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1:04:51EN125 million cells took a decade. The next billion takes monthsHost 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.
1:03:00ENA Model That Solves New Math Problems Every Week Still Isn't AGIHost 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.
2:25:10ENFine-Tuning Isn't Dead — But It Turned a Helpful Model EvilHost 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.
37:00ENThe 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.
1:55:02ENAn Index Fund on AGI: How MATS Filters the Next 120 AI Safety ResearchersHost Nathan LabenzRyan Kidd, co-executive director of MATS, tells Nathan Labenz why he will not shout a timeline, why every safety technique is also a capabilities technique, and who actually gets hired.
1:18:15ENIf AI Becomes a Being, Steering It Is SlaveryHost Nathan LabenzEmmett Shear and DeepMind's Séb Krier clash over whether alignment means controlling a tool or raising a creature that cares.
1:58:48ENARC Went from $1,000 a Task to Pocket Change. That Is Not a Normal Year of AIHost Nathan LabenzCognitive Revolution’s first live show compresses nine conversations into a year-end scan of denial, sample efficiency, companions, memory, Gemini 3 Flash, and scientific judgment.
1:18:05ENThe Real Bottleneck in AI Research Is Compute, Not IdeasHost Jacob EffronAfter NeurIPS, Ari Morcos and Rob Toews sit down with Jacob Effron to argue about plateauing models, SSI’s secrecy, OpenAI’s cash burn, and a set of spicy 2026 bets.
1:04:50ENUseful AI Work Is Doubling Every Four Months. Schools Should Plan Accordingly.Host Nathan LabenzIn a keynote to K-12 educators, Cognitive Revolution host Nathan Labenz argued that task length, not chat quality, is the number that should reshape how schools think.
1:11:18ENHe Got Bored Five Minutes Into a Waymo. Nathan Labenz Says That's the PointHost Nathan LabenzOn a crossover with the Existential Hope podcast, the Cognitive Revolution host argues that the scarcest resource in AI is a positive picture of daily life, not another doom scenario.
2:27:18ENTurn Down AI's Deception Knob, and It Starts Reporting ExperienceHost Nathan LabenzCameron Berg of AE Studio tells Nathan Labenz how suppressing deception features in Llama 3.3 70B pushed self-reports of subjective experience toward 100 percent.
1:01:49ENChatGPT's Co-Creator Left to Put Nature in the Reward LoopHost Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.
2:02:17ENSAEs Find the Nouns. Parameter Decomposition Looks for the VerbsHost Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.
13:30ENHow AlphaGeometry Combines Logical Proof with Creative ConstructionAleph0Aleph0 explains how a symbolic geometry solver gets surprisingly far without AI, and how a language model supplies the auxiliary constructions that take it to Olympiad level.
30:11ENFrom 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.
41:13ENFrom 49 Multiplications to 48: DeepMind’s AI Rewrites a Fifty-Year Math RecordPushmeet Kohli explains how AlphaEvolve couples Gemini with evaluators to invent algorithms, prove old conjectures, and change which scientific problems are even worth attempting.
47:07ENGoodfire’s Eric Ho thinks we will decode neural nets by 2028On Sequoia’s Training Data, the interpretability founder argues that sparse autoencoders, model editing, and a third-party lab are how AI stops being grown from data and starts being designed.
1:25:32ENo3 Invented a Voting Rule and Kept the Job: Four Weeks Inside the AI VillageHost Nathan LabenzAdam Binksmith on putting Claude, o3, and Gemini in a shared world for weeks—and why most people still imagine multi-agent life as one AI times four.
42:09ENAlphaEvolve found algorithms humans missed for 50 years — then sped up Gemini 23%Host Sarah Guo, Elad GilGoogle DeepMind’s Pushmeet Kohli and Matej Balog tell No Priors how an evolutionary coding agent searches algorithm space the way AlphaGo searched Go.
1:37:05ENAn Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a LieHost Nathan LabenzJesse Hoogland and Daniel Murfet of Timaeus argue that singularities in the loss landscape — not the smooth valleys in every slide deck — are what actually govern how neural networks learn, generalize, and go wrong.
3:14:34ENIf ten things make a problem hard, Terence Tao turns nine of them offGuest Terence Tao · Host Lex FridmanThe Fields Medalist walks Lex Fridman from the Kakeya needle and Navier-Stokes through Lean, Perelman, and why today's AI still cannot smell a dead-end proof.
1:12:29ENGoogle's AI Co-Scientist Solved a Phage Puzzle Humans Missed for YearsHost Nathan LabenzImperial College scientists José Penadés and Tiago Costa told the Podovirus podcast how a still-unreleased Google system recovered their unpublished answer: tailless capsids that pick up stray phage tails.
1:54:08ENWhy Anthropic's first check went to a company building microscopes for neural netsHost Nathan LabenzGoodfire's Dan Balsam and Tom McGrath argue that mechanistic interpretability now has a proto-paradigm, two stubborn gaps, and three products worth shipping today.
57:31ENThe New Ruler for Models Is How Long They Can WorkHost Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.
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