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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.
1:12:03ENMice Don't Get Alzheimer's: Daphne Koller on Disease in a DishGuest Daphne Koller · Host Lex FridmanThe Coursera co-founder and insitro CEO on why most diseases are not one disease, how to turn cells into data, and why overconfident neural nets can kill.
1:17:29ENThe Algorithm Is Already Better. The Clinic Still Uses 1967.Guest Regina Barzilay · Host Lex FridmanMIT's Regina Barzilay on why deep learning already beats the breast-cancer 'density' score — and why data, liability, and incentives still will not move.
57:44ENKevin Scott: Treat AI Like a Hash Table, and Measure Platforms by What Others EarnGuest Kevin Scott · Host Lex FridmanMicrosoft's CTO talks radical markets, data dignity, Dunbar's number, and why a platform fails if only a handful of companies decide what AI is for.
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.
1:21:35ENLook Outside the Building: Shaun Clowes on Why Most PMs Fall Short and How AI Changes the JobGuest Shaun Clowes · Host Lenny RachitskyConfluent's chief product officer explains how to become a far better product manager, why data matters more than models, and what AI will and won't do to SaaS.
1:03:59ENBuilding a World Model of Life: Biohub's Alex Rives on Why Better Measurement Must Come Before Virtual CellsHost Nathan LabenzBiohub's head of science explains why today's virtual cell models fall short, how a $1.8 billion data push aims to fix it, and what a five-year information-level model of the cell could unlock.
1:07:08ENIf the Model Is the Product, the Eval Is the PRD: Inside Mercor's RiseGuest Brendan Foody · Host Lenny RachitskyMercor CEO Brendan Foody explains why AI labs will pay almost anything for expert-written evals, how his company grew from $1 million to a $400 million run rate in 16 months, and what that means for jobs and founders.
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.
1:10:19ENThe Robot That Folded the Laundry: Physical Intelligence’s Three FrontiersHost Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.
1:07:35ENWhy AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the LoopHost Lex FridmanIn MIT 6.S093's opening lecture, Lex Fridman lays out five research areas of human-centered AI, from machine teaching to arguing machines that flag their own uncertainty.
1:11:50ENData Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the WheelHost Lex FridmanIn an MIT lecture, Lex Fridman shows how deep learning can sense pedestrians, driver posture, gaze, emotion and cognitive load, and why that matters for human-centered autonomy.
1:24:01ENFrom Sandwich Math to Expert Data: Scale AI's CEO on What Actually Makes Models SmarterHost Lenny RachitskyJason Droege explains what really happened with Meta's investment in Scale AI, how data labeling turned into expert work, and what Uber Eats taught him about building businesses.
15:43ENInside RIDER: The Rugged Black Box Behind MIT's Large-Scale Driving StudyGuest MIT-AVT · Host Lex FridmanA walkthrough of the data-collection device MIT-AVT uses to record drivers and vehicles in semi-autonomous cars month after month.
1:19:56ENAnalytics With a Seat at the Table: How DoorDash Builds a Centralized Data TeamGuest Jessica Lachs · Host Lenny RachitskyDoorDash analytics chief Jessica Lachs explains why she keeps data scientists in one central org, how she hires for curiosity, and how she picks metrics that actually move a business.
11:29ENWhy Tesla AI Day Was the Most Impressive Real-World AI Effort Lex Fridman Has SeenHost Lex FridmanLex Fridman walks through the neural network, data annotation, Dojo and Tesla Bot announcements and what they say about solving real-world robotics.
1:09:51ENFrom Campus Job Board to AI Data Powerhouse: How Handshake Hit $50M ARR in Four MonthsGuest Garrett Lord · Host Lenny RachitskyHandshake CEO Garrett Lord explains what expert data labeling actually involves and how a decade-old student network became a supplier to seven frontier AI labs.
45:12ENInside a Robot Data Factory: How Mecka Sensorizes Humans to Train RobotsMecka AI founder Josh Gao explains how a company collects, quality-checks and licenses egocentric human data, and why robotics will arrive as a steady unlock of tasks rather than a single breakthrough.









