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1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipAnime : 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.
1:13:33ENA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still MissAnime : 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.
4:25:13ENThe Architecture Barely Changed. Data, Cost, and Open Weights Did.Anime : 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.
1:03:00ENA Model That Solves New Math Problems Every Week Still Isn't AGIAnime : 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.
1:18:05ENThe Real Bottleneck in AI Research Is Compute, Not IdeasAnime : 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.
1:39:05ENThe Summer GPT-5 Disappointed Does Not Mean AI Progress Has StalledAnime : Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.
1:02:05EN$100 Million Researchers and a $200 Billion xAI: An AI Roundtable Does the MathAnime : Jacob EffronDatology’s Ari Morcos and Radical Ventures’ Rob Toews argue over whether models are plateauing, whether RL-environment startups will last, and why Google is the lab they would pick.
51:08ENThe Transformer Co-Author Who Says Scaling Hit a WallAnime : Jacob EffronCohere CEO Aidan Gomez tells Redpoint that a $100 million model is still an amnesiac genius, and that enterprise AI money is concentrating in support, research, and the back office.
1:10:23ENA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still UntappedAnime : Jacob EffronThe Transformer co-inventor and Gemini researcher Jack Rae walk through test-time compute, Ilya Sutskever's AGI critique, and a four-year-old who now lectures his teacher about lizards.
48:18ENNoam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of HeadroomAnime : Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.
2:40:58ENZvi Mowshowitz Still Puts P(doom) at 60%—and Says Not Updating Is Self-DeceptionAnime : Nathan LabenzA six-month recap with the Don't Worry About the Vase author covering Ilya’s SSI, OpenAI’s o1, Claude’s computer use, the SB 1047 veto, and Trump’s return.
1:18:32ENA DNA Model That Can Tell Which Genes You Cannot TouchAnime : Nathan LabenzStanford's Brian Hie walks through hybrid architectures, a genome-scale language model trained only on DNA, and an antibody that binds 25 times tighter than nature.
1:23:57ENA Training Run Is a Particle Collider—and the China Contest Is a Fifty-Year RaceAnime : Nathan LabenzSamo Burja tells Nathan Labenz why ChatGPT's virality may have slowed AI science, and why Washington should treat Beijing as an economic rival rather than a military one.
1:01:55ENA $9 AI nurse, an 86 on the medical boards, and a 10% extinction lineAnime : Nathan LabenzNathan Labenz’s solo scouting report walks from medical state of the art through how models actually learn, then to live players, scaling trends, and a control plan that still does not exist.
1:01:39ENGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?Anime : Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take FiveAnime : Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.
1:33:56ENThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandAnime : Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.
1:32:22ENA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer AwayAnime : Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.
26:02ENMoore’s Law Is Not Dead—It Is a Cascade of S-CurvesAnime : Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.
1:10:19ENThe Robot That Folded the Laundry: Physical Intelligence’s Three FrontiersAnime : Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.
1:14:59ENWhy Anthropic's Ben Mann Thinks the Exponential Hasn't Slowed, and Why He Still Worries About SafetyAnime : Lenny RachitskyThe Anthropic co-founder on the AI talent war, scaling laws, the economic Turing test, jobs, leaving OpenAI, constitutional AI and how he forecasts the risk of superintelligence.
1:19:48ENBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep LearningAnime : Lex FridmanIn a 2016 Deep Learning School talk, Andrew Ng distills lessons from leading Baidu's AI team into a workflow for diagnosing models, handling data and building a career.
1:13:40ENThe Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing MomentAnime : Jacob EffronVincent, a distinguished engineer at Waymo, explains how foundation models became teachers for self-driving, why the long tail and causality are the real frontier, and where robotics still lacks its generalist proof point.








