Yann LeCun's $1B Bet Against LLMs [Part 1]

Welch Labs spends 37 minutes on why Yann LeCun walked away from the architecture that made his employer famous, and on the bet he has taken instead. The case against: a model trained to predict the next token learns the statistics of language rather than a model of the world, so scaling it buys fluency and not understanding. Part one builds the groundwork and the history; a second part takes up JEPA, the joint-embedding predictive architecture LeCun proposes in its place. Useful as an explainer even if you think the conclusion is wrong, because it states the position carefully enough to disagree with.

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