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1:29:02ENFrom 50 Designs a Day to 1,500: How AI Changed What Jaguar Land Rover Can ExploreHost Nathan LabenzNeural Concept’s Thomas von Tschammer on specialist physics models, engineering copilots, and why Formula 1 already runs thousands of designs overnight.
1:18:49ENIf AGI Is Cheap Intelligent Labor, Cyber Defense Should Become Continuous ForensicsHost Nathan LabenzAsymmetric Security founder Alexis Carlier argues that assuming AGI means redesigning defense around proactive digital forensics instead of alert triage.
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.
48:00ENA Two-Second Vote Is Training Models to Be ClickbaitHost Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.
43:38ENA 2 A.M. Codename and a Face That Finally Looked Like YouGoogle's product and model leads walk Sequoia through how Nano Banana made single-photo character consistency feel real — and why fun, craft, and human evals did as much work as scale.
44:47ENThe Last Mile Is Where Contact-Center AI Gets RealCresta CEO Ping Wu and Sequoia's Doug Leone on why 17 to 20 million agents, legacy systems, and last-mile integration will decide who wins customer experience.
32:59ENA Hundred People, a Billion in Revenue, and Zero Venture MoneyHost Sarah Guo, Elad GilSurge AI founder Edwin Chen on why quality human data is a poetry problem, not a bounding-box problem, and why leaderboards keep training models to write clickbait.
37:37ENA dozen people, 28 minutes, and one shared desktopThe OpenAI trio behind ChatGPT Agent explain how Deep Research and Operator became a virtual computer that can reason for half an hour and still ask you before it spends money.
38:48ENTask Length Doubles Every Few Months. Nathan Labenz Still Wants Humans in ChargeHost Nathan LabenzAt Imagine AI Live, the Cognitive Revolution host maps structured AI agents against autonomous agentic systems—and tells businesses to keep final control.
41:25ENBen Mann thinks superintelligence by 2028 is quite possibleHost Sarah Guo, Elad GilAnthropic's co-founder tells No Priors how Claude 4 unlocked hours of unattended agent work, why the lab built Claude Code, and what an economic Turing test would actually measure.
37:45ENCodex Is a Thought Experiment: What If the Agent Worked on Its Own Computer?OpenAI’s Codex team on training o3 to write mergeable PRs, running four copies of a 1 a.m. bug, and why the product only clicks once you stop pairing and start delegating.
44:21ENMercor's CEO: Knowledge Work's Next Job Is Writing EvalsHost Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.
51:19ENSal Khan's $15 Tutor Hit 1.4 Million Users. Making Kids Actually Use It Is the ProductHost Jacob EffronThe Khan Academy founder tells Redpoint why an AI tutor still cannot teach most students alone, and why districts are paying $15 a year after an $86 billion tutoring hangover.
1:11:05ENThe Man Who Took HubSpot CRM to $40 Million Says the Data Model Has to Be Thrown OutDay.ai founder Christopher O’Donnell tells Sequoia’s Pat Grady why classic CRM is compressed pixel art, why full self-driving scared his users, and why every line of code has to make every other line more valuable.
38:43ENTwo in Five Doctors Want Out. An On-Call Cardiologist Built the Note That Lets Them StayHost Sarah Guo, Elad GilAbridge CEO Shiv Rao on treating the doctor-patient conversation as healthcare's source of truth, and why the company ran straight at the hardest health systems.
50:35ENCopilot Already Writes Half the Code. GitHub's CEO Says Agents Still Have Four Gates to ClearHost Sarah Guo, Elad GilThomas Dohmke on agent mode, why median developers are not going anywhere, and how AI is repricing the tools that write software.
57:11ENA Million Tokens Cannot Write a Good JokeHost Jacob EffronPrinceton's Arvind Narayanan on why agent evals mislead, inference scaling hits a wall, and AI looks more like the internet than an overnight revolution.
1:33:01ENRare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already MissedHost Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.
1:01:53ENHumans Wrote 1.3 Percent of the Tokens. The AI Lab Still Designed Working Nanobodies.Host Nathan LabenzStanford's James Zou describes a GPT-4o research group that invented a protein-design pipeline for under $100 — and a sparse autoencoder that pulled an undocumented motif out of ESM-2.
1:29:00EN14,000 people applied. Thirty-three got in. The jobs are at Homeland Security.Host Nathan LabenzMichael Boyce, director of the DHS AI Corps, tells Nathan Labenz how the largest civilian AI team in the U.S. government is being built — and what it is already shipping.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.Host Nathan LabenzAndrew White, Head of Science at Future House, maps what language models can already do in chemistry and biology—and the bottlenecks they do not dissolve.
44:48ENEnterprise Google Failed for Twenty Years. A Search Veteran Finally Made It Work.Glean CEO Arvind Jain on why company search is harder than the public web, why RAG lives or dies on retrieval, and what work looks like when every employee has an AI assistant.
1:23:37ENNine of the top ten research ideas were Claude'sHost Nathan LabenzStanford PhD student Chenglei Si ran a blind study pitting Claude 3.5 Sonnet against more than a hundred NLP researchers on novel research ideas.
31:29ENRippling skipped the chatbot and stamped employees after 90 days of workHost Sarah Guo, Elad GilCOO Matt MacInnis tells No Priors how Talent Signal reads source code, tickets, and customer conversations, then gives managers one calibrated label: high potential, typical, or pay attention.
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