This paper has argued that Large [[Language Models]] can be aesthetically appreciated, but not through the anthropomorphic lens that we might be tempted to adopt. Following Carlson’s principle that [[aesthetic appreciation]] must be grounded in what an object in fact is, I have argued that treating LLMs as persons or even pretending they are persons misdescribes them, and therefore is not **an** appropriate foundation for aesthetically appreciating them. LLMs are learned [[continuation systems]]—artefacts that predict the [[next token]] based on statistical patterns acquired during training. Any apparent personality or agency is a projection onto patterns in outputs, not a property of the systems themselves. The positive proposal has been that we can aesthetically appreciate LLMs through the framework of [[semiotic physics]], which reveals how learned [[linguistic patterns]] mechanically unfold during text generation. Just as geological knowledge makes rock formations aesthetically appreciable by revealing [[the forces that]] shaped them, [[semiotic physics]] makes visible [[the order]] in LLM outputs—the persistence of registers, the smooth blending of incompatible genres, the mechanical precision of constraint satisfaction. This mode of appreciation fulfils Carlson’s requirement that we appreciate objects as what they in [[fact are]], guided by appropriate knowledge. [[The result]] is a distinctive [[aesthetic experience]]: we appreciate not an ersatz mind but a [[novel kind]] of artefact, one whose outputs exhibit law-like regularities at [[the level]] of sign-relations, beautiful in their linguistic necessity and mechanistic grace.