# 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 --- # big prompt for aesthetics of ai Hi. In this message is an unfinished draft of a philosophy paper that I am writing. In a moment I would like you to FINISH the draft. Before starting, however, please confirm you understand the instructions below in THEIR ENTIRETY: I’m going to give you [[the structure]] of the draft as I see it. The intro and first three sections I want kept more or less as they are (although fill in this placeholder in the introduction **%%Succinct one paragraph summary of [[the structure]] of the paper. %%**. But the following sections will require varying degrees of input from you. Often, you will need to write entire sections pretty much from scratch. Follow [[the structure]] to get an idea what i mean STRUCTURE In the introduction, we have Sections One and Two. The overall goal of Sections One and Two is to describe an agent-based or quasi-agent-based account, and then, in Section Two, to note how this might allow for an aesthetics of LLMs. Sections One and Two should be reproduced more or less verbatim in your final text. I like how they’re done; I like how their sections work out. Section Three is similar. I would like you to keep it more or less verbatim—just reproduce it verbatim. [[What Section]] Three does is [[introduce Carlson]]’s view about how the environment should be appreciated, and the specific details to do with that. But after we’ve done that original quote, we talk about his ideas on [[aesthetic appreciation]] and understanding more generally, and how they apply not only to the environment but also to works and created artefacts such as architecture. Section Four is where things get a bit messy. The preceding three sections I’d like you to leave more or less verbatim. Section Four you can be a little bit looser with. I like quite a lot of my text there. You’re welcome to appropriate it or reuse it in your version of Section Four if you want, or as much or as little of my text as you want can be used in your version. In section four of your version, I want to focus much more on applying the methods we described earlier in the paper—namely, how to determine what something "in fact is"—to LLMs. This section 4, I think, could be written without using any “environment” terminology or anything like that. It should be a very clear yet dry description of how LLMs are trained and operate, focusing on their function of predicting the [[next token]] in a sequence and how they go about realising that function, which alludes to their training and how it leads to the predictions they make when they are functioning. This should all be clear if you look closely at the details of the texts in your project folder and my draft as well. The [[second half]] of section 4 should show that, at least prima facie, agentive views are not especially good bases on which to appreciate LLMs. The reason for this lies in the way LLMs are trained, as has just been described in this section. In terms of token prediction, it seems only slightly agentic, if at all. And if we are to use Carlsen’s motto—that we need to appreciate things for what they, in fact, are—this does not seem to fit with the persona idea at all, because we can now see that, as token predictors, LLMs are not, in fact, agent-like or anything like that. At the very end of the section, mention that we will return to these sorts of views, and a potential way in which they might be saved, later in the paper. Okay, and then, in section five, we go back to Carlson and we talk about how he understands the [[natural environment]] to be. That is the first step of his approach to aesthetics: fixing what the thing in fact is. It says in the quotes. Playing on this [[first part]] of [[the Carlson]] idea, it should then say that Spinozian phrases, [[natura naturans]], [[natura naturata]], are a good way to characterise what Carlson means when he talks about an environment—what a [[natural environment]] is—and the sort of dynamic unruliness of it. The second half of this section should discuss the second half of Carlson’s ideas about the appreciation of the environment—namely, the claim that we need to understand the natural environment in the light of the natural sciences: biology, geology, physics, and so on. Once that is introduced, I want you to explain how this second part of Carlson’s view connects to the natura naturans ideas introduced earlier in the section. The best way to frame them, I think, is that they are all good lenses for understanding the natural environment because they each pick out, or correctly track, the relevant types of relations between natura naturata and natura naturans, and among different levels within natura naturata and natura naturans. Something like that. The overall aim of Section Five is to introduce Carlson’s specific ideas about appreciating nature and to tie them to this Spinozist vocabulary. the terms 'generative'. or generative environment will be useful as another way to talk about the natura naturata aspect of nature. Okay, moving on to section six. The overall aim of section six is to show that *individual chats* with LLMs should be considered generative environments in a way that echoes the elaboration of carlson's ideas that we have just provided. This should be achieved by showing how there are new natura naturans and natura naturata that can be found in the way LLMs function. This will involve talking a lot about the teleological finish state of how the LLM operates when it’s being prompted, and it should be talking about text generation in Machina naturans terms, and the text that is generated in Machina naturata terms. It should basically make the case not that LLMs themselves are generative environments, but that they allow for the thoughts of generative environments—sorry, they allow for instances of generative environments, that is, chats—to be instantiated. section seven considers the possibility that this might allow for a form of the agentive view of appreciation to be ressurected: an agent view is a light in which to understand LLMs –although LLMs at root are not person or agent like, viewing them in the light of the psychological, cognitive etc. sciences is a way of understanding them. This idea could be strengthened by pointing out that this is what we already do with our natural environment. For the parts of our natural environment that are people—human beings—we can draw on biological, cognitive, or neuroscientific understanding to make sense of this level of the environment: the person-level of the natural environment. But then, in the second half of this section, we’re going to criticise this idea. We’re going to say that psychology, etc., are not good lights in which to understand LLMs, and thereby are not good ways or bases for appreciating LLMs. The reasons for this can be found in the "Not minds but signs" paper, and the "Simulators" paper in the projects folder, so reference them. Okay we get to section eight. Section eight is going to introduce the idea of semiotic physics, or semioticology—you choose which phrase you think is best. Both of them can be found in different papers in the project. The idea here is, first of all, to explain what semiotic physics is, or at least to give a version of what semiotic physics is. You will see in one of the summaries in the Projects folder exactly the sort of level of detail I want this idea to be conveyed with. So I’ll leave that up to you. But my preferences should be clear if you study the previous chat in the projects folder well. This idea should be introduced with, or just before, an explanation of why semiotic physics correctly latches onto some aspect of machina naturans and machina naturata. Finally, section 9 is where we show how semiotic physics and the understanding of machina allows for an aesthetics of LLMs. This section should not only show how this approach fits with all the ideas about the environment and this being one (of potentially various, mention mechanistic interpretability) light in which llms can be aesthetically appreciated. The conclusion should be succinct. HOW TO WRITE THE PAPER Here are some do’s and don’ts in terms of style and content. When it comes to sections 1, 2, and 3, and the introduction, retain almost all of the text verbatim. I don’t really want these sections touched by you right now. I’ll allow minor editorial changes, but nothing more than that. After these early sections, you are much freer to write your own text, and this is important. Follow the style guide, which you will find in your project instructions under a letter. Pay attention to the advice on tone: these words should be at the front of your mind when thinking about writing style: analytic, clear, straightforward, unpretentious. I do not want subsections. I hate them. For example do not write anything like: 2.2, 3.4, etc. Only top level headings are allowed (e.g. 1., 2., 3.,) When you need to add titles or new sections to the paper, make sure the titles are very succinct and very pithy The full text will be between 8,000 and 10,000 words. A great deal of consideration should be given to how many words each section of the paper needs, some will be more complex than others (note also that detail in the plan given above is no indication as to the length of a section). As you write the text, be sure to take all of the documents in the files folder into account. In there, you will find various academic texts, an old draft of the paper, and the new draft attached at the bottom of this message, as well as a conversation with an LLM about semiotic physics. Obviously, you need to be very discerning about when you refer to which texts. Remember, the overall goal is to produce a high-quality piece of analytic philosophy suitable for sending to a publication such as Philosophical Studies or Nous. DRAFT: ** the environmental aesthetics of generative AI # Introduction # 1. AI as (quasi) Agents People treat and talk about LLMs such as OpenAI's ChatGPT, Anthropic's Claude, or Google's Gemini as if they were persons. We refer to them with personal pronouns like 'he' or 'she' without hesitation; when they do not do what we ask, we try to persuade or cajole, explain, or even 'shout' by typing in ALL CAPS. We thank them for their assistance and apologise when we phrase requests poorly, as though courtesy might affect their responses. Perhaps our everyday talk captures something true: LLMs might be agentive or person-like in some sense. If so, this allows for the possibility that they can be aesthetically appreciated in something like the same way that real people can be aesthetically appreciated (we shall see some examples of this in the next section). This section will sketch three different flavours of this view. The first sort of agent view that one might hold is literalism. If one is a literalist, then one thinks that LLMs are persons or agents in some substantial way. This does not necessarily mean thinking that LLMs are just like human persons; rather, it consists of a commitment that humans and LLMs have something person-like in common. David Chalmers argues that successors to current LLMs may be conscious (Chalmers 2024); Eric Schwitzgebel and Henry Shevlin argue we should be ready to extend personhood rights to AIs with a non-negligible chance of consciousness (Schwitzgebel & Shevlin 2023); and Jeff Sebo urges extending moral consideration and preparing for rights on precautionary grounds (Sebo 2023). A second position we might call the pseudo-agent or as-if participant view. On this account, without attributing literal beliefs or intentions to the model, we treat its stable interactional regularities as practically agent-like for purposes of coordination and collaboration. The stance is pragmatic rather than ontological and concedes the prediction-and-training story; nonetheless, it licenses participant or collaborator talk. Cross (2024), writing on AI art, exemplifies this approach when he argues that prompting, iteration, and sampling structure a dialogue where value lies in the interaction itself. "By adjusting inputs, iterating, and sampling," he writes, "an AI artist is engaged in a process of mapping – and perhaps interrogating – the way that the algorithm sees and understands" (Cross 2024, 7–8), although he concedes that "the analogy with performance art isn't a perfect one" (Cross 2024, 9). Anscomb could also be thought of as endorsing a pseudo-agent view. She denies literal mentality and creativity in present systems – "we are not yet at the stage where an AI can formulate intentions … I argue that AI agents cannot be artistically creative" – and adopts a procedural label for "AI agent" as "a self-contained ('autonomous') procedure". Yet she also holds that an AI "may work iteratively without human intervention to non-accidentally generate the formal features of an image" and that it can merit "some share of production credit", which positions her as treating AI as a pseudo-agent, an as-if collaborator rather than a mind-bearing agent. Nonetheless, her vocabulary pressures toward minimal literalism: AICAN is said to be "able to self-assess these products", and "an AI agent arguably deserves credit for its contribution to the production of a work qua art" – locutions typically reserved for bearers of credit rather than mere instruments. A third approach is chatbot fictionalism, according to which human interaction with an AI chatbot is analogous to engaging with a work of fiction in the Waltonian sense of make-believe. Users knowingly participate in a game of imagination, treating the chatbot as if it were a sentient agent with thoughts and feelings. This position has been developed by Mallory (2023), Krueger & Roberts (2024), and Krueger & Osler (2022), whilst Friend & Goffin (2025) discuss the view without endorsing it. As Friend & Goffin observe, prop-oriented make-believe explains ordinary exchanges with Alexa and ChatGPT, where users converse "as when we say 'thank you' … while knowing that there is no real agent producing the replies" (Friend & Goffin 2025, 9). By contrast, content-oriented make-believe clarifies richer, empathetic uses where "it is the interaction itself that matters", with users imaginatively treating the chatbot as a person within the ongoing exchange. %%Succinct one paragraph summary of the structure of the paper. %% # 2. Appreciating LLMs as agents People, and perhaps other conscious beings (see Marchetti?), seem like a sort of thing that can be aesthetically appreciated. Most importantly for our interests here, this appreciation is not limited to physical beauty. If LLMs are a type of agent or quasi-agent, this might well allow for them to be aesthetically appreciated in a somewhat similar manner. In this section, we look at two ways in which this idea might be elaborated. The first route treats the system as a participant within an artist-structured interaction. On this approach, the evaluative focus shifts away from freestanding outputs toward what the duo does together through prompting, iterating, and mapping the system’s way of seeing. As Cross observes: #### By way of their selection of prompts, they elicit a sort of "participation" on the part of the AI … This participation allows them to interrogate the algorithm's latent space. (Cross 2024, 8) According to Cross, the interactional performance is primary; the images or videos are often documentation of that practice rather than the locus of value. He argues that "what is centrally important … is that it is less centrally focused on the output of the AI image generator. Instead, what matters is the interaction between the artist and the algorithm" (Cross 2024, 8). The product can thus function as documentation of the interactional process. Cross calls this the “exploration paradigm”: artists “relate to AI as a participant” and “create a space for interaction … by way of their prompts,” shifting appreciation “towards the artist’s interaction with the AI and the way in which the artist structures this interaction.” The generated images “function as a kind of documentation or evidence” of that exploration rather than the locus of value (Cross 2024, abstract; 9). The agent‑based framing is explicit: the work lies in a temporally extended dialogue in which the artist elicits “participation” from the model to “interrogate the algorithm’s latent space,” making the algorithm’s ways of seeing and representing the object of appreciation (Cross 2024, 8–9). Cross presents this as an as‑if stance and concedes that the performance‑art analogy “isn’t a perfect one” (Cross 2024, 10). The second route would be to appreciate the AI’s 'personality'. In ordinary life, we can enjoy the personality traits of others, things like quick wit, poise, intellect, modesty.  There seems little reason to deny that such enjoyment is aesthetic. Indeed it seems to be a good example of everyday aesthetics. It is plausible that this sort of personal aesthetics is the foundation of our appreciation of what we might call performance personalities. Appreciating the improvisational agility of a comedian or the gravitas of a great orator seems like an extension of our more personal aesthetic appreciation of each other. It is easy to see how these ideas can be applied to LLMs and appreciation. Rather than treating AI as participant, we meet them on a more level playing field, as interlocutor. It is also tempting to say that, if one has interacted with many models, they have different personalities. OpenAI’s GPT‑4.5, for example, was sold as having a better personality than its predecessor. Indeed, after OpenAI replaced GPT‑4o with GPT‑5 in August 2025, user backlash over GPT‑5’s tone and 4o’s perceived ‘feel’ led OpenAI to reinstate GPT‑4o for Plus users and to adjust its model‑retirement policy. [Reuters](https://www.reuters.com/technology/artificial-intelligence/ai-intelligencer-art-chip-deal-2025-08-14/), [The Verge](https://www.theverge.com/openai/758537/chatgpt-4o-gpt-5-model-backlash-replacement). Also in August, fans organised a mock ‘funeral’ after Anthropic retired Claude 3 Sonnet on 21 July 2025 ([WIRED](https://www.wired.com/story/claude-3-sonnet-funeral-san-francisco/)).  ## 3 Appreciating something for what it in fact is In the rest of this paper we will set about showing why treating LLMs as (quasi) agents is not a satisfactory way of aesthetically appreciating them, and propose an alternative account on which appreciation of LLMs is modelled on environmental aesthetics. Both our criticism of agentive views and our positive account will draw from Carlson’s Environmental Aesthetics, as laid out in his 2000 book Aesthetics and the Environment. Carlson’s approach to environmental aesthetics is encapsulated nicely in the following passage: #### 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) Before turning to the details of this view, we should note that Carlson's approach to environmental aesthetics is an instantiation of a more general view towards aesthetics in general. This general view involves two components: first, aesthetic appreciation of a thing should be grounded in the real nature of that thing, what it in fact is—appreciation that is "centred on and driven by the real nature of the object of appreciation itself" (2000 p. 12); second, one must perceive it in light of the right knowledge for that kind. He calls this two-part approach a "blueprint for aesthetic appreciation in general"(ibid.). Within this schema, kind is fixed by domain‑constitutive facts, not by ad hoc choice: at the extremes of nature and art, by history of production; in the middle domain of designed artefacts, by function and the mode of its realisation (2000 pp. 133–134). On this view, correct kind‑knowledge yields the appropriate boundaries and foci and indicates the relevant act or acts of aspection by which one attends (2000 p. 50; cf. 68). The content of the appropriate knowledge is category‑dependent. For nature, the kind is natural environment, fixed by natural history of production; the relevant knowledge is drawn from the natural sciences appropriate to that environment. From such knowledge follow boundaries, foci, and aspection – for example, surveying a prairie differs from scrutinising a forest floor (2000 p. 119). For art, the kind is a work of art fixed by art category and history of production; the relevant knowledge is art‑historical and generic – what counts as part of the work, which features are aesthetically salient, and how to attend to them. Hence frames, media, and genre conventions set boundaries and foci, and aspection tracks the work’s category (2000 p. 12; 50). Example: appreciating Guernica as a painting – as opposed to a relief or a photograph – uses painting‑specific knowledge (medium, cubist conventions) to fix boundaries (the canvas and its surface) and foci (pictorial structure), whereas misclassifying it as a tapestry would misdirect attention. For designed artefacts in the middle domain “between nature and art,” kind is fixed by function and the mode of its realisation: such things “have a function, a purpose; and they are what they are in virtue of what they are meant or intended to accomplish… what is absolutely necessary… is information about their functions… The key to their natures is the purpose or the function they are meant to serve” (2000 pp. 133–134). In addition, Carlson requires attention to how the function is carried out – “how… they are designed to perform these functions” – since production history alone is typically not sufficient here (2000 p. 189; pp. 133–134). Example: a thermostat’s kind is fixed by its function (holding a setpoint) and realised by feedback; a bimetallic‑strip mechanism or a digital sensor‑controller both instantiate the same function under different modes of realisation. This mirrors the thermometer contrast: mercury‑in‑glass versus electronic sensing realise the same function under different mechanisms. In §4, we apply this two‑step schema to large language models: first fix what they are under the artefact reading; then use purpose‑and‑mechanism knowledge to discipline how they are to be perceived under that category. On Carlson’s blueprint, appropriate appreciation proceeds in linked stages: first, classify the object under the correct broad kind—nature, artwork, or artefactual environment—by reference to domain‑constitutive facts rather than ad hoc choice (2000 p. 12; pp. 133–134); second, for artefacts, fix the kind by function and mode of realisation (2000 pp. 133–134; p. 189); third, apply a boundary test to determine what counts as part of the setting for appreciation now, thereby fixing foci and excluding irrelevant intrusions (2000 p. 50; cf. 68; 119); fourth, derive appropriate acts of aspection from the foregoing so that attention tracks the object’s nature rather than projection (2000 p. 50). In what follows, the same method is applied without deviation: the kind is fixed, the relevant function and realisation are stated, the boundary is delimited, and the acts of aspection are derived accordingly. ## 4. What LLMs in fact are The following passage from Carlson provides three ideas that will be useful as this paper progresses: [add  Something more robust here. The idea on the table is that we are going to draw heavily on Carlson’s “aesthetics to the environment” for the rest of this paper. Not only are we going to say that some of his ideas about agent motivation and related approaches are perhaps fundamentally flawed; it will also provide us with material for our positive account. That is, we should apply an environmental aesthetics to the aesthetics of LLMs, specifically] #### 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) In this section, we focus on a general principle that Carlson thinks grounds all types of aesthetic appreciation: to appreciate something appropriately, we should appreciate it as what it in fact is. #### aesthetic appreciation of anything, be it people or pets, farmyards or neighborhoods, shoes or shopping malls, appreciation must be centered on and driven by the real nature of the object of appreciation itself.1 Carlson considers this “a blueprint for aesthetic appreciation in general” (Carlson 2000, 12. Emphasis ours). At its core, the position rejects both formalism and radical subjectivism. Against the former, which restricts appreciation to sensory properties abstracted from context and knowledge, Carlson holds that even basic forms are not properly appreciated without understanding what they are. Against the latter, he argues that aesthetic response is constrained by what the object is – by its real nature – rather than by personal preference (Carlson 2000, 12).  The principle operates via what Carlson – following Paul Ziff – calls “acts of aspection”, the different ways we attend to and appreciate objects. With artworks, we must know not only that something is a painting but what kind of painting it is. Carlson cites Ziff’s examples: “I survey a Tintoretto, while I scan an H. Bosch… look for light in a Claude, for colour in a Bonnard, for contoured volume in a Signorelli.” The thought is that, in knowing the type, we know what and how to appreciate (Carlson 2000). For artworks, this knowledge is readily available because, as Carlson notes, “Works of art are our own creations; it is for this reason that we know what is and what is not a part of a work, which of its aspects are of aesthetic significance, and how to appreciate them.” We attend to the piano’s sound rather than the coughing that interrupts it, recognise where a painting ends at its frame, and look at paintings rather than listen to them. This is built into what it is to be a painting, a symphony, or a sculpture (Carlson 2000). With nature and other non‑art objects, matters are different. Natural environments, unlike artworks, “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” (Carlson 2000). Appropriate appreciation therefore draws not on art‑historical knowledge but on knowledge of natural processes and environmental systems: #### The fact that nature is natural – not our creation – does not mean, however, that we must be without knowledge of it. Natural objects are such that we can discover things about them that are independent of any involvement by us in their creation… This knowledge, essentially common‑sense/scientific knowledge, seems to me the only viable candidate for playing the role concerning the appreciation of nature that our knowledge of types of art, artistic traditions, and the like plays concerning the appreciation of art. (Carlson 2000) Carlson contrasts modes of attention appropriate to different environments: “We must survey a prairie environment, looking at the subtle contours of the land, feeling the wind across the open space, and smelling the mix of prairie grasses and flowers; but such an act of aspection has little place in a dense forest environment. There we examine and scrutinise, inspecting the detail of the forest floor, listening for the sounds of birds, and smelling for the scent of spruce and pine” (Carlson 2000, 119). The principle that we ought to appreciate things as what they are thereby rejects both restriction to form and unconstrained relativism. Different kinds – natural environments, architectural works, agricultural landscapes, artefacts – make different demands on attention, and appropriate appreciation employs knowledge relevant to the kind in view. On this understanding, the appreciator’s role is active yet disciplined by the object’s nature (Carlson 2000). Carlson’s approach begins with kind‑fixing. Appropriate appreciation requires that we attend to an object “as what it in fact is” and “in light of our knowledge of what it is” (Carlson 2000, 5). In the middle domain between pristine nature and pure art, that knowledge is function‑centred: designed things “have a function, a purpose; and they are what they are in virtue of what they are meant or intended to accomplish… what is absolutely necessary… is information about their functions… The key to their natures is the purpose or the function they are meant to serve” (Carlson 2000, 133–134). In short, for designed artefacts, their natures are fixed by *function and the mode of its realisation* (Carlson 2000, 133–134; cf. 189).  Applying this to large language models: on Carlson’s approach, the kind is fixed by what the artefact is made to do *and* how that purpose is realised. An LLM is made to continue token sequences learned from tokenised corpora; at use it selects the next token given the preceding context. Nothing in this essence requires that the tokens be human words: the relevant “language” is any learned code; English is a frequent but non‑essential instance of the token‑sequence the artefact continues.  How this purpose is realised belongs to what the object is. Text is tokenised into subword units and mapped to vectors with positional information; during training the model reduces next-token error on sequences, thereby learning a conditional continuation rule; at use it applies self-attention over a bounded context window to compute a next-token distribution, a decoding policy selects one token, and the loop repeats; post-training alignment and interface choices bias which continuations are chosen without altering the underlying continuation function. A common objection says: the function is to produce human‑like dialogue. Carlson’s framework rebuts this. Dialogue is a *use‑case‑level* aim set by alignment and interface conventions; it is neither necessary nor sufficient for the artefact’s identity. Some deployments never present dialogue; some non‑LLM systems produce dialogue. By contrast, learned token continuation is both necessary to and characteristic of the kind across deployments. On Carlson’s account, that is the tighter essence claim: the purpose that organises correct appreciation is the continuation of token sequences under a learned predictor realised at inference, while dialogue is a contingent manifestation shaped by external aims. Following Carlson's principle that aesthetic appreciation must be informed by knowledge of what the object in fact is, we turn to fixing the nature of LLMs so that our attention can be disciplined by kind. In making this determination, we accept a shift in our descriptive framework from the natural sciences – which Carlson employs for natural environments – to computer science. This shift represents not a departure from Carlson's method but rather its direct application to a different domain of objects. We begin by establishing the broad category: an LLM is an engineered artefact. It is neither a person nor a natural object, neither a freestanding artwork nor an autonomous agent. Whilst its outputs can constitute artworks under certain conditions, the model itself remains a functional system designed for specific computational tasks. Correct appreciation should therefore track its function and origin rather than any projected persona or imagined interiority. At its core, the technical objective of an LLM is *next-token prediction*. The model learns to continue sequences by minimising predictive error across large text corpora. Through training, its parameters – or "weights" – are adjusted so that each subsequent token becomes less surprising given the preceding context. This objective fundamentally shapes both what the model learns and how it generalises to new inputs. Before the learning process begins, the training corpus undergoes *tokenisation*, whereby text is segmented into discrete units that may be complete words or sub-word fragments such as "un-" or "-tion". The model never manipulates ideas or concepts directly; rather, it operates on these token indices. Tokenisation thus establishes the grain of what the system can notice and reproduce – a constraint that matters for any appreciation of its handling of style, rhythm, or phrasing. The training process itself is *self-supervised* because the data supply their own supervisory signal: the correct answer for any prediction task is simply the following token in the sequence. Loss reduction emerges not from memorising specific sentences but from discovering regularities that effectively compress the data. These regularities encompass grammatical structures, collocational patterns, semantic associations, and narrative conventions – the full range of statistical dependencies present in natural language. Modern LLMs employ *attention-based architectures* that model dependencies across variable spans of context. The active computational state exists entirely within the context window presented at inference time. There are no persistent goals, no diachronic self, no memory beyond what fits in this window – unless external memory systems are explicitly added. Whilst increases in model capacity and window length affect performance characteristics, they do not alter the fundamental kind of system we are considering. After training completes, the model's weights become fixed, and it applies its learned predictive function through an *autoregressive* process. At each step, the model generates a probability distribution over possible next tokens; *decoding policies* then convert this probability mass into actual text, with different policies striking different balances between determinacy and variety. The precise behaviour of the system depends heavily on prompt design and the ordering of contextual information. Variation across multiple runs with identical inputs often reflects these sampling choices rather than any underlying intention or creative agency. Contemporary LLMs undergo additional *post-training alignment* through techniques such as instruction tuning and preference optimisation. These processes reshape the model's responses toward greater follow-ability and policy compliance. What users encounter as guardrails, refusals, or safety measures are learned behaviours induced through this alignment rather than hard-coded rules. Similarly, the interfaces through which we interact with these models – including system prompts, external tools, and retrieval augmentation – mediate our inputs and the model's outputs without fundamentally altering the trained conditional model beneath. Phenomena such as hallucination, sycophancy, and extreme sensitivity to prompt phrasing emerge as expected outcomes of the training objective and data distribution rather than as bugs or personality quirks. When contexts depart substantially from training regimes – a phenomenon known as *distribution shift* – performance degrades in predictable ways. These limitations should be understood as properties of a conditional probability model rather than as character traits of an agent. To mis-categorise the system encourages over-reading of its outputs as expressions of intention, emotion, or belief. With the nature of LLMs thus established, we can identify appropriate *acts of aspection* – to use Carlson's term – suited to their appreciation. Where one might survey a prairie environment or scrutinise a forest floor, with LLMs we might *survey* their affordance ranges by systematically varying tasks, prompts, and decoding policies. We might *scrutinise* their local behaviour under fixed conditions to assess calibration, robustness, and consistency. These modes of attention replace folk-psychological projection with observation disciplined by the system's actual nature. On this understanding, the essence of an LLM for appreciative purposes is fixed by its computational role, training history, inference regime, and alignment constraints. Vendor identity, exact parameter count within a broad range, and conversational persona are not essential properties. What appears as "personality" represents a stable response profile induced by the interaction of training data, alignment procedures, and system prompts – nothing more, nothing less. Whilst agent language may persist as convenient shorthand in everyday interaction, aesthetic evaluation should answer to what the system in fact is: a sophisticated conditional model trained to predict text. This technical primer equips us to appreciate LLMs according to Carlson's blueprint, employing categories and criteria appropriate to engineered artefacts rather than those borrowed from interpersonal aesthetics. In the following section, we shall apply these acts of aspection to concrete cases, developing criteria that might include legibility of affordances, responsiveness to contextual framing, and stability under perturbation. The goal throughout remains disciplined appreciation grounded in accurate categorisation rather than anthropomorphic projection. **