Tao draws a parallel to the foundational crisis that rocked mathematics between 1900 and 1930, when Russell's paradox and Gödel's incompleteness theorems forced mathematicians to spell out assumptions they had always left implicit. That crisis produced a rigorous framework that has held up for a century.
Today, Tao argues, the stress test has shifted. It's no longer about mathematical truth but about "the largely implicit framework of mathematical values and practices": what counts as a contribution, what gets rewarded, what it means to understand something, and whether a machine can be said to have done the work.
His working hypothesis: "AI tools will, reasonably soon, become capable of performing a reasonable fraction of research-level mathematical tasks, with reasonable levels of success, quality, supervision, and cost."
As evidence, he points to the First-Proof Project. In the second round, ten never-published research problems were tested against four AI systems under controlled conditions. Seven of the ten received at least one passing grade from at least one system, meaning a solution judged essentially flawless or needing only minor revisions, at costs in the tens to hundreds of dollars per problem.
