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Articles about AI scientific discovery 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.
On Moonshots, Peter Diamandis and his panel seized on an exploratory analysis of rentosertib, a drug whose target and molecule were chosen by Insilico Medicine's AI. In 42 patients with a fatal lung disease, blood-protein "aging clocks" shifted a few years younger by week four. The clocks tied to mortality risk did not move significantly, the signal plateaued by week 12, and the study's authors call the result a biomarker finding, not evidence of a longer life.
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.
Philip Johnston spent six months failing to find a cheap trajectory to the nearest star system. A research campaign at the AI physics startup PSI, run on roughly 10 billion tokens and five or six hours of human time, returned an unintuitive answer: slow the spacecraft down first and let it fall toward the sun. The resulting Fermi Explorer mission proposes a 100-kilogram probe, a sub-$15 million budget, a launch by the end of 2029 and a journey of roughly 77,500 years.
On The Diary of a CEO, investor David Friedberg described a cell as a city of 10 billion workers and argued that AI lets researchers screen a million protein ideas on computers before touching a lab bench. His worked example is partial epigenetic reprogramming — and a June 2026 announcement shows that work has reached a first safety trial in human eyes, not restored sight.
GenBio AI's AIDO Cell simulates a human cell that holds its state across a sequence of interventions, and the Moonshots panel watched a demo and began sketching the end of medicine. The article explains what the simulator does today — prioritizing experiments in two prototype cell lines, with laboratory validation of novel predictions still underway — and separates that from the panel's proposals: an AlphaGo-style search from diseased to healthy cells, open public biology data, and frontier labs paying for all of it.
Merck and Moderna said on August 19 that intismeran autogene, an mRNA therapy manufactured separately for every patient, met its main endpoints alongside Keytruda in a 1,137-patient melanoma trial. On the Moonshots podcast, the panel walked through the sequencing-and-machine-learning workflow behind it and argued about whether drug regulation can cope with a treatment that is different for each person.
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.
Bernie Sanders invoked the AI labs’ own safety commitments in demanding a development pause. On Moonshots, Emad Mostaque and the panel argued instead for controls on biological synthesis and faster detection of threats—but a proposal to monitor every AI prompt raised a different question: how much privacy would those defenses cost?