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1:27:44ENEverybody Can Cook: Guillermo Rauch on v0, Vercel and the Collapse of SpecializationInvitado: Guillermo Rauch · Presenta: Lenny RachitskyVercel's CEO explains how v0 turns intent into shipped products, why understanding how software works still matters, and how teams can build taste through exposure.
1:09:51ENPick Channels Like an Improviser: Adam Grenier on New Acquisition Bets, Growth CMOs and BurnoutInvitado: Adam Grenier · Presenta: Lenny RachitskyFormer Uber and Masterclass marketer Adam Grenier explains how to decide whether an emerging channel deserves your time, what a Growth CMO is, and how to tell burnout from depression.
1:06:10ENColumn B Thinking: How Melanie Perkins Built Canva by Starting From the DreamInvitado: Melanie Perkins · Presenta: Lenny RachitskyCanva's co-founder and CEO explains how working backward from a wild vision, rejection from over a hundred investors and a two-year rewrite shaped a company valued above $42 billion.
11:23ENScience Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence ExplosionInvitado: François Chollet · Presenta: Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.
51:18ENEngineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI CourseInvitado: MIT AGI · Presenta: Lex FridmanLex Fridman lays out an engineering-first approach to artificial general intelligence, previews the course's projects and guest speakers, and frames deep learning's limits.
1:10:25ENSoftware Is Not a Moat: Evan Spiegel on Distribution, Ecosystems and a Design Team That Never Stops MakingPresenta: Lenny RachitskySnap's CEO explains why distribution is the hard part of consumer tech, how a 9-to-12-person design team keeps innovating, and why 2026 is Snap's crucible year.
1:02:06ENDeep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real ProblemPresenta: Lex FridmanLex Fridman opens MIT's 6.S094 with a tour of what deep learning is, how it works, what it has achieved and where it still breaks, framed around autonomous vehicles.









