OpenAI reportedly solved a Millennium Prize Problem using 10,000 agents, researcher says
In a recent podcast, OpenAI researcher Noam Brown discussed a system of about 10,000 AI agents that consumed roughly 130 billion tokens over 88 hours to tackle a Millennium Prize Problem. Brown emphasized that the breakthrough stems mainly from a very powerful underlying model rather than the multi‑agent architecture itself, and that the parallel‑agent approach helps scale test‑time compute…
Key points
- OpenAI allegedly used 10,000 agents processing 130 billion tokens in 88 hours to solve a Millennium Prize Problem.
- Performance scales sub‑linearly: four agents halve time at double cost; 16 agents give diminishing returns.
- Brown says the core success comes from a very strong base model, not primarily from the multi‑agent setup.
Brown described how performance improves when multiple agents work together: four agents roughly halve the time at double the cost, while 16 agents give diminishing returns depending on the task. He noted that math problems appear highly parallelizable, whereas tasks like novel writing are not. The conversation also touched on the rapid progress in AI‑driven mathematics, with models moving from high‑school benchmarks in 2024 to International Math Olympiad gold in 2025, and now solving open problems faster than expected. Brown warned that the science of scaling to tens of thousands of agents is still nascent, and that measuring the exact speedup from 10,000 agents remains difficult.
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The story so far
5 episodes →- OpenAI reportedly solved a Millennium Prize Problem using 10,000 agents, researcher saysthis story
Dwarkesh's podcast with noam brown of openai
dwarkesh.com · 18 September 2026
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- OpenAI’s Noam Brown reveals how AI agents solved a Millennium Prize Problem in 88 hoursPress · cryptobriefing.com ·
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