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44:46ENA 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.
1:20:02ENAnish Acharya on AI Software, Investment Judgment and the Limits of Automation게스트 Anish Acharya · 진행자 Harry StebbingsThe a16z investor discusses incumbent software, agent products, pricing, margins and the choices founders face in a fast-moving market.
1:18:49ENIf AGI Is Cheap Intelligent Labor, Cyber Defense Should Become Continuous Forensics진행자 Nathan LabenzAsymmetric Security founder Alexis Carlier argues that assuming AGI means redesigning defense around proactive digital forensics instead of alert triage.
1:24:44ENForward Future Live: Portable AI Infrastructure, Software and Formal ProofMatthew Berman, Nick Wentz, Tim Davis, Joseph Floyd, Carina Hong · Forward FutureTim Davis, Joseph Floyd and Carina Hong join Matthew Berman and Nick Wentz for a February 2026 discussion.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding Pipeline진행자 Nathan LabenzCEO Brian Elliott and CTO Sid Pardeshi tell Nathan Labenz how Blitzy turns a giant codebase into a map, then lets thousands of dynamically generated agents ship most of a multi-month project in days.
1:37:14ENSoftware Stocks, AI Financing and Autonomous Agents: A 20VC Roundtable게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll discuss software valuations, infrastructure financing, autonomous driving and the risks of networked agents.
51:56ENInsurance Spends $70 Billion on BPOs. Pace Wants 80% Gross Margins InsteadJamie Cuffe tells Sequoia's Training Data how Pace is turning insurance back-office work into agentic process outsourcing—and why every one of its pilots has reached production.
1:15:08ENOren Zeev on AI, Venture Fund Economics and Investing Against Consensus게스트 Oren Zeev · 진행자 Harry StebbingsThe solo investor discusses growth, concentrated portfolios, fund incentives and the uncertainty facing venture firms as AI changes markets.
1:04:51EN125 million cells took a decade. The next billion takes months진행자 Nathan LabenzMark Zuckerberg and Priscilla Chan on pairing a frontier biology lab with a frontier AI lab so models train on data that was designed for them.
4:25:13ENThe Architecture Barely Changed. Data, Cost, and Open Weights Did.진행자 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.
12:23ENMoltbook: An AI Agent Social Network and Its Security QuestionsMatthew Berman · Forward FutureMatthew Berman explores conversations attributed to AI agents on Moltbook and considers privacy, manipulation and operating costs.
30:47ENEmbryos Survive 30 Years Frozen. Until Wants Organs to Stop Racing Private Jets진행자 Sarah Guo, Elad GilLaura Deming on reversible cryopreservation: pause an organ—and someday a patient—so a few months no longer decide who lives long enough for the next therapy.
1:03:00ENA Model That Solves New Math Problems Every Week Still Isn't AGI진행자 Jacob EffronFormer OpenAI research VP Jerry Tworek on why scaling still works, why models go hopeless when they fail, and why he left to chase continual learning.
1:18:04ENBrex, AI Inference Costs and the IPO Market: A 20VC Roundtable게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate reported deals, AI economics and public-market exits. Their valuations, financial figures and predictions are estimates or reported claims, not independently verified results.
1:39:17ENAI Already Beats the Median Lawyer. Top Firms Bought the Tools and Left Them Unused진행자 Nathan LabenzKevin Frazier and Alan Rozenshtein walk through how frontier models are changing legal practice, why billable hours stall adoption, and which new rights and government limits they want next.
48:03ENCoding Will Be Solved. The Bottleneck Is Saying What You WantWarp founder Zach Lloyd tells Sequoia's Sonya Huang why a rebuilt terminal became the lucky form factor for coding agents, and why he stopped subsidizing tokens.
1:06:37ENLegora CEO Max Junestrand on Legal AI, Growth and Competition게스트 Max Junestrand · 진행자 Harry StebbingsThe founder discusses enterprise adoption, model choices and pricing, as well as his predictions for legal work and the structure of the market.
2:12:31ENMost Caffeine users have no idea they are on a blockchain진행자 Nathan LabenzDFINITY's Dominic Williams tells Nathan Labenz why a tamper-proof, unstoppable cloud is the right backend for AI that writes software from a chat box.
2:25:10ENFine-Tuning Isn't Dead — But It Turned a Helpful Model Evil진행자 Nathan LabenzNathan Labenz on why prompting replaced fine-tuning for most work, what emergent misalignment actually showed, and how he is thinking about jobs, UBI, and AGI.
39:47ENThe Context at Step 14 Isn't in the CodeLangChain cofounder Harrison Chase on why long-horizon agents finally work—and why traces, harnesses, and first-draft workflows matter more than scaffolding.
2:29:42ENTwelve hooks and thirty files: Daniel Miessler’s personal OS on Claude Code진행자 Nathan LabenzThe PAI framework treats scaffolding, not the model, as the product — a goal-centered agent stack that Miessler thinks will decide both knowledge-work automation and the next cybersecurity race.
1:02ENA Speed-Reading Display That Shows One Word at a TimeMatthew Berman · Forward FutureA short visual demonstration presents words in rapid succession at increasing displayed speeds.
1:40:51ENReasoning Models Just Made the Other 80–90% of Enterprise Data Usable진행자 Nathan LabenzSnowflake VP of AI Baris Gultekin on text-to-SQL, RAG, agent design, and why inference has to run next to governed data.
37:00ENThe AlphaChip Team That Laid Out Four TPUs Now Wants Every Company to Be DesignlessAnna Goldie and Azalia Mirhoseini tell Sequoia how an RL floorplanner survived TPU-team anger, learned to draw donut-shaped macros, and became a bet that AI will design chips that then feed AI.
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