Dennett's Intentional Stance Applied to LLMs on LessWrong

LessWrong is a community blog for people who want to reason carefully about minds, human and artificial alike, and this post by Eleni Angelou earns a spot in the catalog by testing philosopher Daniel Dennett's framework directly against how large language models behave.

The piece walks through why Dennett's intentional stance — treating a system as if it has beliefs, desires, and goals, whenever that lens predicts its behavior better than tracing its code or hardware does — fits LLMs better than the alternatives. Angelou points to concrete evidence: models scoring at or above human level on tasks like bar exams and reasoning benchmarks, and outputs that are, in practice, hard to tell apart from a person's. She borrows Dennett's own comparison to intelligent aliens, arguing that once a system's behavior gets sophisticated enough, treating it as if it has a mind becomes the most useful predictive strategy, not a claim about what is actually happening inside it.

The argument stays pragmatic rather than metaphysical: Angelou proposes a strategy for prediction and explanation, not a claim that LLMs literally hold beliefs or want things. The post is candid about its limits, too — commenters push back with cases like hallucinations, where treating the model as an intentional agent stops predicting what it actually does. Anyone curious how philosophy of mind maps onto today's AI systems, with no computer science background required, will find this a readable entry point, though it stays at the level of argument rather than technical implementation.