When a new AI model drops, it’s judged based on a static benchmark grid that doesn’t account for how long the model is allowed to think. How then should we measure a model’s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry’s traditional benchmark grids are broken, and how large-scale test-time compute is fundamentally changing how models are evaluated. Noam explains how, if properly scaffolded, today’s models can reason for weeks or even months on complex tasks. He also discusses real-world implications of test-time compute, from building poker solver bots to disproving legendary math conjectures. Together, they also unpack the large gaps in current AI safety frameworks, explore the bottlenecks for recursive self-improvement, and look ahead at the future of multi-agent collaboration and global knowledge sharing.
Read more: ⁠Implications of Large-Scale Test-Time Compute⁠
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Chapters:
00:00 – Cold Open
00:43 – Noam Brown Introduction
01:23 – Why Benchmarks Are Broken
04:19 – Compute Budgets and Projections
05:34 – How Long Should Models Think?
06:47 – Benchmark-Maxxing
08:34 – Using Poker Bots as Evals
11:26 – Safety Evals When Model Capability Scales With Budget 
14:41 – Release Cycle vs. Agent Runtime 
17:06 – Latent Model Capability 
20:59 – Limits on Recursive Self-Improvement
27:09 – Large-Scale Multi-Agent Coordination 
29:11 – Competition at the Frontier 
31:51 – Breaking the Benchmark Grid Equilibrium 
33:29 – Why Benchmarks Should be Evaluated by Cost
36:18 – Conclusion

Why Traditional Benchmarks Fail Modern AI Models with OpenAI Research Scientist Noam Brown