OpenAI wrote research pace out of its new math advisory group's remit

"Importantly, the group will not be responsible for advising us on how to pace our internal progress on mathematics," OpenAI writes ^[11]. Members are unpaid, they pick who joins, they can offer advice nobody asked for, they can speak publicly about the company's influence on mathematics, and they can publish their recommendations ^[10]. The Neuron reports that the group has no decision-making authority over OpenAI ^[12].

A publication right without a decision right means the group can put a disagreement on the record and OpenAI can proceed anyway. The Neuron notes that a slower release schedule gives mathematicians more time per result and does not reduce the rate at which new work enters OpenAI's internal queue ^[13].

The one run OpenAI attached numbers to is Navier-Stokes. The company says that effort used roughly 10,000 concurrent agents with access to cached internet material and code execution, produced the proposed solution in about 88 hours, and then took roughly 17 hours of Lean formalization ^[5][6]. That is 105 hours, about 4.4 days ^[23]. Training began on August 28 and the announcement came on September 21, a window of 24 days ^[2][22]. Five runs of that length fit end to end in 24 days. Reaching 100 results inside the same window would need roughly 18 of them at once, and at 10,000 agents each that is about 180,000 concurrent agents ^[24]. That assumes the other results cost what Navier-Stokes cost. The company has not released most of them and did not say how long those runs took ^[4].

OpenAI published the Navier-Stokes proof in prose and in Lean, which checks reasoning against defined assumptions ^[7]. Reviewers then do the parts Lean cannot: confirm the proof answers the intended problem, check whether someone already established part of the result, and identify which ideas are actually new ^[8]. The published solution has already sparked heated debate in parts of the scientific community, according to the-decoder ^[20].

The September 11 declaration "A Severe Misalignment of AI in Mathematics" argues against using famous open problems as benchmarks for AI systems, on the grounds that progress also depends on understanding why a result works and whether its ideas can support further research ^[14]. Timothy Gowers agrees with much of the letter and thinks mathematics is in trouble. Where he splits from it is on what mathematics is for: the letter treats conceptual understanding as the goal and problem-solving as the means to it ^[17]. He set that out on his blog ^[15].

According to the-decoder, OpenAI acknowledged that it took on the Millennium Prize Problem only after hearing rumors that another team, partly made up of Anthropic researchers, had already cracked it ^[19]. OpenAI calls the collaboration a "first step," with "difficult questions ahead about how AI can support mathematical understanding and how the benefits of these capabilities can reach the wider community" ^[21].