I’m generally optimistic about AI, but the hype surrounding the recent Navier-Stokes proof announcement misrepresents where frontier models actually stand. Yes, AI formalisation tools like Lean have advanced, and seeing models assist human mathematicians with real breakthroughs is genuinely impressive. But claiming the core model itself has reached human-level mathematical brilliance ignores what actually happened behind the scenes. This wasn’t a single super-smart AI having a lightbulb moment. It took 10,000 concurrent agents running for 88 hours, exchanging 2.7 million messages and burning through roughly 130 billion tokens. If you talk to a frontier LLM directly right now, it isn’t giving you these kinds of insights natively. What happened here wasn’t a leap in base reasoning capacity; it was an industrial-scale tree-search. The orchestrating framework spun up a massive web of parallel loops to kind of brute-force possibilities, prune dead ends, and cross-pollinate the few branches that didn’t fail. That is a triumph of massive compute infrastructure, automated verification, and cluster orchestration—not an indicator that the base neural network possesses human-level domain intuition. Anyone who thinks AI won’t keep improving is blind to the trajectory. But we need to separate agentic scale from model intelligence. Throwing a century’s worth of parallel human work-hours at a single problem until the math compiles in Lean isn’t AGI; it’s just raw compute applied to an automated search space. Edit When I say brute force, I don’t mean it in the literal sense as AI does have some form of intelligence that’s obvious, just not at the level where these models can solve millennium problems. submitted by /u/Healthy_Outcome7897
Originally posted by u/Healthy_Outcome7897 on r/ArtificialInteligence