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Articles about Test-time compute 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.
Jakub Pachocki, OpenAI's chief scientist, published an essay saying no lab has solved alignment and monitoring well enough to keep scaling at full speed, and called for voluntary slowdowns until shared safety thresholds exist. On Moonshots, four panelists agreed the systems are extraordinary and disagreed with almost everything else in his argument.
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.
OpenAI's GPT-6 Astra nearly saturates the interactive ARC-AGI-3 benchmark and leads Epoch AI's composite capability index, yet sits third on Artificial Analysis's suite, behind Claude Fable 5.1 and Muse Spark. On Moonshots EP #286, the panel works through what each ruler measures — and argues that Astra's real target was doing tasks with fewer output tokens, so a model can drive a desktop at conversational speed.
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 Training Data, Parag Agrawal explains how his company Parallel entered web search without first building a giant index: it launched a search agent that crawled after a request arrived, replaced outsourced human data collection for insurance, sales and finance customers, and treated the index as a latency optimization to be grown later. He describes the agent-specific architecture behind it, the 200-millisecond Turbo mode Parallel announced in July, and a Google Cloud deal that puts Parallel Search beside Google Search as a grounding option.