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Articles about AI mathematics from podcasts, articles and papers, with links to the original sources.
OpenAI's claimed Millennium Prize result used roughly 10,000 agents on a problem that was, as one entrepreneur on Moonshots put it, unusually easy to specify. The panel's argument: as the price of that kind of compute falls, the scarce skill becomes writing the target — and today's models, asked for ten ideas to cure cancer, produce a bad list.
Google DeepMind's AlphaGenome Atlas stores predicted molecular effects for every possible single-letter change in the human genome. On Moonshots, the panel called it the "bulk solution" to variant effect prediction, proposed it as a naming system that could help people with the same rare mutation find each other — and argued about why it is not really a lookup table.
OpenAI said on 8 September that an internal model, running roughly 10,000 agents for 88 hours, produced a forced blowup construction for the Navier–Stokes equations and a machine-checked proof of it. On Moonshots with Peter Diamandis, the panel worked through what the result is — a statement about idealized fluids, not a device — what it cost, and why the credit for it was contested within hours.
On Moonshots with Peter Diamandis, a panelist interrupted an argument about AI regulation to read a headline off his feed: Anthropic had formalized Fermat's Last Theorem. Anthropic's report describes dozens of agents working eleven days, about six billion output tokens and 30,300 intermediate theorems — plus a piece of bookkeeping software that stopped runs from losing track of their own work. The panel's takeaway was about how to narrow enormous machine output into one result you can build on.
A TIME report has Sam Altman expecting an internal system he would call AGI within four months, and OpenAI's chief scientist saying its unreleased Astra model has met an internal benchmark for an automated research intern. On the Moonshots panel, the label mattered less than a practical test: whether the next model can finally keep hold of what it has learned over a long job, instead of handing a summary to a successor and starting again.
After losing an MIT recruit to a Princeton doctoral program, the Moonshots panel argued that the next two or three years are the last window in which humans steering fleets of AI agents will be unusually valuable — and that a PhD spends that window badly. Then the objection arrived from their own side of the table: everyone there holds the degrees they are telling young people to skip.
A colleague told Brian Greene that physicists should choose their final research problems before AI takes over. Greene finds the prospect both exciting and terrifying. His account of three kinds of scientific creativity explains why he thinks machines could make foundational discoveries—and why collaboration might change, rather than simply erase, the human scientist’s role.