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2:59:44ENTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Host Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.
1:01:14ENThe Model Eats the Scaffolding: How Google Plans to Ship Agents EverywhereHost Nathan LabenzThree years after the leaked “no moat” memo, DeepMind’s Gemini leads walk through 3.5 Flash, Omni, and the shared agent harness now running through Google’s products.
44:34ENThe Cheaper Intelligence Gets, the More of It We'll BuyHost Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.
2:49:35ZH → EnglishPan Jiayi on R1, o1, and Kimi K1.5: How Reinforcement Learning Teaches Models to ReasonGuest 潘嘉怡 · Host 张小珺Berkeley PhD candidate Pan Jiayi walks through reasoning-model reports from DeepSeek, OpenAI, and Kimi, unpacking long chains of thought, GRPO, reward design, and distillation. R1-Zero’s “aha moment” shows how reasoning strategies can emerge through reinforcement learning.
42:08ENMeta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.
1:12:15ENThe Open-Model Vibe Check: China, Containment, and the Data MoatHost Jacob EffronJacob Effron and guests debate Chinese open-weight models, distillation, containment policy, the OpenAI–Hugging Face incident, and Cursor's data advantage.
1:13:40ENThe Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing MomentHost 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.









