Technology

World models aren't just for robotics (though they definitely WILL be used for that). They're for reasoning under uncertainty in domains where you can't see the outcome in advance. Eg:

Medical diagnosis: you can't physically "embody" whether a treatment will work. But a system that understands disease progression, drug interactions, and physiological constraints (not by pattern-matching text, but by learning causal structure) - well, that's fundamentally different from an LLM hallucinating plausible-sounding symptoms.

Financial modeling, engineering simulations, climate prediction...all domains where the "embodied experience" is simulation, not physical interaction. You learn how the world actually works by understanding constraint and causality, not by predicting the next token in a Bloomberg article.

The point isn't "robots will finally work." The point is: understanding causality is cheaper in the long run and more reliable than memorizing correlations. Embodiment is just the training signal that forces you to learn causality instead of surface patterns.

My read is that LeCun's betting that a system trained to predict abstract state transitions in any domain (be that medical, financial, physical) will generalize better / hallucinate less than one trained to predict text.

Whether that's true? Fucked if I know - that's why it's (literally) the billion-dollar question. If he cracks it....it's big.

But "it won't cook dinner" misses the point (and besides which, it might actually cook dinner and change lightbulbs, so....)