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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
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
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
41:36EN
Sequoia Capital· 18 nov. 2025PodcastFour Months to Drive Tokyo: How Wayve Bet the Company on One End-to-End NetworkWayve CEO Alex Kendall tells Sequoia’s Training Data why AV 2.0 ditches HD maps and hand-written stacks for a foundation model that can adapt to a new car and a new country in weeks.Modèles et entraînementAutonomous DrivingWorld models+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
48:51EN
Sequoia Capital· 17 juin 2025PodcastThe Breakthroughs for AGI Are Already Done, Says OpenAI’s Former Research HeadBob McGrew tells Sequoia’s Training Data that 2025 is the year of reasoning, agents will be priced at the cost of compute, and nothing fundamental will be added to the stack by 2035.Modèles et entraînementReasoning & test-time computePretraining & midtraining+3
58:11EN
Sequoia Capital· 15 avr. 2025PodcastPatrick Hsu Wants an App Store for Biology, Not Just Better DrugsThe Arc Institute co-founder argues that Evo 2, trained on genomes rather than proteins, should underpin tools for every step of the scientific method.Modèles et entraînementAI for sciencePretraining & midtraining+3
41:42EN
Sequoia Capital· 22 oct. 2024PodcastNeural Networks Are Still Waiting for Their ThermodynamicsOpenAI researcher Dan Roberts on why physicists keep migrating into AI, and why the field still lacks the equivalent of the ideal gas law.Modèles et entraînementAI researchScaling Laws+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
45:12EN
Sequoia CapitalPodcastInside a Robot Data Factory: How Mecka Sensorizes Humans to Train RobotsMecka AI founder Josh Gao explains how a company collects, quality-checks and licenses egocentric human data, and why robotics will arrive as a steady unlock of tasks rather than a single breakthrough.Modèles et entraînementRoboticsData quality & curation+2