AI math isn’t mathing

TL;DR: Right now, the math world is in an uproar. The breakneck pace of AI-made proofs and other discoveries has left human mathematicians worried—not just about machines taking their jobs, but that something gets lost as math is automated. And it puts the field squarely in the middle of an existential anxiety other disciplines have already faced—or could soon.

What happened: Earlier this week, OpenAI posted 722 papers showing progress that an unreleased model had made on open math problems, grouped into 372 “families.” (Raise your hand if you’re also getting number fatigue already.)

But according to some mathematicians, this kind of mass unloading of findings does more harm than good and isn’t all that helpful to the field. It’s part of an escalating debate over the cost of AI’s rapid push into math. Last month, 28 Fields medalists (math’s closest thing to a Nobel Prize) signed a statement saying AI companies’ goals were “severely misaligned” with mathematicians’.

AI’s math speedrun: OpenAI’s dump this week is just the latest in a string of dizzying AI math progress, which has come with a fair share of controversy—including over the issue of proper credit and how much AI discoveries lean on human mathematicians’ prior work.

But the worry over machine-made math goes beyond a credit fight: Some mathematicians say AI’s “skip to the end” approach, as Quanta Magazine puts it, cuts out a crucial middle stretch of doing math. What’s often a long slog of working toward a proof can turn up new math tools and ideas along the way. And so far, critics argue, AI companies have put little effort into helping humans understand their models’ solutions, which leaves the field with less “fertile ground” to build on from each breakthrough, as one mathematician noted.

The revenge of the humans: Plenty of mathematicians are anxious and angry, and there’s even a perception that AI companies are engaging in “mobster behavior,” one math professor told Wired. Some have stopped including open conjectures in their papers so AI scrapers can’t grab them.

Bottom line: Software engineers have already had to rethink their place in a field AI is reshaping. Now it’s mathematicians’ turn to weigh how much human involvement is worth when AI can potentially get from Point A to B faster. And more industries—including your own—could start doing the same. —WK