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Nathan Lambert, former post-training co-lead at the Allen Institute for AI, now launching Trillium, a nonprofit open AI lab, joins a16z's Lisha Li to discuss whether open models can really keep up with the frontier. When he tells researchers in China that an average OpenAI researcher reportedly has five thousand H100s to work with, "their jaw just falls to the floor." In his view, Chinese labs have the talent but are mostly short on compute. He also thinks the distillation story is overblown. Based on the research, he puts its edge at a few months, and says "there's a bit of cope" in thinking only American labs can build good models. A lot of frontier progress outside math and code comes down to labs buying 1) data and 2) RL environments. It's a multibillion-dollar market, and some of it is sloppy enough to teach models to reward hack. That's a big part of why Trillium exists. Nathan wants to give away the environments other people would sell, and show exactly how his team uses AI to build AI, including where the models still get stuck. His bet is that the more people can see how this works, the better prepared everyone is for what's coming. 0:55 Can open models keep up with the frontier? 2:05 What it takes to reach the frontier 3:55 Open weights and the fast-follower playbook 8:25 Chinese labs: equal talent, less compute 9:55 Why distillation is harder than it looks 14:55 Is distillation theft or competition? 16:20 The lab flywheel vs owning your weights 21:45 Can general models beat every specialist? 26:30 AI at 1% of today's cost in 4 years 27:15 The RL env boom and reward hacking 31:30 What Trillium will build in the open 35:30 Breaking into AI in the age of AI slop 38:05 Open-sourcing how AI builds AI 44:25 Why Nathan isn't pre-training 49:45 Agent swarms and why lab secrets leak 54:25 Fast takeoff vs risks already here YouTube: https://youtu.be/BKuK0M6oDTI @natolambert @trillium_labs @lishali88

https://x.com/i/article/2110168864101163008

Announcing the beta release of the Pi API. We’ve partnered with companies across diverse industries to adapt our models to their robots and applications, from harvesting strawberries to assembling servers. Now we’re opening access to more teams.

Unpacking Marvell's Analyst Day, OpenAI Revenue Rumors, and the Heterogeneous Rack with @benbajarin and @jaygoldberg (00:00) Intro: Semicon West, Applied Materials EPIC Center & OCP Summit (02:51) Marvell Analyst Day: Shifting the Narrative Beyond Amazon (05:23) Has Marvell Found Its Lane in Data Center Infrastructure? (13:39) Fungible Workloads & Heterogeneous Scale-Up Compute Domains (16:33) Specialized Inference Silicon: Prefill vs. Decode Chips (24:03) Marvell's New AI Infrastructure Management Silicon vs. BMCs & FPGAs (35:27) OpenAI ARR Rumors & The Fragility of the Semiconductor Trade (40:09) The 20-30 GW Grid Bottleneck & Data Center Delays (44:56) Why the AI Cycle Differs From Previous Tech Mega-Cycles (53:58) TSMC's Wafer Allocation, AMD's Upside & The Intel Foundry Threat (57:20) How a Terrible PC Market Frees Up Server & AI Wafers

Introducing Natura-1, our first general foundation model for the physical world. It follows natural-language instructions zero-shot and turns them into action with dexterous hands. Natura-1 is a 50-billion-parameter mixture-of-experts (MoE) model pretrained on one million hours of egocentric human experience. On top of this foundation, we conduct efficient post-training on a limited amount of robot data to adapt pretrained world knowledge to dexterous manipulation, yielding a model that generalizes to a wide range of unseen scenarios. Natura-1 follows complex natural-language instructions zero-shot, reasoning through spatial constraints, multi-object relationships, and negative conditions. It can also autonomously carry out long and continuous workflows to perform long-horizon executions. Natura-1 provides early evidence that generalization—and even emergent behavior—can coexist with the complexity of dexterous hands. Further progress depends on the full stack: infrastructure for faster training and real-time inference, pretraining data at greater scale and coverage, and models that transfer to robots with less adaptation. We expect it to be a long-term research and engineering effort.

You'll Thank Me Later, arriving back into the Cape from Starbase with Starship hardware. We're getting closer to this trip involving Starship vehicles. https://nsf.live/spacecoast

突发消息：$ASML 2026年第三季度总净销售额达117亿欧元，毛利率57.5%，净利润50亿欧元。 https://ms.spr.ly/6017aszI1