OpenAI has admitted that rumors of a solution to the Navier-Stokes equations prompted the company to direct its resources toward the problem, following accusations from researcher Tristan Buckmaster that the lab engaged in misconduct and pressured him to remove co-author Levent Alpöge from a paper.
The dispute
The controversy surrounds a mathematical challenge among the Clay Millennium Problems, which carries a $1 million prize. Buckmaster claims that after details of his work leaked, OpenAI attempted to sideline Alpöge, who is employed by Anthropic, and threatened him with career repercussions. There is also suspicion that the company trained its models on drafts the two researchers uploaded to Codex.
One fact stands firm. OpenAI confirmed it heard rumors that Anthropic’s models had cracked a Millennium Problem and then pointed its own resources at the same issue. On other points, the accounts differ significantly.
Buckmaster calls it “absolute academic malpractice”
Alpöge and Buckmaster dispute OpenAI’s claim that its solution differs substantially from theirs. They state they entered a similar approach into the company’s systems. As far as Buckmaster understands, that input ended up in the training data. He views this as “absolute academic malpractice.”
OpenAI employees say the chance that the company actually trained on the submitted solutions is low, particularly if the researchers disabled the option to exclude their inputs from training. Whether they did so isn’t publicly known. OpenAI employee Boaz Barak also pushed back on the idea that the model needed outside help at all. “It’s just cope to think that the model would have needed this. It actually started off by proving a stronger claim than they did. Anyone who has seen this model at work would not think it needs ‘hints.'”
In its official blog post, OpenAI acknowledges the gap in certainty. “While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.”
Altman backs Bubeck while Alpöge pushes back
OpenAI CEO Sam Altman backed Sébastien Bubeck on X, saying the team had “acted with integrity and generosity throughout.” He said it had been suggested that Buckmaster and Alpöge should receive the award, but the team was met with “unfounded accusations of plagiarism.”
Altman also wrote that the effort started because of “rumors on the internet last week that Anthropic’s models had solved a millennium problem and we were curious if ours could do it too.”
Alpöge contradicts Altman on one key point. Altman writes that it was difficult to make the same offer to Alpöge because he “who was not willing to talk or coordinate with us anyway.” Alpöge says he would have liked to work with OpenAI, and the authorship question did not matter to him. “I also like the idea of the labs cooperating, and even better on scientific progress. It’s a shame!” Alpöge writes.
Impact on open science
Regardless of who is right on every detail, the case raises a basic question about how AI labs interact with the research community. Rumors alone were enough for OpenAI to throw massive resources at a research problem on short notice, racing to solve it and possibly publish first. And it cannot be ruled out that the company trained on data fed into its own systems.
For academics and companies alike, the takeaway is simple. Anyone who feeds research data into OpenAI’s systems risks being beaten by their own findings. Opting out of data training through settings offers thin protection at best, especially since AI labs have not earned the benefit of the doubt on training and data practices.
Mathematician Terence Tao, one of the most influential living mathematicians, warns on Mastodon that “even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential.”
“The incentives may now be pointing in the direction of no longer sharing any promising research directions with the broader community, which would reverse centuries of traditions of open science and do serious long-term damage to the future of the field,” Tao writes.
What it means
Researchers must now consider whether sharing early drafts or data with large models is safe. The risk is that a company could use those inputs to train a model that solves the problem before the researchers can publish their findings. This dynamic encourages secrecy, which runs counter to the established norms of academic collaboration.
