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1:35:29ENUber Burned a Year of Tokens in 13 Weeks. Retrieval Is How Agents Win진행자 Nathan LabenzMongoDB Field CTO of AI Pete Johnson argues that agent performance now hinges on retrieval quality, not on stuffing million-token context windows.
44:52ENAn 80GB Wikipedia Cache Versus a 100GB Model That Holds the InternetEngram’s founders argue that stuffing a company into the context window is the expensive kind of memory—and that baking it into the weights can cut token use by two orders of magnitude.
2:38:48ENA thousand searches for five cents, and a high score that did not require cheating진행자 Nathan LabenzOn a Cognitive Revolution morning show, Ceramic’s Anna Patterson collides cheap search with GPT-5.5’s clean Vending-Bench run, Zvi’s case for model welfare, and analog chips that cut matrix-multiply energy about thirtyfold.
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.
1:41:34ENChat Is the Universal Interface—and Often the Worst One진행자 Nathan LabenzAtlassian's head of AI on fighting slop with taste, knowledge, and workflow, and why chat is a terminal, not a product.
1:55:12EN28,000 Documents, a 3B Model, 22 People: The Technical Ledger of Catholic AI진행자 Nathan LabenzLongbeard CEO Matthew Harvey Sanders tells The Cognitive Revolution how a mission-driven team is aligning models to the Catholic magisterium — and why fine-tuning was not enough.
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.
44:49ENHow OpenEvidence Became the Clinical OS for 40% of U.S. Doctors진행자 Sarah Guo, Elad GilFounder Daniel Nadler tells No Priors how a free search tool for physicians, not an answer engine for patients, took high-stakes clinical knowledge in 18 months.
1:25:12ENHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line Repo진행자 Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
44:26ENAn AI Application Is Still an ApplicationMongoDB's Sahir Azam tells Sequoia why vector search is a new primitive for unstructured data, not a replacement for transactions, and why retrieval quality is the last mile that gets AI into conservative enterprises.
56:07ENBox Holds 100 Billion Files. Its CEO Says Enterprises Only Move for a 10x진행자 Nathan LabenzAaron Levie tells Nathan Labenz why AI excitement in the enterprise already outruns the early cloud — and why 98% reliability, seat-based SaaS, and incremental savings still will not ship.
1:29:00EN14,000 people applied. Thirty-three got in. The jobs are at Homeland Security.진행자 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.진행자 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.
1:03:07ENReasoning 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.
44:16ENCohere's Aidan Gomez: The Model Curve Is Flattening, and $20 Million Now Buys GPT-4진행자 Sarah Guo, Elad GilThe Attention Is All You Need co-author tells No Priors why enterprises should stop waiting for the next model and start putting today's models into production.
55:16ENGemini’s API Grew 14× in Six Months. Google’s PMs Explain the Catch-Up.진행자 Nathan LabenzLogan Kilpatrick and Shrestha Basu Mallick walk through search grounding, Gemini Flash’s price-latency quadrant, and why a near-tied leaderboard still leaves Google third in most developers’ queues.
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.
38:28ENHalf of Production AI Is RAG. Fine-Tuning Almost Disappeared.진행자 Sarah Guo, Elad GilBraintrust CEO Ankur Goyal tells No Priors what Notion, Zapier, and Vercel actually ship — and which hyped techniques they quietly dropped.
2:03:55ENThe More You Constrain an Agent, the More You Can Trust It진행자 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.
1:51:00ENStart With Ten Gold Examples: A Three-Step Playbook for AI Automation진행자 Nathan LabenzThe day after OpenAI opened GPT-4o fine-tuning, Nathan Labenz unpacks a three-year method for choosing work, extracting tacit judgment, and raising model performance.
1:21:53ENHippoRAG Matches Iterative Retrieval at One-Tenth the Online Cost진행자 Nathan LabenzA hippocampus-inspired knowledge graph lets multi-hop questions skip the brute-force document crawl.
3:02:16ENThe journey begins after the answer: Perplexity CEO Aravind Srinivas on search and curiosity게스트 Aravind Srinivas · 진행자 Lex FridmanIn 2022, Perplexity's first hire asked about health insurance — and four PhDs could not get a clear answer from Google.
36:20ENA Hundred Million Tokens in Context Would Cost $10 Million. Tengyu Ma Runs the RAG Math진행자 Sarah Guo, Elad GilThe Stanford professor and Voyage AI founder argues that retrieval, not a giant context window, is how enterprises should manage private knowledge.
1:27:01ENThree Million Emails, Ten LLM Calls, Six Models: Shortwave's RAG Playbook진행자 Nathan LabenzFirebase founder Andrew Lee walks Nathan Labenz through the retrieval stack that turned a Gmail inbox into a working AI assistant.
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