Exclusive: Economists have been teaching a broken proof for 50 years. AI just found it

Scott Kominers has taught Robert Aumann’s 1976 theorem dozens of times. He’s assigned it in economics courses at Harvard. He’s built on it in his own research. So when Axiom Math’s formal verification system flagged a gap in the proof’s foundations earlier this year — an assumption Aumann stated but never actually proved — Kominers did what any rigorous economist would do. He called his colleagues. “They all sort of said, ‘Oh, well, that makes sense. Aumann knows this,'” Kominers told Fortune in a recent interview. The problem: Aumann never proved it. And almost every theorem built on top of it — in information economics, in platform design, in the merger guidelines used in federal antitrust cases — was resting on foundations no one had formally examined. Until now. That finding is the first public result of EconLib, a project Fortune can exclusively reveal that Axiom Math is building — and which its founders believe could reshape how economic theory is used in American law. What Lean Catches That Mathematicians Miss To understand why this matters, you have to understand what Axiom is actually building. Axiom Math was founded by its CEO, Carina Hong, an MIT and Oxford-trained mathematician who dropped out of Stanford to launch it, raising $200 million at a $1.6 billion valuation in March, led by Menlo Ventures. Axiom is building what Hong calls “verified AI.” The system doesn’t just generate mathematical proofs — it writes them in Lean, an open-source formal programming language that behaves like code: either every logical step compiles, or the program won’t run. No hallucination tucked into step 47. No drift between premises and conclusions. Just true or false, with no wiggle room in between. It is, as Axiom sees it, the only honest way to let AI near mathematics — and, as it turns out, near economics. courtesy of Axiom Hong joined my interview with Kominers and told me about a saying in the Le