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","datePublished":"2026-07-13","dateModified":"2026-07-13","author":{"@type":"Person","@id":"https:\/\/blog.terabox.com\/author\/flextech-admin\/#Person","name":"flextech-admin","url":"https:\/\/blog.terabox.com\/author\/flextech-admin\/","image":{"@type":"ImageObject","@id":"https:\/\/secure.gravatar.com\/avatar\/ad516503a11cd5ca435acc9bb6523536?s=150&d=mm&r=gforcedefault=1","url":"https:\/\/secure.gravatar.com\/avatar\/ad516503a11cd5ca435acc9bb6523536?s=150&d=mm&r=gforcedefault=1","height":96,"width":96}},"publisher":{"@type":"Organization","name":"terabox","logo":{"@type":"ImageObject","@id":"http:\/\/blog.terabox.com\/wp-content\/uploads\/2021\/11\/logo\u4ea7\u54c1\u540d-\u7ad6\u7248.png","url":"http:\/\/blog.terabox.com\/wp-content\/uploads\/2021\/11\/logo\u4ea7\u54c1\u540d-\u7ad6\u7248.png","width":900,"height":900}},"image":{"@type":"ImageObject","@id":"https:\/\/img.youtube.com\/vi\/AZrU6y3pUcU\/maxresdefault.jpg","url":"https:\/\/img.youtube.com\/vi\/AZrU6y3pUcU\/maxresdefault.jpg","height":"","width":""},"url":"https:\/\/blog.terabox.com\/insights\/noam-brown-test-time-compute-ai-benchmarks","video":{"@context":"http:\/\/schema.org\/","@type":"VideoObject","@id":"https:\/\/www.youtube.com\/watch?v=AZrU6y3pUcU#VideoObject","contentUrl":"https:\/\/www.youtube.com\/watch?v=AZrU6y3pUcU","name":"Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown","description":"When a new AI model drops, it\u2019s judged based on a static benchmark grid that doesn\u2019t account for how long the model is allowed to think. How then should we measure a model\u2019s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry\u2019s 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\u2019s 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.\n\nRead more:\n\n- Implications of Large-Scale Test-Time Compute\u2060: https:\/\/x.com\/polynoamial\/status\/2064210146558136827?s=20\n\nSign up for new podcasts every week. Email feedback to show@no-priors.com\n\nFollow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @polynoamial | @OpenAI\n\nChapters:\n00:00 \u2013 Cold Open\n00:43 \u2013 Noam Brown Introduction\n01:23 \u2013 Why Benchmarks Are Broken\n04:19 \u2013 Compute Budgets and Projections\n05:34 \u2013 How Long Should Models Think?\n06:47 \u2013 Benchmark-Maxxing\n08:34 \u2013 Using Poker Bots as Evals\n11:26 \u2013 Safety Evals When Model Capability Scales With Budget\u00a0\n14:41 \u2013 Release Cycle vs. Agent Runtime\u00a0\n17:06 \u2013 Latent Model Capability\u00a0\n20:59 \u2013 Limits on Recursive Self-Improvement\n27:09 \u2013 Large-Scale Multi-Agent Coordination\u00a0\n29:11 \u2013 Competition at the Frontier\u00a0\n31:51 \u2013 Breaking the Benchmark Grid Equilibrium\u00a0\n33:29 \u2013 Why Benchmarks Should be Evaluated by Cost\n36:18 \u2013 Conclusion","thumbnailUrl":["https:\/\/i.ytimg.com\/vi\/AZrU6y3pUcU\/default.jpg","https:\/\/i.ytimg.com\/vi\/AZrU6y3pUcU\/mqdefault.jpg","https:\/\/i.ytimg.com\/vi\/AZrU6y3pUcU\/hqdefault.jpg","https:\/\/i.ytimg.com\/vi\/AZrU6y3pUcU\/sddefault.jpg","https:\/\/i.ytimg.com\/vi\/AZrU6y3pUcU\/maxresdefault.jpg"],"uploadDate":"2026-06-26T10:25:40+00:00","duration":"PT36M19S","embedUrl":"https:\/\/www.youtube.com\/embed\/AZrU6y3pUcU","publisher":{"@type":"Organization","@id":"https:\/\/www.youtube.com\/channel\/UCSI7h9hydQ40K5MJHnCrQvw#Organization","url":"https:\/\/www.youtube.com\/channel\/UCSI7h9hydQ40K5MJHnCrQvw","name":"No Priors: AI, Machine Learning, Tech, & Startups","description":"Your guide to the AI revolution, co-hosts Elad Gil and Sarah Guo talk to the world's leading engineers, researchers and founders about the biggest questions:\n\nHow far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What\u2019s happening in state-of-the-art in research? Email feedback to show@no-priors.com.\n\nSarah 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.\n\nElad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). 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