Terence Tao, a UCLA mathematics professor who earned his doctorate at age 20, says artificial intelligence is ending mathematics’ “romantic era” by producing solutions to hundreds of problems with simple prompting. He describes a transition towards industrial-scale research, where leading mathematicians increasingly examine AI-generated proofs rather than solve every problem themselves. The change is already unsettling researchers.
OpenAI reportedly addressed the Navier-Stokes equation, one of mathematics’ millennium problems, in early September and subsequently announced solutions to another 700 outstanding problems. Questions remain over verification, since researchers have complained that some AI-generated results do not show their working. Tristan Buckmaster, a New York University mathematics professor, said an OpenAI release had disrupted early-career researchers’ projects. Separately, OpenAI withdrew sponsorship from an AI mathematics event organised by Caltech undergraduates after objections from senior academics. Tao remains less pessimistic than some colleagues, but questions the enduring appeal of traditional stories about individual mathematical achievement, including Sir Andrew Wiles’s prolonged work on a difficult equation. The debate now concerns both research methods and the future of academic careers.
The central institutional question is how mathematical research establishes reliable results when AI systems can generate proofs faster than researchers can examine them. A proof is more than a plausible answer: its reasoning must withstand scrutiny, and each logical step must support the conclusion. Terence Tao’s account points to a potential imbalance between automated production and human verification. Research capacity increasingly depends on the ability to distinguish genuine advances from errors, repetitions or results whose significance remains unclear.
A second-order consequence concerns how research opportunities are distributed. If AI-generated results rapidly occupy previously open problems, early-career mathematicians could lose projects that once provided opportunities to develop expertise and establish independent reputations. The effect would extend beyond individual researchers to the informal system through which mathematical knowledge, professional standing and academic employment develop. OpenAI’s reported release of hundreds of solutions illustrates the scale of the disruption described by researchers, although the extract does not establish how many results are independently valid or how much research was genuinely displaced.
The dispute over OpenAI’s withdrawal from the Caltech AI mathematics event also exposes a divide over legitimate research practice. Undergraduates saw AI-assisted problem-solving as preparation for an emerging discipline, while senior academics objected to the event. The disagreement suggests that mathematical institutions are contesting both the reliability of machine-generated results and the standards by which researchers earn recognition. Terence Tao’s account of the transition therefore raises a question about how academic credibility will be established when proof generation becomes increasingly automated.
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