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48:18ENNoam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of HeadroomPresenta: Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.Presenta: 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.
1:27:33ENA 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a DayPresenta: 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.
1:03:07ENReasoning Hasn't Budged in Two Years. Dust Still Saved a Customer 8,000 HoursDust co-founders Gabriel Hubert and Stanislas Polu on multi-model agents, refusing to buy GPUs before product-market fit, and the jump from calculator tools to stochastic ones.
1:42:56ENBuilding a Research and Writing Flow with CrewAIMatthew Berman, João Moura · Forward FutureMatthew Berman and CrewAI founder João Moura build and debug a two-crew workflow for planning and drafting educational articles.
38:28ENHalf of Production AI Is RAG. Fine-Tuning Almost Disappeared.Presenta: Sarah Guo, Elad GilBraintrust CEO Ankur Goyal tells No Priors what Notion, Zapier, and Vercel actually ship — and which hyped techniques they quietly dropped.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually ThinkPresenta: Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
48:31ENA Call Center Contact Costs $13. A Company Agent Can Do It for Under a DollarPresenta: Sarah Guo, Elad GilSierra cofounder and OpenAI chairman Bret Taylor tells No Priors why branded customer agents are shovel-ready, why RAG demos are not products, and how AI software should charge for the job done.
2:03:55ENThe More You Constrain an Agent, the More You Can Trust ItPresenta: Nathan LabenzAgent.ai engineering lead Andrei Oprisan tells Nathan Labenz why narrow, highly prescribed workflows beat open-ended autonomy — and how a 12-person team shipped a free agent network past 40,000 users in a few months.
1:12:31ENA Bare Model Closes 35% of Airline Changes. Sierra's Agents Clear 70% of Real Tickets.Clay Bavor on why putting a raw LLM in front of customers does not math, and how AgentOS, supervisor agents, and outcome pricing change the arithmetic.
9:58ENAbstract AI: A Proposed Routing Layer for Language ModelsMatthew Berman · Forward FutureMatthew Berman outlines a startup idea for choosing models and workflows by cost, speed and quality.
1:07:05ENAlphaGo Combined Learning and Search. Language Models Only Did the First Half.Reflection AI's Misha Laskin tells Sequoia why prompted coding agents stall in the teens, and why the missing piece is still a ground-truth reward.
1:16:22ENHe Spent 20 Hours Crafting a Prompt. DSPy Beat Him AnywayPresenta: Nathan LabenzSander Schulhoff walks through The Prompt Report — a 78-page survey of what actually works when you prompt large language models.
59:11ENDon't Train a Model. Factory's Founders Say Go Dig for GoldMatan Grinberg and Eno Reyes tell Sequoia's Training Data why they sell a fleet of job-specific droids on top of other people's models — and how that stack just took SWE-bench from 14% to 19%.
1:37:07ENA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.Presenta: Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.
49:50ENUnconstrained agents captivated Twitter. Custom ones are what ship.LangChain founder Harrison Chase tells Sequoia’s Training Data what an agent actually is, why AutoGPT stalled, and which cognitive architectures reach production.
1:00ENAndrew Ng on Four Agentic AI Design PatternsMatthew Berman, Andrew Ng · Forward FutureA short excerpt identifies reflection, tools, planning and multi-agent collaboration as approaches to building AI agents.
29:28ENDevin Will Not Replace Engineers. It Will Multiply Them, Says Cognition’s Scott WuPresenta: Sarah Guo, Elad GilThe IOI champion behind Devin explains why an AI software engineer that you can watch over the shoulder creates more human engineers, not fewer.
16:22ENHarrison Chase on Planning, Interface Design and Memory for AI AgentsMatthew Berman, Harrison Chase · Forward FutureThe LangChain CEO outlines open questions for agent builders, with commentary from Matthew Berman on oversight, workflows and memory.
13:59ENSWE-agent Tutorial: Navigating Repositories and Attempting GitHub FixesMatthew Berman, Carlos · Forward FutureMatthew Berman sets up a research coding agent, hits an installation problem and a cost limit, then shows an author’s successful SymPy benchmark example.
27:32ENFrom a side project to 700 integrations: Harrison Chase on what still blocks agentsPresenta: Sarah Guo, Elad GilThe LangChain CEO walks through a two-year churn of patterns, and why memory, planning, and few-shot data still matter more than swapping models.
42:14ENMeta’s $197 Billion Day Was a Partial Answer to AI’s $200 Billion QuestionPresenta: Sarah Guo, Elad GilSarah Guo and Elad Gil walk through Gemini, Mistral, NVIDIA’s upgrade cycle, Klarna’s support bot, and why the real AI spend still sits with the hyperscalers.
55:29ENBuilding a CrewAI Newsletter Team: Scraping, Summarizing and DebuggingMatthew Berman, Joe · Forward FutureMatthew Berman builds a two-agent newsletter prototype, works through input and scraping errors, and shows what still needs to be fixed.
4:56ENIf the Mind Is a Tool, AI Looks Less Like a ThreatPresenta: Nathan LabenzYohei Nakajima and Nathan Labenz pause a TED AI talk to connect agent-building habits with East–West ideas of identity.
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