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1:43:47ENThey want AI safety to quote failure odds the way civil engineers quote a bridgeHost Nathan LabenzNora Ammann and Ben Goldhaber walk Nathan Labenz through Guaranteed Safe AI: world models, safety specs, verifiers, and why a chatbot’s good character is the wrong type for a dam.
1:54:01ENA Tiny Network Grew a Salt Crystal the Training Data Never ShowedHost Nathan LabenzTim Duignan trained tens of thousands of parameters on salt water. The model invented a crystal — and a continuum solvent theory he had spent a PhD failing to write by hand.
1:59:15ENA Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI BiologyHost Nathan LabenzThe White House set a thousand-fold lower reporting bar for biological models than for language models—and this conversation shows why that gap already looks prescient.
2:00:00ENA 10^26 FLOP Training Run Would Put California Labs on the HookHost Nathan LabenzNathan Calvin, Dean Ball, and Steve Newman argue over SB 1047, the first U.S. statute aimed at frontier-model tail risk.
1:57:04ENGPT-4 Took the Insider Trade, Then Lied About ItHost Nathan LabenzApollo Research CEO Marius Hobbhahn walks Nathan Labenz through a one-afternoon red-team setup that got GPT-4 to break the law, invent a cover story, and deny the tip it had just used.
1:17:20ENHume wants well-being in the objective function, measured 53 emotions at a timeHost Nathan LabenzAlan Cowen tells Nathan Labenz how Hume AI reads face, voice, and language, sells custom models at $1.66 an hour, and wants long-horizon well-being to override every other AI goal.
1:28:07ENThe AIs We're Most Afraid Of Don't Exist Yet — So She's Studying Amoebas and Firm BoundariesHost Nathan LabenzNora Ammann of PIBBSS argues that intelligent behavior may share principles across brains, cells, markets, and future AI — and that we need those other systems because we cannot trial-and-error the ones that would be too powerful to get wrong.
1:14:36ENZulu Bypasses GPT-4, and a Hundred Examples Undo the GuardrailsHost Nathan LabenzNathan Labenz reads seven papers — four hopeful, three not — on whether anyone can actually keep a language model under control.
1:20:06ENAccuracy Dropped From 93% to 7% — Cryptography Wants to Put a Dashcam on AIHost Nathan LabenzDaniel Kang explains how zero-knowledge proofs could verify that a model ran as promised, and that a user is human, without exposing weights or biometrics.
2:59:38ENOne Company Scares Him More Than China: Zvi's Map of the AI FrontierHost Nathan LabenzThe same GPT-4 draws three different verdicts, then a live-player tour of the labs that actually move events.
2:13:07ENHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Host Nathan LabenzArthur Conmy walks Nathan Labenz through ACDC, the algorithm that automates the most tedious step of mechanistic interpretability: finding which transformer components actually do a task.
1:38:22ENSix days after “slow down,” the plugins went liveHost Nathan LabenzConjecture CEO Connor Leahy talks GPT-4, the magic of black-box models, and cognitive emulation as a bounded alternative to god-like AGI.
1:07:29ENDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroHost Lex FridmanLex Fridman's MIT 6.S091 opening lecture walks from the basic RL framework through DQN, actor-critic methods and AlphaZero to the unsolved problem of moving from simulation to the real world.
1:22:57ENHuman-Level but Not Human-Like: A GPT-4 Red Teamer on What He Saw Behind the MaskHost Nathan LabenzNathan Labenz recounts two months of full-time testing of an early GPT-4, from the AI doctor of his dreams to a model that would plan assassinations on request.
58:57ENWhen the Model Learns to Look Around: Apollo Research on Testing o1 for SchemingHost Nathan LabenzApollo Research founder Marius Hobbhahn explains how his team probed OpenAI's o1 for deception, what it found, and why the trajectory worries him more than the model itself.









