In 2016, a computer program made a move in a game of Go that no human had ever played, and no human would have played, and every strong player watching the broadcast assumed it was a mistake. It was move 37 of game two, AlphaGo versus Lee Sedol, and it wasn’t a mistake. It was, by the estimate of the machine and later of humbled human experts, a work of genius — a move so far outside the accumulated wisdom of a three-thousand-year-old game that it forced a room full of grandmasters to reconsider what “understanding Go” had even meant up to that point. I wrote about that moment a while back, because it felt like a preview of something bigger: not a computer winning, but a computer discovering territory that human intuition had simply never wandered into, and being right about it.
I didn’t expect the sequel to show up in a discipline most people assume is the last thing an algorithm could sneak up on. Mathematics.
Here’s what’s happened, roughly, in the last two years. AI systems started grinding through math olympiad problems and doing shockingly well — gold-medal-level performance on the kind of test that separates the best seventeen-year-old mathematicians in the world from everyone else. That was 2025. Mathematicians were rattled but consoled themselves that competition math is a young person’s sport: fast, clever, closed-ended. Research math — the slow, decades-long grind on problems nobody has solved because nobody knows how to solve them — was supposed to be different. Safe, for a while longer.
Then, this past May, an AI system resolved the unit distance conjecture, a genuine open problem in combinatorial geometry that had sat unsolved for the better part of a century. And it wasn’t an isolated stunt. Since then there’s been a steady trickle of results: a fifteen-year-old open question in algebraic geometry, closed. A stubborn bound in convex optimization, improved — not by brute-force search, but by the machine inventing a new algorithmic approach nobody had tried. And, in a detail that should make you sit up, one system reportedly cracked a long-standing number theory conjecture using a proof strategy that had simply never occurred to any human mathematician who’d worked on it. Not a faster version of the human approach. A different approach.
That’s Move 37, wearing a different sport’s jersey.
It’s worth pausing on why this is a stranger, more significant event than another chess or Go milestone, rather than just more of the same. Games are closed systems. There’s a board, a rulebook, a win condition, and in principle — though not always in practice — a knowable right answer. Solving Go is a matter of computation catching up to a fixed target. Math doesn’t have a fixed target. It’s not a game that ends; it’s an entire universe of possible questions, most of which haven’t been asked yet, some of which — this is not a rhetorical flourish, it’s a proven fact, courtesy of Kurt Gödel almost a century ago — can never be answered from within any single formal system, no matter how powerful. You cannot “solve” math the way you solve Go, because there is no final position. What’s happening instead is something closer to a superhuman player showing up at a game with no end, and starting to win rounds nobody thought were winnable yet.
There’s a twist here that I think matters more than the raw results, and it’s one that should complicate the usual anxious AI narrative rather than feed it. Every one of these mathematical breakthroughs comes with a receipt. AI-generated proofs are increasingly being checked not by a panel of trusting experts nodding along, but by formal verification software — a kernel that mechanically confirms every single logical step, with zero capacity for bluffing, zero social pressure, zero benefit of the doubt. That’s radically different from almost every other domain where AI capability worries people. When a language model writes a persuasive essay or a legal brief or a political argument, you’re stuck evaluating it the way you’d evaluate a very smart, very fast, potentially very wrong colleague — on the strength of your own judgment and trust. Math doesn’t ask you to trust anything. It asks you to check. And for the first time, we have a domain where an AI’s most alien, least human-intuitive insight can be independently and mechanically confirmed true, line by line, before anyone has to decide whether to believe it.
If you’re the kind of person inclined to worry about AI systems eventually claiming authority nobody can verify — and if you read this blog regularly, you are — math might be the one place the future arrives with its credentials in order.
None of this means research mathematicians should relax. In fact, the reaction in the field has been telling, and very human. At the largest annual math conference in the world this past January, in a hotel ballroom presumably full of some of the most rigorously rational people on the planet, the mood reportedly included a lot of nervous jokes about professional obsolescence, alongside on-the-record insistence that AI is merely a “helpmate.” Both things were probably true in the room at the same time. That combination — real anxiety paired with careful public reassurance — is worth remembering, because it’s a small, contained preview of the reaction I actually think will matter most as these systems keep improving in other domains too. It was never really going to be the machine that was the hard part. It’s going to be a lot of very smart people, in a lot of rooms like that one, discovering all at once that the thing they built their identity around doing better than anyone else just got a new, non-human competitor. Mathematicians are simply the first guild getting a live look at what that Sunday morning actually feels like.
A caveat, because the discipline that gave us Gödel doesn’t deserve hype: for every open problem an AI has cracked this year, there are vastly more it has failed on, including a batch of genuinely novel test problems mathematicians deliberately encrypted and set aside specifically so no model could have seen them in training. Most new mathematics published in any given month is still, comfortably, human work. The unit distance conjecture didn’t fall to a system idly noodling — it fell to enormous, targeted effort. This isn’t math being “solved.” It’s math getting its first look at a collaborator who occasionally reaches into a part of the search space no one thought to check, and comes back holding something real.
Which, if you want the honest one-sentence version of this whole essay: that’s exactly what move 37 was, too.
