AI math breakthroughs get audited and the cracks show

Two threads dissect AI-announced algorithm results. A writeup on the math behind OpenAI's claimed faster Fourier transforms drew commenters who noticed the algorithm needs complex numbers whose magnitudes grow superpolynomially with n. One asked directly whether that invalidates the big-O analysis. Another pointed out that the headline complexity results from last week assumed 'exact complex arithmetic.' In the Auto Research thread on integer multiplication below n log n, people were asking whether the proof lives in Lean, whether it extends OpenAI's proof, and where the speedup intuitively comes from.

The pattern here: the claims land fast and the explanations lag. Theorists show up within a day to check the model of computation, and the question 'what exactly was proven?' matters more than the headline. A related thread on research showing AI models can provably hide arbitrary capabilities feeds the same mood. People want to verify outputs, not just be impressed by them.

The counterpoint came from a theorist who objected to the writeup's tone toward theory researchers. Skepticism is not the same as hostility, and some commenters think the critics are being unfair.