No Priors: Artificial Intelligence | Technology | Startups
Why Traditional Benchmarks Fail Modern AI Models with OpenAI Research Scientist Noam Brown
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com.
Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners.
Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.
Show Notes
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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:
– Cold Open
– Noam Brown Introduction
– Why Benchmarks Are Broken
– Compute Budgets and Projections
– How Long Should Models Think?
– Benchmark-Maxxing
– Using Poker Bots as Evals
– Safety Evals When Model Capability Scales With Budget
– Release Cycle vs. Agent Runtime
– Latent Model Capability
– Limits on Recursive Self-Improvement
– Large-Scale Multi-Agent Coordination
– Competition at the Frontier
– Breaking the Benchmark Grid Equilibrium
– Why Benchmarks Should be Evaluated by Cost
– Conclusion
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