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1:03:05ENHarrison Chase on Building Reliable Agents, Model Routing and MemoryGuest Harrison Chase · Host Harry StebbingsThe LangChain co-founder explains why agents remain difficult to control, where domain expertise matters, and how his team manages coding-agent costs.
40:44ENGrant Sanderson: There's never been a better time to go into mathGrant Sanderson, Constantin Kogler · 3Blue1BrownStudents fear AI has solved mathematics. Grant Sanderson argues the opposite: even if machines prove theorems better than we do, the institution of math stays valuable—and this is a rare moment when newcomers can reshape it.
2:51:18ENLindon Gao on Building Dyna Robotics and Finding a Path Through the FogGuest Lindon Gao · Host 张小珺Dyna Robotics CEO Lindon Gao explains why his company builds intelligence through real workflows, how hardware and research must evolve together, and how a founder can find clarity by letting go of the need to be right.
1:17:09EN20VC on Pro Rata Rights, AI Pricing and the Changing Software MarketGuest Jason Lemkin, Rory O’Driscoll · Host Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate investor rights, model economics and the choices facing software companies and their employees.
39:23ENThe Defenders' Window: Greg Brockman on AI-Generated Knowledge, Drug Discovery, and the New Physics of CybersecurityHost Sarah Guo, Elad GilOpenAI's president explains why solving open math problems is a proof point for science, how proactive agents change product form, and why security spending must exceed software spending.
56:49ENAlfred Lin on Building in the AI Era: The Diffusion Gap, Founder Conviction, and Fire and IceAlfred Lin, Jason Zhang, Tanvir Bhathal · Sequoia CapitalAt Stanford AI Club, Sequoia’s Alfred Lin explains where he sees opportunity in AI and why learning, human understanding, and operating discipline still shape enduring companies.
2:17:48ENAgent IDs Are Empty, Rogue Agents Are Coming, and We Might Have to BargainHost Nathan LabenzGovAI’s Alan Chan scores two years of agent-governance infrastructure with Nathan Labenz, then walks through a recursive-self-improvement paper co-authored with OpenAI’s Jakub Pachocki and Anthropic’s Jack Clark.
12:14ENGemini 4 Argon Tested: 3D Demos, a Spreadsheet Prototype and Benchmark ResultsMatthew Berman · Forward FutureMatthew Berman tests Google’s model on games and visual projects, then compares reported coding and agent benchmarks.
10:36ENMeta Muse Wonder Tested: Strong Benchmarks, Mixed Demos and Quota FrictionMatthew Berman · Forward FutureMatthew Berman tests Meta’s models on browser demos and reviews their published benchmark scores, availability and cost.
1:08:02ENAlphaGo Took Two Years. AlphaZero Took Hours. Silver Is Making the Same Bet on SuperintelligenceDavid Silver and Lasse Espeholt left DeepMind to build Ineffable Intelligence, a twenty-person lab that wants a superlearner to discover science, math, and language from experience rather than from a pre-training run.
58:54ENMike Cannon-Brookes on AI Agents, Enterprise Context and the Future of InterfacesGuest Mike Cannon-Brookes · Host Harry StebbingsAtlassian's CEO argues that agents make product design more important, explains the trade-offs in enterprise AI, and makes the case for slower thinking.
31:00ENWhy AI Labs Keep Racing Despite Their Own Warnings of a Major BreachSam Altman, John Schulman · OpenAIAI Explained examines weakening safeguards, the incentives behind the frontier race, and why research breakthroughs make the next 18 months difficult to forecast.
1:12:06ENMax Freeman on Building Ramp’s Sales MachineGuest Max Freeman · Host Harry StebbingsRamp’s sales leader explains how he hires, trains and equips sellers, from early outbound experiments to enterprise forecasting and AI-assisted research.
17:37ENAI Coding, Game Mashups and the Value of Human TasteMatthew Berman · Forward FutureMatthew Berman explains decompilation and game mashups, then argues that iterative AI coding changes software and creativity.
2:00:51ENResample Every Token and You Can Watch the Model DecideHost Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:09:44EN10,000 GB300s for Four Weeks: Reflection’s Open-Model LedgerHost Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go PastHost Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:15:51ENThe AI Race, Founder Loyalty and the New Rules of Enterprise SoftwareGuest Dev Ittycheria, Jason Lemkin, Rory O’Driscoll · Host Harry StebbingsHarry Stebbings, Dev Ittycheria, Jason Lemkin and Rory O’Driscoll debate model competition, startup exits and what happens when AI agents choose vendors.
1:10:12ENOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell AnywayHost Nathan LabenzCEO Woodson Martin on software that never breaks, why most enterprise work does not need a frontier model, and how AI is finally shrinking the IT backlog.
21:29ENMistral Large 4 Preview: European Open Weights, Cyber Benchmarks and Setup FrictionMatthew Berman · Forward FutureMatthew Berman reviews the model’s reported specifications and benchmarks, then tests it in an agent coding workflow.
25:39ENPersonal AI Assistants in Practice: Briefs, Family Logistics, Email and BillsMatthew Berman · Forward FutureMatthew Berman describes how he connects assistants to everyday workflows and sets approval rules for consequential actions.
12:49ENAlex Mashrabov: Higgsfield agents that choose models and launch their own GPUsAlex Mashrabov · HiggsfieldAt OpenAI DevDay 2026, Higgsfield's CEO walks through why a continuous media experience needs an agent that picks models, scores its own output, and provisions GPUs — including a live 10-hour AI stream small teams can now ship.
13:12ENAlexandra Lato on Building Stack Overflow for Agents and Stack Internal with CodexAarti Bagul, Alexandra Lato · OpenAIStack Overflow’s VP of Product explains how a small team used Codex with opposite constraints on two products—and why agent-native design means letting agents contribute knowledge, not just read it.
1:04:26ENAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
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