Mathematics in the Age of AI Terence Tao — essay for the ICM 2026 Proceedings, posted August 17, 2026 (12 pages) read it here Tao does something unusual in this essay: he refuses the argument everyone else is having. Rather than debating whether AI will ever reach research-level mathematics, he simply assumes that it will, and then turns to the question that assumption makes urgent — what are the goals and values of mathematical research actually? What is it that mathematicians are doing when they do mathematics? Using problem-solving as his lens, he works through what would remain if the answers themselves became cheap. The essay is short, readable, and written for a general mathematical audience rather than a specialist one. It is the rare piece on AI and mathematics that spends almost no time on capability claims and almost all of it on purpose. For anyone who teaches, the framing lands close to home. The question Tao is asking about his discipline is the question a mathematics teacher has always had to answer for a student holding a calculator, a solutions manual, or now a chatbot: if the answer is available, what are we here for? CREATIVE FORCE: This is the clearest statement yet that mathematics was never primarily a machine for producing answers. When a Fields Medalist grants the machines the answers and finds the discipline still standing, what he is pointing at is the creative act — the framing of a problem worth solving, the taste that distinguishes an interesting question from a merely hard one, the sense of what a proof is for beyond its truth value. _________________________ Report of MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Assessment Massachusetts Institute of Technology — institutional research report, released August 13, 2026; widely covered August 25–28 read it here MIT convened a committee to look honestly at what AI has done to undergraduate teaching, and the committee came back with a finding the institution could not manage around: AI can now credibly complete most undergraduate assignments. From there the report catalogs the damage — faculty who cannot tell what students have actually learned, students who are more isolated than before, and a corrosion of trust running in both directions. One line has been quoted everywhere since publication: “Such an underground river of mutual suspicion is no foundation for a healthy classroom.”

Are We Thinking Correctly About AI Intelligence? The Joy of Why (Quanta Magazine) · Steven Strogatz & Janna Levin with Melanie Mitchell · Aug 20, 2026 · Podcast, 52 min · Read it here ___________________________ Melanie Mitchell of the Santa Fe Institute makes a case that should interest anyone who has ever graded a paper: we do not currently have good methods for finding out what these systems actually understand, and the field of AI has largely not been trained to build them. Her proposed fix borrows from developmental and comparative psychology — the disciplines that figured out how to probe the minds of babies and animals, which cannot explain themselves either. She offers six principles for assessing machine cognition, including watching for our own tendency to read humanity into anything that speaks fluent English, building genuine control conditions, and treating failures as more informative than successes. Her anchor example is Clever Hans, the early-1900s horse who appeared to do arithmetic until a psychologist ran the obvious control and discovered the animal was reading the questioner's face. The horse was a genius, Mitchell notes — just not at what everyone assumed. The last ten minutes are the reason to listen if you find some time this week. Strogatz says plainly that he believes humans will not be at the cutting edge of mathematics much longer — and then argues, without flinching, that this does not empty the work of meaning. He compares it to what he discovered about mathematics in high school: real discoveries to him, not to the world. Levin pushes back, distinguishing making a discovery from understanding one. CREATIVE FORCE: This is the most direct challenge to "mathematics as a creative force" that we have run in the Dispatch, and it comes from people who love the subject. If creativity in mathematics is defined as producing a surprising valid connection, machines are already doing it, and the question is settled unhappily. Mitchell and Strogatz both point somewhere better: toward competence over performance, toward theory-building over problem-solving, toward the embodied and metaphorical origins of mathematical ideas that Lakoff and Núñez wrote about. Strogatz even revives von Neumann's warning that mathematics drifting too far from its source in the physical world becomes sterile — and suggests this could be a great era for pure mathematics precisely if it turns back toward nature. For our classrooms, the practical inheritance is Mitchell's competence-versus-performance distinction, which she and Strogatz illustrate with the student in office hours who memorized the problem and its solution but cannot handle a variant. We have always known that student. We now have a much more urgent reason to be able to tell them apart, and a shared vocabulary to do so.

Maths Shows Why Your Vote May Not Count the Way You Think It Does Read it here • University of Cambridge — research news, July 31, 2026 Frederik Ravn Klausen of Cambridge and Sebastian Tim Holdum of the University of Copenhagen have proved an impossibility theorem for electoral systems, and the result is unusually easy to state. A fair national election, they argue, would satisfy three conditions at once: regionality, meaning everyone who wins a local seat keeps it; proportionality, meaning national seat totals match national vote shares; and a fixed parliament size. Once enough parties are competing, no system can deliver all three. As Klausen puts it, this is not corruption or conspiracy — it is simply mathematics. The work began with the 2022 Danish election, where for the first time in seventy-five years the seat count stopped tracking the vote count — and the resulting single-seat discrepancy decided the election. The paper traces the same strain elsewhere: Germany's Bundestag swelled from 598 seats to 736 before 2023 reforms capped it, at the cost of guaranteed local representation, and the UK's 2024 general election was its least proportional ever, with Labour taking 63% of the seats on 33.7% of the vote. Crucially, the authors do not stop at the negative result. They propose an algorithm called geographically ranked guaranteed proportionality, which sets national totals before any voting occurs and then allocates seats by ranked local performance. It works — and it pays for itself in regionality, since a candidate can win comfortably and still lose the seat once the party's quota is spent. The paper appears in the Annals of Operations Research. CREATIVE FORCE: This is what a constraint looks like when it is honest. Most of what students meet in school mathematics is a problem with an answer waiting at the end; here the mathematics arrives to tell us that a thing we badly want is not available, and then — this is the creative turn — goes to work on what we can have instead. The impossibility is not the end of the inquiry; it is the beginning of design. For the classroom, it is a rare gift: an accessible impossibility students can hold in their heads all at once and feel the pinch of before anyone writes a proof. And the follow-on question is the better one — given that something must give, which one, and who decides?

Eric was formed by having room. He was given a level of autonomy in his first year that essentially no first-year teacher receives today, and he used it to become the kind of educator who is still, twenty-six years later, describing seventh-grade lesson planning with real pleasure. The room made him. He is now the person removing the room. And he is right to. Full link to the article here: https://www.linkedin.com/pulse/nobody-knew-dr-kevin-berkopes-ytitc

Monique Harrison, Ed.S. was walking the aisles of a classroom that was not hers. The kids in that room, she noticed, had not seen many adults who looked like her. She was there because the teacher at the front had been assigned mathematics she did not know, and helping with that is Monique's job. One little Black girl looked up at her and said it. Math don't like me. Monique's full story here: https://www.linkedin.com/pulse/math-dont-like-me-dr-kevin-berkopes-id3cc