For three years, the industry has aggressively sold the idea that if we just shove enough electricity and data into next-token predictors, true reasoning will magically emerge… we all know how that’s going. You simply cannot run critical infrastructure or write provably secure code using a stochastic parrot that occasionally hallucinates a logic gate. And the people at the very top of the food chain know it… Yann LeCun’s massive $1B seed round (contex from Bloomberg ) isn’t just another Valley hype cycle. It’s a direct, billion-dollar financial short against the pure Scaling Hypothesis. His new venture, Logical Intelligence , is completely ditching Transformers to focus on Energy-Based Models (EBMs). Instead of autoregressively guessing the next piece of a solution, they treat formal verification as an energy minimization problem. You map the mathematical constraints, and the model is forced to settle into a provably correct state. No probabilistic vibes… just rigid, mathematical proof. It is a beautiful concept for finally moving past the hallucination era. But let’s be real… mapping discrete, rigid logic into continuous energy landscapes is going to hit an absolute brick wall of computational cost at inference time. Are we finally seeing the inevitable architectural reset toward verifiable AI, or are we just trading the LLM hallucination problem for a mathematically impossible compute bottleneck? submitted by /u/rennan
Originally posted by u/rennan on r/ArtificialInteligence