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1:08:02EN
Sequoia Capital· 13 oct 2026PódcastAlphaGo 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.Modelos y entrenamientoReinforcement LearningRL environments & simulators+3
56:31EN
Unsupervised Learning: With Jacob Effron· 6 oct 2026PódcastPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabPresenta: Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.Modelos y entrenamientoPost-trainingReinforcement Learning+3
38:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 18 sept 2026Pódcast · 2 vocesDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom SiliconPresenta: Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.Modelos y entrenamientoInferenceAI cost & performance+3
1:56:40EN
Cognitive Revolution "How AI Changes Everything"· 8 ago 2026PódcastThe Periodic Table Was Already in the WeightsPresenta: Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.Modelos y entrenamientoModel InterpretabilityIngeniería de agentes+3
47:22EN
Sequoia Capital· 4 ago 2026PódcastFrom 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.Modelos y entrenamientoAI for scienceScaling Laws+3
49:12EN
Sequoia Capital· 29 jul 2026PódcastThe 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.Modelos y entrenamientoAI researchPretraining & midtraining+3
2:06:41EN
Cognitive Revolution "How AI Changes Everything"· 9 jul 2026PódcastZero the J-Space and a Model's Hidden Goal Lights Up on Token OnePresenta: Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.Modelos y entrenamientoModel InterpretabilitySeguridad y gobernanza+3
50:57EN
Unsupervised Learning: With Jacob Effron· 9 jul 2026PódcastA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen SchmidhuberPresenta: Jacob EffronThe AI pioneer tells Unsupervised Learning's Jacob Effron why he is wildly optimistic about the technology and deeply pessimistic about the model companies buying the chips.Modelos y entrenamientoAGIAI research+3
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· 4 jul 2026Pódcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipPresenta: Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.Modelos y entrenamientoLocal inferenceScaling Laws+3
2:59:44EN
Cognitive Revolution "How AI Changes Everything"· 3 jun 2026PódcastTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Presenta: Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.Modelos y entrenamientoContinual learningTransformers & attention+3
1:13:33EN
Unsupervised Learning: With Jacob Effron· 3 jun 2026PódcastA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still MissPresenta: Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.Modelos y entrenamientoAI researchAgentes de programación+3
59:41EN
Unsupervised Learning: With Jacob Effron· 22 may 2026PódcastThe GPT moment for video hasn’t arrived, but AGI may already be herePresenta: Jacob EffronA day after Google I/O 2026, Gemini co-lead Oriol Vinyals argues that world models, file-system memory, and infinite-complexity RL environments will define the next era of frontier models.Modelos y entrenamientoWorld modelsReinforcement Learning+3
1:21:56EN
Unsupervised Learning: With Jacob Effron· 15 may 2026PódcastYann LeCun Left Meta Because LLMs Will Not Get You to IntelligencePresenta: Jacob EffronThe Turing Award winner tells Jacob Effron why he spun AMI Labs out of Meta, why JEPA-style world models beat pixel generators, and why he thinks Hinton changed his mind in 2023.Modelos y entrenamientoWorld modelsAI research+3
1:48:43EN
Cognitive Revolution "How AI Changes Everything"· 1 may 2026PódcastThe reward jumped from 3 to 180. Every Hacker News title was a Google layoff.Presenta: Nathan LabenzCoreWeave's Kyle Corbitt walks Nathan Labenz through GRPO, rubrics, RL environments, and why most reward hacks are easy to catch once you know to look.Modelos y entrenamientoReinforcement LearningPost-training+3
58:46EN
Unsupervised Learning: With Jacob Effron· 9 abr 2026PódcastTwo Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028Presenta: Jacob EffronJakub Pachocki tells Jacob Effron why those timelines still hold, why continual learning is not a detour, and why showing chain of thought in the product would train the monitor away.Modelos y entrenamientoContinual learningReinforcement Learning+3
1:49:53EN
Cognitive Revolution "How AI Changes Everything"· 5 mar 2026PódcastDon't Fight Backprop: Goodfire's Bet on Designing What Models LearnPresenta: Nathan LabenzDan Balsam and Tom McGrath return to The Cognitive Revolution with a $150 million Series B, a new research pillar called intentional design, and a rule for anyone who wants to steer training: stop trying to out-muscle gradient descent.Modelos y entrenamientoModel InterpretabilitySeguridad y gobernanza+3
1:34:29EN
Cognitive Revolution "How AI Changes Everything"· 18 feb 2026PódcastThree IMO Golds Missed the Same Problem. Harmonic Wants Math to CompilePresenta: Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.Modelos y entrenamientoReasoning & test-time computeAI research+3
1:04:51EN
Cognitive Revolution "How AI Changes Everything"· 1 feb 2026Pódcast125 million cells took a decade. The next billion takes monthsPresenta: Nathan LabenzMark Zuckerberg and Priscilla Chan on pairing a frontier biology lab with a frontier AI lab so models train on data that was designed for them.Modelos y entrenamientoAI for scienceData quality & curation+3
4:25:13EN
Lex Fridman· 31 ene 2026PódcastThe Architecture Barely Changed. Data, Cost, and Open Weights Did.Presenta: Lex FridmanNathan Lambert and Sebastian Raschka walk Lex Fridman through a year in which training methods and datasets moved more than model design, and 2026 looks like a fight over inference cost, agents, and who owns truly open models.Modelos y entrenamientoScaling LawsOpen-weight models+3
1:03:00EN
Unsupervised Learning: With Jacob Effron· 29 ene 2026PódcastA Model That Solves New Math Problems Every Week Still Isn't AGIPresenta: Jacob EffronFormer OpenAI research VP Jerry Tworek on why scaling still works, why models go hopeless when they fail, and why he left to chase continual learning.Modelos y entrenamientoReinforcement LearningAGI Timelines+3
2:25:10EN
Cognitive Revolution "How AI Changes Everything"· 22 ene 2026PódcastFine-Tuning Isn't Dead — But It Turned a Helpful Model EvilPresenta: Nathan LabenzNathan Labenz on why prompting replaced fine-tuning for most work, what emergent misalignment actually showed, and how he is thinking about jobs, UBI, and AGI.Modelos y entrenamientoAjuste finoSeguridad y gobernanza+3
1:01:38EN
Sequoia Capital· 6 ene 2026Pódcast13-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.Modelos y entrenamientoRoboticsReinforcement Learning+3
41:36EN
Sequoia Capital· 18 nov 2025PódcastFour 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.Modelos y entrenamientoAutonomous DrivingWorld models+3
1:55:12EN
Cognitive Revolution "How AI Changes Everything"· 8 nov 2025Pódcast28,000 Documents, a 3B Model, 22 People: The Technical Ledger of Catholic AIPresenta: Nathan LabenzLongbeard CEO Matthew Harvey Sanders tells The Cognitive Revolution how a mission-driven team is aligning models to the Catholic magisterium — and why fine-tuning was not enough.Modelos y entrenamientoGeneración aumentada por recuperaciónAjuste fino+3
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