Can Open Models Keep Up With OpenAI and Anthropic? | Nathan Lambert
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摘要
a16z Partner Lisha Li sits down with AI researcher and Trillium founder Nathan Lambert to ask how durable the gap really is between frontier AI labs and open models.
Nathan argues that OpenAI and Anthropic have structural advantages in compute, capital, and the ability to pioneer new training paradigms, but open-model labs, particularly in China, have proven remarkably capable of following close behind. They unpack how much of that comes from distillation versus technical talent, why compute remains a major constraint, and how data, RL environments, and post-training are increasingly shaping model progress.
They also discuss Nathan’s new nonprofit research lab, Trillium, and its plans to open up parts of AI research that are increasingly difficult to study from the outside. That includes releasing RL environments and documenting how AI is actually being used to automate AI research, with the goal of demystifying recursive self-improvement and giving researchers and policymakers a clearer picture of what frontier systems can and cannot do.
Timestamps:
00:00 - Intro
00:58 - Compute, data, and talent
08:48 Compute-starved, and the distillation debate
14:57 Is distillation just competition?
19:24 Open and closed on different timescales
25:00 Frontier progress is really about data
31:29 What Trillium will build
38:05 Demystifying RSI
44:32 Can open source pre-train?
49:32 Harnesses and agent swarms
Resources:
Learn more about Trillium: https://trilliumlabs.org/
Follow Nathan Lambert on X: https://x.com/natolambert
Nathan Lambert’s Newsletter: https://www.interconnects.ai/
Follow Lisha Li on X: https://x.com/lishali88
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