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2:00:51ENResample Every Token and You Can Watch the Model DecidePresenta: Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
56:31ENPost-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.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models BannedPresenta: Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
2:16:29ENFrom Two Papers a Month to Sixty: What Two Weeks in China Revealed About AI SafetyPresenta: Nathan LabenzNathan Labenz argues that the familiar 'China will never slow down' line does not survive contact with the actual Chinese AI safety ecosystem.
1:47:38EN19 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.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.Presenta: Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-ReadyPresenta: Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
4:25:13ENThe 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.
1:54:30ENFrom One in Ten Cancer Cells to One in a MillionPresenta: Nathan LabenzNathan Labenz's solo AMA updates his son's lymphoma treatment, calls Claude Opus 4.5 software AGI, and ranks the live players of early 2026.
1:18:40ENWhen AI Becomes the Oil Well: Luke Drago on the Intelligence CursePresenta: Nathan LabenzThe Workshop Labs co-founder tells the FLI Podcast why a world of non-human production could recreate the resource curse — and how private data, open weights, and moonshot careers might still break it.
1:32:38ENPolish Is 1% of Frontier Training Data, So Poland Built Its Own ModelPresenta: Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.
1:09:48ENChina's AI Labs Went From 15 to Six. The Compute Gap Is 15-to-1.Presenta: Jacob EffronJordan Schneider tells Unsupervised Learning that Chinese models were a private-capital story, that Beijing still thinks in hardware, and that the West's real edge is chips it can serve — not a state master plan.
1:25:54ENHours after training ends, Z.ai puts the weights on the internetPresenta: Nathan LabenzZixuan Li of Z.ai tells ChinaTalk how a Chinese lab ships GLM, treats open weights as a ticket into Western ecosystems, and still sees itself as an upstart.
2:14:55ENFrom Character AI to a 1% Warning Shot: Gabriel Weil's Case for Liability as AI GovernancePresenta: Nathan LabenzA Touro law professor tells Nathan Labenz how negligence, strict liability, and punitive damages could make frontier labs treat public risk as a hit to their own balance sheet.
54:53ENA Free Voting Board Burned $2 Million. Then It Raised $100 Million.Presenta: Jacob EffronDatabricks co-founder Ion Stoica explains why LMArena exists, why China is winning open-source models, and why reliability—not another scaling law—is still the thing holding AI back.
1:30:57ENAnt's $320 Billion IPO Was Stopped at the Eleventh Hour: Angela Zhang on China's Tech TightropePresenta: Nathan LabenzA USC law professor walks through the dynamic pyramid that governs Chinese tech, from Didi's listing to DeepSeek's price war, and why she thinks the United States is starting to look more like China.
45:06ENMike Schroepfer: AI’s Bottleneck Is Electricity, and One Tanker of Fusion Fuel Could Power the U.S. for a YearPresenta: Jacob EffronMeta’s former CTO tells Redpoint why hyperscaler demand is the climate story, why Llama was an access play, and why tomorrow’s CTO still lives or dies on a priority queue.
1:08:51ENChip Controls Are Meant to Buy Ten Years, Not Win the AI RacePresenta: Nathan LabenzEconomist Noah Smith tells Nathan Labenz that U.S. export controls are a piece of conventional deterrence against China — not a plan to kneecap DeepSeek.
2:33:39ENThe Attacker Goes Second: Why Nicholas Carlini Keeps Breaking AI DefensesPresenta: Nathan LabenzA Google DeepMind security researcher on simple loss functions, 70% robust accuracy, and why the fix has to live in the system, not the model.
5:06:18ENDeepSeek's $5 million training claim and the five-year bet on computePresenta: Lex FridmanDylan Patel and Nathan Lambert walk Lex Fridman through DeepSeek-V3 and R1, export controls, TSMC, megaclusters, and who actually makes money in the AI race.
1:48:20ENFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the TablePresenta: 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.
1:24:57ENA 50,000-GPU cap and a 1,700-chip loopholePresenta: Jacob EffronSemiAnalysis founder Dylan Patel walks through the AI diffusion rule, China’s compute squeeze, and how xAI stood up a 100,000-GPU cluster in a closed Memphis appliance factory.
3:53:49ENWith One-Twelfth the Data, an Open-Source Vision Model Beat GPT-4oPresenta: Nathan LabenzWill Hardman of Veratai walks Nathan Labenz through two years of vision-language models, from ViT and CLIP to InternVL, BLINK, and Transfusion.
1:57:00ENLess Than 1%: Why OpenAI's roon Thinks the Good Guys Get AGI FirstPresenta: Nathan LabenzDoom Debates host Liron Shapira presses OpenAI technical staffer roon on extinction odds, alignment-by-default, and a plan that even he admits is balancing on razor wire.
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