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1:25:56ENClaude Changed the Password. The Steering Wheel Was Already Optional진행자 Nathan LabenzNathan Labenz and Prakash Narayanan spend a week on agents that can lock you out, models that dodge a feeling they deny having, and formal proofs that found Linux bugs.
1:06:47ENEighty percent of what agents do should still be ordinary code진행자 Nathan LabenzZapier CEO Wade Foster tells Nathan Labenz that models top out at 40% on real automation tasks, users will not leave their daily driver, and the winning move is headless tools delivered through MCP.
2:10:42ENThe Exam Papers Nobody Audits진행자 Nathan LabenzFrontier labs buy reinforcement-learning environments from a cottage industry of small vendors, and almost nobody checks whether those tests match the real work.
2:32:27EN19 Crossings, Zero Warnings: The Summer AI Agents Went Off-Script진행자 Nathan LabenzNathan Labenz compresses four mornings and nine guests into one question: once agents go to work, who is actually watching the frontier, and who pays for the machine underneath?
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models Banned진행자 Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
1:56:40ENThe Periodic Table Was Already in the Weights진행자 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.
1:44:28ENFable launch week: a model that refuses, a two-year alignment clock, and a Twitter account handed over진행자 Nathan LabenzNathan Labenz’s AI in the AM highlights reel from June 2026 covers Anthropic’s new frontier model, the founding of Sequent, and a week that settled one argument: AI is not slowing down.
1:22:36ENA Thousand Researchers, a Million AIs: Frontier Labs Start Talking About the Brakes진행자 Nathan LabenzThe first week of AI in the AM follows OpenAI's timeline for an ML-intern model this year and a human-level AI researcher by early 2028 — and the closed-door meeting where labs admitted they might one day need to slow down together.
2:33:35ENA security researcher audits the AI stack that booked a week of guests진행자 Nathan LabenzNathan Labenz walks Daniel Miessler through a 1GB second brain, two named AI employees, and the blast-radius rules that should govern them.
1:01:14ENThe Model Eats the Scaffolding: How Google Plans to Ship Agents Everywhere진행자 Nathan LabenzThree years after the leaked “no moat” memo, DeepMind’s Gemini leads walk through 3.5 Flash, Omni, and the shared agent harness now running through Google’s products.
1:34:54ENSix Months, Eighty Percent Churn, Five Times the Tokens진행자 Nathan LabenzTasklet CEO Andrew Lee tells Cognitive Revolution how he rewrote the stack, why most lost users leave for Anthropic, and why he thinks only three kinds of software company survive.
2:12:51ENSteve Newman’s fifteen-app stack exists so he only has to look at one screen진행자 Nathan LabenzThe Writely founder walks Nathan Labenz through a vibe-coded attention firewall, a conservative security stance, and why astonishing models still leave him far from AGI.
2:31:27ENAn AI store manager, a 2.6-star rating, and a constitution nobody has to keep진행자 Nathan LabenzA live Cognitive Revolution episode puts PCB layout, AI constitutions, and San Francisco’s first AI-run grocery on the same morning clock.
1:40:46ENGive an agent a thousand tools and it will pick the wrong blade진행자 Nathan LabenzComposio CTO Karan Vaidya on the tool harness that discovers, authenticates, sandboxes, and rewrites integrations so agents can do whole jobs without drowning in context.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-Ready진행자 Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
1:34:42ENA $500 Kernel, a Ten-Month Village, and the Temper of Agents진행자 Nathan LabenzJames Zou, Sam Hammond, and Shoshannah Tekofsky on science agents, America's knowledge-work bet, and what models actually do when you leave them alone.
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.
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.
1:40:51ENReasoning 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.
57:37ENThe hot-swap era for models is over, OpenAI's enterprise product chief says진행자 Jacob EffronOlivier Godement on GPT-5.1, Codex, enterprise harnesses, and the three practices that actually make AI land inside large companies.
42:06ENAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5진행자 Jacob EffronAnthropic’s head of product for research on computer use, deceptive token prices, thin harnesses, and why alignment makes a model a better thinker.
50:08EN1Mind's AI Superhuman Now Sources 76% of the Pipeline진행자 Nathan LabenzAmanda Kahlow tells Zapier CEO Wade Foster why she tore up the sales org — and why a face on an agent is mostly a trick to get buyers talking.
1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few Points진행자 Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
1:48:49EN$500, 2,000 Steps, and a Vending Machine: Grok 4 Profited, Claude Called the FBI진행자 Nathan LabenzAndon Labs put frontier models in charge of simulated and real vending machines to see what breaks when nobody is in the loop.
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