, browser, option state or runtime behaviour.
Several iterations made that division of labour visible. I rejected implementations that were technically correct because their visual behaviour was wrong. At other moments an unexpected output disclosed a direction better than the one I had requested. I sometimes rejected an architecture proposed by AI because it violated a constraint the model could not yet see; at other times I accepted an AI proposal over my original preference because the running evidence showed that my first design was inferior. The collaboration did not preserve a sovereign intention and merely automate its execution. Encounter with the artifact transformed the intention itself.
Some failures changed more than one line of code. An early installer rejected a plausible video-support candidate with ERROR: video preview calls are incomplete. My first temptation could have been to infer that video support itself was defective. The evidence did not justify that conclusion: the validator had stopped the deployment because it expected a particular JavaScript structure. We therefore separated several claims that the first error message had collapsed—front-end rendering, administrative preview, preservation of existing image handlers, syntax validity and structural expectations. A validator is useful evidence, but capital letters do not make it an oracle.
At another stage, a MutationObserver recursion could freeze the administration page even though PHP, WordPress and stored settings remained healthy. That failure forced a more precise model. “The website works” was not one indivisible fact. Backend bootstrap, syntax, stored data, administrative interaction, public rendering and browser behaviour were different claims requiring different tests. Later, when the system migrated from theme ownership to a plugin, the transition was divided into bounded states: install the validated plugin inactive, make the old theme fall back unambiguously, activate and verify the plugin, and only then remove the legacy implementation. Thirty-three old files eventually left the theme, but the saved visual vocabulary remained intact.
Those episodes belong in this article because they show how evidence changed understanding. They also explain why my role cannot be reduced to either “I wrote the code” or “AI wrote the code.” AI generated substantial implementation and reasoning. Deterministic tools established whether syntax, structure and stored state satisfied specific claims. The browser supplied visual evidence. I decided which problem mattered, which constraint could not be sacrificed, whether a technically valid output was artistically acceptable, and what the next question should become. The specification emerged through seeing real outputs and revising my own language.
Only later did I recognize how closely this practical pattern resembled the structure Prof. Dohna was describing through artistic Gestalt, service and metanoia. The thing being created was educating the creator about what he was trying to create. That does not mean the software possessed a mystical will. It means the work disclosed possibilities and failures that did not exist for me as explicit knowledge before I encountered them.
Refresh brought the visitor inside the work
The next conceptual change came from one of the most ordinary actions on the web. Normally a refresh requests the same resource again. In the Background Studio, it can instantiate another artistic state. A visitor opens an article and receives one composition; refresh produces another; navigation to a different article may produce another again. The work exists less like one definitive picture and more like a structured population of potential manifestations. Lev Manovich’s discussion of variability is relevant because a digital object’s capacity to exist in changing versions can become a formal property of the cultural work rather than an incidental technical feature (Manovich, 2001).
The browser consequently became more than a neutral display case. Viewport size, CSS layering, JavaScript execution, cropping, decoding, animation phase, the article in the foreground and the timing of the visit all participate in the manifestation. A screenshot preserves a specimen, not the whole work. The work also includes the rules that make another specimen possible.
I first called the visitor a co-creator. Then I hesitated. The word granted more control than the present interface actually offers. A visitor cannot drag Russell into the upper-right corner, change a Voronoi parameter or demand that the next state contain a fox. The more accurate phrase became a performer of selection without complete authorship. The visitor arrives, navigates, refreshes, attends, remains or leaves. That modest gesture nevertheless determines which possible manifestation becomes actual for that person at that time.
Umberto Eco’s open work helped me understand that openness does not mean the absence of artistic structure. A work can define constraints while leaving aspects of realization or meaningful completion to performance, chance and interpretation (Eco, 1989). Here, openness is partly executable. Seed, record selection, geometry, viewport, animation, article context and visitor action prevent one fixed visual state from exhausting the work.
I had to correct another attractive phrase. I initially wanted to say that every visitor receives an absolutely unique composition. Technically, “practically unique in ordinary use” is more defensible. A finite configured system using deterministic seeds can theoretically revisit an earlier state. Artistic enthusiasm is valuable, but occasionally the hash function deserves a vote.
Repeated visits introduced memory into the generative system
Once I took the visitor seriously, time entered the artwork. Russell may appear beside the sea lion today, disappear during several visits and return later beside a Hilbert animation. The visitor brings previous manifestations into the present one. Familiarity, absence, return and rarity can acquire significance. A record that once seemed merely comic may become affectionate when it reappears; a relation that was initially unnoticed may become legible only after another composition interrupts it.
I gradually distinguished three forms of meaning. Local meaning can arise in one adjacency. Sequential meaning can arise through the order of appearances, disappearances and returns. Distributional meaning can arise through frequency, rarity, weight and eligibility across many manifestations. An improbable relation can matter partly because the visitor has learned, without calculating, that it is improbable.
The system was no longer producing only compositions. It was producing histories of encounters. Yet the history currently lives largely in the visitor rather than in the document object model. The browser does not need to know that the fox has been absent for three days for its return to matter to someone who remembers it. This clarified an important distinction: a work can generate temporal experience without yet storing an explicit institutional memory of that experience.
A future preservation or contribution layer could make part of that private history explicit. A saved reconstructable state would record one encounter; a visitor-supplied image could enter later combinations; an interpreted relation could influence future weights. But those are further steps. The present work already depends on remembered recurrence, even before the software learns to remember on the visitor’s behalf.
AI helped build the generator rather than each final image
The phrase “AI-generated art” initially seemed convenient, but it obscured the architecture. I did not type one prompt into an image model, receive a finished artifact and publish it. AI helped me build the infrastructure from which later visual states could emerge. Once deployed, that infrastructure can produce a new manifestation in a visitor’s browser without calling the development AI again.
| Direct AI-generated artifact | AI-assisted generative infrastructure |
|---|---|
| A model synthesizes a particular output. | AI helps construct a persistent system capable of producing many later outputs. |
| The primary generative act usually happens before an ordinary viewer arrives. | The visitor’s arrival, navigation or refresh instantiates a runtime manifestation. |
| The delivered artifact may remain fixed until another generation is requested. | Variability belongs to ordinary use of the work. |
| The model’s latent possibility space remains central to production. | Part of the possibility space is externalized into inspectable records, seeds, weights, algorithms and browser behaviour. |
| The viewer primarily encounters a selected output. | The visitor participates in deciding when another output becomes actual. |
Before I could articulate the distinction in a table, I drew it as two production paths:
DIRECT AI GENERATION
human prompt → AI model → image → viewer
AI-ASSISTED GENERATIVE INFRASTRUCTURE
human intention ⇄ development AI
↓
persistent generative system
↓
visitor action + browser runtime
↓
manifestation
Even the second diagram was too linear. The manifestation could change the visitor, the artist or the next specification and therefore return upstream. But drawing the two paths first prevented “AI-generated” from hiding where generation actually occurred.
The distinction appeared architectural at first. Then I realized that it changed the artistic question. Direct generation often asks, “What image should exist?” This project increasingly asks, “What conditions should exist so that unforeseeable forms and encounters can continue occurring?” AI did not principally make the artwork as one object. It helped construct an apparatus capable of producing occasions for art-events after the development conversation had ended.
I briefly considered the phrase “AI-assisted meta-generative art” because AI operated at the level of constructing the generator. I still find the phrase descriptively useful, but I do not present it as a settled art-historical category. The stronger claim does not depend on naming a new genre. The architecture is enough: human intention and AI-assisted development produce an inspectable generative environment; a visitor later activates a manifestation that neither the visitor, I nor the development AI explicitly composed as a finished arrangement beforehand.
My first defence of human agency became insufficient
My earlier technical articles had already reached a conclusion I considered important: human agency does not depend on manually typing every line of code. AI could generate most of an implementation while I retained agency through choosing objectives, defining constraints, interpreting failures, deciding which evidence counted and determining what qualified as success. This accurately described much of the project’s history.
Then I challenged my own answer. What happens when agentic AI automates the very transitions on which that account depends? A coding agent can increasingly inspect a repository, plan changes, edit files, run tests, use a browser, evaluate a candidate, revise it and continue through a long task. OpenAI has described an internal software project whose code, tests, documentation and tooling were written by Codex while humans steered through intent, environment and feedback structures (Lopopolo, 2026). Anthropic has described a planner–generator–evaluator harness that built full-stack applications during multi-hour autonomous runs (Rajasekaran, 2026). These reports do not mean autonomous development is universally solved. They do mean that “the human still performs this technical step” is an unstable foundation for a theory of the human.
I could say, “AI writes the code, but I approve deployment.” Yet deployment can become automated. I could retreat to visual judgment, but evaluator agents increasingly participate there. I could say that I formulate the goal, but systems can already propose and critique goals. If human distinctiveness is defined by whatever remains inconvenient to automate this year, the definition will move every time the toolchain improves.
The question therefore changed. It was no longer, “Which actions did I personally execute?” It became: where do purpose, judgment, responsibility, significance and stakes move as operational agency becomes increasingly distributed?
This did not erase my agency. It made the layers more precise. I choose and transform the core vocabulary, establish commitments, judge artistic direction and remain responsible for publication. AI contributes technical construction, interpretation, alternatives and conceptual reorganization. The generative engine performs procedural choices within bounded rules. The browser materially realizes a state. The visitor activates and interprets a manifestation. These contributions are real, but they are not equivalent. Distributed authorship does not mean equal responsibility.
This was the first point at which a simple human–machine opposition became less useful than an architecture of relationships. The problem was no longer to identify one participant who secretly did everything. It was to distinguish the different kinds of causality, judgment, exposure and responsibility that met in the work.
An invitation arrived before the theory existed
On 18 August 2026, three days after I published Refreshing the Webpage as an Act of (Artistic) Composition, Prof. Dohna contacted me about her pilot project, METANOIA BEYOND EAST AND WEST—Contemplative Seeing of the (W)hole and AI—From the School of Athens to Magnifica Humanitas: Research–Work–Friendship. The chronology matters. We had collaborated before, and she knew other aspects of my work, but she had not seen this specific Background Studio when she decided to invite me.
Her message was unusually warm. She described the project as only beginning and wrote that she did not want to undertake it without me. That personal context mattered because I did not receive the invitation as a neutral request for expertise. Friendship, trust and previous collaboration were already part of the encounter before the new artwork entered it.
Her wording already exposed the difficulty of categorization. She wanted to invite me “come ARTISTA …” and then immediately added “a scientifico!” I had spent the preceding days wondering whether the project was web engineering, generative art, computational art, creative coding, visual autobiography or an inconvenient mixture of all of them. Apparently the invitation had encountered the same classification problem.
I sent her the article and explained that code, mathematics, movement and controlled randomness were materials of the work. I called it generative art, computational art and creative coding in quick succession because each name showed something and concealed something else. I asked her to refresh the page and open different articles. I explained that I choose the visual vocabulary and establish the rules, but do not completely predetermine each visible arrangement. At first I described the visitor as almost a co-creator. Then I corrected myself: under the current interface, the visitor is more precisely a performer who actualizes one possibility without fully authoring it.
I then described two possible developments in sequence. First, a visitor could save or download a composition that had affected them, turning a fleeting state into an encounter trace. Later, a visitor might contribute an image to the vocabulary itself, allowing someone else’s visual history to enter combinations that I could not have prepared alone. The second possibility was not yet implemented, but it made the distribution of creation harder to dismiss.
Then came the first message:
“Incredible.”
Twenty-five seconds later came the second:
“Allora il mio progetto non ha tanto senso?”
I felt I had to answer quickly because I thought almost the opposite. I also experienced the sequence as a small intellectual shock, but that is my interpretation of the exchange, not proof of what she privately intended. What is confirmed is the chronology and the wording. What followed is also confirmed: her question changed what I investigated next.
I reread her project before answering the larger question
My first impulse was simply to reassure her: no, the technological work did not make her project meaningless. Before making a stronger claim, however, I returned to the proposal. I wanted to know whether I was imposing my new vocabulary of “encounter” upon her project because it happened to fit my artwork.
Rereading changed my model. Encounter was already structural to her proposal even where the English noun did not appear in every paragraph. The course develops around Bildung, self-Bildung, metanoia, contemplative seeing, research, artistic work and friendship. It begins with Romano Guardini’s formulation that “the researcher serves the problem.” The researcher does not treat the problem as raw material for a predetermined result. The artist similarly becomes responsive to an emerging Gestalt. In friendship, the friend does not reduce the other person to usefulness but allows the other to “come into their own.”
The tree joins the proposed modules because research, friendship and artistic work can each involve meeting it free from immediate utility. Her text repeatedly resists a world made only of objects to classify, optimize and use. In that context, AI is not merely a new production tool. It intensifies a question already present in modern rationality: what happens to knowing, creating and becoming when information, deduction and formal production can be detached from the person who undergoes them?
Prof. Dohna’s published writing confirms that this concern predates our exchange. In “What we see looks back at us,” she connects Guardini’s phenomenology of the gaze with love, artistic creation, knowledge and encounter. The work of art and love each configure a space of knowledge in which things and people may reveal themselves in their deeper being (Dohna-Schlobitten, 2022). Her later study of Guardini’s Weltanschauung places seeing, artistic form and the whole into a relationship that resists reducing knowledge to detached possession (Dohna-Schlobitten, 2024).
A Guardini passage quoted by Pope Francis in his 2023 address to artists became newly important to me. The artwork “opens a space” into which a person can enter, move and encounter things and persons as they become open before them (Francis, 2023). I had already called the browser a small theatre. The philosophical scale is different, but the structural connection is striking. Perhaps one artistic act consists in creating a space where encounter can occur.
I therefore wrote back that my work did not make her project less meaningful. I thought it made the project more meaningful because it gave the question a concrete and difficult case. That answer was more honest than simply placing “human creativity” on one side and “machine reproduction” on the other, although I had not yet worked out why.
The reassuring answer became a stress test
My first reassurance was still too easy: the machine can arrange forms, but the human remains the real creator. The history I had just documented undermined that sentence. AI helped build the system. The runtime algorithms produced relations I did not manually compose. The browser performed the state. The visitor decided when another manifestation appeared. Future visitors might contribute the visual vocabulary. Where, exactly, should I draw a line around “the real creator” without making every other causal contribution disappear?
One AI formulation changed the direction of the discussion:
“Your project makes that problem visible rather than merely theoretical.”
I immediately recognized part of the sentence. Prof. Dohna’s proposal asks what happens to human knowing, creation, contemplation and transformation in an age of capable AI. My work places part of that problem in an operating system: AI participated in construction; mathematical procedures generate formal novelty; a visitor participates in actualization; interpretation can exceed what I explicitly encoded.
Then another formulation sharpened it:
“Your project does not eliminate her question. It makes it more acute.”
I accepted that too, but with a consequence. The artwork should not be presented merely as a friendly example proving her theory. It could also be a stress test. If the theory depended upon “humans create; machines only repeat,” the case would challenge it. If the deeper issue concerned encounter, sense, responsibility and transformation, then the challenge could strengthen the project by forcing its vocabulary to become more precise.
The weaker opposition was:
human creates meaningfully / machine reproduces mechanically
The operating artwork replaced it with harder questions:
What is creation when generation is distributed?
What is encounter when its conditions can be produced algorithmically?
What is meaning when artist, AI, algorithm, artwork and beholder do not individually contain the complete event?
What is metanoia if the decisive distinction cannot simply be identified with producing a novel form?
Her momentary question was therefore more productive than agreement would have been. “Beautiful” might have confirmed the work. “Does my project still make sense?” disturbed the framework. That disturbance became an input to the research.
I separated generation from encounter and then had to reconnect them
Another AI sentence initially seemed to solve the central problem:
“AI can participate in the generation of form without itself undergoing the meaning of that form.”
I found this powerful because it abandoned the increasingly indefensible claim that machines cannot contribute to creation. It distinguished several levels that I had been blending together:
generation of form
↓
emergence of meaningful relations
↓
experience of meaning
The runtime algorithm clearly participates in the first stage. It causally contributes to the second by creating adjacency, scale, rhythm and recurrence. A person may then experience the relation as comic, theological, autobiographical, tender or disturbing.
That seemed convincing until I noticed that the arrows were pointing only downward. The actual history contradicted the diagram. A generated result changed my interpretation; my interpretation changed the next request; the revised system changed future results. Prof. Dohna’s encounter changed my theoretical vocabulary. This article may change the implementation. Generation and encounter could not remain permanently separated stages.
The model became:
generation ⇄ encounter
production ⇄ reception ⇄ interpretation
⇄ transformation ⇄ renewed production
The distinction still mattered, but it was no longer a wall. It named different moments in a recursive process. A form can occasion an encounter, and the encounter can return upstream to alter the conditions of future form. This was the point when the project stopped looking like a pipeline and began looking like a processual ecology.
The question changed from who creates to where sense happens
One short question from the AI dialogue reorganized the problem:
“Where exactly is sense?”
I had already been using words such as meaning and Sinn, but the word “where” exposed an assumption. I was imagining sense as data stored somewhere. Perhaps the artwork contains it. Perhaps the artist deposits it. Perhaps the viewer manufactures it. Perhaps an AI represents it. Perhaps, in Prof. Dohna’s theological horizon, it comes from God. Which participant owns the canonical copy?
Then I began to suspect that storage was the wrong metaphor.
Russell’s image carries historical and cultural associations before it enters my system. A sacred image arrives with iconographic and theological histories. My chemical equation has autobiographical significance because of my own past. A visitor does not create those inheritances from nothing. I began calling them sedimented semantic possibilities.
The algorithm then establishes a concrete relation that did not exist as part of either isolated source: this Russell, at this scale, beside this sea lion, around this article, during this visit. The relation is real even though no caption fixes its interpretation.
Then someone encounters it. Perhaps philosophical seriousness seems to be interrupted by bodily exuberance. Perhaps the relation is affectionate. Perhaps it is absurd. Perhaps it recalls another memory. Perhaps nothing interesting happens. The encounter does not guarantee significance.
The revised model became:
semantic inheritance
↓
relational configuration
↓
encounter
↓
actualized significance
↓
memory / biography / culture
↓
future interpretation
This led to a proposition I still treat as interpretation rather than confirmed fact:
Sense may be neither arbitrarily manufactured by the subject nor stored as a finished property inside the object; it may become disclosed through encounter.
When that sentence first appeared, I wondered whether this was what Prof. Dohna wanted to prove. “Prove” was too strong. Her question had helped me see a structure, and the artwork gave me a concrete case through which to examine it, but neither an affecting experience nor a persuasive formulation settled the metaphysics. A WordPress page cannot prove Guardini merely by returning HTTP 200.
What the case does show is why two easy accounts are insufficient. The meaning was not completely encoded by me beforehand, yet the visitor did not freely invent the algorithmic juxtaposition afterward. The relation itself contributes something. That contribution is philosophically interesting even before one decides where its ultimate ground lies.
I also had to resist an overstatement here. The encounter does not create everything from nothing, and the thing does not arrive empty. Historical meanings, personal memories, algorithmic configuration, present attention and later recollection participate differently. “Meaning emerges relationally” is useful only if it does not erase those asymmetries.
I corrected the claim that the algorithm was culturally illiterate
My earlier art essay had called the algorithm “culturally illiterate.” For the runtime geometry, the phrase remains useful. A Voronoi function does not need Russell’s biography to assign his image to a polygon. But our discussion contained two very different computational systems: the narrow mathematical generator and the conversational AI that helped construct and interpret the project.
The AI could identify Russell, discuss Christian imagery, connect Guardini with art and encounter, recognize why the sea lion might make the relation comic, and formulate the philosophical problem more fluently than many casual viewers. Saying simply “the machine knows nothing” would erase observable semantic competence.
This correction generated the next question:
Does representing the meaning of an encounter amount to undergoing its meaning?
My first answer again wanted to be too clean: AI represents; humans experience. I then asked how I could establish that boundary without placing the conclusion inside the definition. AI systems can perform substantial semantic operations, maintain context, alter human understanding and revise plans. Future systems may preserve longer histories and more stable self-models.
The more defensible distinction became:
semantic competence ≠ lived significance
The inequality sign matters, and it is deliberately modest. An AI may explain perfectly why Russell beside the sea lion is funny. That does not by itself establish that the image has become significant to the AI within a biography—something it will miss later, regret misunderstanding, assume responsibility for, allow to change whom it loves, or organize a promise around. Successful representation is evidence of semantic competence. It is not, by itself, evidence that something matters to the system in that stronger sense.
I do not use this distinction to close the question forever. I use it to prevent two opposite simplifications: “AI understands nothing” and “fluent semantic performance proves lived significance.” The present evidence supports neither absolute conclusion.
The person can become implicated
When I asked what can happen to a person that is not captured by generation, representation or adaptation, the first answer was:
“The person can become implicated.”
I did not immediately understand why that word mattered, so I asked for another explanation. The crucial correction was that “change” alone is far too weak. Algorithms change state. Learning systems update. Agents revise plans. A theory in which humans change and machines do not collapses immediately.
Implication names another possibility: something can become at stake. I may encounter a work and discover that I was wrong. I may recognize myself, reinterpret my past, feel responsible, become grateful or ashamed, change how I treat another person, or make a promise. The encounter enters the history through which I understand who I am. It is no longer information only about an external object; it makes a claim upon how I judge or live.
This was where Prof. Dohna’s use of metanoia stopped looking like adjacent theological language and became central to the computational problem. I began distinguishing adaptation from metanoia provisionally:
| Adaptation | Metanoia as provisionally used here |
|---|---|
| A system changes behaviour or state in response to information. | A subject’s orientation toward self, world, others, truth, value or action changes. |
| The objective may remain stable while the strategy improves. | The encounter may alter what the participant understands the objective to be. |
| A failed output leads to a better output under an existing criterion. | The criterion itself becomes one of the things questioned. |
The distinction became concrete in the changing objective of my own project. I began with: “I want better visual backgrounds.” The objective then became: “I want a generative composition engine.” Later it became: “I want to construct conditions under which encounters can occur.” If a composition leaves too much empty space and I adjust a density parameter, the system adapts within an existing criterion. If the artwork leads me to realize that I have misdescribed the whole objective, the criterion itself changes. That new understanding may alter the title, visitor features, moderation model, archive design and the reason for continuing the work.
This looked much closer to metanoia. Then I challenged the distinction as well. Suppose a future agent maintains a persistent history, revises not only strategy but its hierarchy of goals, relates present action to previous commitments and reorganizes its future behaviour around what it identifies as significant. Would “adaptation” remain sufficient? Would something still be missing—vulnerability, responsibility, the possibility of loss, a first-person relation, something genuinely at stake?
I still do not know. The stronger question is:
Can there be metanoia without someone for whom the transformation is at stake?
Leaving it open is not a failure of the article. It is a correction to my earlier confidence.
The mechanism can produce surprise without being astonished
Another AI formulation became a hinge:
“The algorithm may make something unexpected, but it is not astonished by what it has made.”
My immediate response was “yes, but the person who encounters it can be astonished.” That objection produced a better sentence:
The encounter can contain astonishment even when the mechanism does not.
The generator does not need to intend a relation for the relation to become meaningful. It supplies conditions: adjacency, scale, recurrence, rarity, interruption, disappearance, return and rhythm. A person may actualize one possibility as significance. The system cannot force this to happen, and it should not claim credit for every interpretation.
I therefore began calling the Background Studio something more ambitious than a generative composition engine:
an engine for generating conditions of encounter
The phrase felt right because it preserved both power and limit. The engine can stage relations but cannot guarantee that they matter. This also gave the work a curatorial dimension. A curator does not manufacture every interpretation in an exhibition. Curation constructs a field—this work beside that work, this interval, this route, this return. My system performs a narrower procedural version of that activity and continuously restages the field.
Even this formulation later became incomplete. Once encounters return as feedback and change the system, the engine does more than generate conditions. It participates in a cycle through which conditions and participants transform one another.
Prof. Dohna’s response entered the artwork’s causal history
At one point I realized that I had been theorizing encounter while overlooking the most immediate encounter in the story. Prof. Dohna’s invitation arrived independently of this particular artwork. I showed her the work. She responded first with wonder and then with a question about the meaning of her own project. I encountered that question. It changed what I read, what I asked AI, which distinctions I challenged and what I began writing.
artwork ↓ Prof. Dohna’s encounter ↓ question / conceptual disturbance ↓ my encounter with her response ↓ AI-assisted reflection ↓ new interpretation ↓ new article ↓ new technical possibilities ↓ future artwork ↺
This is why her invitation and reaction belong to the artwork’s history. They are not JavaScript modules inside the plugin. They operate at another causal level. A manifestation affects a person; that person’s response affects the developer; the changed developer will make different artistic decisions.
One sentence from the AI dialogue gave me a strong emotional reaction:
“The artwork is partially shaping the process that continues creating the artwork.”
My response was immediate: this gives me goosebumps, because it is what is happening now. The emotion did not prove the proposition. It did direct my attention to a structure I had not yet named.
I made the system. The system produced forms. The forms motivated an art essay. The essay entered an encounter with Prof. Dohna. Her response generated a new inquiry. That inquiry suggested visitor contribution, preservation and feedback. If those ideas enter the code, the artwork will have participated causally in its own future development—not because software secretly intends its destiny, but because it changes the humans who can change it.
The artist creates the work, and the work participates in creating the future artist.
There is nothing supernatural in that claim. The observer of the artifact is also its developer; what the observer learns can become a design decision. Yet the ordinary mechanism should not make the artistic consequence disappear:
The artist can be surprised.
The artist can learn.
The work can disclose a possibility its maker had not yet formulated.
Guardini’s language of serving an emerging form became concrete at this point. Responsibility does not vanish, and receptivity is not an excuse for surrendering judgment. What weakens is the fantasy that authorship requires every significant possibility to exist fully inside the creator’s intention before execution. The artist can also become a beholder of the work, learn from it and allow that encounter to redirect the next act.
The writing process repeated the structure it was describing
The recursion became clearer when I noticed that the article itself was no longer external commentary. The first artistic essay changed how I explained the project. That explanation changed Prof. Dohna’s encounter. Her reaction changed the theory. The theory suggested features. Features will change future manifestations.
The theory has entered the causal loop of the artwork.
The drafts of this article then reproduced the same method. An early draft was factually rich but treated engineering as the main subject. I said that the centre of gravity was wrong. The next draft recentered the work on art, encounter, Sinn, metanoia and Prof. Dohna’s response. It was conceptually stronger, but it made the conclusions look as though they had arrived in orderly succession. I objected again: where were the repeated questions, the confusion, the provisional agreements and the corrections to both my own views and the AI’s?
A further revision restored the genealogy and strengthened the argument’s architecture. That was an improvement, but comparison with the earlier telling revealed that disciplined structure had also compressed some of the evidence, humour, personal immediacy and intermediate distinctions through which the argument had become possible. The present third version therefore performs another return. It does not revert to the earlier architecture or add every discarded sentence. It restores only those details that change the evidence, reveal a genuine stage of thought or allow the reader to feel why a distinction became necessary.
Each objection changed the objective of revision. The goal moved from “make the article complete,” to “preserve the genealogy of understanding,” and then to “let a sound architecture carry the full evidential and human richness of that genealogy.” The revision method itself thus supplied another example of human agency. My agency did not consist merely in approving generated paragraphs. It appeared in noticing that a polished result had erased something methodologically essential, rejecting its implicit model of knowledge, and redefining what the article had to preserve.
This is why the history of thinking is not decorative metadata attached to the final research. In this case it is part of the research. Without it, the article would state that creation is recursive while presenting its own conclusions as linear and predetermined. Its form would contradict its argument.
The human–AI dialogue became another form of encounter
Was AI merely a tool for expressing thoughts I already possessed? That description also became insufficient. Several decisive phrases were neither dictated by me nor merely returned as editorial polish. They appeared through interaction: “Where exactly is sense?”, “The person can become implicated”, and “The artwork is partially shaping the process that continues creating the artwork.” Each phrase opened a path I had not yet formulated in those terms.
But the collaboration did not work because I accepted fluent language as truth. It worked because generation and resistance were both present. AI proposed an articulation; I experienced recognition, doubt or disagreement; I supplied a technical fact, personal history, counterexample or distinction; the explanation changed; that revised explanation changed my next question. My contribution was not the residue left after automation. It included deciding which anomaly deserved attention, which analogy was false, what evidence counted and when the question itself had changed.
The intellectual pattern was:
partial intuition
↓
AI formulation
↓
my recognition or objection
↓
qualification / counterexample / evidence
↓
revised formulation
↓
new connection
↓
new question
↺
The interaction accumulated a history. A later answer became possible only because earlier formulations had been challenged and the context had changed. AI could reason differently because I introduced the distinction between the runtime geometry and the conversational model; I could reason differently because AI gave language to implication and recursive feedback. Neither contribution can be understood by isolating one final paragraph from the sequence that produced it.
I do not claim that this dialogue was phenomenologically identical to an encounter between two human persons. I do claim that it was operationally recursive and intellectually productive. It generated formulations, resistance, revision and new attention. That limited claim is sufficient to make the dialogue part of the method rather than an invisible instrument behind the prose.
Encounter turned a pipeline into feedback
Once outputs began returning upstream, engineering and cybernetics supplied a useful vocabulary. In control engineering, feedback occurs when information about an output returns to influence subsequent behaviour. My development process already had that structure:
intention ↓ AI-assisted implementation ↓ output ↓ observation and evidence ↓ changed intention └──────────↺
The artistic process added another loop:
generative system ↓ manifestation ↓ encounter ↓ interpretation ↓ changed artist ↓ changed system └────────────↺
Prof. Dohna’s response added a social loop:
work / result
↓
another person encounters it
↓
response / objection / surprise
↓
I encounter that response
↓
changed interpretation and action
↓
future work
↺
The human–AI writing process added an epistemic loop. At different moments I was artist, developer and beholder. Prof. Dohna was observer, interlocutor and participant. AI was technical collaborator, conceptual mediator and generator of formulations. None of us occupied only one stable position.
At that point, second-order cybernetics became relevant. Heinz von Foerster’s work directs attention toward observing systems and what changes when the observer cannot be treated as external to the system of inquiry (von Foerster, 2003). I do not claim that a WordPress artwork resolves second-order cybernetics. The narrower structural connection is enough: I design the system, observe it, am changed by what I observe, write about the change, and return that writing to the system’s development.
Still, I needed one more technical distinction. The current visitor experience is interactive, but it is not yet a complete adaptive learning loop. A visitor can refresh and thereby cause another state to appear. Their private reaction does not automatically change later weights, rules or eligibility. The present fact is:
visitor action → another manifestation
The proposed future system would add:
composition ↓ encounter ↓ explicit response or contribution ↓ system memory ↓ modified future conditions ↓ new composition ↺
That distinction matters because a theoretical possibility should not be narrated as an implemented result. Today, the artwork supports runtime variability and visitor activation. Saving reconstructable states, accepting visitor material and adapting future generation remain proposed experiments.
Visitor contribution could turn private memory into system history
The first future extension is comparatively conservative: allow a visitor to preserve a composition that affects them. A useful saved state would contain more than a screenshot. It could record the algorithm, seed, selected record identities, relevant configuration and enough viewport information to reconstruct the manifestation. The visitor’s act of recognition would then become materially visible as a trace of one encounter.
The second extension changes the category of the work: allow visitors to contribute visual records. At present, the thirteen-record vocabulary is largely autobiographical. Once another person can introduce an image, the engine no longer recombines only my cultural and personal archive. It can place someone else’s visual memory beside my childhood chemical equation. A third person may encounter the relation and preserve it. I may later encounter that saved state and discover a significance neither the contributor nor I anticipated.
my archive
+
visitor archive
↓
generative system
↓
unexpected relation
↓
another visitor
↓
interpretation
↓
preserved encounter
↓
my later encounter
↓
new work
↺
The artwork would now contain intersecting biographies. Olga Goriunova’s account of participatory platforms is useful because digital artistic activity can emerge through infrastructures and collective processes rather than remain contained in a closed artifact (Goriunova, 2016). Katja Kwastek similarly treats action, system and aesthetic experience as integral to interactive digital art rather than as optional additions to a completed object (Kwastek, 2013).
Participation, however, is not automatically artistic progress. Visitor material would require provenance, moderation, controlled file handling, permissions, deletion procedures and a visible distinction between the autobiographical core and contributed records. A public upload field can turn participatory art into malware curation with impressive speed if one becomes too spiritually receptive to arbitrary MIME types.
If visitor response changes future generation, the loop becomes computational
At present, interpretation changes me and may therefore change later development. The proposed adaptive system would make another path explicit: a visitor response could alter the probability space from which later compositions emerge. Meaning would then have a computational consequence—not because the software had understood the encounter in a human sense, but because a trace of the encounter had become one of the system’s future conditions.
If implemented, the work might be described provisionally as a co-adaptive participatory generative system. The phrase should remain conditional. “Participatory” would not mean that every visitor becomes an equal author; “adaptive” would not mean that the system undergoes metanoia; and “co-adaptive” would not erase the asymmetry between a person whose orientation can be at stake and a mechanism whose parameters change.
The design questions are therefore part of the artwork rather than merely administrative details. Should memory belong to an individual visitor, to a temporary group or to the whole public system? When a contributed record enters a later composition, does it carry the contributor’s explanation with it, or can its meaning be transformed by another encounter? If the contributor requests removal, should later saved states disappear, retain a historical trace or become unreconstructable records of something that once existed?
A separate risk concerns probability. I am reluctant to make popular saved states automatically more likely to recur. If every heart icon increases a record’s weight, the work may converge upon the safest and most familiar combinations. After months of building an ecology of encounter, I could accidentally rediscover the recommender system. A non-adaptive baseline, transparent experiments and reversible weighting would preserve the possibility that the most important encounter is the one no popularity metric would select.
Encounter is richer than feedback because a response can introduce a new category, not merely a new value. visitor_liked = true can alter a weight. A visitor’s explanation—“this combination changed how I thought about exile”—can change what the artist believes the system is for. When feedback alters the criterion rather than only a parameter, the process approaches the metanoia problem again.
Agency became distributed, layered and unequal
By this stage, the phrase “human agency migrates” was useful but incomplete. Agency migrates, distributes and layers itself across the project. The layers have different powers, histories and responsibilities:
| Participant or layer | Present contribution | Limit or unresolved issue |
|---|---|---|
| Artist–developer | Purpose, curation, constraints, evaluation, publication, responsibility and revision of the objective. | Does not manually determine every manifestation or foresee every interpretation. |
| Development AI | Code generation, architectural proposals, diagnosis, conceptual formulation and reorganization. | Semantic contribution does not by itself establish lived significance or equivalent responsibility. |
| Runtime generator | Procedural selection and geometry within configured rules. | Does not possess the cultural biographies attached to the records. |
| Browser | Material execution, layout, crop, animation, timing and viewport-dependent performance. | Performance is causal without becoming curatorial responsibility. |
| Visitor | Arrival, refresh, attention, interpretation, memory and possible preservation. | Present control is participatory but limited; contribution remains prospective. |
| Encounter | A relation in which significance and transformation may arise. | Cannot be guaranteed, owned completely or reduced to one participant. |
The table is not a final ontology. It protects two distinctions that the more elegant prose can blur. First, causal participation does not make every participant an author in the same sense. Second, distributed agency does not dissolve responsibility. A browser executing CSS is not ethically equivalent to the person who publishes a record. An AI proposing code is not automatically responsible in the same way as the person who deploys it. A visitor refreshing a page does not become the sole creator.
The better question is not “Who possesses all agency?” It is what kind of agency operates at each layer, how the layers change one another, and where responsibility remains when no participant controls the whole result. Research on generative AI and art already shows why conventional authorship becomes unstable when creative work is automated or redistributed (Epstein et al., 2023). This project makes that instability visible across development, runtime generation, performance, interpretation and future revision rather than only at the moment of image synthesis.
The artwork increasingly resembles a processual ecology
By this stage no single pipeline could represent the project. Technical production, artistic experience, social response, conceptual interpretation and future modification were feeding one another at different speeds:
writing / theory ⇄ artist–developer ⇄ development AI
↓
generative system
↓
manifested form
↓
beholder
↓
encounter
↓
interpretation / transformation
↓
future work
↺
I use processual ecology to name this interdependence, not to suggest that every element is alive or equivalent. The ecology includes code, records, browser behaviour, people, memories, institutional relationships, theory and maintenance. Its participants have unequal capacities and responsibilities, but an event at one layer can reorganize conditions at another. The artwork is less a single object moving down a production line than a maintained field in which forms, encounters and decisions recur.
Creation began moving from object-making toward world-building
The distinction between an AI-generated artifact and AI-assisted generative infrastructure now returned at a deeper level. A prompt-to-image workflow usually places the main generative event before the ordinary viewer arrives. The viewer encounters an artifact. In my project, AI helped construct an environment whose future manifestations were not all present during development. Visitors arrive later and instantiate forms. Future visitors may add source material. Their responses may eventually alter later conditions.
The artistic question therefore moved from:
What should I make?
toward:
What conditions should I construct and tend so that forms, relations and encounters I cannot completely foresee can continue becoming possible?
This is closer to world-building than object-making. In language nearer to Prof. Dohna’s project, it moves from producing one finished Gestalt toward creating a space in which figuration continues. The artist still chooses, limits and takes responsibility. Yet the work is no longer exhausted by one completed surface.
At the beginning I thought the central unit was the background image. Then it became the generated composition. Later I focused on juxtaposition. Now I suspect the more important unit may be the encounter and its capacity to return as a condition of further creation. That shift was not planned in advance. It emerged because each proposed unit proved too narrow for the evidence.
Prof. Dohna and I became participants in what we were theorizing
There is something almost suspiciously elegant about the chronology. Prof. Dohna did not invite me because she already knew this artwork. I did not build it as an illustration of her new course. Two independently developing projects met.
Her project gave me concepts—metanoia, contemplative seeing, serving, Gestalt, Sinn and encounter. My artwork gave her a working case that disturbed an easy distinction between human creation and machine production. Her response disturbed my interpretation. The human–AI dialogue produced further formulations. My objections changed those formulations. This article may now return to her and change the discussion again.
Neither project contains the other. Her theory does not explain the artwork completely from above. My software does not refute her project from below. Something emerged between them.
That emergent insight was not fully stored inside either project beforehand. It became available when they met, when she responded, when I encountered that response and when the dialogue changed what each of us could ask next. The history itself became evidence for the relational account of meaning.
Why her question may have been more valuable than agreement
This is also why I am increasingly grateful for her question rather than wishing she had offered only agreement. If the threatened claim was “machines cannot create,” the technological evidence makes it vulnerable. If the deeper question concerns what can matter, transform, obligate and reorient a participant, then the artwork does not abolish it. It makes the question more demanding.
Agreement would have allowed my first explanation to remain comfortable. Her doubt exposed its weakest possible form: the reassuring opposition in which the human creates and the machine merely reproduces. Because that answer failed under the actual architecture of the work, I had to distinguish generation from encounter, then reconnect them; distinguish semantic competence from lived significance; and ask whether adaptation can ever become metanoia. The objection did not destroy the project. It forced both the artwork and the theory to become less easy.
What remains unresolved
I do not know where sense ultimately “resides,” or whether residence is the right metaphor. The proposal that sense is disclosed relationally remains an interpretation, not a result demonstrated by software. Prof. Dohna’s Guardinian framework retains a theological horizon that this computational case can neither prove nor disprove. The artwork shows that relations can become meaningful without one participant explicitly precomposing their full interpretation. It does not establish the ultimate ground of that meaning.
I also do not know whether a sufficiently persistent, self-modifying AI could satisfy criteria associated with lived significance or metanoia. Present semantic competence is real. Claims about subjective significance require different evidence. Future systems may make the distinction harder; that is a reason to refine the question, not to declare an answer by definition.
Visitor preservation, contribution and adaptive weighting are not current achievements. They are design possibilities generated by the inquiry. Their implementation may enrich the work, produce moderation problems, flatten surprise or reveal entirely different questions. The article should not grant future software the evidential status of deployed behaviour.
Nor is every composition meaningful. Sometimes a sea lion appears beside a rectangle because JavaScript assigned two records to neighbouring regions. Sometimes the layout is weak. Sometimes the visitor is busy. Sometimes nothing happens. If every refresh reliably produced metanoia, I would check whether a theology plugin had been installed with unusually aggressive caching.
The openness of the artwork includes triviality, boredom, failure and nonsense. The engine can create conditions of encounter; it cannot command revelation.
What I want to test next
The next stage should test the theory through practice while keeping present facts separate from plans. First, I want to implement preservation of reconstructable compositions. This would make an encounter traceable without pretending that a screenshot alone contains the entire event.
Second, I want to experiment with a moderated visitor vocabulary. Contributed records should remain distinguishable from the autobiographical core, preserve provenance and support removal. The purpose is not to maximize uploads. It is to see whether relations among different personal histories change the aesthetic and interpretive field.
Third, only after observing those interactions would I test explicit adaptive feedback. I would begin with reversible experiments and retain a non-adaptive baseline. Before increasing the probability of a saved state, I want to know whether preservation signifies aesthetic value, personal memory, curiosity, humour or simply the convenience of the button.
Finally, I want to preserve the intellectual history around the code. If theory, conversation and response are causal inputs to later versions, a source repository records only part of the work. The technical articles, the first art essay, Prof. Dohna’s messages, the human–AI dialogue, the rejected explanations and the revised objectives form a developmental archive. The genealogy of thought is not separate from the artwork’s evolution.
When the artwork answered back
I began with a practical background feature. It became a structured selector, then a mosaic and scattered composition system, then a family of mathematical generators, then a maintainable plugin. Real outputs changed the specification. The foreground and background became one visual environment. Refresh became a performative gesture. Repetition produced histories of encounter. AI-assisted development complicated authorship. Agentic AI destabilized my first defence of human agency.
Then Prof. Dohna invited me as an artist before knowing this specific work. Her reaction made me reread her project. The rereading shifted the question from generation to encounter. AI proposed formulations that I accepted, resisted and revised. “Where exactly is sense?” changed the model of meaning. “The person can become implicated” led to metanoia. “The artwork is partially shaping the process that continues creating the artwork” made the recursion visible. An early draft preserved evidence but chose the wrong centre. A later draft found the centre but compressed the genealogy. A further revision restored that genealogy but still lost some of the earlier account’s concrete richness. Comparing the versions changed the method once more: stronger reasoning and richer detail had to become mutually supporting rather than competing editorial goals.
The movement has therefore been closer to this:
creation ↓ unexpected form ↓ encounter ↓ question ↓ provisional explanation ↓ objection / evidence / correction ↓ re-interpretation ↓ transformation of the objective ↓ new creation ↺
The added middle matters. Encounter does not mechanically yield understanding. It may create confusion. An elegant answer may prove insufficient. Evidence may force a distinction. A counterexample may reopen it. The work becomes intellectually generative because the process does not stop at the first phrase that sounds complete.
I initially thought I had made an engine for generative composition.
Then I thought I had made an engine for generating conditions of encounter.
Now even that description seems incomplete, because encounters have begun returning into the system that generated them. They alter the artist, the theory, the dialogue, the proposed participation model and potentially the future algorithms. The artwork is becoming a place where generation and encounter feed one another: forms create occasions for response, and responses return as conditions for later forms.
I am grateful to Prof. Dohna for inspiring this stage precisely because she did not hand me a conclusion. Her project encountered mine, mine encountered hers, and the disturbance between them created questions neither one fully contained beforehand.
The question I now carry is:
Where does meaning happen when a human creates conditions, AI helps construct them, algorithms generate relations, a person encounters those relations, the encounter changes what matters to the person, and that transformation becomes part of the conditions from which the next creation emerges?
I still do not know.
But three days earlier, when I published the first art essay, I did not even know that this was the question! When the first complete draft was finished, I did not yet know that preserving how the question emerged was part of the answer. When the structurally stronger revision was finished, I had not yet seen how much concrete life it had left behind. That continuing change in understanding is not closure.
Perhaps that change is itself the first piece of evidence that the artwork has already answered back.
References
- Boden, M. A., & Edmonds, E. A. (2009). What is generative art? Digital Creativity, 20(1–2), 21–46. https://doi.org/10.1080/14626260902867915
- Dohna Schlobitten, Y. (2022, June 4). What we see looks back at us. L’Osservatore Romano. https://www.osservatoreromano.va/en/news/2022-06/dcm-006/what-we-see-looks-back-at-us.html
- Dohna-Schlobitten, Y. (2024). Romano Guardini und der Ursprung seiner katholischen Weltanschauung—Ein Blick auf das Ganze des Kunstwerks und der Welt. zur debatte, 1/2024; lecture presented 15 June 2023. Katholische Akademie in Bayern. https://kath-akademie-bayern.de/mediathek-eintrag/romano-guardini-und-der-ursprung-seiner-katholischen-weltanschauung-ein-blick-auf-das-ganze-des-kunstwerks-und-der-welt/
- Eco, U. (1989). The Open Work (A. Cancogni, Trans.; D. Robey, Introduction). Harvard University Press.
- Epstein, Z., Hertzmann, A., Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., Leach, N., Mahari, R., Pentland, A., Russakovsky, O., Schroeder, H., & Smith, A. (2023). Art and the science of generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
- Francis. (2023, June 23). Address to artists for the 50th anniversary of the inauguration of the Vatican Museums’ Collection of Modern Art. The Holy See. https://www.vatican.va/content/francesco/en/speeches/2023/june/documents/20230623-artisti.html
- Galanter, P. (2016). Generative art theory. In C. Paul (Ed.), A Companion to Digital Art (pp. 146–180). Wiley-Blackwell. https://doi.org/10.1002/9781118475249.ch5
- Goriunova, O. (2016). Participatory platforms and the emergence of art. In C. Paul (Ed.), A Companion to Digital Art (pp. 297–309). Wiley-Blackwell. https://doi.org/10.1002/9781118475249.ch12
- Kwastek, K. (2013). Aesthetics of Interaction in Digital Art. MIT Press. https://doi.org/10.7551/mitpress/9592.001.0001
- Lopopolo, R. (2026, February 11). Harness engineering: Leveraging Codex in an agent-first world. OpenAI. https://openai.com/index/harness-engineering/
- Manovich, L. (2001). The Language of New Media. MIT Press. https://mitpress.mit.edu/9780262632553/the-language-of-new-media/
- Rajasekaran, P. (2026, March 24). Harness design for long-running application development. Anthropic. https://www.anthropic.com/engineering/harness-design-long-running-apps
- von Foerster, H. (2003). Understanding Understanding: Essays on Cybernetics and Cognition. Springer. https://doi.org/10.1007/b97451