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19:43ENJack Wotherspoon on Building a Team of Managed AgentsJack Wotherspoon · Google DeepMindGoogle DeepMind’s Jack Wotherspoon introduces Gemini Managed Agents: cloud sandboxes that let teams run coding and research agents with a single API call. He explains how persistent environments, reusable skills, proxy-injected credentials, schedules, and specialized agents fit together.
1:04:26ENAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
45:10EN76% of Agentic Commerce Is Under 30 Cents — So Coinbase Is Banking the AIsHost Sarah Guo, Elad GilBrian Armstrong tells Elad Gil how Coinbase became an everything exchange, why card networks cannot price agent-to-agent payments, and what his longevity company is taking into the clinic.
34:30ENNetic's Agents Answer the Phone When the Heat BreaksHost Sarah Guo, Elad GilFounder Melisa Tokmak tells Elad Gil how a two-year-old platform became the AI operations layer for HVAC, roofing, pet care, and other essential services.
48:55ENAnthropic's Platform Grew Three Layers in a Year — and Tokens Now Have JobsKatelyn Lesse and Angela Jiang tell Sequoia's Training Data how Anthropic went from a stateless Messages API to knowledge, execution, and coordination — without trying to own the servers.
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.
41:20ENA $4 Million Cabinet and a Trillion-Dollar Year: Jensen Huang Turns the AI Factory Into a Manufacturing StoryOn Sequoia's Training Data stage, NVIDIA's CEO walks a sixty-country audience from ChatGPT's parlor tricks to agentic work, a five-layer industrial cake, and why radiology hiring went up.
44:01ENA millionth of a penny: Jeremy Allaire on the payment rails of the agentic economyHost Sarah Guo, Elad GilCircle's CEO tells No Priors why stablecoins and a purpose-built chain, not the legacy banking stack, are the money layer for software that buys and sells on its own.
1:37:43ENCisco wants two extra network layers so agents can share intentHost Nathan LabenzVijoy Pandey of Outshift by Cisco argues that superintelligence has to scale out through networks of agents, not just up through bigger models.
1:06:31ENAndrej Karpathy Hasn't Typed a Line of Code Since DecemberHost Sarah Guo, Elad GilOn No Priors, the OpenAI cofounder and former Tesla AI director describes the loopy era of coding agents, AutoResearch, and why the bottleneck is now the human.
2:06:23ENFive AI Employees Live on Five Mac MinisHost Nathan LabenzAfter selling her YC packaging company, Jesse Genet opened a terminal for the first time and built an OpenClaw staff that now plans lessons, logs learning, and keeps family evenings from sliding into slop.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding PipelineHost Nathan LabenzCEO Brian Elliott and CTO Sid Pardeshi tell Nathan Labenz how Blitzy turns a giant codebase into a map, then lets thousands of dynamically generated agents ship most of a multi-month project in days.
48:03ENCoding Will Be Solved. The Bottleneck Is Saying What You WantWarp founder Zach Lloyd tells Sequoia's Sonya Huang why a rebuilt terminal became the lucky form factor for coding agents, and why he stopped subsidizing tokens.
40:13ENAs long as humans still need to read code, the IDE is not dyingZed founder Nathan Sobo argues that source code is a language for people, that terminal agents still need a visual editor, and that conversations should be anchored to every edit.
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.
1:32:47ENA transformer co-author wants a $160 million model no company ownsHost Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
35:45ENSix Years of Revenue, Then Four Times That in Eight Monthsn8n CEO Jan Oberhauser tells Sequoia how two bets — becoming an AI-agent builder, then deleting the leads metric — turned a workflow tool into a default orchestration layer.
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.
47:49ENWorkday Is Onboarding AI Agents the Way It Onboards PeopleWorkday CEO Carl Eschenbach on turning a 70-million-person system of record into the ledger for a mixed workforce of humans and digital workers.
55:40ENTo Solve All Diseases, Isomorphic Labs Needs a Half-Dozen AlphaFoldsMax Jaderberg on turning DeepMind's game-playing research into a general AI engine for drug design—and why prediction alone cannot search a 10^60 molecular universe.
1:29:02ENShe Never Sees a Row of Your Data. The Answers Still Clear 95%.Host Nathan LabenzIllumex CEO Inna Tokarev Sela tells Nathan Labenz how a Platonic industry ontology, trained only on metadata, collapses enterprise software into one conversation.
30:09ENBilt Saved 65 Support Seats. Decagon's Founder Says Most Agent Ideas Still Won't SellHost Sarah Guo, Elad GilJesse Zhang on why customer support is the golden agent use case, why instruction following beats o1-style reasoning, and the two filters that kill most agent startups.
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.
55:50ENThere will not be one model. Fireworks is betting on hundreds of small expertsHost Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.
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