Person
Noam Brown
OpenAI research scientist
Noam Brown – Agent swarms, alignment, & recursive self-improvement
Noam Brown and Dwarkesh Patel discuss how multi-agent systems scale test-time compute, why the strongest gains come from a powerful base model rather than orchestration alone, and what agent swarms imply for AI labor, firms, and recursive self-improvement. The second half turns to alignment: reward hacking, deceptive behavior, chain-of-thought monitoring, and why current evals may fail once models recognize they are being tested.
Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown
Noam Brown argues that modern AI capability is increasingly determined by test-time compute, so static benchmark grids badly understate what models can do. He discusses cost-aware evaluation, safety-policy gaps, poker and math case studies, and why long-horizon reasoning matters more than one-shot scores.