Tristan Buckmaster’s guest essay on Terence Tao’s blog
AI companies ‘strip mine’ mathematicians’ data; calls for opt-in training, zero-retention deals and open-weight infrastructure
Confirmed
The takeaway
On Oct 21, 2026 Terence Tao’s blog published a guest essay by NYU mathematician Tristan Buckmaster arguing that mathematics is at a “Deep Blue–Kasparov moment” and will be “unrecognizable” within a few years.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- 3 of 5
- Last verified
- 22 October 2026
Your AI and this story
- GPT-6 Astra174 days after its cutoff
- Claude Opus 5.5113 days after its cutoff
- Gemini 4 Argon204 days after its cutoff
- Grok 4.7143 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 113 days before it.
Key facts
- Expands on Buckmaster’s Oct 19 talk at the Institute for Advanced Study; Tao notes the post was converted from another format using AI, and Buckmaster discloses LLM help with editing
- On cost: OpenAI’s Navier–Stokes proof ‘reportedly required the coordination of 10,000 agents over 88 hours, at an estimated cost in the millions’
- Charge: ‘AI companies, who strip mine our data, including unpublished work, private notes and research grant applications, have an egregious conflict of interest in competing against their own customers with unlimited resources and internal models unavailable to the public’
- On the Oct 6 release: manuscripts ‘mostly unreadable and in some cases false’; the average-three-hours claim was ‘essentially meaningless’ because failed attempts and earlier agent work were not counted; no due diligence on building on customers’ unreleased work, ‘some of whom were graduate students and postdocs who were literally days away from releasing their own work’
- Says OpenAI’s Millennium-problem push was, by OpenAI’s own account, spurred by rumours that a rival had solved two of them; after Navier–Stokes, teased further Millennium problems ‘yet they achieved none’
- Proposals: collective enterprise agreements with zero-data-retention and audit clauses for all academics; opt-in (not opt-out) training on user data; ban on absorbing a whole session when a user rates one reply; independently funded open-weight infrastructure; credit for exposition, shared tools and detailed AI disclosures
- ‘Chess did not end in 1997. Mathematics will not end in 2026.’
What happened
Buckmaster, whose Euler blow-up result with Levent Alpöge came out the night before OpenAI’s Navier–Stokes announcement and who was in a priority dispute with OpenAI, set out the case that AI labs’ data practices threaten academic mathematicians. Publication on Tao’s blog gives it wide reach in the field.
Why it matters
It is the most detailed public statement from a leading mathematician involved in the 2026 AI-maths results about privacy, credit and the conduct of labs, with concrete policy demands. His claims about OpenAI are his own and have not been answered in the sources here.
Sources
1 source from 1 site. Numbers match the chips in the text.
1 source: 1 primary
Primary
- Terence Tao’s blog: Mathematics is a subject for the young (guest post by Tristan Buckmaster)terrytao.wordpress.com, official
Changes
- Filed (Terence Tao’s blog; coordinator request)