OpenAI’s Navier-Stokes release included a Lean 4 formal proof

Yesterday OpenAI announced a proof that settled a long-standing question about the Navier-Stokes equations from fluid dynamics. The announcement has created a lot of buzz, as one would expect. But there’s an aspect of OpenAI’s work that I haven’t seen anyone talk about: they posted a Lean 4 formal proof at the same time as their conventional human-readable proof.

Quite a few other mathematical conjectures have been settled recently using AI, and these have also been accompanied with formal proofs, using Lean 4 in particular.

Until very recently, generating machine-verifiable formal proofs has been excruciatingly tedious. In 2005, Henk Barendregt and Freek Wiedijk wrote

To give an indication of how much work is needed for formalisation, we estimate that it takes approximately one work-week (five work-days of eight work-hours) to formalise one page from an undergraduate mathematics textbook.

That was the rule of thumb: forty hours per page. And this in the context of undergraduate textbooks. Research publications are much denser than textbooks. Furthermore, page 100 of a textbook probably depends mostly on material on pages 1 through 99. A sentence in a research article could cite anything that has been published before.

Say a research article takes 20 times more effort to formalize than page in an undergraduate textbook. Then formalizing the 166-page paper from OpenAI would take 132,800 person-hours. It took OpenAI 17 hours to verify their proof in Lean. I hesitate to use the word “revolutionary,” but lowering the cost of anything by four orders of magnitude is revolutionary.

I’ve used AI to generate formal proofs to check my work just for a little blog post. I wouldn’t dream of doing that if I had to pay someone a week’s salary to check my work.

Formal verification doesn’t just apply to mathematics. You could, for example, formally verify that a set of security policies are consistent and that, given certain assumptions, they accomplish their purpose. You could formally verify that a smart contract imposes a certain maximum liability. You could verify the correctness of mission-critical algorithms. These problems are easier than formalizing mathematics research, and it is easier to quantify the return on investment.