arXiv · 2609.32028
This is a personal essay about how I see math, what I value about it, and what it means to me.
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Larry Guth. 2026-09-25. What math means to me. https://arxiv.org/abs/2609.32028
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This declaration calls for action to address the challenges posed by the use of artificial intelligence within mathematics research. Technological developments have repeatedly transformed the practice of mathematics. Recent artificial intelligence technologies, including symbolic and neural methods for the generation and formalization of mathematics, may already have initiated a significant chapter in this long history. Among researchers, artificial intelligence has produced a wide range of reactions: enthusiasm for its potential to yield new discoveries; intimidation by the pace of developments; indifference to these rapid changes; and concern for the implications, both for mathematics and in wider society. Mathematicians have a choice about whether and how to adopt artificial intelligence in the conduct of their research. They also have a responsibility to ensure the continued flourishing of the discipline. This Declaration calls upon mathematicians to exercise this responsibility, and provides recommendations for individuals, institutions, government, and industry. Although we adopt the perspective of mathematical research, much of what we write applies equally to other aspects of mathematics. This includes work in the broader mathematical sciences, education, mentoring, publishing, funding, science policy, and use of mathematics in the wider world.
On 8 September 2026 OpenAI announced a proof of finite-time blowup for the three-dimensional Navier-Stokes equations with smooth data and forcing: 166 pages produced in 88 hours by ten thousand agents, certified by 616,000 lines of Lean, and read in full, at the moment of this writing (20 September 2026), by no human being. This essay asks what such an artifact -- text, certificate and announcement -- is, and what follows from accepting it as a proof. Mathematics was the Enlightenment's existence proof of autonomous reason: for three centuries every certified theorem could be understood by anyone who followed its demonstration, and the distance between the two was zero by construction. A certified proof no one can follow reopens that distance, and a community that accepts it adopts, without a vote, the constitution Hobbes drafted for the Leviathan, in which authority and not truth makes the law. The essay distinguishes the demonstrated from the revealed (certified); names, in Panofsky's terms, the coming age a Middle Ages in reverse and its authority a subhuman superintelligence; and locates the turning point not in what the machine produces but in what we accept. Since acceptance is the one sovereign sanction the companies cannot manufacture, it proposes a covenant in place of either boycott or capitulation: the community's cooperation given to that producer which strictly observes its practices of legibility, disclosure and responsibility, withheld from any that does not, and the covenant kept plural, with the history of the Indigenous nations among rival empires as its guide. The Sirens of the title promise knowledge, not understanding. Daemmerung is the light at both ends of the day, and whether this Aufklaerungsdaemmerung is a dusk or a dawn depends on what is done at the moment of acceptance, which is not yet past.
Powerful artificial intelligence is weakening the traditional relationship between mathematical output and evidence of mathematical expertise. In particular, the production of an original theorem or a polished dissertation can no longer, by itself, certify the intellectual formation of its nominal author. I argue that graduate mathematical education should therefore be organized around the formation of intellectual agency: internal technical competence, mathematical judgment, understanding, and responsible participation in a shared intellectual culture. I distinguish productive from premature cognitive offloading, propose complementary independent and AI-augmented modes of training, and suggest a corresponding reformulation of the role of the PhD thesis and dissertation defense. More broadly, I argue that academic mathematics should understand itself increasingly as an institution for the reproduction and stewardship of human mathematical expertise rather than primarily as a mechanism for producing theorems.