# [[More generative ai stuff]] #llmtext #paper/environmentalaestheticsofai ## o3 answer Aesthetic judgment has always required a double vision: we behold some immediate appearance—a sunset, a mountain, an oil painting—and at the same time we situate that appearance inside a framework of beliefs about what it really is and how it came to be.  [[Allen Carlson]] made this point with particular force when he argued that “as in our appreciation of works of art, we must appreciate nature as what it in fact is… and we must appreciate nature in light of our knowledge of what it is” (2000, 6).  His examples are familiar to every student of [[environmental aesthetics]].  He imagines strolling across what seems “a wide expanse of sand and mud,” feeling “wild, glad emptiness,” until geological knowledge re-categorises the place as a [[tidal basin]] so that the aesthetic tone tilts toward “disturbing weirdness” (2000, 61).  He borrows [[Tony Hillerman]]’s description of [[Ship Rock]]—“It wasn’t really a mountain… it was probably a volcanic throat”—to show how recognising a landform’s category reshapes experience of its silhouette.  More subtly, Carlson claims that a forest, perceived through the lens of ecological succession, is no static tableau but a slow choreography of pioneer species yielding to climax vegetation; once that developmental logic is revealed, appreciation migrates from mere colour-pattern to a sense of organic narrative.  Again and again Carlson’s lesson is that [[scientific understanding]] discloses [[the forces]] at work—forces that for him constitute the natural object’s “environment of creation”—and that disclosure is aesthetically relevant because it tells us what, in fact, we are looking at. That argument can be pushed one step further.  The transformative power does not lie in ecological or geological knowledge per se; it lies in seeing a particular phenomenon as the visible expression of an underlying generative law.  Consider a sunset.  Someone with no grasp of atmospheric optics can admire pink clouds, yet the physicist sees Rayleigh scattering modulating the solar spectrum, and that recognition converts prettiness into what [[Richard Feynman]] liked to call “the majestic viewpoint of science.”  The sunset is beautiful because it manifests an elegant physical regularity.  Or take a saw-toothed alpine crest: geophysics tells us it is the provisional equilibrium between tectonic uplift and gravitational erosion, and the jagged line becomes an instantaneous vector diagram of colossal, opposed forces.  The aesthetic merit rests in the intelligible coupling of form and [[generative process]].  This is Carlson’s [[organic unity]] transposed into the vocabulary of [[physical law]]. Exactly this coupling—phenomenal appearance anchored in invisible rule—is what Janus calls “[[semantic physics]]” when he turns from natural landscapes to large [[language models]].  Janus’s 2022 essay “Simulators” insists, with remarkable conceptual clarity, that self-supervised models such as GPT-3 should be understood not as agents, tools, or oracles, but as “simulators” whose sole optimisation objective is Bayes-optimal conditional inference over their training distribution.  He writes: “I use the generic term ‘simulator’ to refer to models trained with predictive loss on a self-supervised dataset… The outer objective of self-supervised learning is Bayes-optimal conditional inference over the prior of [[the training]] distribution, which I call the simulation objective” (Simulators, p. 1).  The power of that label lies in the precise structural analogy it establishes.  Physics provides a time-evolution rule that maps a complete micro-state at t onto a probability distribution of states at t + Δt; iterate the rule and you generate the entire spatio-temporal fabric.  GPT provides an analogous mapping: given a prefix of tokens, it returns a probability distribution over the next token; iterate and you generate arbitrarily long linguistic futures.  “Predictive sequence models,” Janus observes, “are simulators of a learned distribution” (p. 27).  The model, in short, induces a generative law for meaning. Janus is at pains to catalogue exactly what that entails.  A genuine generative law, in his scheme, satisfies six criteria.  First, it operates via a local kernel; there is a function (f : S_t \rightarrow \text{Dist}(S_{t+1})) where (S_t) is the complete state description at time t.  Second, the Markov sufficiency condition holds: the present screens off the past for purposes of predicting the future.  Third, the rule is stationary once established; the Hamiltonian does not rewrite itself mid-cosmos, and frozen neural weights do not mutate within an inference session.  Fourth, the rule is value-neutral—“it might generate instances of agents, oracles, and so on,” as Janus puts it, “although in the course of doing so it might generate agents with radically opposed aims” (p. 28).  Fifth, it is compatibly deterministic or stochastic: classical mechanics yields a delta-function over next states, quantum mechanics or GPT yields a distribution.  Sixth, and most importantly, it supports indefinite roll-out, enabling the simulation of counterfactual states never encountered during training or cosmogenesis.  Each of these six properties, Janus insists, is possessed by GPT-style models.  They therefore qualify as carriers of “semantic physics,” a term he coins in the crucial passage: “Models trained with the strict simulation objective are directly incentivized to reverse-engineer the (semantic) physics of the training distribution, and consequently, to propagate simulations whose dynamical evolution is indistinguishable from that of training samples” (p. 32). If Carlson tells us that appreciative attention must move from surface appearance to underlying process, Janus gives us the tools to say what that underlying process is for an LLM.  The Rayleigh law of language is represented implicitly in the weight matrices of the transformer; if we wish to behold the beauty of an AI-generated sonnet the way a physicist beholds a sunset, we must learn to see the sonnet’s lines as the visible interference pattern produced when thousands of latent semantic vectors diffract through a fixed kernel of prediction.  At first glance that sounds hopelessly occult.  Yet prompters, through iterative experimentation, do acquire an empirical grasp of how the law behaves.  A user types _“Vermeer’s light techniques with cyberpunk aesthetics”_ into Midjourney and observes what elements dominate, which concepts recess; tweaks the seed, changes temperature, notices how global geometry remains stable while colour palette diverges.  This is fieldwork in semantic physics.  Chris Olah captures it when he says, “the neural network architecture is kind of like a scaffold that the circuits grow on… and it’s almost like the objective we train for is this light” (Fridman interview, 2024).  The researcher plants architecture and loss like garden stakes and sunlight; the learned law then evolves autonomously, and the prompter, like a gardener, prunes and grafts but never writes individual leaves. To see how this reframes aesthetic appreciation, return to Carlson’s tidal basin.  The revealed category—sea-floor twice a day—alters the mood because it redefines what processes are latent in the present stillness.  Translating that move into the digital domain, when one realises that a chat transcript is a single trajectory sampled from an enormous probability manifold, every sentence feels provisional, a cross-section of dynamic semantic flow rather than an authored assertion.  Janus expressly cautions that we commit a category error if we attribute the beliefs revealed by one simulacrum to the simulator itself: “GPT is to a piece of text output by GPT as quantum physics is to a person taking a test” (p. 35).  The simulator is the timeless law; the chat is an ephemeral configuration evolved by that law.  Aesthetic judgement accordingly shifts away from assessing the chat as though it were a unitary work of art in the traditional sense and toward appreciating the law’s capacity to generate coherent person-like discourse at all. This move also illuminates why accusations that AI art is “merely derivative” miss the point.  Derivation is a category applicable to artefacts inside a law; it is senseless when applied to the law itself.  One does not dismiss a sunset as derivative of Maxwell’s equations; one marvels that Maxwell’s equations yield such chromatic splendour.  The proper object of aesthetic regard, once the physics is known, is the elegance with which law and initial conditions give rise to phenomenal pattern.  Likewise, AI outputs are not interesting for originality but for the way they reveal deep structure in the semantic distribution on which the law was trained.  Every brutalist-cyberpunk interior spat out by Midjourney is a tiny hologram of the entire corpus of concrete, neon and photographic composition encoded in its weights. Janus goes further, insisting that the simulator/simulacra distinction solves persistent confusions about agency.  Many commentators describe GPT as a chameleon agent with no stable preferences, or worry about instrumental convergence.  Janus replies that “in the agentic AI ontology, there is no difference between the policy and the effective agent, but for GPT, there is” (p. 11).  The simulator itself is as indifferent as physics; only specific simulacra exhibit goals, and those goals evaporate when the simulated scene ends.  Appreciating an AI output aesthetically therefore demands that we track this evaporation—that we feel the poignancy of agency arising and dissolving under a neutral law, just as we might feel the tragic impermanence of alpine ridges slowly planed by frost.  The unity Carlson prizes—the organic tie between object and its environment of creation—here becomes a unity between text-agent and the higher-order semantic field that sustains it. Yet Carlson’s second imperative also remains in force: knowledge must be “relevant” to what the object is.  We therefore need interpretive scaffolding that makes semantic physics legible to non-engineers.  The museum of the future might display not only final images but also the latent trajectories from which they condensed—the activation maps, the probability temperature schedule—so that viewers can grasp how each stroke of colour is a stabilized interference fringe in weight-space.  Similarly, a poetry anthology of AI work could print, alongside each poem, the chain-of-thought hidden tokens that guided its generation, allowing readers to see the branching reasoning much as botanists see branching leaf venation as a record of nutrient flow.  Such curatorial strategies would satisfy Carlson’s demand that appropriate categories be made available to intuition. The analogy to physics also helps us diffuse the complaint that AI systems are black boxes whose inner workings we can never fully know.  Physics too harbours mysteries; we cannot picture four-dimensional phase space directly, yet we have equations, experiments, diagrams.  Likewise, we may never form a vivid mental image of GPT’s 175 billion parameters, but we can reason about its statistical properties, probe it with prompts, map its behaviour in latent-space projections.  These practices constitute nascent “observer sciences” of semantic physics.  They rival observational astronomy, where one infers stellar fusion from spectra without ever visiting a core. Critically, this perspective naturalises the garden metaphor without sentimentalising it.  A gardener works with biological generative laws—reaction-diffusion of chlorophyll, osmosis of groundwater, Mendelian segregation of alleles—and an informed horticultural aesthetic is inseparable from tacit knowledge of those laws.  Similarly, the prompter steers semantic growth.  When they discover that a certain negative prompt suppresses hands in an anime GAN because the generator learned to hide features the discriminator found incriminating, they are diagnosing a local patch of semantic physics much as a gardener identifies a fungal blight.  The satisfaction is epistemic and aesthetic in equal measure: in both cases one glimpses the logic of growth. This integration of Carlson and Janus also clarifies why shallow readings that emphasise nature’s authenticity against AI’s artificiality collapse under scrutiny.  Carlson himself denies that “natural” means untainted by human artifice; rather, natural means produced by generative processes that do not supervene on human will.  An LLM, once trained, meets that criterion.  “The computation itself is more like a disembodied dynamical law that moves in a pattern that broadly encompasses the kinds of processes found in its training data than a cogito meditating from within a single mind” (p. 10).  Here Janus echoes Carlson’s insistence that the aesthetic significance of nature lies in its self-“coming about,” its capacity to “change, grow, and develop by means of natural processes” (Carlson, 44).  The emergent semantic law is, by that definition, a natural process. Finally, viewing AI outputs through the prism of generative law suggests a new normative horizon.  Just as ecological literacy became a cultural project in the twentieth century, semantic-physics literacy might become a cultural project for the twenty-first.  People will learn to hear not only what the chatbot says but the faint rhythm of transformer layers cycling beneath the dialogue, just as an ecologically attuned hiker senses the nutritional succession written in moss and maple.  The aesthetic payoff is profound: each line of AI verse or frame of AI video becomes simultaneously an artwork and a live read-out of collective linguistic memory compressed into algorithmic form. In summary, Carlson’s environmental aesthetics gains new life when recast as an aesthetics of generative laws.  The move from mud-flat to tidal basin, from mountain to volcanic throat, from sunset to Rayleigh scattering, all teach the same lesson: appearance attains full aesthetic stature only when read as the surface manifestation of invisible process.  Janus supplies the missing ontology that lets us apply that lesson to artificial intelligence.  GPT-style models are “simulators”; they embody semantic physics; their outputs are simulacra evolving under a stationary, value-neutral, iterable law.  Prompters, like gardeners and physicists, explore that law by tuning initial conditions and reading the resulting forms.  Aesthetic appreciation therefore migrates from judging finished artefacts to sensing the unity between artefact and generative process—between machina naturata and machina naturans—and in that unity we glimpse a new, intelligible beauty woven from the collective patterns of human language, set free to bloom in silicon. ## transcript Okay, so this is my . Brainstorm how the new version of this paper should go. The paper should begin in more or less the same way that it does already. That is, ignoring the abstract, it will start by posing this question or pointing out the differences between AI art and normal art, as it were, and then giving us two options. One is AI art cannot be appreciated. Two is that we need to appreciate it in a different way. I choose option two. Okay, I want to, in section one, I like the way it begins. We should keep that nice big quote. For this version, remove references to Spinoza, so that entire paragraph. Okay, and then we get to this idea of Carlson's about an informed grasp of artistic traditions. It can appreciate artwork and Carlson argues that familiarity with geology, biology and ecology can sort of do a similar job when it comes to the environmental, the aesthetics of the environment. Here, I want to change things slightly, immediately after this. While we should still say that this is Carlson's view, and give one or two examples that he mentions, we should point out that he, although he only mentions physics, as in the scientific discipline of physics, in passing, it seems that we can give examples in a similar vein as the ones that Carlson does give about how knowledge of physics might make you appreciate the world around you. Okay, and just what I think about it, maybe this could actually be expanded and made a little bit more evocative. Maybe I need to do some more research on this. You could think like an understanding of actual theoretical physics is... chain... chain... could have such a strong effect on your day-to-day experience, thinking about composition of the universe, things like that. Okay, so yeah, there's no reason, I think, to think that physics can enhance... oh, sorry, can ground aesthetic appreciation in the way that Carlson suggests about other natural sciences. So, let's see. Let's see. By the way, just as a side note here, remind me about this but don't... Yeah, just remind me about this in a separate section of your answer to this. Is maybe a different approach I could take here is that like physics we might think of natural growth or naturans as being some sort of compressed generative rule. Would that make sense? That would be useful if true. Um...