OpenAI researchers explain why math is the road to AGI

That's what the researchers want to carry over from math training into other fields, from biology to materials science. Bubeck draws a parallel with how people are educated: students learn math not because they'll go on to write proofs, but because the subject forces them to think logically.

Math also has practical advantages as a benchmark. Problems are clearly stated, answers can be checked, and nobody argues about whether a result is correct. Bubeck introduces the idea of "AGI time": two years ago, models could simulate a student's thinking for minutes. Today, they're up to days or even a week. The next target is weeks and months.

OpenAI's training methods aren't specific to math, Bubeck says, but general, which means progress in other sciences should follow. The researchers are building an "automated researcher" that can work on problems on its own over long stretches of time.

The Erdős problems and the fight over what they mean

Bubeck and Ryu also dig into the Erdős problems, a collection of open questions left behind by the late Hungarian mathematician. Bubeck says internal models initially found solutions to ten problems marked as open, mostly through deep literature searches. His misleading tweet about it sparked a public spat with Google CEO Demis Hassabis, since many people read it as a claim that OpenAI had produced new proofs. By now, Bubeck says, ChatGPT and internal models have actually produced more than ten genuinely new solutions worthy of publication in academic journals.

What seemed like an unrealistic claim is now reality, and the pace is picking up. Bubeck sees this as evidence that the models are making the leap from recombining existing knowledge to producing new mathematics. Even if the philosophical question of whether scientific progress is anything more than clever recombination plus a bit of reasoning remains open.

The risks: mental atrophy and fake proofs

Both researchers warn against using these tools superficially. Expertise matters more than ever, they argue, because only trained mathematicians can put the models to productive use. Non-mathematicians who post long AI-generated proofs on social media are usually wrong. Ryu sees the same pattern in programming, where a whole generation is losing the ability to use debuggers.

Bubeck says claims that scientists are no longer needed are therefore dangerous. Academic institutions need to actively reclaim their role. At the same time, AI can speed up proof verification - a process that currently takes years - and flag problems in published papers.