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Articles about Google DeepMind from podcasts, articles and papers, with links to the original sources.
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.
Nvidia's chief executive declared AGI achieved on September 6 while announcing more GPU capacity, and the Moonshots panel split between calling the label meaningless and calling the underlying capability the most important moment in history. A second claim on the same show — that OpenAI's agents now do 3.1 days of research work per human day — comes from an internal report that measures how long agents ran, not how much research they finished.
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, the panel read out a new leaderboard result: Google's Gemini 3.7 Flash on top of the AA-AnalystAgent benchmark with 60%, ahead of Claude Opus 5 at 54%. Diamandis called it proof that Google is back; Alex argued the score measures repeated reliability on spreadsheet analysis rather than frontier capability, and blamed Google Search for pushing Gemini toward speed and determinism. Emad Mostaque agreed the model was decent but said Google's problem is institutional, not a shortage of chips.
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.