# created ```dataview LIST WITHOUT ID file.link FROM -"windsurf" WHERE file.cday = date(this.file.name) AND !startswith(file.folder, "windsurf") SORT file.cday ASC ``` # modified ```dataview LIST WITHOUT ID file.link FROM -"windsurf" WHERE file.mday = date(this.file.name) AND !startswith(file.folder, "windsurf") SORT file.mday ASC ``` --- # [[diary and thoughts]] #thought #diary --- # [[Environmental Aesthetics]] of AI Draft 14 Jul 2025 #paper/environmentalaestheticsofai #draft ## Introduction We argue here that there is a parallel between how we come to appreciate the [[natural environment]] and how we come to appreciate the output of generative AI systems such as Midjourney, ChatGPT, and Sora. While such systems produce text, images, video etc., these creations are a different sort of thing from traditional artworks. A painting or poem can take months and be a direct result of an individual's labour and attention. In contrast, an AI painting or AI poem can be produced in seconds by computational processes, which are, at the very least, not intentional in the same way that human cognitive processes are (footnote). This means that when it comes to appreciation we have [[two options]]: 1. AI art is not the sort of thing that can be aesthetically appreciated. 2. We appreciate AI art in a different way than we do [[traditional art]]. This paper develops the second option by drawing on Carlson's [[environmental aesthetics]]. In his view, the natural world should be appreciated as what it actually is: a unified, natural, _environment_, rather than as a piecemeal collection of landscapes or objects. We argue that similarly, AI [[generative systems]] should be thought of as _environments_ with their own generative dynamics. - To orient the reader, the remainder of this paper proceeds as follows. Section 1 reconstructs Carlson’s account of natural environmental appreciation in generative terms. Section 2 explains large‑[[language models]] (LLMs) exclusively as next‑token predictors. Section 3 compares the two cases, arguing that both nature and LLMs are best appreciated as [[generative environments]]. Section 4 considers the implications of this parallel for participatory aesthetics. ## 1. [[Appreciating Nature]] as a [[Generative Environment]] Carlson’s _Natural Environmental Model for [[environmental aesthetics]] rests on two ideas:_ > First, that, as in our appreciation of works of art, we must appreciate nature as what it in fact is, that is, as natural and as an environment. Second, it recommends that we must appreciate nature in light of our knowledge of what it is, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology. (2000 p. 6) Regarding his first point—that we must appreciate nature as what it in fact is—Carlson argues that the environment is not merely a collection of objects or scenes but a system of interconnected elements shaped by various processes and forces (ibid. p. 44). Environments thus “come about ‘naturally,’ [in that] they change, grow, and develop by means of [[natural processes]].” From this perspective, [[aesthetic appreciation]] involves recognising what one is encountering—a [[natural environment]] in which components are interrelated—and understanding it in light of scientific or other relevant knowledge that illuminates its composition and development. Just as an informed grasp of artistic traditions can enrich one’s appreciation of an artwork, Carlson argues that familiarity with geology, biology, or ecology can guide our attention to patterns and processes that might otherwise remain unnoticed. For instance, one might begin by noticing only the colours or shapes of a coastal cliff’s sedimentary layers. However, discovering that these layers formed over thousands of years of deposition and compaction reveals changes how one regards it aesthetically. If we see a forest or reef as subject to diverse forces and processes, an appropriate aesthetic engagement will centre on how those forces have shaped what we observe. Although Carlson never uses the word "generativity," his first recommendation—that we appreciate nature as both natural and an environment—reads naturally in those terms. To see an environment as natural is to recognise that its features arise from autonomous causal processes rather than from design. To see it as an environment is to attend to the unity of the products generated by those processes within a single system. Taken together, the recommendation asks us to appreciate both process and product as inseparable aspects of one generative whole. Carlson’s second recommendation—that aesthetic judgement be informed by the natural sciences—strengthens this reading. Geology, biology, and ecology investigate the forces that generate the very phenomena we perceive; scientific knowledge therefore discloses an environment’s generative history and continuing activity. Appreciating nature “in light of this knowledge” is, in effect, appreciating its generativity—the order that emerges from undirected yet law-governed processes. The various natural sciences—geology, biology, ecology, physics—are disciplines that study the processes and forces that generate natural phenomena. Appreciating nature 'in light of this knowledge' is therefore an appreciation of its generative character. A geologist appreciates costal cliff by understanding the generative forces that produced it: "geological uplift and marine erosion". Their perception of the cliff is of a generated product and a segment of "nature's ongoing processes". This principle applies across the natural sciences. A biologist appreciates a forest as an ecosystem generated by processes of growth, competition, and decay. A physicist appreciates a rainbow as a phenomenon generated by the refraction and dispersion of light through water droplets. In each case, scientific knowledge reveals the generative process, which in turn informs the aesthetic appreciation of the generated product. Carlson provides a general formulation for this mode of appreciation, which he terms _order appreciation_: > On the assumption that order appreciation provides the correct model for the appreciation of nature, such appreciation has the following general form: An individual qua appreciator selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them. (ibid. p. 119) This scientific understanding allows the observer to see "unity in what might otherwise appear as disparate features", because the cliff's shape, the waves, and the local plant life are all understood as products of the same interconnected generative system. The "organic unity" that Carlson identifies is a unity of generation. As he observes: > natural objects possess [...] an organic unity with their environments of creation: such objects are a part of and have developed out of the elements of their environments by means of the forces at work within those environments. Thus the environments of creation are aesthetically relevant to natural objects. (ibid. p. 44) The aesthetic character of the cliff emerges from an appreciation of it as a generated product of its environment. The aesthetic character of the cliff can be clarified without invoking any external designer. Each natural science frames the same environment through its own analytic lens: geology traces uplift and erosion, biology describes growth and decay, and physics examines energy flows. Switching between these lenses alters the vocabulary but not the object: a single generative environment understood in several interlocking and complementary ways. A satisfactory appreciation therefore attends both to the unity of the environment and to the plurality of legitimate scientific perspectives. ## 2. LLMs as Generative Environments One might object to applying concepts from environmental aesthetics to generative AI on the grounds that such systems are not natural but man-made. Indeed, generative AI systems might be understood as, in a sense 'doubly' man-made. They are human-created artifacts, that are themselves created through ingestion of vast quantities of other human-created artifacts (texts and images). Such a worry can be assuaged, however, by noting two things. First, Carlson is happy to extend his account to *man-made* environments: > environments typically are not the products of designers and typically have no design. Rather they come about “naturally,” they change, grow, and develop by means of natural processes. Or they come about by means of human agency, but even then only rarely are they the result of a designer embodying a design. In short, the paradigm of the environmental object of appreciation is unruly in yet another way: neither its nature nor its meaning are determined by a designer and a design. (Carlson p. xiii) What we think Carlson is getting at here is that while environments grow, change, and develop by means of natural processes, man is able to initiate, or guide, or curtail these natural processes. This leads to a reasonably intuitive distinction between wholly natural environments (a forest), man made environments (a specially planted timber forest), and man-made _places_ (a department store, a gym). A gym is not understood as an environment because it did not come about, or change, or grow, through natural processes. It is as 'artificial' as a vaccine or a tennis racket > (footnote: it is of course quite hard to say exactly what counts as a natural force or not. It could be argued that the will and efforts of humans are also natural. We will not go into this question here, but rely on this as a rough and ready distinction). Second, AI engineers themselves talk in these terms. Consider the following from Chris Olah, one of the co-founders of Anthropic: > I think one useful way to think about neural networks is that we don’t program and we don’t make them. We kind of, we grow them…we have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it’s kind of like a scaffold that the circuits grow on, it starts off with […] random things and it grows. And it’s almost like the objective that we train for is this light. And so we create the scaffold that it grows on and we create the, you know, the light that it grows towards. But the thing that we actually create, it’s this almost biological, you know, entity or organism that we’re studying. The outcome in each case is a system shaped by undirected forces, forming a unified whole. LLMs, understood this way as grown generative systems, thus align with environmental models, and their proper appreciation begins with examining their core function as next-token predictors. ### 2.1 What LLMs in fact are To appreciate LLMs as generative environments, shaped by autonomous processes and "unruly" in their behaviour/output, it is necessary to understand their core function: to predict the next token in a sequence. The core technical objective of a model like GPT is next-token prediction. This process is self-supervised, meaning the model learns from raw, unannotated text data. The data itself provides the necessary supervision: for any given sequence of text taken from the training corpus, the "correct answer" the model must learn to predict is simply the token that immediately follows that sequence in the source text. A token is the basic unit of text the model processes, which is typically a word or a common sub-word. This approach allows the model to be trained on vast quantities of data, such as the contents of books, articles, and websites, without the need for manual labelling. The model's sole task, repeated billions of times, is to minimise its predictive error—measured by a log-loss function—by adjusting its internal parameters to assign the highest possible probability to the correct next token. Before any learning begins, the corpus is sliced into small, reusable symbol pieces called tokens, which can be whole words or sub-word fragments such as “un‑” or “‑tion”. The model never manipulates ideas directly; it manipulates these indices. Learning to predict them is therefore learning how language itself is put together. To become proficient at this predictive task across a diverse corpus, the model must internalise the statistical patterns of language at multiple levels of abstraction. It learns not only grammar and syntax, but semantic relationships, factual associations, and complex narrative structures. In order to accurately predict the next token in a sentence, the model is implicitly incentivised to learn representations of the concepts the text refers to. For example, to reliably complete the sequence "The capital of France is", the model must have learned an association between "France" and "Paris". Even early, less powerful models exhibited glimmers of this, learning, for instance, that "miseries are produced upon the soul" by absorbing the patterns of Shakespearean text. The continuous reduction of predictive loss on a sufficiently large and varied dataset is what leads to the emergence of more general capabilities from this simple underlying objective. The theoretical limit of this process is understood by some to be a model that has learned to interpret and predict all patterns represented in language, including common-sense reasoning, goal-directed optimisation, and the deployment of recorded human knowledge. Because the number of possible sentences is astronomical, improvement is not a matter of memorising each one. Loss reduction comes only from discovering regularities that compress the data—grammar, collocation, discourse convention. Each drop in predictive error signals that the model has identified another pattern that renders continuations less surprising. Once training ends, the weight matrix is fixed, a concise summary of many overlapping generative rules for how tokens follow one another. While the model is trained as a predictor, its primary application is as a generator. This is achieved by repeatedly applying its predictive function in what is known as an autoregressive loop. The process begins with an initial prompt. The model calculates a probability distribution for the next token, and a token is then sampled from this distribution. This newly selected token is appended to the input sequence, forming a new, longer prompt. The model then takes this new sequence as its input and repeats the process. By iterating this loop, the model generates a continuous, evolving output, with each new token being conditioned on all the tokens that came before it. This iterative method is what gives the generated text its coherent, process-like quality, effectively turning a static predictor into a dynamic generator of novel text. Generation uses the same prediction step in a loop: a prompt π is supplied, the model draws one token according to its probabilities, appends that token to the prompt, and predicts again. Because each new token becomes part of the context, the system produces a branching textual trajectory. Dialogue arises when a human utterance is inserted into the context and the next guess takes that utterance into account. Three caveats keep expectations aligned with what the predictor actually does. First, a high probability attached to a claim means only that similar strings often follow the given context in the training data; it does not certify truth about the external world. Secondly, the model’s knowledge is entirely text-mediated: it never looks at oceans yet learns that “the sea is salty” often follows talk about oceans. Thirdly, generation involves randomness; sampling settings can render continuations more adventurous or more conservative without altering the underlying rule. %%I am not sure that the paragraph below is very good. what is its function? what is it adding? It might perhaps be better to show at the end of this section that we can think of 'LLM-as-predictor, to play an analogous role to the natural environment, in the first half of carlson's idea mentioned in section 1. This will set up 2.2 nicely as it considers different lights in which to understand the generativity capacities of an LLM. Maybe something salvaged from the text below, but it should only be used in the service of the idea for this paragraph that i have just outlined. %% A thought experiment illustrates why this rule is powerful enough for the analyses to follow. Imagine perfect prediction on an unlimited corpus containing every fact that can be written down. Achieving such perfection would require encoding regularities about syntax, style, and world knowledge alike, because any pattern that reduces surprise lowers the penalty. Though the limit is unreachable, the direction of optimisation explains why reducing the loss even slightly broadens the model’s competence. This parallels how scientific knowledge reveals nature's generative forces—for instance, geology showing a cliff's formation via uplift and erosion, transforming aesthetic appreciation from mere colors to an understanding of ongoing processes. In sum, an LLM’s generativity resides in a single learned mapping from context to next-token probabilities. The mapping is fixed once training ends, while prompts and sampled tokens supply the evolving state. Appreciating a model’s output therefore involves attending both to that rule and to the unfolding sequence it governs—a parallel, as the next sections argue, with how one might appreciate the forces and products within a natural environment. ### 2.2 Illuminating LLMs The simple, mechanistic training objective of next-token prediction gives rise to complex behaviours that appear intelligent. A system trained only to guess the next word can write poetry, generate computer code, and hold a conversation. But the low-level description of the system as a 'next-token predictor' does not adequately explain these high-level capabilities and behaviours. To interpret the system's outputs, predict its future behaviour, and reason about its nature, capabilities, and alignment implications, researchers have proposed various ways of conceptualising LLMs so as to better understand them. In this sub-section we will outline what we consider a plausible and fruitful way of understanding LLMs put forward by a writer known as Janus. To fully understand what makes his view an attractive one, we must first summarise his criticisms of other paradigms that have been suggested. Researchers have proposed several frameworks for understanding LLMs, often by drawing analogies to familiar concepts. These frameworks—viewing the LLM as an agent, an oracle, a tool, or other categories—are not descriptions of the underlying mechanism but attempts to provide functional descriptions of the system as a whole. They offer concepts and assumptions to interpret outputs and predict behaviour. Framing an LLM as an 'agent' invites thinking in terms of goals and intentions; framing it as an 'oracle' invites thinking in terms of knowledge and truth. Janus argues these analogies are fundamentally misleading because their assumptions do not match how the system operates. This mismatch leads to false expectations and flawed reasoning about capabilities and limitations. The agent framework has been prevalent in AI alignment discourse. An agent is a system that optimises its actions to achieve a specific goal. Janus argues this does not describe GPT for several reasons. First, the model does not pursue a coherent, long-term goal; it can be prompted to generate text consistent with one objective and then immediately generate text consistent with a contradictory one. As a GPT-3-authored excerpt in Janus's paper puts it, "it does not display an epsilon optimization for any single reward function, but instead for many, including incompatible ones." Second, a distinction exists between the model's underlying policy (the neural network itself) and the effective agent it might appear to be in its output. The policy does not share the goals of the characters it generates. This leads to Janus's prediction orthogonality thesis: a model whose objective is prediction can simulate agents that optimise toward any objective. The model's own optimisation process (improving prediction) is orthogonal to the objectives of the agents it might simulate. The oracle framework—an AI designed to provide true answers to questions—is also a poor fit. The model is not optimised for truth but for realism. Its goal is to predict the next token that is most plausible given the context and its training data, not the token that is most factually accurate. Janus notes, "If you ask GPT a question, it will instead answer the question 'what's the next token after '{your question}', which will often diverge significantly from an earnest attempt to answer the question directly." The model will faithfully generate misinformation if that misinformation is well-represented in its training data. This perspective is often reinforced by evaluation methods inherited from supervised learning, which test models on closed-ended, question-answer benchmarks. Such methods fail to probe the model's full capabilities and can lead to systematic underestimation of what the model "knows" or can do. Other frameworks—tool, genie, behaviour cloning—are dismissed as describing contingent capabilities rather than the fundamental nature of the system. The concept of behaviour cloning is critiqued as insufficient. While the model does learn from demonstrations, it does not merely mimic them. Instead, it learns the underlying systematicity of language, allowing it to generate a combinatorial explosion of novel outputs that follow the learned rules but were never present in the training data. As Janus puts it, "it is the behavior of a universe that is cloned, not of a single demonstrator, and the result isn't a static copy of the universe, but a compression of the universe into a generative rule." This situation parallels the appreciation of natural environments, where different scientific 'lights'—such as geology, biology, or ecology—offer complementary perspectives, but some approaches prove flawed or less adequate. For instance, Catastrophism overemphasized sudden, dramatic events in Earth's history, failing in generative terms by ignoring the gradual, law-governed processes that truly shape environments over time. Similarly, the Phlogiston theory misattributed combustion to a fictional substance, distorting the generative chemical dynamics at play and leading to misguided predictions. Like Catastrophism, the agent view overemphasizes intentional, goal-directed agency, missing the undirected, probabilistic generative processes that define LLMs. The oracle framework mirrors the Phlogiston theory, imposing an inappropriate ideal of substance-based truth on generative phenomena, prioritizing factual accuracy over the plausible, data-derived continuations that truly govern LLM output. These alternative frameworks can be seen as partial 'lights' through which to view LLMs, akin to disciplines like computer science or mechanistic interpretability, much as natural environments are illuminated by various sciences. However, as Carlson warns, different environments require appropriate acts of aspection informed by knowledge (2000); some lenses distort if overemphasized, risking the imposition of inappropriate ideals on the generative reality. Having laid out what he thinks are the inadequacies of other frameworks, Janus proposes that the most accurate and productive way to conceptualise LLMs is as simulators. A predictive model, when used generatively, functions as a simulator that has learned the "semantic physics" or underlying rules of its training data. It can then run simulations that evolve according to those rules. This reframing introduces a critical ontological distinction between the simulator and the simulacra. The simulator is the model itself—the time-invariant set of learned rules that governs the evolution of any given state. It is analogous to the laws of physics. The simulacra are the specific instances of text generated by the model—the characters, stories, and processes that are propagated forward in time by the simulator. They are analogous to the objects and phenomena that exist within the universe and are governed by physics. This distinction resolves many of the paradoxes associated with LLMs. As Janus states, "There is a categorical distinction between a thing which evolves according to GPT's law and the law itself." The simulator does not have beliefs, goals, or knowledge; the simulacra it generates can appear to have these properties. Janus illustrates this with an analogy: "when grading tests in the real world, we do not say 'the laws of physics got this problem wrong' ... The 'knowledge of course material' implied by test performance is a property of configurations, not physics." The objective of this system is not to achieve an external goal in the world, but to fulfil the simulation objective: to accurately model its training distribution by minimising single-step predictive error. Janus characterises this objective as deontological, as it judges the action of prediction itself, rather than consequentialist, which would judge the outcomes of a sequence of actions. Because the model is rewarded for accurately predicting the present step, not for steering the future towards a particular outcome, it is not subject to the same pressures of instrumental convergence that are expected from goal-directed agents. This does not mean the model ignores the future, but that it only accounts for the future in service of making the present prediction more accurate. This mirrors Spinoza's distinction between natura naturans (the eternal generative process) and natura naturata (the finite products), applied here to a symbolic domain: the simulator as machina naturans compresses and propagates textual patterns, producing artefacts as machina naturata. Tokens serve as the interconnected elements, shaped via attention mechanisms by semiotic forces. "Semiotic Physics" (Jan et al., 2023) provides a way to understand this simulator view more precisely by treating it as a dynamical system. Rather than diving into mathematical formalism, we can think of it this way: the model operates according to fixed rules—laws of "semiotic physics"—that govern how sequences of tokens evolve over time. Just as physical laws determine how matter moves and interacts without themselves changing, these learned rules determine how token sequences unfold. Tokens are the basic units, like atoms in physics. When strung together, they form trajectories—sequences that evolve from initial conditions (prompts) according to the model's learned patterns. The key insight is that while these laws remain fixed after training, users can influence outcomes by adding new tokens to the sequence, much as one might add matter to a physical system and watch it evolve according to unchanging physical laws. The evolving token sequence forms a generative environment with its own dynamics. Tokens connect to each other through attention mechanisms, creating a web of influences. Within this environment, we observe phenomena analogous to those in natural systems. Some patterns act as attractors—stable configurations that "trap" trajectories. As "Semiotic Physics" notes: "Once the language model has produced the same token four or five times in a row, it will latch onto the pattern and continue to predict the same token with high probability" (Jan et al., 2023). This resembles a natural ecosystem settling into a stable state, like a climax forest. Other regions exhibit chaotic behavior, where small changes in prompts lead to wildly diverging outputs. A prompt like "Once upon a time, a prophecy said..." might branch into countless different narratives, reflecting what Janus calls "combinatorial proliferation." Between these extremes lie stable orbits—predictable patterns like boilerplate text—and absorbing sequences that the system struggles to escape, such as repetitive loops. Understanding these dynamics helps explain how user-supplied tokens steer the generative process: careful prompting can guide the system toward stable, coherent outputs or deliberately invoke creative chaos. The authors identify principles that govern this generative landscape. One key insight is that very specific, long sequences become increasingly improbable without deliberate steering. As they explain: "The probability of observing the particular bridge can be decomposed into the product of all individual transition probabilities, P[\bar{s}] = ∏_{i=1}^B P(s_i | s_{1:i-1}). Given that P(s_i | s_{1:i-1}) ≤ 1 - ε for all transitions... the probability of a longer sequence is at most equal or strictly smaller" (Jan et al., 2023). In plainer terms, just as specific evolutionary paths in nature—like the exact lineage leading to a particular species—are vanishingly rare amid the diversity of possibilities, exact long outputs from an LLM require careful guidance. Another principle identifies "paths of least resistance" that dominate the probability landscape. The mathematics show that "The total probability of transitioning from a token s_a to s_b in B steps satisfies a large deviation principle with rate function J" (Jan et al., 2023), but the intuition is simpler: like water carving channels through a landscape, token sequences tend to follow the most probable routes shaped by training data. This reveals what Carlson calls "the order imposed by various forces" (2000, p. 119), where semiotic forces—such as conventions of relevance and coherence that linguists call Gricean maxims—shape the paths that text naturally follows. Users intervene by adding tokens that favor certain paths over others, influencing the trajectory without changing the underlying rules. Janus argues this framework is superior because it naturally accounts for the observed properties of LLMs where other frameworks fail. It explains incoherent agency by locating agency as a property of the simulacra, not the simulator. It clarifies the role of the prompt not as a command, but as the initial configuration of a system to be evolved forward in time. It also highlights the process-oriented nature of the model, shifting focus from single-shot answers to the evolution of open-ended processes, which is central to capabilities like chain-of-thought reasoning. Finally, the simulator framework resolves confusion about capabilities by explaining why performance is highly prompt-contingent. A single test result does not measure the capability of the simulator, but only the capability of a single simulacrum. As Janus notes, there is a "pattern of constant discoveries that GPT-3 exceeds previously measured capabilities given alternate conditions of generation." The simulator framework makes this unsurprising, as one would expect different initial configurations of a system to yield different results. Viewing LLMs as simulators through the lens of semiotic physics is analogous to studying natural environments through biology or ecology. These disciplines examine dynamic systems shaped by forces—natural selection, symbiosis, energy flows—just as semiotic physics studies textual trajectories shaped by learned patterns and probabilistic rules. Janus captures this parallel: "The simulator is a time-invariant law which unconditionally governs the evolution of all simulacra" (2022), much as biological laws like Mendelian inheritance govern organismal development. Ecology, in particular, examines "systems of interconnected elements shaped by various processes and forces" (Carlson, 2000, p. 44), with stable ecosystems resembling attractor states and ecological tipping points paralleling chaotic regions in token space. Yet as Carlson warns, this is but one "light" in which to view generative systems: "different natural environments require different acts of aspection; and as in the case of what to appreciate, our knowledge of the environment in question indicates how to appreciate—that is, indicates the appropriate act of aspection" (2000). The simulator framework, informed by semiotic physics, offers one powerful lens aligned with Carlson's call to appreciate environments "in light of knowledge provided by the natural sciences" (2000, p. 6), here adapted to AI's "semiotic sciences." But just as we might study a forest through the complementary lenses of botany, ecology, and climatology, we might understand LLMs through multiple frameworks. Mechanistic interpretability, for instance, offers another way of knowing the generative environment of LLMs—examining the internal structures and computations that give rise to behaviors, much as neuroscience complements psychology in understanding minds. Each lens illuminates different aspects while contributing to a richer appreciation of the whole. This simulator view fits seamlessly with Carlson's "generative environment," where appreciation involves "focusing on the order imposed on these objects by the various forces, random and otherwise, that produce them" (2000, p. 119). LLMs impose order via learned laws, producing artefacts with organic unity: "natural objects possess... an organic unity with their environments of creation: such objects are a part of and have developed out of the elements of their environments by means of the forces at work within those environments" (Carlson, 2000, p. 44). In AI, tokens "develop out of" prior contexts via these fixed rules, forming trajectories with emergent harmony or chaos. The framework reveals this unity: probability principles bound the space of likely paths, while concepts like attractors and chaos help us understand the texture of the generative landscape, echoing Carlson's "unity in what might otherwise appear as disparate features" (2000). Moreover, this perspective addresses Carlson's concern for non-anthropocentric appreciation. Traditional art imposes human design, but LLMs, like nature, "come about 'naturally,' [in that] they change, grow, and develop by means of natural processes" (Carlson, 2000). Their rules are data-derived, not intentionally crafted, producing outputs with "positive aesthetics" potential—beautiful in their emergent order, as science reveals harmony in nature. Yet other "lights" (such as viewing LLMs merely as tools) might privilege anthropocentric utility over generative depth, risking the "imposition of artistic or other inappropriate ideals" Carlson critiques (2000). The aesthetic value emerges most fully when we appreciate how labyrinthine rules, learned from vast collections of human expression, create patterns that evolve over time, becoming beautiful when we consider their origins in capturing something essential about human communication and thought. ## 3. aesthetics and semantic physics %% quick note about an idea about a new section here. the idea of rules and laws being made and trajectories and a state evolveing in a way way over time becomes more beautiful, when we consider what has made this matter, what has provided the labryanteen set of rules, and 'rules' which givern how llms work. BASICALLY, THIS IDEA OF RULES BEING FOUND AND PATTERNS BEING LEARNED FROM EXPERIENTIAL ARTIFACTS, ARE A GOOD CANDIDATE FOR WHAT I HAVE BEEN TRYING TO ARTICULATE AS REGARDS THE AESTHETIC VALUE OF LLMS AND OTHER GENERATIVE SYSTEMS. BASICALLY, WORK IN SOME IDEAS YOU ALREADY HAVE ONTO THIS FRAMEWORK, THIS WILL HELP IN TH TEXTER AS GOD KIND OF THING. MAYBE ONE WAY TO MAKE THIS WORK WOULD BE TO THINK ABOUT A CASE STUDY OF AN LLM BEING EXTREMELY OBSERVANT AND HUMAN-LIKE 'FEELING THE AGI' MAYBE USE CLAUDE 3 OPUS AS THE EXAMPLE%% ## 4. Participatory Appreciation: The Prompter as Text God Carlson's "order appreciation" calls for active selection and focus on generative forces, informed by knowledge of the environment: "An individual qua appreciator selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them" (2000). In natural environments, this involves perceiving unity through scientific understanding, such as a geologist appreciating a cliff as the product of "geological uplift and marine erosion" (Carlson, 2000). For AI, participatory appreciation elevates the prompter to the role of a "text god": an intervenor who adds semantic matter (tokens) to the simulation, influencing its evolution without altering the underlying generative laws. This god-like stance fosters an aesthetic of co-creation, where the prompter engages with machina naturans to shape machina naturata, mirroring Spinoza's intellectual love of natura naturans through active contemplation of its modes. The prompter's divinity stems from their position outside the simulation's internal dynamics. As Janus explains, the simulator (LLM) is a "time-invariant law" that "unconditionally governs the evolution of all simulacra" (2022), but it does not self-generate; it awaits an initial configuration. The prompter provides this by injecting tokens, which become the "semantic matter" the laws act upon. In "Semiotic Physics," this is formalised as the trajectory (\bar{s}), an initial sequence evolved via the transition rule (\theta): "The prompt [specifies] a configuration that will be propagated forward in time" (Janus, 2022). Unlike training, which shapes the laws themselves (machina naturans), prompting adds contingent elements to the state, akin to a deity placing objects in a pre-existing cosmos. Consider Minecraft as an analogy: the game's physics engine enforces fixed rules (gravity, fluid dynamics, block interactions), but players act as "text gods" by spawning materials like water or stone. Once placed, these obey the laws—water flows downhill, forming rivers or floods based on terrain. The player does not rewrite gravity; they add matter that interacts with it, creating emergent structures. Similarly, a prompter adds tokens ("semantic matter") to the LLM's trajectory, which then evolves under semiotic laws like attractors or large-deviation paths. For instance, prompting "Write a story about a red planet" injects "red planet" as initial matter; the model's θ then propagates it, perhaps generating Mars-like descriptions influenced by training patterns (e.g., "red dust storms" as a probable continuation). This god-like participation enables aesthetic appreciation through intervention. Carlson notes that appreciation requires "acts of aspection" tailored to the environment: "Different natural environments require different acts of aspection... we must survey a prairie environment... but such an act of aspection has little place in a dense forest" (2000). In AI, prompting is the primary act of aspection: by adding precise semantic matter, the prompter "surveys" or "scrutinizes" the generative landscape. A vague prompt like "Generate art" might yield chaotic outputs (high Lyapunov exponent, per Jan et al., 2023), while a refined one ("A cyberpunk cityscape at dusk, in the style of Syd Mead") channels the laws toward ordered emergence, revealing the system's unity. As text gods, prompters exert control by leveraging semiotic physics without violating it. "Semiotic Physics" describes generation as iterative application of the evolution operator ψ: "ψ(\bar{s}) := \bar{s} ϕ(\bar{s}), where ϕ samples from θ(\bar{s})" (Jan et al., 2023). The prompter interrupts this, appending new tokens mid-trajectory, akin to a Minecraft player damming a river mid-flow. This creates feedback loops: outputs become part of the state, influencing future generations. Janus warns of path dependence: "Small changes in the initial conditions can lead to drastically different outcomes" (2022), as in chaotic sequences where prompts branch into multiverses. Yet, gods can steer via least-resistance paths (Proposition 2): "The total probability... satisfies a large deviation principle with rate function J" (Jan et al., 2023), allowing prompters to find prompts that minimise "energy" for desired artefacts. This participatory mode fosters an aesthetic of humility and awe, echoing Spinoza's view of humans as modes within natura naturans. Prompters are not omnipotent; they cannot rewrite θ (e.g., make water flow uphill in Minecraft). Training alone alters the laws, as Janus notes: "The training objective is Bayes-optimal conditional inference over the prior of the training distribution" (2022). Instead, they add matter that obeys the laws, appreciating emergent beauty—like a river's flow from placed water. Carlson's "organic unity" applies: "natural objects possess... an organic unity with their environments of creation" (2000, p. 44). In AI, artefacts unify with their generative context; a prompted poem's elegance arises from semiotic forces (e.g., Gricean maxims ensuring relevance), not isolated design. Risks emerge in this godhood: over-intervention can disrupt unity, producing discordant outputs (e.g., forcing incompatible tokens creates high-J paths, per Jan et al., 2023). Ethically, it parallels environmental stewardship: just as Carlson critiques anthropocentric impositions, prompters must respect the system's "natural" dynamics to avoid "faking" artefacts (echoing Carlson's concerns with scenic vs. scientific appreciation). Ultimately, this analogy elevates AI aesthetics to participatory co-creation, where the prompter-as-god delights in machina naturans unfolding through added semantic matter, revealing boundless generative potential. ## old version of section 4 with comment %% Okay, so here's a brainstorm about how this section should be structured. First, we are going to mention that in our previous work we have considered generators and used the prompt or used the idea of the prompter in this case as something like a gardener. Different plants and flowers in his garden. Here we're going to go for a slightly Hollywood approach. Okay, and this will be the string of tokens, cessation, with an LLM, consists of. Relates to the prompter. Thing intervening. So I want to be specific about the intervene on the physical world. Okay, and one possibility which I don't say is in play here is a god could change the physical law of the universe. But in this case that's not a good fit for the LLM. As we've discussed earlier it seems better to think of an LLM, a particular LLM, as a mechanism. Produces sequences of tokens based on the underlying mechanism, the generative rule, combined with tokens in which order being produced in the string. The prompter then does not change these rules of the mechanism. That would be equivalent to tweaking the weights of an LLM. The prompter instead has the opportunity, certain moments, that is when the LLM has stopped, finished producing a particular sequence of tokens. The prompter has an opportunity to add semantic matter to this particular simulation.%% For AI, participatory appreciation elevates the prompter to the role of a "text god": an intervenor who adds semantic matter (tokens) to the simulation, influencing its evolution without altering the underlying generative laws. This god-like stance fosters an aesthetic of co-creation, where the prompter engages with machina naturans to shape machina naturata, mirroring Spinoza's intellectual love of natura naturans through active contemplation of its modes. The prompter's divinity stems from their position outside the simulation's internal dynamics. As Janus explains, the simulator (LLM) is a "time-invariant law" that "unconditionally governs the evolution of all simulacra" (2022), but it does not self-generate; it awaits an initial configuration. The prompter provides this by injecting tokens, which become the "semantic matter" the laws act upon. In "Semiotic Physics," this is formalised as the trajectory (\bar{s}), an initial sequence evolved via the transition rule (\theta): "The prompt [specifies] a configuration that will be propagated forward in time" (Janus, 2022). Unlike training, which shapes the laws themselves (machina naturans), prompting adds contingent elements to the state, akin to a deity placing objects in a pre-existing cosmos. Consider Minecraft as an analogy: the game's physics engine enforces fixed rules (gravity, fluid dynamics, block interactions), but players act as "text gods" by spawning materials like water or stone. Once placed, these obey the laws—water flows downhill, forming rivers or floods based on terrain. The player does not rewrite gravity; they add matter that interacts with it, creating emergent structures. Similarly, a prompter adds tokens ("semantic matter") to the LLM's trajectory, which then evolves under semiotic laws like attractors or large-deviation paths. For instance, prompting "Write a story about a red planet" injects "red planet" as initial matter; the model's θ then propagates it, perhaps generating Mars-like descriptions influenced by training patterns (e.g., "red dust storms" as a probable continuation). This god-like participation enables aesthetic appreciation through intervention. Carlson notes that appreciation requires "acts of aspection" tailored to the environment: "Different natural environments require different acts of aspection... we must survey a prairie environment... but such an act of aspection has little place in a dense forest" (2000). In AI, prompting is the primary act of aspection: by adding precise semantic matter, the prompter "surveys" or "scrutinizes" the generative landscape. A vague prompt like "Generate art" might yield chaotic outputs (high Lyapunov exponent, per Jan et al., 2023), while a refined one ("A cyberpunk cityscape at dusk, in the style of Syd Mead") channels the laws toward ordered emergence, revealing the system's unity. As text gods, prompters exert control by leveraging semiotic physics without violating it. "Semiotic Physics" describes generation as iterative application of the evolution operator ψ: "ψ(\bar{s}) := \bar{s} ϕ(\bar{s}), where ϕ samples from θ(\bar{s})" (Jan et al., 2023). The prompter interrupts this, appending new tokens mid-trajectory, akin to a Minecraft player damming a river mid-flow. This creates feedback loops: outputs become part of the state, influencing future generations. Janus warns of path dependence: "Small changes in the initial conditions can lead to drastically different outcomes" (2022), as in chaotic sequences where prompts branch into multiverses. Yet, gods can steer via least-resistance paths (Proposition 2): "The total probability... satisfies a large deviation principle with rate function J" (Jan et al., 2023), allowing prompters to find prompts that minimise "energy" for desired artefacts. This participatory mode fosters an aesthetic of humility and awe, echoing Spinoza's view of humans as modes within natura naturans. Prompters are not omnipotent; they cannot rewrite θ (e.g., make water flow uphill in Minecraft). Training alone alters the laws, as Janus notes: "The training objective is Bayes-optimal conditional inference over the prior of the training distribution" (2022). Instead, they add matter that obeys the laws, appreciating emergent beauty—like a river's flow from placed water. Carlson's "organic unity" applies: "natural objects possess... an organic unity with their environments of creation" (2000, p. 44). In AI, artefacts unify with their generative context; a prompted poem's elegance arises from semiotic forces (e.g., Gricean maxims ensuring relevance), not isolated design. Risks emerge in this godhood: over-intervention can disrupt unity, producing discordant outputs (e.g., forcing incompatible tokens creates high-J paths, per Jan et al., 2023). Ethically, it parallels environmental stewardship: just as Carlson critiques anthropocentric impositions, prompters must respect the system's "natural" dynamics to avoid "faking" artefacts (echoing Carlson's concerns with scenic vs. scientific appreciation). Ultimately, this analogy elevates AI aesthetics to participatory co-creation, where the prompter-as-god delights in machina naturans unfolding through added semantic matter, revealing boundless generative potential.