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1:37:52ENChina Has 17 Million Chatbot Users and No OpenAIHost Nathan LabenzA Beijing-based watcher walks through China's LLM players, products, regulation, and the delayed bill from the U.S. chip ban.
1:28:37ENTwelve Hours After a GPT-4 Demo, He Turned a Decade-Old Legal Startup AroundHost Nathan LabenzPablo Arredondo, co-founder of CaseText and now VP of CoCounsel at Thomson Reuters, traces legal research from 19th-century citators to a retrieval-anchored GPT-4 product that law firms treat more like a colleague than a search box.
52:41ENA Broken Belt Buckle Turned Gucci's Call Center Into a Sales FloorHost Jacob EffronSalesforce AI CEO Clara Shih on Service GPT, Einstein Copilot, and why trust—not cost—is the real bar to enterprise scale.
1:30:41ENThe Model Is a Baked Cake. NYT v. OpenAI Will Likely Cut a Middle PathHost Nathan LabenzTech lawyer Cecilia Ziniti maps the Constitution’s IP clause onto the New York Times lawsuit — and explains why a court is unlikely to order GPT-4 destroyed.
1:53:08ENWhen GPT-4 Writes SQL, Four Tables Is the Breaking PointHost Nathan LabenzJuan Sequeda on why a knowledge graph is the accurate, explainable brain that foundation models do not have.
1:22:43ENFive billion minutes of calls: why Dialpad trained its own business LLMHost Nathan LabenzDan O’Connell on real-time speech, verticalizing the stack, and why he and Nathan Labenz disagree about when AI employees show up.
59:28ENFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductHost Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
1:10:11ENTrying ChatGPT is not the same as using itHost Nathan LabenzRachel Woods of The AI Exchange tells Nathan Labenz what she learned from more than a hundred company calls: start inside the business, skip the annual tool contract, and expect to be using only a sliver of what the models can already do.
1:27:23ENGoing All In on AI at Cisco: Jeetu Patel on Platforms, Packet Loss and Telling People the TruthHost Lenny RachitskyCisco's president and chief product officer explains how a 90,000-person company went AI-first, what Cisco does in the AI infrastructure stack, and the leadership and career lessons behind it.
1:37:29ENInside Palantir's Founder Factory: Forward-Deployed Engineers, Ontologies and Deliberately Off-Putting HiringHost Lenny RachitskyFormer Palantir engineer Nabeel Qureshi explains how a services-heavy company became a data-platform giant, and why it keeps producing founders and product leaders.
54:44ENLegal Tech's Step-Function Moment: Why Legora Bet on the Last 20%Host Jacob EffronLegora CEO Max explains the $550M Series D, AI-native legal work, foundation-model competition, and the culture required to keep moving fast.
1:01:47ENInside Notion AI: Prototypes That Annoy Everyone, Evals Built In-House, and Models Rented from PartnersHost Jacob EffronNotion AI engineer Linus Lee explains how a dozen-person team went from a hotel-room hackathon to Writer, Autofill and workspace Q&A.
1:52:49ENInside Anthropic's Growth Team: Big Bets, Automated Experiments and Firefighting SuccessHost Lenny RachitskyAmol Avasare, Anthropic's Head of Growth, explains how a roughly 40-person team handles hypergrowth, automates experimentation with Claude, and rethinks the PM and engineer ratio.
51:30ENLess Email, Better Thinking: Grammarly’s CEO on AI That Augments CommunicationHost Jacob EffronGrammarly CEO Rahul explains how the company is moving from grammar checks to end-to-end communication assistance—and why evals, user data, and measurable outcomes matter.
1:04:25ENInside GitHub's Copilot Bet: Agents, Open Source, and Why the Developer Job EnduresHost Jacob EffronGitHub CEO Thomas explains how Copilot went from a research bet to 1.3 million developers, and what agents, fine-tuning and a multi-model world mean for the next five years of software.
57:08ENShipping Finn: Intercom's Product Playbook for the ChatGPT EraHost Jacob EffronDes Traynor explains how Intercom pivoted to AI, shipped Finn, built guardrails, and turned customer support into a product lab for LLMs.
50:03ENCopilot Is a Copilot, Not a Pilot: Inbal Shani on the Future of AI in Software DevelopmentHost Lenny RachitskyGitHub's chief product officer explains what AI changes for developers, what it does not replace, and how teams should adopt it.
1:09:00ENThe Meeting Is the Moat: Fireflies.ai’s Path to 16 Million UsersHost Jacob EffronKrish Ramani explains how Fireflies.ai turned AI note-taking into a meeting chief of staff, why he avoids fine-tuning, and how a startup competes with Microsoft, Zoom, and Google.
1:04:35ENOscar Health's AI Playbook: Translate the Bureaucracy First, Then Reinvent CareHost Jacob EffronMario, Oscar Health's CTO and co-founder, explains how a regulated insurer deploys LLMs today and what stands between healthcare and AI doctors.
54:05ENServal's Jake Stout on Outbuilding ServiceNow: Mirror the System of Record, Bet on Code, and Ship Before the Incumbent Can Book a MeetingHost Jacob EffronHow an AI-native employee-support startup attacks a legacy ITSM incumbent with velocity, code-generated workflows and a permissions-first architecture.
57:25ENFrom RAG to Reasoning: Why the Future of AI Is a System, Not a ModelHost Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.
51:55ENThe Search Engine Under AI Apps: Why Bigger Context Windows Aren't EnoughHost Jacob EffronTurbopuffer CEO Simon on object-storage databases, vector-search trade-offs, and the AI features that will become SaaS table stakes.
40:58ENBuilding AI Products When the Ground Keeps MovingHost Lenny RachitskyOpenAI's Kevin Weil and Anthropic's Mike Krieger discuss how AI product management differs from traditional software, from evals and enterprise buyers to computer use, reasoning models, and proactive agents.
1:42:11ENTwenty Agents, 1.2 Humans: Inside SaaStr's AI-Run Sales TeamHost Lenny RachitskyJason Lemkin explains how SaaStr replaced a ten-person go-to-market team with AI agents, what it took to train them, and who survives the shift.
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