Yann LeCun on Why LLMs Can't Reason (and What Would Fix It)

LeCun's most-cited social media thread on LLM limitations: LLMs confabulate because they have no world model. They predict the next token based on pattern-matching in text, but they don't have an internal model of how the world works — so they generate plausible-sounding but factually wrong answers. His argument: humans learn world models from observing and interacting with the physical world from birth, which grounds their reasoning in causality. LLMs learn from text, which is a highly filtered, bandwidth-limited representation of human experience. You can't learn physics from reading a physics textbook — you have to interact with the world. The fix, in LeCun's view: multimodal learning (learning from video, audio, sensorimotor data) combined with architectures like JEPA that learn abstract representations rather than predicting raw pixels. This thread was widely shared in 2025 after a series of high-profile LLM failures and is the best accessible entry point for LeCun's core argument.