[D] Is LeCun’s $1B seed round the signal that autoregressive LLMs have actually hit a wall for formal reasoning?

I’m still trying to wrap my head around the Bloomberg news from a couple of weeks ago. A $1 billion seed round is wild enough, but the actual technical bet they are making is what’s really keeping me up.

LeCun has been loudly arguing for years that next-token predictors are fundamentally incapable of actual planning. Now, his new shop, Logical Intelligence, is attempting to completely bypass Transformers to generate mathematically verified code using Energy-Based Models. They are essentially treating logical constraints as an energy minimization problem rather than a probabilistic guessing game.

It sounds beautiful in theory for AppSec and critical infrastructure where you absolutely cannot afford a hallucinated library. But practically? We all know how notoriously painful EBMs are to train and stabilize. Mapping continuous energy landscapes to discrete, rigid outputs like code sounds incredibly computationally expensive at inference time.

Are we finally seeing a genuine paradigm shift away from LLMs for rigorous, high-stakes tasks, or is this just a billion-dollar physics experiment that will eventually get beaten by a brute-forced GPT-5 wrapped in a good symbolic solver? Curious to hear from anyone who has actually tried forcing EBMs into discrete generation tasks lately.