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22:32ENThe $5.5 Million Was Only the Last MileAnime : Sarah Guo, Elad GilOn No Priors' 100th episode, Sarah Guo and Elad Gil separate DeepSeek's paper cost from the compute behind it, then walk through OpenAI's Deep Research, Stargate, and their bets for 2025.
1:48:20ENFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the TableAnime : Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.
1:40:22ENTwenty-atom crystals taught an AI the physics of a heartbeatAnime : Nathan LabenzOrbital Materials' Jonathan Godwin and Tim Duignan tell Nathan Labenz how a neural network trained on tiny crystals simulated a potassium ion channel on a single 2017 GPU.
2:04:45ENo3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-ToeAnime : Nathan LabenzNathan Labenz walks through OpenAI's o3 scores, deliberative alignment, and why 2024's biggest surprise was beating PhDs while failing to book a calendar invite.
1:07:32ENo1 Is a New Generation — and the Day AGI Arrives Will Feel BoringAnime : Jacob EffronOpenAI’s former chief research officer Bob McGrew on why o1 is a 100x leap, why agents stall on reliability, and why AGI may land as office tedium rather than a single dramatic moment.
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.
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own BaseAnime : Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.
5:15:00ENDario Amodei Has Seen This Scaling Movie Enough TimesInvité : Dario Amodei · Anime : Lex FridmanAnthropic’s CEO puts numbers on frontier training costs and AGI, then hands the mic to the people who shape Claude’s character and look inside its weights.
2:24:18ENMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.Anime : Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.
41:42ENNeural 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.
1:06:48ENo1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning PointAnime : Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.
45:22ENAlphaGo 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.
39:45ENHarmonic’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.
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.
2:04:15ENA parrot cannot paraphrase — and Claude cannot be talked into harmAnime : Nathan LabenzRobert Wright of the Nonzero podcast interviews Nathan Labenz on whether large language models understand anything, what Anthropic’s 10 million features actually show, and why open weights collide with U.S.–China policy.
46:09ENOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are NumberedAnime : Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.
1:42:16EN267 Papers in Seven Months: Albert Gu Explains the State Space Model RevolutionAnime : Nathan LabenzThe Mamba co-author walks through five years of recurrent-model work, why selective compression beats a growing KV cache, and what Mamba 2 trades away to train faster.
34:09ENFive Independent Teams Found the Same 10-to-1 Mix That Beats TransformersAnime : Sarah Guo, Elad GilThe authors of S4 and Mamba argue that mostly state-space layers plus a dash of attention is the architecture that actually works—and they are shipping it first as real-time speech.
1:54:01ENA Tiny Network Grew a Salt Crystal the Training Data Never ShowedAnime : 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.
1:37:07ENA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.Anime : Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.
1:59:15ENA Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI BiologyAnime : 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.
58:28ENFrom Jailbreaking ChatGPT to Anthropic’s First Prompt Engineer: How Alex Albert Ships Claude 3Anime : Nathan LabenzA year after building jailbreakchat.com, Alex Albert walks Nathan Labenz through Claude 3’s writing, orchestration, character work, and the guardrails app developers still skip.
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.
1:19:24ENA Gibberish Prompt Unlocks What GPT-J Plans Three Tokens AheadAnime : Nathan LabenzNortheastern’s Bau Lab shows that a 40,000-parameter soft prompt can read a model’s future words, and that about ten attention heads carry the task itself.
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