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> What exactly was he dead wrong about that is proven by any of this?

He said as you need more and more tokens models will fall apart because each additional token is a chance for a mistake and they will just exponentially fall apart. But in practice models have learned to identify and self-correct mistakes and if you look at the graphs more inference reasoning tokens almost always give far better accuracy.

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That's true, but he's still correct, it's just that the context is now so large that only people using agent loops see "context rot"

His other criticism of LLMs that I like better is that they try to predict tokens instead of learned embeddings. Tokens are arbitrary and in order to decode LLMs you need technical analysis (see mechanistic interpretability).

With JEPA models so far, it seems that PCA on latent vectors suffices.

tldr: embeddings have a lot more room for improvement

One of the big things that enabled extended context to work was training techniques for extending LLMs with reasoning, part of the thing he was saying wouldn't work.

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