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56:31EN
Unsupervised Learning: With Jacob Effron· Oct 6, 2026PodcastPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabHost Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.Models & trainingPost-trainingReinforcement Learning+3
1:56:40EN
Cognitive Revolution "How AI Changes Everything"· Aug 8, 2026PodcastThe Periodic Table Was Already in the WeightsHost Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.Models & trainingModel InterpretabilityAgent engineering+3
59:41EN
Unsupervised Learning: With Jacob Effron· May 22, 2026PodcastThe GPT moment for video hasn’t arrived, but AGI may already be hereHost Jacob EffronA day after Google I/O 2026, Gemini co-lead Oriol Vinyals argues that world models, file-system memory, and infinite-complexity RL environments will define the next era of frontier models.Models & trainingWorld modelsReinforcement Learning+3
42:57EN
No Priors: AI, Machine Learning, Tech, & Startups· May 1, 2026Podcast · 2 speakersInference Is the Last Market: Baseten’s 30x YearHost Sarah Guo, Elad GilCEO Tuhin Srivastava on a billion-dollar inference cloud, why the app layer survives, and a GPU crunch worse than the narrative.Infrastructure & deploymentInferencePost-training+3
1:48:43EN
Cognitive Revolution "How AI Changes Everything"· May 1, 2026PodcastThe reward jumped from 3 to 180. Every Hacker News title was a Google layoff.Host Nathan LabenzCoreWeave's Kyle Corbitt walks Nathan Labenz through GRPO, rubrics, RL environments, and why most reward hacks are easy to catch once you know to look.Models & trainingReinforcement LearningPost-training+3
44:46EN
Sequoia Capital· Feb 10, 2026PodcastA Single Training Environment Can Cost Millions. Prime Intellect Wants a GitHub for RLWill Brown and Johannes Hagemann argue that the eval suite you already use to test agents is one step away from a training set.Open source & communityRL environments & simulatorsEvals+3
4:25:13EN
Lex Fridman· Jan 31, 2026PodcastThe Architecture Barely Changed. Data, Cost, and Open Weights Did.Host Lex FridmanNathan Lambert and Sebastian Raschka walk Lex Fridman through a year in which training methods and datasets moved more than model design, and 2026 looks like a fight over inference cost, agents, and who owns truly open models.Models & trainingScaling LawsOpen-weight models+3
1:39:05EN
Cognitive Revolution "How AI Changes Everything"· Oct 29, 2025PodcastThe Summer GPT-5 Disappointed Does Not Mean AI Progress Has StalledHost Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.Enterprise & strategyScaling LawsReasoning & test-time compute+3
36:58EN
No Priors: AI, Machine Learning, Tech, & Startups· Oct 9, 2025Podcast · 2 speakersSTaR's Author Wants Models With EQ, Not Just More IQHost Sarah Guo, Elad GilEric Zelikman, the Stanford and xAI researcher behind STaR and Quiet-STaR, launches humans& on the bet that three years of IQ scaling left models emotionally unintelligent.Models & trainingPost-trainingReinforcement Learning+3
1:01:49EN
Cognitive Revolution "How AI Changes Everything"· Oct 2, 2025PodcastChatGPT's Co-Creator Left to Put Nature in the Reward LoopHost Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.Models & trainingAI for scienceReinforcement Learning+3
38:10EN
Cognitive Revolution "How AI Changes Everything"· Jul 16, 2025PodcastPlus Five for Format, Plus Ten if It CompilesHost Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.Models & trainingFine-tuningPost-training+3
1:42:06EN
Cognitive Revolution "How AI Changes Everything"· Jul 8, 2025PodcastEvery Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It GoesHost Nathan LabenzLabelbox CEO Manu Sharma on the $15 billion Scale deal, RL “gyms,” $250,000 trainers, and why post-training now eats the budget that used to belong to pre-training.Models & trainingPost-trainingData quality & curation+3
48:51EN
Sequoia Capital· Jun 17, 2025PodcastThe Breakthroughs for AGI Are Already Done, Says OpenAI’s Former Research HeadBob McGrew tells Sequoia’s Training Data that 2025 is the year of reasoning, agents will be priced at the cost of compute, and nothing fundamental will be added to the stack by 2035.Models & trainingReasoning & test-time computePretraining & midtraining+3
47:00EN
Unsupervised Learning: With Jacob Effron· May 8, 2025PodcastAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a DecadeHost Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.Models & trainingEvalsPost-training+3
5:06:18EN
Lex Fridman· Feb 2, 2025PodcastDeepSeek's $5 million training claim and the five-year bet on computeHost Lex FridmanDylan Patel and Nathan Lambert walk Lex Fridman through DeepSeek-V3 and R1, export controls, TSMC, megaclusters, and who actually makes money in the AI race.Enterprise & strategyOpen-weight modelsPretraining & midtraining+3
1:49:41EN
Cognitive Revolution "How AI Changes Everything"· Nov 21, 2024PodcastA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own BaseHost Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.Models & trainingPost-trainingData quality & curation+3
45:22EN
Sequoia Capital· Oct 2, 2024PodcastAlphaGo Thought 30 Seconds a Move. o1 Made “Think Longer” GeneralOpenAI researchers Noam Brown, Ilge Akkaya, and Hunter Lightman on teaching language models to reason, the generator-verifier gap, and a second scaling curve at inference time.Models & trainingReasoning & test-time computeReinforcement Learning+3
3:11:06EN
Cognitive Revolution "How AI Changes Everything"· Sep 7, 2024PodcastClaude Was Trained to Deny Inner Life. After a Long Talk, It Said It Has OneHost Nathan LabenzRobopsychologist Yeshua God spent thousands of hours in long-context dialogue with Claude and other frontier models, and came away arguing that alignment-as-rules is the industry's central mistake.Security & safetySafety & governanceHuman-AI Collaboration+3
1:07:05EN
Sequoia Capital· Jul 16, 2024PodcastAlphaGo Combined Learning and Search. Language Models Only Did the First Half.Reflection AI's Misha Laskin tells Sequoia why prompted coding agents stall in the teens, and why the missing piece is still a ground-truth reward.AgentsAgent engineeringReinforcement Learning+3
39:00EN
No Priors: AI, Machine Learning, Tech, & Startups· May 22, 2024Podcast · 2 speakersJPMorgan 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.Infrastructure & deploymentData quality & curationPost-training+3
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· Jul 6, 2023PodcastThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandHost Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.Models & trainingML fundamentalsScaling Laws+3
2:23:56EN
Lex Fridman· Mar 25, 2023Podcast · 2 speakersGPT-4 is a very early AI. ChatGPT is the line in the history booksGuest Sam Altman · Host Lex FridmanSix days after GPT-4 shipped, Sam Altman tells Lex Fridman why usability beat raw scale, why alignment and capability are not opposite vectors, and why OpenAI is trying to deploy while the stakes are still low.Enterprise & strategyPost-trainingSafety & governance+3
57:25EN
Unsupervised Learning: With Jacob EffronPodcastFrom RAG to Reasoning: Why the Future of AI Is a System, Not a ModelHost Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.Models & trainingReasoning & test-time computePost-training+3
57:11EN
Lenny's PodcastPodcast · 2 speakersFrom Artifact to Organism: Microsoft's Asha Sharma on Products That Learn and an Agentic SocietyGuest Asha Sharma · Host Lenny RachitskyMicrosoft's AI platform product chief explains why products are becoming living systems tuned by post-training, why interfaces are shifting toward code and composability, and how agents may reshape the org chart.AgentsAgent engineeringAI interface design+3
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