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2:59:44ENTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Presenta: 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 EverywherePresenta: 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 BuyPresenta: 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.
26:28ENAPI prices fell 200x. The LLM table still is not set.Presenta: Sarah Guo, Elad GilSarah Guo and Elad Gil, with no guest this week, argue over whether the language-model market has already consolidated — and why falling prices, five-person labs, and AMD’s shopping spree all cut against a simple ending.
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 MoatPresenta: 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 MomentPresenta: 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.








