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Sébastien Bubeck

Researcher, OpenAI · as of 2026-10-04 · source

@SebastienBubeck · Wikipedia

Former Microsoft VP of AI research; at OpenAI he leads AI-for-mathematics work, including the disputed 2026 Navier-Stokes blow-up claim.

News mentioning Sébastien Bubeck (5)

  1. OpenAI claims a Millennium Prize problem: 10,000 AI agents prove forced Navier–Stokes blow-up; priority dispute erupts ★★★★★

    On 8 Sep 2026 OpenAI released a 166-page paper and a Lean formalisation proving that the 3D incompressible Navier–Stokes equations with a smooth external force can develop a finite-time singularity from smooth initial data. This fits option (C) of Fefferman's official Clay problem statement. About…

  2. OpenAI hosts a closed-door summit of about 40 mathematicians on the future of mathematics ★★★

    In early August 2026, shortly after its "ten advances in mathematics" post, OpenAI hosted about 40 mathematicians at its San Francisco offices to discuss what will be left for human mathematicians if AI surpasses them. Sébastien Bubeck asked Daniel Litt to describe "the future we'd all like to…

  3. New Fields Medalist Jacob Tsimerman takes leave from Toronto to work on AI safety at OpenAI ★★★

    After receiving the 2026 Fields Medal at ICM Philadelphia on July 23, 2026, University of Toronto mathematician Jacob Tsimerman said at a press conference that he is taking leave to join OpenAI as an AI-safety researcher in San Francisco. He called AI safety the most important issue of the time…

  4. OpenAI publishes 'Early science acceleration experiments with GPT-5', including four new math results ★★★

    On 20 Nov 2025 OpenAI and academic co-authors, including Timothy Gowers, released case studies of GPT-5 contributing to research in maths, physics, astronomy, computer science, biology and materials science. The paper includes four new mathematical results checked by the human authors. It frames…

  5. GPT-5 Pro proves an improved convex-optimisation bound, which humans had already surpassed ★★

    OpenAI's Sébastien Bubeck reported that GPT-5 Pro, in about 17 minutes, proved that gradient descent on L-smooth convex functions yields a convex sequence of function values for step sizes up to 1.5/L. The paper's v1 had proved it for 1/L. However, the authors' own v2 had already proved the tight…

Posts (4)

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