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50:33ENThe 40% data-center save started with an email that ended in a question markPhaidra founder Jim Gao tells Sequoia how an AlphaGo-inspired bet at Google became reinforcement learning that runs billion-dollar plants.
49:50ENUnconstrained agents captivated Twitter. Custom ones are what ship.LangChain founder Harrison Chase tells Sequoia’s Training Data what an agent actually is, why AutoGPT stalled, and which cognitive architectures reach production.
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.
1:23:09ENThe Right Size of an AI Task Is Whatever You Can CheckHost Nathan LabenzElicit founders Andreas Stuhlmüller and Jungwon Byun walk through how a twelve-person research assistant cut summary hallucinations from 1.5 to 0.5, ships about every week and a half, and treats task decomposition as the way to spend more compute without a black box.
1:28:37ENTwelve Hours After a GPT-4 Demo, He Turned a Decade-Old Legal Startup AroundHost Nathan LabenzPablo Arredondo, co-founder of CaseText and now VP of CoCounsel at Thomson Reuters, traces legal research from 19th-century citators to a retrieval-anchored GPT-4 product that law firms treat more like a colleague than a search box.
38:48ENShopify Built a Copilot, Not an Autopilot, for Every MerchantHost Sarah Guo, Elad GilShopify VP of Core Product Glen Coates on collapsing duplicate engines, shipping AI that proposes but never commits, and teaching storefront search to understand a sweater.
50:07ENCheap Volume Beats Gold-Plated Jets: Anduril’s Brian Schimpf on AI and DefenseHost Sarah Guo, Elad GilThe Anduril CEO on Lattice, a school-bus-sized autonomous submarine, why Ukraine made fighter attrition unworkable, and how you actually sell a capability to the Pentagon.
1:33:51ENGPT-4 still writes SQL that will not run. She put a guardrail on the modelHost Nathan LabenzShreya Rajpal built Guardrails AI so developers can encode what “correct” means, validate the pieces, and send failures back to the model instead of hoping the prompt holds.
42:18ENTen Percent of Search Is Health — and the Doctor Is Googling TooHost Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.
1:28:43ENClinical Guidelines Are 11% Evidence. Curai’s Founder Still Wants GPT-4 in the Exam RoomHost Nathan LabenzNeal Khosla of Curai Health argues that large language models already match median U.S. physicians on today’s tests — and that the real work is scaffolding, supervision, and a doctor’s signature.
58:24ENAmong 100 Random Doctors, Zak Kohane Would Be Surprised If One Beat GPT-4Host Nathan LabenzHarvard's biomedical informatics chair on a rare newborn case, why drug-style trials will not certify a general medical model, and why the AI should still work for the patient.
58:26ENSeventy Percent of the Inbox Is a Pitch: Matt Welsh on the First Wave of AI AgentsHost Nathan LabenzThe Fixie.ai founder maps copilot versus delegation, the missing appendages into our systems, and why agents will need guardrails, memory hygiene, and probably an earpiece.
1:56:16ENThey Used Humans as Stand-In AIs. Now 250,000 People Run Elicit.Host Nathan LabenzOught's founders tell Nathan Labenz why open-ended reasoning has to be decomposed, supervised, and grounded—not scaled until it merely looks persuasive.
39:10ENNever Cite More Than Five Links: Perplexity's Bet on Answer EnginesHost Sarah Guo, Elad GilFounded in August 2022, Perplexity was barely seven months old when its CEO and CTO sat down with No Priors to explain how an answer engine takes on Google.
1:25:46ENDon't Make the Tools Agentic: Flo Crivello on What Production Agents Actually NeedHost Nathan LabenzThe Lindy CEO tells Nathan Labenz that most working agents are intelligent workflows with human-designed control flow — and that scaffolding may still matter even if AGI arrives.
48:52ENUsers Wanted a Better Brush. Runway Built One-Click GreenscreenHost Sarah Guo, Elad GilRunway CEO Cristobal Valenzuela on why a model is not a product, why filmmakers hacked a four-frame-per-second tool, and why 80 percent is enough when nobody dies.
1:28:46ENThe Speech Company That Gets Paid to Label Its Own Training DataGuest Dan Kokotov · Host Lex FridmanRev VP of engineering Dan Kokotov tells Lex Fridman why a $1.25-a-minute human transcript, a 14% machine word-error rate, and a two-sided freelancer market are the same flywheel.
1:30:52ENCristos Goodrow: There Is No YouTube Algorithm, Only People Moving Through VideoGuest Cristos Goodrow · Host Lex FridmanYouTube’s head of search and discovery tells Lex Fridman why collaborative filtering learned language on its own, why watch time is not satisfaction, and why nobody can predict a viral hit.
1:13:06ENLockheed Martin's CTO on Autonomy, the Moon, and the Culture of Not Getting It WrongGuest Keoki Jackson · Host Lex FridmanKeoki Jackson tells Lex Fridman why going back to the Moon is the easy part—and why proving a learning system will still do the right thing is not.
32:45ENElon Musk: Every Driver Input Is an Error Tesla Autopilot Must EliminateGuest Elon Musk · Host Lex FridmanAfter MIT published a study of functional vigilance on Autopilot, Lex Fridman sat down with Musk to argue about cameras on drivers, fleet data, and whether humans still belong in the loop.
1:00:38ENThe Race Car and the Rulebook: Chris Gerdes on Automated Vehicle SafetyHost Lex FridmanA Stanford engineer and former DOT innovation chief explains how autonomous racing, voluntary safety guidance, and data sharing could shape safer self-driving cars.
1:10:32ENA Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is HeadingGuest Edwin Chen · Host Lenny RachitskySurge AI's founder explains how a bootstrapped team built the leading human-data company, why he distrusts benchmarks, and how reinforcement learning environments are the next frontier.
1:46:33ENFrom Chaos to a Failure Map: A Hands-On Primer on AI EvalsGuest Hamel Husain, Shreya Shankar · Host Lenny RachitskyHamel Husain and Shreya Shankar walk Lenny Rachitsky through a real error-analysis workflow, from reading traces to building a trustworthy LLM judge.







