OpenAI hands mathematicians 372 AI-generated proofs to check on GitHub

Three hours is an average across 372 results ^[3][1]. OpenAI shared only averages, with no per-problem figures and no prompts ^[7]. Across the whole set that is roughly 1,116 hours of ChatGPT Pro Thinking compute ^[22]. Treat it as a number about OpenAI's problem list. For it to transfer to another group's list, their problems would have to resemble the ones this model was given, and failed attempts would have to be counted the same way. Some results took multiple attempts ^[10]. The Decoder's report does not say whether those retries are inside the average.

The Navier-Stokes run needed a 10,000-agent swarm and millions of dollars ^[14]. Next to that, one agent per result is a large drop in what a proof costs to find. Checking is slower. The Navier-Stokes solution has been under formal review for weeks, according to OpenAI ^[15].

Lean is aimed at that gap. It is a programming language built so a machine can check a proof ^[4]. When the checker accepts a formalized proof, the logic is correct ^[11]. No referee has to do the line-by-line pass. Lean cannot judge whether a result is relevant or original ^[11]. Each result is meant to solve or advance an open problem, some tied to the Riemann hypothesis ^[21]. Whether a given result is new and matters is still a mathematician's call. I think formalization is the right engineering choice for the half of review it covers. I'd want The Decoder's "many" formalized proofs ^[4] turned into a count before treating the bottleneck as cleared.

The packaging deserves credit. The repository keeps revision logs and citations ^[2]. OpenAI also published reasoning summaries, statistics on how many problems the model attempted, and compute estimates ^[6]. A versioned history suits results that will be corrected in public. OpenAI has said it wants to improve its citations and presentation ^[12]. The Decoder calls the choice of GitHub over journals a statement that the traditional process is too slow for this volume ^[17].

The doubts reach OpenAI's own advisers. The advisory group it consulted includes Fields Medal winner Timothy Gowers ^[8][7]. Gowers has warned that within one to two decades the mathematical literature could grow enormously while no human community remains that truly understands it ^[19]. Terence Tao has argued that training young mathematicians should emphasize the human side and tightly limit AI tool use ^[20].

OpenAI plans to fund workshops and conferences on understanding AI-produced results ^[9]. It says it is working on a responsible release of the model to "directly empower scientists with state-of-the-art capabilities" ^[13]. A wider release would put proof generation at roughly this cost in more hands, with the same human reviewers on the other end.