Person-directed knowledge is a natural candidate for what design knowledge leaves out. This temptation arises because the way we interact with LLMs is so similar to interacting with actual human interlocutors. 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 in Section 2 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. The person-directed thought can be preserved in two ways. The first 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 second argues that the LLM is an intentional system in a thin sense, and that this is enough to license some person-directed appreciation. Mallory (2023) takes engagement with a chatbot to be a game of prop-oriented make-believe. The chatbot provides text that functions as a prop for imagining an interlocutor. Its outputs are "literally meaningless but fictionally meaningful" (Mallory 2023, p. 1082). On Mallory’s account, generated strings can function as props: they make it fictional that a character has said something, while the system that generates them remains distinct from that character. Mallory develops this as a metasemantic and epistemic account rather than an aesthetic one. Adapted to the present question, it suggests that the person-like object of appreciation is the fictional interlocutor made available by the game. The warmth or wit a user finds in Claude or in ChatGPT is the warmth or wit of a fictional interlocutor that playing with the prop brings into the game. Mallory’s fictionalism depends on keeping the prop apart from the character it makes available. Mallory is explicit: "the character is not this technological infrastructure any more than a character in a play is a human body or a costume" (Mallory 2023, p. 1091). The fictional interlocutor enters the game; the LLM is what makes the game possible. Person-directed appreciation can therefore attach to the interlocutor, but the interlocutor is not the LLM. The trained continuation system Section 2 identified remains to be appreciated as what it is, and Mallory's account leaves that task untouched. Frankish offers a more direct route, because he keeps the intentional description attached to the system itself. Drawing on Dennett’s intentional stance, he argues that we can ascribe beliefs and desires to a system when doing so yields a simple and fruitful account of its behaviour. On the shallow view he adopts, these attitudes need not be inner episodes or conscious states but can be dispositional patterns in the behaviour of a whole system. This makes the view unusually hospitable to LLMs: if treating a model as having beliefs and desires helps predict its textual behaviour, then the intentional description is not merely a fiction imposed from outside. Frankish’s own account also marks the limits of this move. LLMs are static systems with no needs and no social life, and their inner architecture does not update through interaction. For that reason, he does not credit them with the range of communicative desires we ascribe to human speakers. The thin agency he allows them is organised around a single goal: to play the chat game, producing textual responses that are cooperative by human conversational standards, given the context. The chat-game agent, however, falls short of what person-appreciation needs. A person's linguistic behaviour, as Frankish himself notes, is embedded in a wider web of non-linguistic behaviour, and tracking the predictive patterns of that whole web involves ascribing a much wider range of desires than the chat game alone calls for (Frankish 2024, p. 15). The chat-game agent has nothing answering to that web. The intentional structure Frankish licenses is restricted to making moves in the chat game; it does not place the text within a life in which earlier conduct informs later conduct. The categories person-appreciation works with depend on this kind of life. Steadiness, for example, can only be shown across the situations that make up someone's life. The chat-game agent does not have one. Post-training shapes generated text into stable patterns, and this might seem enough for person-appreciation. Users do say that one model feels friendlier than another, and they are picking up on something — a regularity in how chat-optimised systems tend to respond across many prompts. The regularity belongs to the way post-training disposes the system to continue prompts in assistant-like ways. It is not a trait of a temporally extended subject whose responses today are continuous with their responses last week. Applied here, the categories of person-directed appreciation lack the kind of subject they normally require. Neither person-directed route reaches the object isolated in Section 2: the trained continuation system whose responses develop from acquired dispositions operating on context. The remaining question is what kind of knowledge makes such generated text appreciable as the product of a trained continuation system.