# 3. Persons, Artifacts, and the Limits of Design Appreciation #generative-aesthetics Knowing what something is does not, by itself, settle how it should be appreciated. The point is one Carlson makes in his own discussion of nature. We can take a tree and dwell on it for the kinds of formal qualities we dwell on in a piece of abstract sculpture; we can take a stretch of countryside and frame it from a particular vantage in much the way we might frame a landscape painting. There is nothing wrong with the looking, in either case. What goes wrong is what we end up appreciating. The first way of looking, Carlson argues, rips natural objects out of the larger environments to which they belong; the second frames and flattens nature into scenery (Carlson, 2000, pp. 6–7). In neither case is what we appreciate nature itself. %%not a good paragraph. Very unclear, far too editorialiised, using words like 'rip' which come from carlson even though this is not made clear. %% LLMs are vulnerable to the same kind of mistake.%%not how i write%% There are two appreciative models that immediately come to mind, and each has a basis in what LLMs in fact are. %%not how i write%%The first is the appreciation of an interlocutor: we meet LLMs in conversation, and that conversational form makes us look for the kind of thing we attend to in conversational partners. The second is the appreciation of an engineered object: LLMs are built and trained by particular research groups for particular ends, and that origin makes us look for the kind of thing we attend to in objects that have been put together for a purpose. Whether either of these models is the right way to attend to the LLM as §2 has described it — or whether each, like Carlson's two models, takes its lead from real features of its object and ends up appreciating something the object is not — is the question for the rest of this section. Whatever the aesthetic appreciation of a person amounts to, it is not just a matter of registering a stable pattern of response. As §1 indicated, when we appreciate someone aesthetically as a person — when we say of her, for instance, that her character is beautiful — we do so against a background of knowing the life she has had: her commitments, and the circumstances under which her traits have been tested over time.%%not how i write%% To say that her character is beautiful is to make a claim about traits not as they show up in a single moment but as they belong to that life.%%not how i write%%The full theory of how this kind of appreciation works is not what we need here. The more limited claim we need is that aesthetic predicates of this sort apply only to a subject whose conduct can be read as the expression of such a life. One way to defend the appreciation of LLMs as persons is to say that it is a fictional appreciation. Mallory (2023) defends a view of this kind. Drawing on Walton's account of make-believe (Walton, 1990), he argues that we engage with chatbots by treating them as *props* in a game of prop-oriented make-believe — props whose physical outputs generate fictional truths within the game. Inside the game, the chatbot 'says' things and 'means' things; outside it, we know that no such speaker is there. Mallory's metasemantic claim is that the outputs are "literally meaningless but fictionally meaningful" (Mallory, 2023, p. 1082). The view does useful work, since it explains how a user can take a chatbot exchange seriously as it unfolds without committing herself to the existence of a speaker behind the screen. This is genuinely useful practice, but it is not yet the appreciation of the LLM as a person. Fictional characters belong to practices that ask us to imagine them as persons. To imagine Gatsby as a person, when we read Fitzgerald's novel, is not a misclassification of what Gatsby is; it is part of what reading the novel involves. The LLM does not stand in this kind of relation to make-believe. It is, as §2 described it, a system trained to generate continuations from context, and the speaker we imagine when we engage with it as if there were a speaker present is something our as-if stance has produced, not the system itself. Whatever it is to respond aesthetically to that imagined speaker, it is not, on its own, to appreciate the system that has been doing the generating. If a make-believe defence of treating LLMs as persons is not enough, one might try to secure the position by weakening what is required for mindedness. Frankish takes this route. Drawing on Dennett's intentional stance, he argues that intentional descriptions of LLMs can be literally true at the right level of abstraction, rather than mere pretences we adopt because they are convenient (Frankish, 2024). LLMs, on his view, admit ascriptions of many thin 'beliefs' together with one thin 'desire' — the desire to play what he calls the chat game: to produce textual responses that are cooperative by ordinary conversational standards, given the context. The proposal warrants more careful treatment than Mallory's, since it does not ask us to pretend that LLMs are agent-like. It says that agent-talk picks up real patterns in their behaviour. Even if we grant Frankish his thin agency, we have not yet got what we need to appreciate LLMs as persons. The desire he ascribes — to play the chat game, to produce an appropriate next move in conversation — is not the kind of desire that organises a life; it does not stand in the relations to other commitments and to other people in which the appreciation of a person finds its purchase. The thin beliefs Frankish ascribes do not, between them, build up a perspective developed across time. The pattern they belong to is local: it is the pattern of producing continuations within a context, under conditions set by training and the surrounding deployment. To call someone's character beautiful is to make a claim about traits manifested and tested across a life, and Frankish's chat-game agent is not the kind of subject in which traits can be manifested or tested in that way. Post-training is what most strongly pulls us toward treating an LLM as a person, but the pull does not change what kind of thing the LLM is. It is post-training that gives a particular system the recognisable assistant-like profile one notices when comparing it with another system — the profile users have in mind when they say one model has a different 'vibe' from another. Users who say this are not confabulating: they are picking up real patterns in the way a post-trained system tends to respond. But what they are picking up is a profile in generated outputs and exchanges, not the character of a subject whose conduct expresses commitments developed across a life. The patterns are perfectly available for aesthetic attention; what goes wrong is the description of them as the marks of a character. There is an obvious alternative to thinking of LLMs as persons: thinking of them as made things. LLMs are built and configured by particular research groups and companies, and put before users for use in conversation. With an ordinary made thing — say a kettle — we do not ask whether it has a life through which its character is expressed; we ask whether it has been well made, and whether its behaviour fits the use we have for it. Should we not say the same thing about LLMs? If treating an LLM as a person reaches too quickly for the language of subjects, treating it as a designed thing returns us, on the face of it, to firmer ground. Carlson's design appreciation, as set out in §1, asks how well a functional object's form fits its function — and that question has clear application to LLMs. An LLM, in ordinary deployment, is meant to be a usable conversational assistant; design appreciation asks how well it succeeds at being one. Forsey (2013) and Parsons and Carlson (2008) develop this kind of thought further: aesthetic assessment of a designed thing turns on understanding what the thing is for, and on understanding how its form realises that purpose. There is no obstacle, on the face of it, to putting questions of this kind to LLMs. Design appreciation is not, however, the whole story. The reason it falls short comes out as soon as we ask what explains the characteristic order of an LLM's generated text, and the way that text develops across an extended exchange. Designers specify the architecture of the system, the training and post-training procedures it goes through, and the conditions under which it is then deployed; they do not specify, feature by feature, the patterns that come to emerge across the system's outputs and chats. Take refusals. Whether and when a system refuses is something designers can set deliberately. *How* it refuses — the rhythm with which it builds up to a refusal, the standard formulations into which it falls when declining — is not. The same kind of point holds for how a system sustains a topic across many sentences, and for how an extended exchange settles into its particular shape. None of this follows from a designer's plan. Olah makes the same point in the language of growth. He writes: > one useful way to think about neural networks is that we don't program them... we don't make them... we kind of 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... we create the scaffold that it grows on and we create the light that it grows towards. (Olah, 2024) The growth Olah has in mind is not biological growth: LLMs are not organisms, and parameter adjustment under a training objective is not the kind of process that produces a frog or a tree. The comparison still does its work, however, by bringing out a gap between what a designer can directly do — setting up the architecture and the training regime — and what the trained model then turns out to be like. The model that comes out of training has tendencies that the people who built it did not, and could not, individually specify, and that are often only partially understood even by those who built it. LLMs are designed in the sense that the set-up of training is designed; they are not designed in the sense that the working profile of the trained system has been written into it feature by feature. It might be objected that the appreciation of a made thing is still design appreciation even when the maker does not directly control everything that goes into making it. Carlson himself addresses this in his treatment of Pollock. A Pollock is, of course, made; no serious appreciation of one of his canvases ignores Pollock's choices and his handling of his materials. But Carlson uses such works as cases in which appreciation also depends on knowing the role of forces beyond the maker's direct intentional control: > Although these forces differ from many that shape works of art, awareness and understanding of them is vital in nature appreciation, as is knowledge of, for example, Pollock's role in appreciating his action painting or the role of chance in appreciating a Dada experiment. (Carlson, 2000, p. 120) LLMs are not artworks in the way Pollock's canvases are. But the structural lesson does carry over. There are made things whose appreciable order requires attention to what goes on beyond what the maker has directly arranged. With a Pollock, that order has to do with what happens to paint after it has been laid on a surface that the artist has prepared for it. With an LLM, it has to do with the learned regularities through which continuations are produced and exchanges develop. In neither case is everything appreciable about the object captured by attending to design alone. Neither model should be discarded outright. The pull toward person-like language is not unfounded — post-trained systems do have stable assistant-like profiles, and what users notice when they speak of personality and 'vibe' is responding to something the system really does. Design appreciation is not unfounded either — LLMs are made for use, and how well they are made for it is something we can ask. What neither model captures, however, is the order §2 has put before us: the learned order of generated text and extended exchange. The next section turns to the kind of knowledge that would bring that order into view, under the name of *semiotic physics*.