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1:27:33ENA 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a DayModeration: Nathan LabenzMultiOn CEO Div Garg returns to The Cognitive Revolution to recap 18 months of agent hype, the AgentQ stack, and why 2025 might still be the year of vertical agents.
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own BaseModeration: 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.
56:10ENDeepL Beat Google at Translation by Refusing to Be a General ModelModeration: Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.
1:13:23ENAn AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.Moderation: Nathan LabenzMichael Levin and Leo Pio Lopez on a multilayer embedding that predicted melanoma, a cancer with no mutations, and why biology's real interface is cognitive.
59:30ENGPT-4 Gets Half Your Data Questions Wrong. Snowflake's Answer Is Systems DesignUsing an off-the-shelf model on enterprise data yields about 45% reliability—one wrong answer out of every two.
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.
1:16:11ENScooping Food Into Boxes Is 70 Percent of Plant LaborModeration: Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.
1:00:28ENScaling Laws Aren't Done. Don't Lock Yourself Into Yesterday's Architecture.Microsoft CTO Kevin Scott on why the exponential is still compounding, why inference will dwarf training, and why a language model is the world's crappiest database.
39:00ENJPMorgan Has 150 Petabytes. GPT-4 Trained on Less Than One.Moderation: 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?Moderation: 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.
38:48ENShopify Built a Copilot, Not an Autopilot, for Every MerchantModeration: 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.
40:58EN99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses FirstModeration: Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.
27:14ENSeven People Did the Work of 700. Salesforce's AI Chief Says Enterprise AI Is Still in the Second InningModeration: Sarah Guo, Elad GilClara Shih tells Sarah Guo and Elad Gil why Salesforce built a shared LLM platform, why data cleanup beats model shopping, and why AI is becoming the enterprise UI.
1:21:50ENA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.Moderation: 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.
1:32:40ENThe Historian Who Taught GPT-3.5 to Beat GPT-4Moderation: Nathan LabenzMark Humphries on a kilometer of unread paper, fur-trade jargon that embeddings cannot see, and why cheap processing will force history to be rewritten.
37:01ENDNA Is Code, but You Compile It with a PipetteModeration: Sarah Guo, Elad GilGinkgo Bioworks CEO Jason Kelly tells No Priors why cells are worth the magic, why biology has no AWS, and why a pandemic should look like cybersecurity.
2:15:11ENTwenty Gibberish Tokens Jailbroke ChatGPT and BardModeration: Nathan LabenzCarnegie Mellon’s Zico Kolter, Andy Zou, and Asher Trockman walk through a universal jailbreak that transfers from open models to commercial APIs, and a training-free way to steal some of pretraining’s benefit.
51:22ENThree IPOs Later, Frank Slootman Still Thinks the Job Is UrgencyModeration: Sarah Guo, Elad GilSnowflake's CEO tells No Priors how he picked the right elevator, turned a 15-person backup startup into an EMC acquisition, and why a data lake is a landfill you should not train on.
3:15:07ENWikipedia Worked Once Writing Stopped Feeling Like Grad SchoolGast: Jimmy Wales · Moderation: Lex FridmanJimmy Wales tells Lex Fridman how Nupedia's seven-stage review died, why Wikipedia still refuses ads and governments, and what large language models should actually do with an encyclopedia.
59:28ENFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductModeration: Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
2:00:19EN1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Moderation: 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…
1:29:31EN130,000 Teleop Demos: Google's GPT Playbook for RobotsModeration: Nathan LabenzKeerthana Gopalakrishnan of Google Robotics tells Nathan Labenz how RT-1, SayCan, and PaLM-E turn language-model scaling into kitchen-ready mobile manipulators.
2:31:48ENA Car That Cannot Drive Itself Will Be About as Useful as a HorseGast: Elon Musk · Moderation: Lex FridmanIn a Lex Fridman conversation built around Tesla Autopilot, Elon Musk argues that every driver input is an error, that today’s hardware is already enough for full self-driving, and that watching the human will soon matter less than making the system safer than a person.
1:28:46ENThe Speech Company That Gets Paid to Label Its Own Training DataGast: Dan Kokotov · Moderation: 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.
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