OpenAI Irritates Mathematicians Once More
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| Source: Mastodon | Original article
OpenAI's latest moves have again drawn criticism from mathematicians, who say the company's behavior resembles that of a mob.
OpenAI’s upcoming rollout of more than 100 solutions to long‑standing mathematical problems has reignited a simmering feud with the research community. The company announced the release as part of a broader batch that also covers 377 problems, a follow‑up to the 700 preprints of proofs and counterexamples it disclosed earlier this month [as we reported on Oct 7, 2026].
The latest wave has provoked a fresh outcry because of how the work was generated and credited. NYU professor Tristan Buckmaster told WIRED that OpenAI pressured him not to acknowledge a collaborator from rival lab Anthropic after the pair made progress on a difficult Euler‑equations question. Buckmaster’s allegation that OpenAI subsequently published its own version of the proof has deepened mistrust.
Mathematicians fear that the models powering these breakthroughs have been trained on their own unpublished code and preprints without permission, a concern echoed in an open letter signed by 25 leading scholars in early September 2026. The letter accused OpenAI and other AI firms of scraping proofs, preprints and problem sets without consent, credit or compensation, and warned that such practices could erode the culture of open research.
Why it matters is twofold: first, the dispute raises fundamental questions about intellectual‑property rights and attribution when AI systems produce novel scientific results; second, it threatens to polarise a community that has been cautiously embracing AI tools like Codex, fearing that their contributions are being harvested for commercial gain.
The next weeks will reveal whether OpenAI will adjust its attribution policies, issue a formal apology, or double down on the releases. Academic institutions are likely to scrutinise their own data‑sharing agreements, and regulators in the EU and elsewhere may be asked to weigh in on the balance between open science and proprietary AI development. The outcome could set a precedent for how AI‑generated research is credited across all scientific fields.
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