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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
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
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
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
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
37:00EN
Sequoia Capital· 14 janv. 2026PodcastThe AlphaChip Team That Laid Out Four TPUs Now Wants Every Company to Be DesignlessAnna Goldie and Azalia Mirhoseini tell Sequoia how an RL floorplanner survived TPU-team anger, learned to draw donut-shaped macros, and became a bet that AI will design chips that then feed AI.Infrastructure et déploiementReinforcement LearningAI startups & small teams+3
1:01:38EN
Sequoia Capital· 6 janv. 2026Podcast13-Hour Coffee and 2x Throughput: Physical Intelligence Crosses the Deployment LineKarol Hausman and Tobi Springenberg tell Sequoia’s Training Data how a general robot brain, plus real-world RL, turned coffee, laundry, and chocolate boxes into jobs that actually run.Modèles et entraînementRoboticsReinforcement Learning+3
30:11EN
Sequoia Capital· 30 juil. 2025PodcastFrom 12 Percent on AIME to IMO Gold in Two MonthsAlex Wei, Sheryl Hsu, and Noam Brown tell Sequoia's Sonya Huang how a three-person OpenAI sprint used general-purpose reinforcement learning, not Lean, to reach gold-level on the 2025 International Mathematical Olympiad.Modèles et entraînementReasoning & test-time computeBenchmarks+3
32:46EN
Sequoia Capital· 25 févr. 2025PodcastDon't draw the flowchart: OpenAI's Deep Research team on training agents end to endIsa Fulford and Josh Tobin explain why Deep Research was trained with reinforcement learning on hard browsing tasks instead of a human-written graph of model calls.AgentsIngénierie d’agentsReinforcement Learning+3
52:30EN
Sequoia Capital· 28 janv. 2025PodcastMove 37 looked like a bug. LLMs still need their AlphaZero momentIoannis Antonoglou, from AlphaGo to Reflection AI, on self-play, reliability, and why compute can still buy intelligence.AgentsReinforcement LearningIngénierie d’agents+3
45:22EN
Sequoia Capital· 2 oct. 2024PodcastAlphaGo Thought 30 Seconds a Move. o1 Made “Think Longer” GeneralOpenAI researchers Noam Brown, Ilge Akkaya, and Hunter Lightman on teaching language models to reason, the generator-verifier gap, and a second scaling curve at inference time.Modèles et entraînementReasoning & test-time computeReinforcement Learning+3
39:45EN
Sequoia Capital· 24 sept. 2024PodcastHarmonic’s Bet: Teach AI Math Directly, and the Riemann Hypothesis Falls by 2029Robinhood’s Vlad Tenev and Harmonic co-founder Tudor Achim tell Sequoia why math is reasoning, why Lean is the verifier, and why they refuse to wait for mathematical skill to emerge from scale.Modèles et entraînementSynthetic dataReinforcement Learning+3
49:14EN
Sequoia Capital· 17 sept. 2024PodcastHumanoid robots fell from $1.5 million to $30,000. The GPT-3 moment is still a verbNvidia’s Jim Fan tells Sequoia how three kinds of data, a humanoid bet, and a System-1 motor-control model could unlock general-purpose robots.AgentsRoboticsScaling Laws+3
51:15EN
Sequoia Capital· 10 sept. 2024PodcastAGI Has One Problem Left: Spend a Million Times More on a TokenMagic founder Eric Steinberger tells Sequoia’s Training Data why he trains his own models, why a software engineer is the MVP of AGI, and why 96% still is not a colleague.Agents de codeAGIReasoning & test-time compute+3
50:33EN
Sequoia Capital· 20 août 2024PodcastThe 40% data-center save started with an email that ended in a question markPhaidra founder Jim Gao tells Sequoia how an AlphaGo-inspired bet at Google became reinforcement learning that runs billion-dollar plants.Infrastructure et déploiementReinforcement LearningData quality & curation+3
1:07:05EN
Sequoia Capital· 16 juil. 2024PodcastAlphaGo 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.AgentsIngénierie d’agentsReinforcement Learning+3