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36:19ENThe Benchmark Grid Is Broken: Why Test-Time Compute Has Outrun How We Score Models진행자 Sarah Guo, Elad GilOpenAI researcher Noam Brown argues that static leaderboards hide how long a model is allowed to think — and that safety evals, research taste, and the release cycle have not caught up.
1:28:02EN20VC Roundtable: Open Models, AI Spending and the Cost of Competition게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate AI talent, model economics, consulting and whether labs should build their own chips.
44:52ENAn 80GB Wikipedia Cache Versus a 100GB Model That Holds the InternetEngram’s founders argue that stuffing a company into the context window is the expensive kind of memory—and that baking it into the weights can cut token use by two orders of magnitude.
1:34:29ENJacob Baskin on Building Superstore and Scheduling the HiveYaron Minsky, Jacob Baskin · Jane StreetThe Jane Street engineer traces the design choices behind a distributed analytical database and the challenge of allocating a growing research cluster.
15:12ENTwelve Open-Source AI Projects for Agents, Video, Coding and VoiceMatthew Berman · Forward FutureMatthew Berman surveys tools ranging from long-running agent frameworks to document OCR, skill security and local speech generation.
2:29:20ENThe Market Will Not Choose an Honest Model진행자 Nathan LabenzJournalist Robert Wright tells Nathan Labenz that AI is a selection problem, a geopolitical problem, and a moral test humanity may not pass.
1:16:37ENNikesh Arora on Enterprise AI, Cybersecurity and Model Economics게스트 Nikesh Arora · 진행자 Harry StebbingsPalo Alto Networks’ CEO discusses model depth, token costs, security and how leaders can adapt large organizations.
2:14:33ENA 90-Minute Ultimatum, an Emoji Wall, and a Rule: Don't Fight Washington진행자 Nathan LabenzZvi Mowshowitz reads Fable's system card, then Nathan Labenz stress-tests the export-control fight against lawyers, safety researchers, and builders who did not pause.
1:20:59ENFlexport CEO Ryan Petersen on Logistics, AI and Fundraising게스트 Ryan Petersen · 진행자 Harry StebbingsPetersen discusses the economics of global freight, automating operational work, venture financing and the lessons he has taken from building Flexport.
2:38:44ENTwenty-one months from an autonomous researcher, the Action Plan's author joins OpenAI진행자 Nathan LabenzDean Ball is building Strategic Futures as OpenAI's public timeline counts down to an AI research intern in three months and a full autonomous researcher in March 2028.
1:28:10EN20VC Roundtable: AI Policy, Software Valuations and Robotics게스트 Rory O’Driscoll, Everett Randle · 진행자 Harry StebbingsHarry Stebbings, Rory O’Driscoll and Everett Randle debate reported AI restrictions, enterprise software competition and the practical challenges of robotics.
45:00ENLip-Bu Tan took the hardest job in chips to save Intel진행자 Sarah Guo, Elad GilThe Cadence veteran and semiconductor investor tells No Priors how he is rebuilding Intel's culture, balance sheet, and foundry, and why he still wants a 10x from here.
38:45ENThe 25-agent town that became a Fortune 500 decision sandboxOn the day before Valentine's Day in a Stanford simulation, cafe owner Isabella decided to throw a party — no human wrote the plot.
1:35:16ENPerplexity CEO Aravind Srinivas on AI Agents, Compute and Competition게스트 Aravind Srinivas · 진행자 Harry StebbingsSrinivas discusses the future of AI products, infrastructure constraints, export controls and the challenge of building a business around model advances.
1:25:00ENMatan Grinberg on AI Coding, Token Costs and Building Factory게스트 Matan Grinberg · 진행자 Harry StebbingsFactory’s CEO discusses enterprise AI budgets, model choice, engineering culture and the uncertain effects of automation on work.
1:44:28ENFable launch week: a model that refuses, a two-year alignment clock, and a Twitter account handed over진행자 Nathan LabenzNathan Labenz’s AI in the AM highlights reel from June 2026 covers Anthropic’s new frontier model, the founding of Sequent, and a week that settled one argument: AI is not slowing down.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.진행자 Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
1:15:56EN20VC on IPO Expectations, Lean AI Startups and Investor Risk게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate reported listings, AI company economics and the changing cost of building a startup.
51:09ENBuild like the model will eat your harness in a yearLogan Kilpatrick, who leads Google AI Studio and the Gemini API, tells Sequoia's Sonya Huang why agent scaffolding is a 12-month asset, why coding already feels like narrow superintelligence, and why Google is optimizing for outcomes instead of eyeballs.
1:45:59EN87% of Workers Use AI. Only 13% Say the Company Got Better.진행자 Nathan LabenzGlean’s Work AI Index names the missing hours: botsitting, botshitting, and a hamster wheel of polished nonsense.
41:20ENA $4 Million Cabinet and a Trillion-Dollar Year: Jensen Huang Turns the AI Factory Into a Manufacturing StoryOn Sequoia's Training Data stage, NVIDIA's CEO walks a sixty-country audience from ChatGPT's parlor tricks to agentic work, a five-layer industrial cake, and why radiology hiring went up.
1:14:30ENRoman Chernin on AI Compute Demand, Inference and Infrastructure게스트 Roman Chernin · 진행자 Harry StebbingsNebius’s co-founder discusses customer demand, GPU economics, managed inference and the risks of a concentrated market.
57:42ENJacob Lauritzen on AI Engineering, Legal Work and Building Legora게스트 Jacob Lauritzen · 진행자 Harry StebbingsLegora’s CTO discusses code review, token budgets, enterprise adoption and how legal workflows might change.
1:22:36ENA Thousand Researchers, a Million AIs: Frontier Labs Start Talking About the Brakes진행자 Nathan LabenzThe first week of AI in the AM follows OpenAI's timeline for an ML-intern model this year and a human-level AI researcher by early 2028 — and the closed-door meeting where labs admitted they might one day need to slow down together.
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