Artifact, Forest, Person. The Chimera Aesthetics of Generative AI Introduction In recent years, aestheticians and philosophers of art have turned their attention towards generative AI, investigating whether AI systems can be authors or co-authors, and whether AI-generated work has any aesthetic merit at all (Anscomb 2022; Wojtkiewicz 2023; Cross 2025). Still, there is another interesting philosophical question that has not been properly addressed so far, namely, whether and how AI systems themselves can be objects of aesthetic appreciation. This paper aims at answering this question by focusing on the paradigm case of LLMs. We argue that there are three main ways in which the aesthetics of LLMs might be built. The first, most straightforward way, is within the frame of the aesthetics of design, treating LLMs simply as designed artifacts. Still, LLMs's aesthetically relevant features — the patterns in their outputs, a particular model's characteristic 'feel' — emerge from training and from the way generated continuations develop from context, rather than being specified by designers themselves. The text the appreciator reads is the outcome of a process the trained system has gone on from the prompt — under conditions design has set, but in a direction it has not. LLMs, indeed, are not only designed but also trained. Olah (2024) describes the relation between what design fixes and what training produces as a type of growth. I think one useful way to think about neural networks is that we don't program, we don't make them, 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 some 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 light that it grows towards. But the thing that we actually create, it's this almost biological entity or organism that we're studying. Given the way in which an LLM appears to us, as a subject able to engage in conversation, one might be tempted to identify this almost biological entity or organism with a sort of person. This brings us to the second candidate frame for the aesthetics of LLMs: the aesthetics of persons. Users indeed talk about different models' personalities, and it is natural to respond aesthetically to these apparent traits. Still, LLMs lack the temporally extended life, stable dispositions, and projects that underwrite person appreciation. This suggests that the aesthetic of persons can capture at most the way LLMs appear, not what they are. There is, instead, we argue, a third kind of aesthetic appreciation that fits LLMs' deep nature. That is the aesthetic appreciation of natural objects and environments such as mountains, rivers, and forests. Carlson (REF) argues that we appreciate nature by attending to patterns produced by natural forces, guided by knowledge — geology, ecology, and the like — that makes those patterns visible. He calls this mode of aesthetic engagement "order appreciation" and he contrasts this with "design appreciation", which is, instead, the mode of aesthetic engagement appropriate to artifacts. We argue that LLMs are special artifacts that call not only for design appreciation but also for order appreciation: attention to patterns produced by trained continuation systems, guided by knowledge of how generated text develops from context under learned constraints. We conclude that, in a sense, all the three basic modes of appreciation are relevant to the aesthetics of LLMs, which thus reveal themselves to be especially peculiar aesthetic objects that merit a distinctively threefold appreciation. Specifically, design appreciation accounts for LLMs as designed systems, order appreciation accounts for LLMs as systems that are not only designed but also, as Olah puts it, "grown", and person appreciation accounts for how LLMs appear, that is, the experiential effects they elicit from human users. Still, we contend, order appreciation is the key to the aesthetics of LLMs, the crucial central piece that bridges the gap between the appreciation of their designed structure and the appreciation of their personish behavior. We will proceed as follows. Section 1 sets out Carlson's distinction between design appreciation and order appreciation, and considers why person appreciation has to be discussed alongside it. Section 2 asks whether design appreciation can guide the appreciation of LLMs, and gives only a partially positive answer since LLMs are best understood as trained continuation systems that transcend design. Section 3 shows that order appreciation is the key to the aesthetics of LLMs, and argues that it is based on knowledge of how trained continuation systems develop text from context. Section 4 considers the role of person appreciation in our engagement with LLMs, arguing that it can shed light on their conversational function but it rests upon design-directed knowledge and especially order-directed knowledge, which enable us to properly appreciate the structure in virtue on which that function is fulfilled. We conclude by showing how this framework guides appreciation of an LLM as a whole. 1. Design Appreciation, Order Appreciation and… Person Appreciation Both our criticism of artefactual and agentive conceptions of LLMs and our positive account will draw from Carlson's environmental aesthetics, as laid out in his 2000 book Aesthetics and the Environment. In particular, we adopt Carlson's general recommendation for aesthetic appreciation: take things as what they are, and look at them in the light of the right kind of knowledge. He applies this to the appreciation of the natural environment thusly: 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. The natural environmental model thus accommodates both the true character of nature and our normal experience and understanding of it. (Carlson, 2000, p. 6) This captures something intuitive about how we appreciate nature versus art. Appreciating mountains and cliff faces as the work of a divine artisan, rather than of natural forces, would be wrong-headed (cf. Carlson, 2000, Chapter 8); so would appreciating a Rembrandt as if it were the product of natural forces slopping paint together (cf. Danto 1974, p. 140). In both cases, appreciation is undermined by a failure to recognise what the object really is. Carlson argues that artworks and everyday objects call for design appreciation. With paradigmatic artworks, we recognise them as objects whose features are, as Gombrich (1950, p. 13) puts it, each "the result of a decision by the artist". We appreciate such works by seeing how well the result realises the artist's design. The same approach extends to designed artifacts more generally. Carlson (2000, p. 188) is explicit that functional objects are properly appreciated by seeing how their forms answer to what they are for: With anything functionally designed, not only its form, but much of its aesthetic interest and merit, 'follows function'. So, on this account, a chair, or a bridge invite the same style of attentive appraisal as a painting – guided by knowledge of ends, materials, constraints, and the fit between purpose and realisation. For the natural environment, in contrast, Carlson recommends a different mode: order appreciation. Appreciation of things like trees or valleys cannot be grounded in considerations of how well a designer managed to realise her intentions, because they are not designed objects. Instead, Carlson recommends that the knowledge grounding appreciation of the natural world is knowledge of how the order we find has been shaped by natural forces: 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. Moreover, the objects are selected in part by reference to a general nonaesthetic and nonartistic story that helps make them appreciable by making this order visible and intelligible. (Carlson, 2000, p. 119) Order appreciation is not confined to nature, however. Carlson finds it already required by unconventional works whose patterns are produced rather than designed: Pollock's action painting, where the pattern arises from the behaviour of the paint and the painter's unplanned movements, and the Dada experiments with chance, where Tzara drew the words of poems from a hat and Arp placed cut-outs by chance (Carlson 2000, pp. 110–113). Such works have no initial design against which they could be judged, but they do have an ordered pattern, produced by a combination of forces: the properties of the materials, chance, and an artist who figures as one force among the others and whose remaining role is to select which results are kept. In this respect they are closer to trees and valleys than to the chair or the bridge; Arp wanted works of this kind to "remain anonymous and form a part of nature's great workshop as leaves do, and clouds" (quoted in Carlson 2000, p. 117). In both modes, knowledge guides acts of aspection — ways of attending to an object that partly constitute its appreciation (Carlson, 2000, pp. 41–42). But the character of this knowledge differs. In the case of design, we need functional and technical understanding: what the designer intended and what constraints they faced. In natural cases, we need an account of the processes that produced the order we perceive — knowledge that lets us see natural structures as effects of forces (Carlson, 2000, pp. 60–61). Once the relevant account is in play, some cases will exhibit its order more clearly and interestingly than others; still, Carlson holds that all of nature is more or less equally appreciable (Carlson, 2000, pp. 118–120). So far so good but, we contend, Carlson's approach to aesthetics overlooks another important category of object of appreciation: people. In ordinary life we not only admire landscapes and artifacts; we admire people too – their wit, their manner, their steadiness. Some philosophers have taken this practice seriously, investigating the aesthetic appreciation of personality – sometimes termed "beauty of character" – and asking whether traits such as kindness, wit, or courage can be aesthetically as well as morally valuable (Gaut 2007; Paris 2018). Carlson's recommendation seems naturally extendable here: appropriate aesthetic appreciation of persons will depend on the right kind of person-directed knowledge – familiarity with a life (real or fictional) and a sense of the values and dispositions that organise it. A stranger's single act can of course strike us as kind. The object of beauty-of-character appreciation, however, is a trait, and a trait is a standing disposition which a single act is not sufficient to establish: the same piece of behaviour might be an expression of generosity or of calculated self-interest, and which of these it is depends on how it fits into the rest of the agent's life. Appreciating kindness as a feature of someone's character therefore requires knowing the pattern of their generous responses given who they are and what they have faced. As Parsons stresses, such knowledge is typically built up through direct interaction, careful biography, or the more precarious route of gossip (Parsons 2023, 297–299). Although Carlson does not consider person appreciation, it is not difficult to imagine ways in which his framework might be extended or modified to accommodate it. A first option is to treat the appreciation of character as a special case of order appreciation: we focus on the psychological, social, and biographical forces that shape a life, much as we attend to geological and ecological forces in a landscape. A second option would be to emphasise the ways in which personalities are, at least in part, self-shaped, and to appreciate them as self-designing projects – a thought that has obvious attractions for existentialist traditions. Nothing in what follows requires settling the issue between these options. On the principle implicit in Carlson's account, moreover, modes of appreciation are individuated by the kind of knowledge that guides them. Knowledge of a life is knowledge of a person's reasons and values, and this is not reducible either to knowledge of a designer's intentions or to knowledge of the forces operating on an object; the philosophers of character cited above already treat its appreciation as a practice of its own. We will accordingly speak of persons as a third category alongside natural items and artifacts, with their own distinctive mode of appreciation anchored in their status as subjects, leaving open how this category ultimately relates to the other two. On all of these views, however, Carlson's recommendation still applies: aesthetic appreciation is guided by substantive background understanding of what persons are like and how their traits hang together over time. Person-based aesthetics, in this sense, presupposes a rich conception of the subject as a temporally extended agent with relatively stable dispositions, projects, and evaluative commitments, grasped under a suitable body of knowledge. All this leaves us with three candidates for the aesthetics of LLMs, namely, design appreciation, order appreciation, and person appreciation. As LLMs are made by human beings just like artefacts, the natural first thought is that they admit design appreciation in the way artefacts do. In the next section, we will thus consider design appreciation as the first candidate model for the aesthetics of LLMs. 2. Design Appreciation of LLMs Technical artefacts call for design appreciation, which requires knowledge of the purpose for which the object was made and of how its features serve that purpose. LLMs initially look like straightforward candidates for this mode of appreciation: they are engineered systems, and their responses depend on choices made before any user enters a prompt. On this view, models such as GPT-5.5, Claude 4.8 Opus, and Gemini 3 Pro can be characterised by a relatively unified functional role, namely, a general-purpose conversational assistant, and by a specific way of realising that role through an architecture and a user interface whose design might be admired for elegance, efficiency, or ingenuity. Still, we argue that knowledge of the design and functioning of LLMs cannot, on its own, ground their appreciation. With traditional designed artifacts, design-knowledge illuminates structure because designers specified it. Knowing what the designer intended and what constraints they faced allows us to understand why an artifact has the form that it does. A car is appreciated through its designed form. For example, the shape of its body is at once what the appreciator looks at and what determines how the car moves through air at speed. This shape is what it is because of what the car is for. To appreciate the car aesthetically is to attend to this fit between form and purpose, and design knowledge — knowledge of the car's purpose and of how its features answer to that purpose — reaches the form the appreciator engages with. This sort of approach, however, does not fit so cleanly for LLMs. Nothing the user engages with stands to the LLM as the car's body stands to the car: what she sees is a text box and the text appearing in it, and the items that were designed (the architecture, the training objective) do not appear anywhere in her experience of the system. The interface is visible and designed, and might be admired accordingly, but it is peripheral to what users aesthetically respond to. One might reply that the generated text is where the design is realised, so that design appreciation can proceed there. But the organisation of the trained system emerges from training rather than being specified in advance, and design-knowledge therefore does not illuminate this emergent organisation: there was no designer's specification that laid it out. To understand the system, one must attend to the training process that produced it. Specifically, design does not completely settle how the system will respond to a prompt; it determines only the conditions under which the model is trained. Design fixes the training objective, which provides the system with a standard against which its outputs can be adjusted. Then, given a stretch of text, the model assigns probabilities to possible continuations, and its parameters (i.e. weights) are altered when those probabilities diverge from the continuation found in the training data. Thus, what the trained model eventually does with a prompt is not settled by design choices; it is acquired over the course of training. When the system responds to user input, it uses what it has acquired in training to continue the text it has been given. The prompt, together with whatever the system has already produced, gives it a context (that is, an enriched text). From that context the model assigns probabilities to the possible next tokens, one of which is selected and added to the context before the same step runs again, so that what appears at the end as a single answer is built through successive transitions of this kind. Later parts of the continuation depend on earlier parts, and each step is shaped by the dispositions acquired in training. The text the user reads is what the trained system has produced going on from the prompt — under conditions design has set, but in a direction it has not. Recall Olah's (2024) description of the relation between what design fixes and what training produces as a kind of growth: the neural network architecture, it's kind of like a scaffold that the circuits grow on. It starts off with some 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 light that it grows towards. But the thing that we actually create, it's this almost biological entity or organism that we're studying. What design fixes is the scaffold and the objective; the form that grows under them is acquired through training. While in the car case what the user engages with is the form the designer built, an LLM's response is produced by trained dispositions operating on the prompt and on the continuation as it develops. Design knowledge can explain the conditions under which the response becomes possible, but it does not by itself give us the order of the generated text. Thus, artefact is too coarse a description for the appreciation Carlson aptly requires. LLMs are artefacts, and design knowledge bears on how they should be appreciated; ignoring how they were built would distort that appreciation. Carlson's question, however, concerns what the thing specifically is. At that level, an LLM is a trained continuation system: a made system whose responses are not designed but rather generated from dispositions acquired in training and operating on context. Those dispositions are not written into the code as explicit rules about how to, say, handle metaphors, or politely decline illicit requests. They are emergent regularities in a trained network that has been pushed, by training, to reduce prediction error. What training makes "grow" on Olah's "scaffold" is a system of statistical associations and processing circuits whose internal organisation even designers often understand only partially. These are the features design appreciation cannot reach. Much of what users find aesthetically important in LLM behaviour – the way a model sustains a metaphor or abruptly drops it, the pattern of hedging and self-correction, the texture of its reasoning, the sorts of digression it tends to indulge, the characteristic "feel" of its refusals – is grounded in features that have not been micro-designed but have emerged from optimisation under constraints. Parsons and Carlson (2008) note that, even for simpler artifacts, knowledge of function must include knowledge of how that function is realised if it is to structure appreciation appropriately. In the LLM case, this means including knowledge of the way training and alignment have grown a particular style of continuation on top of the designed architecture. In this sense, knowledge of how function is realised concerns not only design but especially growth. Thus, if we try to make design appreciation do all the work, we mislocate the primary source of what matters aesthetically. In garden-variety technical artifacts, the designer's choices fix most of what matters aesthetically. In the LLM case, by contrast, the order that matters aesthetically is largely the order of a trained statistical system running under its own learned constraints. Designers specify objectives and scaffolds but the particular ways in which text is produced in response to the user's prompt are not written down anywhere as a plan. The upshot is that LLMs are artifacts, and there is a place for design appreciation in their aesthetic appraisal: we can and should evaluate how well their designed forms answer to their engineered functions, as well as how what Olah calls "scaffold" and "light" can differ from model to model. However, the most distinctive and revealing aesthetic phenomena arise not from the execution of a detailed design, but from the emergent linguistic order that these grown systems exhibit when they are run. To appreciate that order, we need knowledge not of what designers intended but of how training shapes text propagation. 3. Order Appreciation of LLMs [Key: **bold** = old text (the 2 Jul draft and earlier settled material); unbolded = written in the current working session.] **LLMs are trained continuation systems, to wit, systems whose responses develop from dispositions acquired in training and operating on context. That is why design knowledge cannot, by itself, guide their appreciation. Design knowledge reaches the scaffold, the objective, and the conditions under which the system is trained and deployed, but it does not by itself make visible the order acquired by a particular continuation as context is extended. The sort of knowledge central to appreciation of LLMs must make the learned order of generated text visible as the order of a trained continuation system.** **We suggest that** such knowledge **is the relevant kind** **for aesthetic appreciation of LLMs in Carlson's sense: the knowledge that makes order visible and intelligible, and that guides acts of aspection** (2000, pp. 41–42). **This order can be encountered at more than one scale. A single output is one bounded continuation from a context. An extended chat is a longer process in which earlier turns condition later ones. The model as a whole is the trained system whose tendencies become visible across many such outputs and chats. These scales do not compete for the appreciator's attention. The system is a set of standing dispositions, and dispositions can only be encountered through their manifestations: the outputs are what the appreciator perceives, and the system is that to which the perceived order belongs. To appreciate an LLM is thus to appreciate it through its outputs, much as a climate can only be appreciated through the weather it produces.** Even if one accepts that generated text has an order of its own, and that knowledge of trained continuation can make that order intelligible, we can still ask whether the plain prose that makes up most of a model's output is appreciable at all. It is easy to think of examples of the prose in question: replies which set out the steps for descaling a kettle, messages politely rescheduling a meeting. While such text does what is asked of it, the common verdict on it is that it is bland, and much of it is now dismissed outright as slop. How can we appreciate ordinary generated text if its order is nowhere on show? The same question arises for nature, where much of what there is to appreciate does not put its order on show. Section 1 set out Carlson's view that all of nature is nevertheless more or less equally appreciable. He grants that the natural order is easier to perceive in some cases than in others, but insists that it is "yet present in every case" (2000, p. 120). In every output, likewise, the dispositions acquired in training are at work; while some continuations display their work more legibly than others, the difference is one of legibility rather than of presence. We might say that the order in a routine reply is appreciable, but not conspicuous enough to invite appreciation unaided. Yet, this is no reason to think that every output is aesthetically good: how an output stands once its order is perceived remains a further question. Which features a reader notices and the way those features strike her depends upon the account under which she reads. The **story takes over the design's role of indicating "if not what is being done, then at least what is going on" (Carlson 2000, p. 113). The question a reader brings to a generated text is accordingly what is going on here.** **Different sub-disciplines of computer science might be put forward as candidates for helping the appreciator to grasp this order. One field that has emerged in connection with neural networks is mechanistic interpretability, which investigates the internal workings of these systems by reverse-engineering them into human-understandable algorithms, identifying which circuits, attention heads, and internal representations handle which linguistic tasks (Olah et al. 2020; Elhage et al. 2021). Consider, though, the difference between chemistry and geology when appreciating a cliff face. Chemistry provides knowledge of molecular bonds within rock, but it operates at a scale invisible to the naked eye. Geology, by contrast, offers concepts – e.g. strata, faults, erosion channels – that connect to what can actually be seen: one can perceive strata without specialist equipment, and knowing how sedimentation works makes the visible layering intelligible. Mechanistic interpretability occupies the position of chemistry in this comparison: the causal, circuit-level knowledge it yields concerns weight matrices, activation patterns, and circuit-level features, none of which is available to readers encountering generated text.** **For an aesthetics of LLMs, we thus need an account whose concepts describe perceivable features and render them intelligible as products of the system's learned regularities: an output-side account of how trained systems develop linguistic forms through iterated continuation from context. At any point in a run, the system receives the context so far and computes a distribution over possible next tokens. Once one token is selected, the context changes, and the next step is produced from that changed context. Training gives the system a graded sensitivity to the regularities of text — to what tends to follow what under what conditions. When the model is run, those regularities operate through a context that changes as the text develops from the starting point fixed by the prompt.** **The path generated by the model is computed token by token but the order in question is encountered by readers as a piece of ordinary language, since the trained system has acquired patterns governing what tends to follow what under given conditions in a given language. This is what Picca (2025, p. 1) captures when he describes LLMs as systems that "recombine, recontextualize, and circulate linguistic forms based on probabilistic associations". Wolfram (2023) puts these patterns in geometrical terms:** > **inside ChatGPT any piece of text is effectively represented by an array of numbers that we can think of as coordinates of a point in some kind of 'linguistic feature space'. So when ChatGPT continues a piece of text this corresponds to tracing out a trajectory in linguistic feature space.** **According to Wolfram, such a trajectory stays within meaningful text because the model has "implicitly discovered" the relevant regularities in training (PAGE); it holds them only implicitly, and no explicit statement of them is yet available. For the appreciator, though, it is enough that the regularities bear on the text in front of her: the continuation leans towards some words and away from others because training has made it so. A reader who knows as much can take the text before her as a trace of that process.** **We saw in Section 1 that order appreciation focuses on the order imposed on objects by the forces that produce them. Carlson summarises the account in terms of three entities and the interplay among them: "the order, the forces that produce it, and the account that illuminates it" (2000, p. 119). For nature, he fills these roles with the natural order, the forces of geology, biology, and meteorology, and the story told by natural science (2000, p. 120). The same roles can be filled for generated text. The order is the organisation a text acquires by being produced stretch by stretch, each stretch generated from those before it. Carlson's general form already provides for forces of two kinds, "random and otherwise", and both kinds are present here. Sampling supplies the random element, resolving each step from the distribution before it. The remaining forces are of the other kind: the learned regularities that weight the distribution, the accumulated context through which they operate, and the prompt, which initiates and conditions the process without determining it in detail. Since no plan for the whole is given in advance of generation, whatever direction a text has depends on what has already been produced and on how the learned regularities respond to it at each step; a direction can therefore strengthen, since each token in a register raises the probability that the next conforms to it, or weaken, since early material makes up a diminishing share of the context. The story is the account of trained continuation. On this scheme, the objects of appreciation are patterns that "are or can be seen as the marks of the forces" that produced them (2000, p. 111).** **It might be objected at this point that talk of forces producing a text is metaphorical, and hence in tension with Carlson's recommendation that things be appreciated as what they in fact are. However, Carlson does not use "force" as a physical notion: the properties of materials, chance, and the artist's own movements all count among the forces at work in the anti-art cases (2000, p. 113). Moreover, each of the forces listed above has a literal referent: the weighted distribution, the sampling step, the context, and the prompt are the factors that in fact produce each token.** **The pattern of a Pollock is conditioned by the pattern already on the canvas. Its visible shapes, in Janson's description, "are largely determined by the internal dynamics of his material and his process: the viscosity of the paint, the speed and direction of its impact upon the canvas, its interaction with other layers of pigment" (quoted in Carlson 2000, p. 110). The same holds of a continuation: each stretch of text is generated from, and conditioned by, the stretches already produced. The painter's involvement does not reintroduce design: in Carlson's analysis he figures as one force among the others, the one that sets the process going, and the prompt occupies the same position among the forces of generation.** **The closest of Carlson's cases to generated text is Arp's automatic poetry:** > **Automatic poetry comes straight out of the poet's bowels or out of any other of his organs that has accumulated reserves… He crows, swears, moans, stammers, yodels, according to his mood… His poems are like nature; they stink, laugh, and rhyme like nature. Foolishness, or at least what men call foolishness, is as precious to him as a sublime piece of rhetoric. For in nature a broken twig is equal in beauty and importance to the clouds and the stars. (quoted in Carlson 2000, pp. 117–118)** **Figure 1 belongs to this class of cases [PROVENANCE NOTE: model, generation regime, and permission to be settled]. When asked for its opinions on bees, a model replied with some three hundred words of near-language. Its invented vocabulary remains morphologically well formed and keeps returning to the subject of bees; its register is maintained throughout; and it preserves an oratorical structure of invocation, interludes, and peroration, detached from any occasion of use, although the words that fill this structure are themselves inventions. The reply differs from Arp's poems in what is going on. In Arp's case the story appealed to the unconscious and to the poet's mood; in this case the story of trained continuation tells us that the reply is a trajectory that remains within one region of feature space under a loosened selection of steps. The forces at work in this reply are the same as those at work in an ordinary one, differing only in their relative strength. Because sampling has been loosened while the learned regularities continue to operate, the contribution of each force is easier to distinguish: the weighting towards bee-related vocabulary is evident in the invented words, and blends such as "sweeat" and "beeings" fuse neighbouring items into single tokens, and so display what Wolfram calls the "fan" of high-probability continuations from which every step is drawn (2023, PAGE). In ordinary output the forces are more evenly weighted, and the pattern they produce is less conspicuous.** **[IMAGE OF BEE TEXT]** The passage reads as English for an obvious reason: everything in it except its vocabulary is behaving as English behaves. When we read it as anomalous, we implicitly recognise that the system possesses a command of English form that exceeds its hold on English sense. While the reply of Figure 1 and a routine reply are similar in being products of the same trained dispositions, they differ in at least one respect, which can be brought out by considering how conspicuously those dispositions show themselves in each case. In the reply of Figure 1 the dispositions appear as disturbance, in a routine reply they appear as composure. It is not simply that a routine reply contains nothing anomalous, it is that at every step something anomalous was available and not taken. Fluent continuations are the sort of thing that the trained distribution makes likely, and a routine reply realises that likelihood step by step: part of reading it as generated is to read its evenness as maintained rather than given. We do not deny that the anomalous case is the easier to read; but this does not force us into treating the routine case as empty of the very order it settles. Ordinary outputs and anomalous ones, being drawn from the same trained distribution, inherit their differences from the sampling that selects among its continuations. Wolfram uses the notion of temperature to explain why text produced by always taking the likeliest word reads as "flat" (2023, PAGE): a sampling parameter fixes how often the model departs from its highest-ranked continuation, and raising it moves the text away from the flattest case. Given that the same distribution underlies both, drawn on at different settings, we should be wary of treating the two sorts of output as two sorts of object; what varies between them is where the trajectory has been allowed to go. In both regions of that space, the same regularities carry the text forward; while one region wears them openly, the other holds them in balance. **Knowing that a text is LLM-generated rather than human-written changes how it is appropriately aspected. We saw in Section 1 that appreciation is undermined when an object is appreciated as something it is not, as when a cliff face is taken for a divine artisan's work or a Rembrandt for the product of natural forces. Reading generated text as authored is a new instance of the first mistake, in which an order produced by forces is credited to a designer. The same words support different appreciation under the two readings. If we read the reply of Figure 1 as human writing, it is a pastiche, and each blend is a witticism to be credited to its author. If we read it as what it is, the blends are fusions of neighbouring items and the register is a trajectory held within its region: marks of forces rather than of authorial choices.** **The same knowledge dictates the relevant acts of aspection (2000, p. 119): a long fluent exchange calls for survey, with attention to whether a direction strengthens or weakens across turns, while a reply like that of Figure 1 calls for word-by-word scrutiny of the items fused in each blend. The appreciator attends to the developing relation between earlier and later parts of the generated text, and understands that relation as a product of iterated continuation.** We can begin to see this by noting that ordinary readers already tell machine-written prose from human writing without being able to say how they do so. Phenomenologically, such readers differentiate generated prose from human prose in terms of its cadence: the sense that each sentence departs very little from what the last made likely. **Consider vocabulary clustering: words do not appear independently but make other related words more probable, so that once a medical term appears, other medical terms become more likely to follow.** In each case, the reader's discrimination stems from regularities of trained continuation, which the account we have given renders visible as such. Recognising machine prose, we suggest, is an untutored perception of this order, one which the relevant knowledge can convert into appreciation. Moreover, there is a dimension of variation between models which helpfulness does not settle: the characteristic feel of their prose. Which words a model favours and the way its paragraphs unfold depends upon its training history rather than upon the task it is set. While post-training contributes regularities of its own to this feel, we will not consider them further here; they belong with the assistant persona, and with Section 4. Consider the practice of running the same prompt more than once. As the variants accumulate, some formulations recur across nearly all of them, others appear once and vanish, and the family as a whole gathers around a recognisable centre. Embedded in this practice is a shift of object: attention falls on the distribution the variants are drawn from rather than on any single text. With a human author there is nothing to rerun, with a model there always is. Rerunning a prompt can accordingly be thought of as an act of aspection proper to this kind of object. One might object here that the smoothness we have been describing is precisely what readers hold against generated prose. Why should evenness merit attention in a generated reply whereas in a student essay it merits only the comment that the writing is lifeless? Against a parallel temptation in the case of clouds, Hepburn advises the appreciator to realise the "inner turbulence" behind a form that looks soft and settled (quoted in Carlson 2000, p. PAGE). Now, for any stretch of generated prose, however plain, a distribution over the entire vocabulary has been formed and collapsed at every word: the calm of the surface is the settling of that process, and can be read as such. **The stories grounding order appreciation are, moreover, "in one sense nonaesthetic", yet "in another sense they are exceedingly aesthetic. They illuminate nature as ordered and in doing so give it meaning, significance, and beauty" (2000, p. 121).** The fact that readers can complain of blandness at all shows that the pull towards the likeliest word is something they already perceive. We are not arguing that blandness is a merit, nor that an output becomes better by being understood; the question of how good any particular reply is survives the account of what it is. **As each model has learned regularities from human text, its order also reflects, in a technologically transformed way, the linguistic culture of its training data.** **As Wolfram (2023) points out, LLMs reveal that** > **human language (and the patterns of thinking behind it) are somehow simpler and more 'law like' in their structure than we thought.** **There is thus a sense in which generative AI is a mirror of culture, not only morally, as Vallor (2024) has argued in her book The AI Mirror, but aesthetically. The model shows us our own linguistic patterns, filtered through statistical learning. The order appreciation of LLMs, from this perspective, can also be cast as the aesthetic appreciation of culture seen through technology. The key to appreciation, however, lies in technological mediation. The order made visible by the relevant knowledge of how LLMs work is not only the order** of **our language but especially the order of linguistic forms carried forward and transformed through trained continuation.**[^1] **The knowledge that guides this appreciation need not be held theoretically. Carlson notes that scientific and everyday knowledge of nature lie on a continuum rather than differing in kind (2000, PAGE): the farmer who knows the soil through planting and tending can appreciate the order in a well-drained field in ways unavailable to someone who merely gazes at the landscape. The experienced user of an LLM develops acquaintance of the same kind. By prompting and observing across many contexts, she builds up a practical sense of a model's characteristic order: she learns which vocabulary it tends towards from given starting points, and how far earlier material continues to condition what comes later. Such knowledge is held practically rather than explicitly, and it is aspectual as well as predictive: it settles what to attend to and where the model's order is likely to be visible. At the scale of the model, familiar talk of one model having a different 'vibe' from another can be understood as a way of registering stable differences in the regularities the models have learned.** **Extended exchanges with an LLM are a natural site for building such acquaintance. Each prompt creates conditions under which the system responds, and each response shows something of how the model carries text forward; the back-and-forth of prompting is itself a mode of aspection, organising appreciative attention over time. Cross (2025) characterises certain AI art-making as an 'exploration paradigm' in which the artist iteratively probes the model, adjusting prompts and sampling variations, and he compares the practice to performance art, where the artist creates a space for the audience's participation. What the artist is doing, on the present account, is a form of interactive aspection: the prompts and adjustments are interventions that make the system's regularities visible, rather than ways of coordinating with a co-creator.** **The practical and the theoretical routes converge on the same object. The farmer's knowledge and the geologist's track the same forces, differing in how the knowledge is held rather than in what it is knowledge of; likewise, the user's feel for a model and the account of trained continuation track the same learned regularities. Those regularities are, as Wolfram puts it, implicit in the model; they are also held implicitly in the practised user's expectations; and they are made explicit, so far as they can be, in the theorist's story. Wherever on this continuum the knowledge is held, it does what Carlson requires of it: it makes the order of generated text visible and intelligible, and it dictates the acts of aspection appropriate to appreciating that order.** [^1]: The order a reader meets in a generated text stands to the order of the language itself as record to source: training compresses into the model's dispositions the regularities of human textual culture, and the trajectory a particular output traces is an exercise of those dispositions. A reader who knows as much can therefore attend, through the text, to the order the text registers, much as tree rings can be read for the growth of the wood or, through that growth, for the climate that set its pace. The reading through is not immediate, however, because the record refracts its source: the dispositions were formed under a training objective and reshaped by post-training, so what returns is the culture transformed by the process that carried it. The appreciation defended in this section takes the first of these orders as its object, and the second is reached only through it. **The regularities of a textual practice are made true by many acts of writing taken together, and are not located in any one of them**, so that **in generated text the conventions of a culture are exercised without being participated in**; **nothing in Carlson's natural case corresponds to this further object of appreciation.** The appreciation of textual culture through generated text might then be order appreciation conducted under a second story, supplied by linguistics and cultural history rather than by an account of trained continuation; or it might have a structure of its own, since the record here is an artefact, and so itself a candidate for design appreciation. We leave the question for future work. 4. Person Appreciation of LLMs We have argued that a proper aesthetic appreciation of LLMs requires supplementing design appreciation with order appreciation. Still, the way we interact with LLMs may look so similar to interacting with actual human interlocutors that one might wonder whether a further supplementation of design appreciation and order appreciation with person appreciation is required. The question concerns chatbots rather than LLMs as such, and the distinction should be made explicit. An LLM is a trained continuation system; a chatbot is one deployment of such a system among others. The same kind of model can be deployed in applications in which no interlocutor appears at all: Cotypist, for instance, uses a language model to provide system-wide predictive text, completing whatever its user is currently typing, and nothing in this use invites conversation.[FOOTNOTE: Cotypist, a macOS application developed by Daniel Gräfe (Accelerated Thought GmbH, 2024–), runs a small language model locally to supply inline predictive completion across applications: https://cotypist.app.] The appearance of a conversational partner belongs to one mode of deployment rather than to LLMs as such. We sometimes appreciate persons aesthetically. A person's warmth may be aesthetically appreciable as a feature of character, rather than as a feature of bodily appearance. Gaut (2007) and Paris (2018) treat such appreciation as directed at the traits and dispositions through which a person's life is intelligible. Something similar is possible with fictional characters: we can aesthetically appreciate a character as a person within a fiction, without believing that the character exists outside it. The account of LLMs we have defended so far, however, puts pressure on this comparison. The system producing the text is a trained continuation system: it generates responses by applying dispositions acquired in training to the context it is given. LLMs are systems that tokenise text, manipulate numerical vectors, and generate continuations by sampling from learnt probability distributions. Nothing in that description straightforwardly resembles a subject with beliefs, intentions, or a life-history; there is no obvious place for character traits, projects, or personal development. That being the case, there are two strategies to preserve person appreciation of LLMs. The first argues that the LLM is an intentional system in a thin sense, and that this is enough to license some person-directed appreciation. The second grants that the LLM is not literally a person but reads its outputs as fictional speech, so that what is appreciable is a fictional character rather than the system itself. The first strategy casts LLMs as agents of a thin and unfamiliar kind. On a suitably liberal conception of mind, perhaps they qualify as intentional systems and that is enough to license some person-based aesthetics. Frankish (2024) offers a sophisticated version of this idea. Drawing on Dennett's intentional stance, he suggests that LLMs can be treated as genuine, if unusual, intentional systems. On this view, we are licensed to ascribe beliefs and desires to an LLM when doing so yields a simple and fruitful account of its behaviour, even if the underlying implementation is purely mechanical. In the case of contemporary chatbots, Frankish proposes that we can ascribe to them a large set of thin 'beliefs' – roughly, informational states distilled from their training – and one thin 'desire': to play what he calls the chat game. A system is playing the chat game when it generates text that looks like a cooperative move in an ongoing conversation, respecting local coherence, relevance to the prompt, and broadly human conversational norms. An LLM, on this picture, is a system whose behaviour can be summarised by saying that it believes many simple things and wants to make an appropriate next move in the chat. Frankish is not inviting us to pretend that LLMs have beliefs and desires; he is claiming that, at the right level of abstraction, it is literally true that they do, in much the same sense in which a thermostat can literally be said to "want" the room to be at a certain temperature when adopting the intentional stance helps us describe its behaviour. The agent-talk is meant to latch onto real, pattern-like features of the system's organisation. Suppose we grant all of this. Does it give us what we need for aesthetic appreciation of LLMs as persons? Not so. A subject of beauty of character is not just any intentional system. It is, minimally, a being with a temporally extended life, with relatively stable value-laden dispositions, with projects and commitments that can succeed or fail, and with a capacity for speech and action to express and reshape its character over time. When we set the chat-game agent against this benchmark, it looks thin. The 'beliefs' are shallow, in the sense that they are confined to what is encoded in the model's parameters and surfaced in the current context, without memory or development across conversations. The "desire" is singular and thin: make an appropriate move now in this exchange. There are no independent projects pursued across episodes, no webs of concern or attachment, no history in which earlier experiences inform later choices. What structure there is, is entirely local to the present stretch of text. The predicates characteristic of person-aesthetics—'beautiful soul,' 'admirable steadiness,' 'ugly character'—presuppose something that can be tested, developed, or refined over time; a thin chat-game agent has no such temporal depth. From this perspective, LLMs may be agents in Frankish's concessive sense, but they are not the sort of agents whose lives and characters can be the object of the aesthetic responses associated with persons. There is nothing like a "beautiful soul" or an "ugly character" here in the relevant sense; there is no enduring set of values and dispositions that could be manifest, challenged, or transformed over time. Given Carlson's recommendation, the right kind of person-directed knowledge for beauty-of-character appreciation is knowledge of a life and its values. The technical and training facts about LLMs do not supply that kind of object. Once again, Carlson's recommendation sharpens the point. If, even on a concessive mindedness view, LLMs lack the life-structure required for person-aesthetics, then to insist on aesthetically appreciating them as persons would be to ignore what they in fact are. It would be to treat the thin chat-game profile as if it were enough to underwrite the rich person categories we apply to human agents, and to let those categories govern appreciation despite knowing that the underlying kind is different. The concessive strategy therefore does not secure an appropriate person-based aesthetics of LLMs. The second strategy takes the make-believe route. If we ask ordinary users whether they literally believe that a chatbot is a person, many will concede that they do not. They may talk to a model as if it were a friend or a colleague, and they may feel heard, reassured, or amused, but when pressed they acknowledge that they are interacting with a computational system rather than a human being. Their stance is, in this sense, already a kind of as-if posture. Mallory offers a way of theorising this posture through what he calls chatbot fictionalism (2023). On his view, we engage with chatbots by entering a game of make-believe in which the exchange is treated as if it were a conversation with an agent. Within the fiction, the chatbot 'says' things and 'means' things; outside the fiction, we know that no such speaker is present. At the metasemantic level, Mallory claims, the outputs lack literal semantic content – they are 'literally meaningless but fictionally meaningful' (Mallory, 2023, p. 1082). This fits the everyday thought that we can take a chatbot seriously in the moment without actually believing that it has a mind. Just as a child treats a banana as a sword in a game, we treat chatbot outputs as utterances within a kind of imaginative practice. This is not delusion but a deliberate, bounded pretence that allows us to coordinate with the system and even gain knowledge from it, much as we might learn geography from a map by imagining countries as two-dimensional shapes. Mallory's account is not itself an aesthetics of LLMs; it is primarily a semantic and epistemic proposal about how we can use them and learn from them. But it highlights one obvious way a person-based aesthetic stance might be defended: one might suggest that we should aesthetically appreciate LLMs as if they were persons or characters, in the same sense in which we respond aesthetically to fictional protagonists whose existence we do not literally believe in. We respond to Gatsby's enigmatic, dream-chasing idealism or Ron Swanson's libertarian gruffness using much the same vocabulary as we use for real people, and we often talk quite straightforwardly about their 'character' or 'personality'. The warmth or wit a user finds in Claude or in ChatGPT can be cast as the warmth or wit of a fictional interlocutor. Fictional characters, however, are not fully-fledged persons but rather—according to a popular view in philosophy of fiction—artifacts that have the function of eliciting imaginings of fictional persons within a story-world (cf. Thomasson 1999; John 2021; Terrone 2021). From this perspective, a proper appreciation of a fictional character should consider not only its person-like appearance but especially the designed representational texture (say, the text of a novel or the images of a film) in virtue of which that appearance shows up in the imagination of the audience. That is to say that fictional characters ultimately call not only for persona appreciation but especially for design appreciation. Thus, even if we concede that the generation of the experience of interacting with a fictional person is an aspect of the function that LLMs fulfil—hence of what they are—the fact remains that the appreciation of that function is grounded in the appreciation of the structure in virtue of which it is fulfilled. While in the case of fictional characters such structure calls for design appreciation all the way through, in the case of LLMs, as argued above, design appreciation should be supplemented with order appreciation. Specifically, LLMs fulfil the fictional-person function in virtue of a structure shaped by post-training. The sort of training we have described so far teaches the model statistical patterns of language and yields a base LLM. In fact, most chat-oriented systems undergo a further post-training phase. After pre-training, the base model is fine-tuned on examples of instructions and responses, and then adjusted by Reinforcement Learning from Human Feedback (RLHF), a process in which human raters evaluate the model's responses for features such as helpfulness, accuracy, appropriate tone. The model then adjusts its parameters to produce more responses like those rated highly and fewer like those rated poorly. Post-training shapes the model's conversational norms: when to express uncertainty ('I'm not sure, but...'), when to decline requests ('I cannot help with...'), how to structure explanations ('Let me break this down...'). RLHF makes responses more consistent, more helpful, and more aligned with human expectations. But it operates through the same fundamental mechanism—adjusting numerical parameters to match patterns in the training data. The model learns which response patterns get high ratings, not why those patterns are appropriate or what social purposes they serve. The result is a chat-optimised model: the same predictive core, now biased towards a certain family of outputs that look like the moves of a cooperative assistant. When this chat-optimised model is embedded in a product—given a system prompt, safety filters, a memory policy, and a user interface—it becomes the chatbot that users encounter. What users describe as a model's "personality" or "vibe" is a stable pattern in its responses under this post-training and product regime, not a separate mechanism or inner subject added on top of the predictive core. The chat-optimised systems exhibit stable patterns of hedging, refusal, politeness, and explanatory structure. Still, this does not mean that post-training turns bare LLMs into conversational agents and that, under Carlson's recommendation, we should take those chat assistants as the "things as they in fact are" and allow some form of person-based aesthetic stance. Indeed, acknowledging that the appearance of a fictional person is grounded in a technical order suggests a more layered picture. On the one hand there is the underlying generative system: the predictive core that, after pre-training, approximates the statistical structure of its training corpus and that, after post-training, remains a text continuation engine with a modified probability landscape. On the other hand, there are patterns in its outputs that, under chat-style prompting and within a product wrapper, look like the moves of a cooperative assistant persona. The assistant is not a new mechanism added on top of the model, but a recurrent pattern in how the model tends to respond when prompted and constrained in certain ways. Seen in this light, post-training does not install a new "assistant mind" with its own independent goals and projects. It biases the predictive core so that prompts issued through the chat interface are much more likely to elicit assistant-like responses – helpful, safe, polite, and structured – and much less likely to elicit, for example, unfiltered reproductions of online arguments or free association. The underlying operation remains next-token prediction; what changes is which regions of its behavioural space are easy to reach in ordinary use. The chat product – with its system prompt, safety filters, and interface – further shapes the environment so that certain person-like patterns are the default. This helps explain why users talk about models having different 'vibes'. If users say that Claude Opus 4.5 feels friendlier than GPT-5.2, they are picking up on a stable pattern in how the chat-optimised systems tend to respond across many prompts and episodes. They track which assistant personae tend to appear and how those personae typically behave. Different base models, post-training regimes, and product designs favour different families of assistant-style responses. It is therefore not surprising that they invite person-like language, but the targets of that language are episodes and recurring response profiles, not underlying subjects. This is the relevant sense in which a sort of person appreciation may supplement design appreciation and order appreciation in our aesthetic engagement with LLMs. One might find a particular model's refusals laboured or concise, its hedging overdone or judicious, its tone soothing or dry. In that sense, we can aesthetically respond to assistant personae in a way that resembles our responses to real people. What matters for present purposes is that such reactions target not only the appearance of a fictional person but especially patterns in outputs and interactional style. They concern how a product behaves under certain constraints, so as to yield the beauty or ugliness of a character in the person-aesthetic sense. Even taking post-training and chat personae fully into account, we do not find a temporally extended life, a network of projects and commitments, or a stable evaluative outlook that could ground a full-fledged person appreciation. What we find is a complex artifact designed and trained to produce certain patterns of text in response to prompts, together with an engineered tendency to exhibit assistant-like behaviour with a person-like appearance. Under Carlson's recommendation to appreciate things as what they are, and in the light of the right kind of knowledge, we should therefore resist a person-centred aesthetics for LLMs even once we take post-training and chat personae into consideration. As Farrell, Gopnik, Shalizi, and Evans (2025) put it, "Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated". We have shown that the novelty of this "new kind of cultural and social technology" lies also in its calling not only for design appreciation but also for order appreciation, while person appreciation enters the picture only when it comes to characterizing the sort of experience that LLMs can generate in virtue of their structure. Conclusion The three modes of appreciation distinguished above are not independent of one another. Design makes order possible: what designers fix, namely the architecture and the training objective (what Olah calls the "scaffold" and the "light"), are the conditions under which a system of learned regularities grows, and the conditions and the growth are distinct objects that call for separate appreciation. Order in turn makes the appearance of a person possible, and the relation here is different in kind. The assistant persona is a recurrent pattern within the grown order, shaped by post-training and sustained by the surrounding product, rather than a further object over and above it. The chain therefore has links of two kinds, since design enables order while order constitutes the person-appearance; and so, although there are three modes of appreciation, there are only two objects. Design appreciation and order appreciation attend to the conditions and to the growth respectively, while person appreciation adds an aspect under which the grown order can be engaged, rather than a third object. The knowledge that guides appreciation runs in the opposite direction to the chain of making possible. To appreciate the persona appropriately is to know it as a pattern in an order, which is order-directed knowledge; to appreciate the order appropriately is to know it as a growth under fixed conditions, which is design-directed knowledge. Order appreciation thus occupies the middle position in both directions: the conditions are appreciable as the conditions of this growth, and the persona is appreciable as a pattern in this order. This is the sense in which order appreciation bridges the appreciation of designed structure and the appreciation of person-like behaviour. The chimera of our title is put together in the same order: the artefactual tail makes the trunk possible, and the head is a pattern that the trunk sustains. To appreciate an LLM ultimately amounts to appreciate a complex entity that originates from design just like technical artifacts but then grows somehow autonomously just like a forest and ends up behaving like a person. An LLM is at once an artefact we can master and a growth we cannot fully comprehend, and its appreciation accordingly carries both of the ambivalences with which Carlson closes his comparison of appreciating art and appreciating nature (2000, p. 122). It is a sort of chimera with an artefactual tail, a forest as trunk—a forest of linguistic signs turned into numeric tokens—and a human head. It may look like a monster, but one with its own distinctive, impressive beauty. References Abell, C. (2020). Fiction: A Philosophical Analysis. Oxford: Oxford University Press. Anscomb, C. (2022). Creating Art with AI. Odradek, 8(1), 13-51. Carlson, A. (2000). Aesthetics and the Environment: The Appreciation of Nature, Art and Architecture. London: Routledge. Carroll, N. (2013). Andy Kaufman and the Philosophy of Interpretation. In Minerva's Night Out: Philosophy, Pop Culture, and Moving Pictures. Malden, MA: Wiley-Blackwell. Cross, A. (2025). Tool, Collaborator, or Participant: AI and Artistic Agency. The British Journal of Aesthetics, 65(4). https://doi.org/10.1093/aesthj/ayae055 Danto, A. C. (1974). The Transfiguration of the Commonplace. The Journal of Aesthetics and Art Criticism, 33(2), 139-148. Davies, S. (2012). The Artful Species: Aesthetics, Art, and Evolution. Oxford: Oxford University Press. Elhage, N., et al. (2021, December 22). A mathematical framework for transformer circuits. Transformer Circuits Thread. https://transformer-circuits.pub/2021/framework/index.html Farrell, H., Gopnik, A., Shalizi, C., & Evans, J. (2025). Large AI models are cultural and social technologies. Science, 387(6739), 1153-1156. https://doi.org/10.1126/science.adt9819 Forsey, J. (2013). The Aesthetics of Design. New York: Oxford University Press. Frankish, K. (2024). What are large language models doing? In A. Strasser (Ed.), How to Live with Smart Machines (pp. 73-110). Vienna: Holzhausen Publishing. Available at: https://keithfrankish.github.io/articles/Frankish_2024_What%20are%20large%20la nguage%20models%20doing.pdf Gaut, B. (2007). Art, Emotion and Ethics. Oxford: Oxford University Press. Janus. (2022, September 2). Simulators. AI Alignment Forum. https://www.alignmentforum.org/posts/vJFdjigzmcXMhNTsx/simulators John, E. (2021). Review of Fiction: A Philosophical Analysis by Catharine Abell. The Journal of Aesthetics and Art Criticism, 79(4), 514-517. Kirchner, J. H., Smith, L. M., Campos, J., Clune, J., & janus. (2023, March 3). [Simulators seminar sequence] #2 Semiotic physics – revamped. AI Alignment Forum. https://www.alignmentforum.org/posts/9kNxhKWvixtKW5anS/simulators-seminar-s equence-2-semiotic-physics-revamped Kirchner, J. H., Steiner, C., Riggs, L., Janus, & Thibodeau, J. (2023, January 3). Semiotic physics. In Simulators seminar sequence (#2). LessWrong. https://www.lesswrong.com/posts/TTn6vTcZ3szBctvgb/simulators-seminar-sequen ce-2-semiotic-physics-revamped Mallory, F. (2023). "Fictionalism about Chatbots." Ergo: An Open Access Journal of Philosophy, 10, 38. https://doi.org/10.3998/ergo.4668 McGinn, C. (1997). Ethics, Evil, and Fiction. Oxford: Oxford University Press. metasemi. (2023, March 20). A note on 'semiotic physics.' LessWrong. https://www.lesswrong.com/posts/AdXzZDoYFqHCfupDB/a-note-on-semiotic-physi cs Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., & Carter, S. (2020). Zoom in: An introduction to circuits. Distill, 5(3). https://doi.org/10.23915/distill.00024.001 Olah, C. (2024, November 11). In D. Amodei, A. Askell, & C. Olah, Interview by Lex Fridman. Lex Fridman Podcast #452. Available at: https://lexfridman.com/dario-amodei-transcript/ Paris, P. (2018a). The empirical case for moral beauty. Australasian Journal of Philosophy, 96(4), 642-656. https://doi.org/10.1080/00048402.2017.1411374 Paris, P. (2018b). On form, and the possibility of moral beauty. Metaphilosophy, 49(5), 711-729. Parsons, G. (2023). Imperfection and Beauty of Character. In P. Cheyne (Ed.), Imperfectionist Aesthetics in Art and Everyday Life (pp. 296-309). New York: Routledge. Parsons, G., & Carlson, A. (2008). Functional beauty. Oxford University Press. Picca, D. (2025). Not minds, but signs: Reframing LLMs through semiotics. arXiv preprint arXiv:2505.17080. https://arxiv.org/abs/2505.17080 Saito, Y. (2008). Everyday Aesthetics. Oxford: Oxford University Press. Terrone, E. (2021). Twofileness. A Functionalist Approach to Fictional Characters and Mental Files. Erkenntnis 86, 129–147. https://doi.org/10.1007/s10670-018-0097-2 Thomasson, A. (1999). Fiction and metaphysics. Cambridge: Cambridge University Press. Vallor, S. (2024). The AI mirror: How to reclaim our humanity in an age of machine thinking. Oxford University Press. Wojtkiewicz, K. (2023). How Do You Solve a Problem like DALL-E 2? The Journal of Aesthetics and Art Criticism, 81(4), 454-467. https://doi.org/10.1093/jaac/article/81/4/454/7571331 Wolfram, S. (2023, February 14). What is ChatGPT doing … and why does it work? Stephen Wolfram Writings. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-doe s-it-work/