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1:08:02EN
Sequoia Capital· 13 oct. 2026PodcastAlphaGo Took Two Years. AlphaZero Took Hours. Silver Is Making the Same Bet on SuperintelligenceDavid Silver and Lasse Espeholt left DeepMind to build Ineffable Intelligence, a twenty-person lab that wants a superlearner to discover science, math, and language from experience rather than from a pre-training run.Modèles et entraînementReinforcement LearningRL environments & simulators+3
1:04:26EN
Sequoia Capital· 6 oct. 2026PodcastAt 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.Infrastructure et déploiementAI cost & performanceInference+3
1:05:21EN
Sequoia Capital· 15 sept. 2026PodcastThe Bridge From Model to Workflow Is a Trillion-Dollar BusinessBox CEO Aaron Levie argues that the money in this cycle sits in the messy gap between frontier models and real enterprise work — and that coding's overnight diffusion will not repeat for everyone else.Entreprise et stratégieIA en entrepriseAI Strategy+3
52:27EN
Sequoia Capital· 1 sept. 2026PodcastAfter Two Dozen Police Departments Said No, They Built an Anti-Network-Effect AI CompanyA Wisconsin county had 300 gigabytes sitting on a cold case—and Peregrine's founders still refuse to take the credit.SécuritéForward-deployed engineeringCompany Building+3
55:19EN
Sequoia Capital· 25 août 2026PodcastHuman Click Data Is a Bug: Rebuilding Search for a Web of AgentsParag Agrawal, former Twitter CEO and founder of Parallel Web Systems, argues that agents will query the web a thousand times more than people, and that both search infrastructure and the ad-supported internet have to be rebuilt for that customer.AgentsSearch & retrievalIngénierie d’agents+3
53:44EN
Sequoia Capital· 18 août 2026PodcastThe Biggest Bug in Today’s Models Is That the Weights Never ChangeTuring Award winner Rich Sutton and Khurram Javed founded Oak Lab to build agents that keep learning after they ship — and they call synthetic data a big mistake.AgentsContinual learningReinforcement Learning+3
47:22EN
Sequoia Capital· 4 août 2026PodcastFrom One in a Thousand to 15 Percent: Chai Discovery Treats Drug Design as a Scaling ProblemJoshua Meier and Matthew McPartlon tell Sequoia’s Training Data why antibody design is an engineering problem, not a bespoke science, and why they sell a molecular CAD suite instead of their own drugs.Modèles et entraînementAI for scienceScaling Laws+3
49:12EN
Sequoia Capital· 29 juil. 2026PodcastThe People Writing the Transformer’s Eulogy Want a Lab That Can Take Vacation Without Themo1’s reasoning lead and a Gemini pre-training lead argue the architecture, not more scale, is now the bottleneck — and that replacing it starts with automating kernel generation.Modèles et entraînementAI researchPretraining & midtraining+3
51:31EN
Sequoia Capital· 21 juil. 2026PodcastThey Refunded Nearly $2 Million. Then Factory’s CEO Explained the Dark FactoryMatan Grinberg on being two years early, why a model-agnostic harness beats lab co-design, and why 90 percent of coding tokens will soon run with the lights off.Agents de codeHarness engineeringModel routing & gateways+3
48:55EN
Sequoia Capital· 14 juil. 2026PodcastAnthropic'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.AgentsIngénierie d’agentsAgent orchestration+3
55:19EN
Sequoia Capital· 7 juil. 2026PodcastThe drone is 15% of Zipline: 140 million miles, zero incidentsKeller Rinaudo Cliffton and Eric Watson explain why the aircraft is the small part of an automated logistics system now cheaper than a car.Infrastructure et déploiementRoboticsAutonomous Systems+3
1:10:15EN
Sequoia Capital· 30 juin 2026PodcastThe 100x lives between the layers: Dylan Patel on co-design and a 60x yearSemiAnalysis’s founder argues that matching models, kernels, and silicon is what turns a 2x here and a 2x there into 100x, while equivalent-quality inference has gotten about 60 times cheaper in a year.Infrastructure et déploiementInferenceBenchmarks+3
44:52EN
Sequoia Capital· 24 juin 2026PodcastAn 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.RAG et connaissancesContinual learningFine-tuning+3
38:45EN
Sequoia Capital· 16 juin 2026PodcastThe 25-agent town that became a Fortune 500 decision sandboxOn the day before Valentine's Day in a Stanford simulation, cafe owner Isabella decided to throw a party — no human wrote the plot.AgentsMulti-agent systemsIngénierie d’agents+3
51:09EN
Sequoia Capital· 11 juin 2026PodcastBuild like the model will eat your harness in a yearLogan Kilpatrick, who leads Google AI Studio and the Gemini API, tells Sequoia's Sonya Huang why agent scaffolding is a 12-month asset, why coding already feels like narrow superintelligence, and why Google is optimizing for outcomes instead of eyeballs.AgentsIngénierie d’agentsHarness engineering+3
41:20EN
Sequoia Capital· 10 juin 2026PodcastA $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.Infrastructure et déploiementAI StrategyAI cost & performance+3
40:22EN
Sequoia Capital· 2 juin 2026PodcastBuilding Gets Easier Near AGI. Knowing What to Build Does Not.Listen Labs CEO Alfred Wahlforss on AI interview agents, a 30-million-person audience, and why transcripts beat credit-card data when you need to predict customers.Produit et UXProduct StrategyAI Product Management+3
45:33EN
Sequoia Capital· 26 mai 2026PodcastCursor Ships 1TB of Composer Weights Across Oceans in Under a MinuteFederico Cassano and Fireworks' Dima walk through the globally split RL stack that trained Composer 2 on a sparse trillion-parameter MoE.Infrastructure et déploiementDistributed training & inferenceReinforcement Learning+3
38:24EN
Sequoia Capital· 19 mai 2026PodcastServal's Bet: Keep ServiceNow's Primitives, Take Humans Out of the BuildJake Stauch tells Sequoia's Pat Grady how a two-year-old ITSM startup uses codegen agents, boring enterprise controls, and "fewer, better" hiring to rebuild employee support for the AI era.Entreprise et stratégieIA en entrepriseWorkflow automation+3
34:56EN
Sequoia Capital· 13 mai 2026PodcastOn Suno, 90% of Daily Users Make a SongCEO Mikey Shulman on modeling raw sound instead of music theory, why music is not an LLM-style scale problem, and treating creation itself as the entertainment.Multimodal et médiasGenerative mediaProduct Strategy+3
31:26EN
Sequoia Capital· 14 avr. 2026PodcastChatGPT Opened 65 Pages to Buy a Shower HeadProfound CEO James Cadwallader argues that the internet's front door is the same, but an agent now walks through it, and marketing has to be rewritten for that visitor.Entreprise et stratégieAI in sales & marketingGo-to-Market+3
58:27EN
Sequoia Capital· 24 mars 2026PodcastEven a dumber AI can win science if it never leaves the labGinkgo Bioworks CEO Jason Kelly tells Sequoia why autonomous labs, not smarter models, will change how experimental science gets done.AgentsAI for scienceIngénierie d’agents+3
44:19EN
Sequoia Capital· 17 mars 2026PodcastThe $500-per-Kilogram Line: Why Starcloud Thinks Data Centers Belong in SpaceStarcloud founder Philip Johnston walks through the physics, launch economics, and business model of putting AI compute in orbit.Infrastructure et déploiementInferenceAI cost & performance+2
40:56EN
Sequoia Capital· 10 mars 2026PodcastFour of the Top Five Defense Primes Still Run Hardware Tests on PowerPointNominal’s cofounders argue that hardware never got the last twenty years of software infrastructure, and that physical AI will stall until test data lives in one place.Infrastructure et déploiementData quality & curationObservabilité et fiabilité+3
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