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53:44ENThe Biggest Bug in Today’s Models Is That the Weights Never ChangeTuring Award winner Rich Sutton and Khurram Javed founded Oak Lab to build agents that keep learning after they ship — and they call synthetic data a big mistake.
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.
2:06:16ZH → EnglishTan Jie on Data Bottlenecks, Generalist Robots, and the Road to Real-World RoboticsGuest 谭捷 · Host 张小珺Google DeepMind robotics researcher Tan Jie traces his path from computer graphics to robotics and from reinforcement learning to Gemini Robotics. He argues that data scale and coverage, cross-embodiment transfer, real-time reasoning, and safety remain key to bringing robot foundation models into the real world.
41:36ENFour Months to Drive Tokyo: How Wayve Bet the Company on One End-to-End NetworkWayve CEO Alex Kendall tells Sequoia’s Training Data why AV 2.0 ditches HD maps and hand-written stacks for a foundation model that can adapt to a new car and a new country in weeks.
32:59ENA Hundred People, a Billion in Revenue, and Zero Venture MoneyHost Sarah Guo, Elad GilSurge AI founder Edwin Chen on why quality human data is a poetry problem, not a bounding-box problem, and why leaderboards keep training models to write clickbait.
45:21ENApplied Intuition Is Worth $15 Billion, Hasn't Spent Its Capital, and Wants to Be the OS for Cars and TanksHost Sarah Guo, Elad GilOn No Priors, Qasar Younis and Peter Ludwig explain the Microsoft playbook for vehicles, why they waited for autonomy techniques to converge, and why Waymo's business model is the question that follows the demo.
38:25ENA thousand airplanes will never train an AI engineerFormer Airbus CTO Paul Eremenko explains why physical-world engineering still has no coding-AI moment, and how P-1 AI's agent Archie is supposed to get there anyway.
55:40ENTo Solve All Diseases, Isomorphic Labs Needs a Half-Dozen AlphaFoldsMax Jaderberg on turning DeepMind's game-playing research into a general AI engine for drug design—and why prediction alone cannot search a 10^60 molecular universe.
51:08ENThe Transformer Co-Author Who Says Scaling Hit a WallHost Jacob EffronCohere CEO Aidan Gomez tells Redpoint that a $100 million model is still an amnesiac genius, and that enterprise AI money is concentrating in support, research, and the back office.
52:30ENMove 37 looked like a bug. LLMs still need their AlphaZero momentIoannis Antonoglou, from AlphaGo to Reflection AI, on self-play, reliability, and why compute can still buy intelligence.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually ThinkHost Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
39:45ENHarmonic’s Bet: Teach AI Math Directly, and the Riemann Hypothesis Falls by 2029Robinhood’s Vlad Tenev and Harmonic co-founder Tudor Achim tell Sequoia why math is reasoning, why Lean is the verifier, and why they refuse to wait for mathematical skill to emerge from scale.
44:16ENA perfect demo is ten years from a product you can pay forHost Sarah Guo, Elad GilAndrej Karpathy uses Waymo's decade-long gap, Tesla's software bet, and a new AI course to argue that autonomous intelligence will stall at product and globalization, not at the first impressive clip.
39:00ENJPMorgan Has 150 Petabytes. GPT-4 Trained on Less Than One.Host Sarah Guo, Elad GilScale AI founder Alexandr Wang tells No Priors why easy internet data is exhausted, why expert-plus-model teams still win, and why the path to AGI looks more like curing cancer than inventing a vaccine.
1:01:39ENGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?Host Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.
1:21:50ENA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.Host Nathan LabenzGretel AI founder Alex Watson tells Nathan Labenz why most company data is still a spreadsheet, why GPT-4 is the wrong tool for generating it, and how differential privacy lets a model learn a disease without learning a patient.
1:17:09ENGPT-4 as Teacher, GPT-3.5 as Student: A Nine-Tenths Cost Cut and an AI Bundle Nobody Will StartHost Nathan LabenzNathan Labenz reports that chain-of-thought traces from GPT-4 train a cheaper, faster 3.5, then argues for flooring the app accelerator, tapping the brake on GPT-5, and bundling a hundred AI tools for a hundred dollars.
3:11:35ENSearch was always a hack: Marc Andreessen on AI, truth, and the next internetGuest Marc Andreessen · Host Lex FridmanLex Fridman sits down with the Mosaic co-creator and Andreessen Horowitz founder to ask whether Google Search, the browser, and open-source AI survive the decade.
2:00:19EN1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Host Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…
1:14:34ENInside the Frontier Lab: How Karina Nguyen Builds Products by Training the ModelGuest Karina Nguyen · Host Lenny RachitskyOpenAI researcher Karina Nguyen explains synthetic data, evals, how Canvas and Tasks were made, and which human skills matter as models get smarter.








