10 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
A declaration from mathematicians calling for action on the challenges AI poses to mathematics research, including proof, credit and publishing.
Why I recommend it: An advocacy statement signed by researchers — it states a position rather than presenting new evidence.
The Institute for Advanced Study's free biographical page on John von Neumann, who joined its School of Mathematics at 30 — covering his work on quantum theory, game theory, the stored-program computer architecture nearly every machine still uses, and his wartime work.
Why I recommend it: Good background for anyone meeting "von Neumann architecture" for the first time. It is written by the institution that employed him, so it reads as tribute — his role in nuclear weapons work and his later strategic writing get far less space than the mathematics.
A communal blog where working mathematicians — professors, PhD students, sceptics and enthusiasts alike — write about what AI is doing to their field: authorship, what counts as understanding, where papers will go, and whether the job changes. Recent pieces include Martin Hairer on why he joined the AGMAI advisory group and Jonny Evans on the choices ahead. Submissions are open to anyone in the field, any length.
Why I recommend it: Free to read and free to write for. Worth reading even if you never touch mathematics: it is one of the few places where a whole profession is arguing in public about what AI does to its craft, in its own words rather than a journalist's. These are individual opinions, not findings — the value is the range of them, and the disagreement is the point.
An independent group of nine mathematicians — including Timothy Gowers, Martin Hairer, Edward Witten, Ravi Vakil and Melanie Matchett Wood — formed to advise AI companies on how mathematical results produced by AI models should be presented and released. The site states its purpose, its members, and its current task: advising OpenAI on how to release a large batch of mathematical results the company says its internal model produced. There is an open form for anyone in the mathematical community to send input.
Why I recommend it: Free, and short enough to read in five minutes — a good example of what independent oversight looks like when it is written down. Read their own two caveats rather than mine: members take no payment and the group is independent of any AI company, but they also say plainly that they hold no decision-making power, so the companies remain free to ignore them. The group formed after OpenAI approached some members about an in-house advisory board and they chose to sit outside it instead.
OpenAI is funding an independent advisory group of mathematicians, hosted at the Institute for Advanced Study, to advise on how AI-generated math results are shared.
Why I recommend it: Announced alongside a claim that an internal model solved 100+ open math problems in a month. The group is explicitly not allowed to slow the pace of research — read it as a communications channel, not a brake. Members include Terence Tao and Timothy Gowers.
Personal site of Andrew Critch, mathematician and AI safety researcher, collecting his papers, talks and writing on multi-agent risk and existential safety.
Why I recommend it: Denser than most safety writing and worth the effort. Note the site refuses automated visits, so the picture here may be a screenshot.
The institute behind the Millennium Prize Problems, with free lecture videos, published proofs, historical mathematics archives and details of its research programmes.
Why I recommend it: Free access to serious mathematics — the lecture library alone is worth bookmarking if you are studying or teaching math.
Live posting for Citi's 2027 Markets Quantitative Analysis Summer Analyst role in New York, working on pricing models, risk analytics and quantitative research alongside trading desks.
Why I recommend it: If you have strong math, statistics or programming coursework, this is the quant route into markets. Expect technical questions on probability and coding, so prepare those before applying.
Announcement of the Leiden Declaration, in which mathematicians warn that AI systems are pressuring the discipline's standards of proof, understanding, and verification.
Why I recommend it: Every field is having this argument right now. Watching mathematics have it clarifies what "understanding" means in your own work.
Organization exploring the mathematical foundations of AI and improving public understanding of it.
