Frontier models are not as good as the navier stokes solution would lead you to believe
尽管人们普遍对人工智能持乐观态度，但最近关于纳维-斯托克斯方程证明的宣传，却误解了前沿模型的实际能力。该解决方案并非由一个超级智能AI独立完成，而是由10,000个并发代理运行88小时，交换了270万条消息，并处理了大约1300亿个tokens。这表明这是一种“暴力破解”的方法，暗示当前的AI模型虽然具有一定智能，但尚未达到能够独立解决复杂千禧年难题的水平。
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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.