a16z 发布对 Nathan Lambert（前 Allen Institute for AI 后训练联合负责人，现创办非营利开源 AI 实验室 Trillium）的访谈，并总结其五个观点。他表示中国实验室人才水平相当，但据他向中国研究者提及 OpenAI 平均研究者可随时调用 5000 张 H100 等效算力时对方反应震惊，认为中国主要缺算力；他估计蒸馏领先优势只有几个月。
AI 生成摘要 · 以原文为准
Five bangers from Nathan Lambert:
1. China's compute gap:
"You talk to people [in China], and you're like, 'Oh, I heard an average OpenAI researcher has five thousand H100 equivalents that they can access at any time,' and like their jaw just falls to the floor. They're just like, 'I can't comprehend.'"
2. China's talent pool:
"The Chinese labs are very technically excellent... To say that China is anything but equal on talent, you're gonna have a hard time because they just have so many people being trained at such a high level."
3. OpenAI's superpower:
"[The frontier labs] have just taken the most ambitious bets the earliest in order to get there, and that's why OpenAI has done things like kickstart the reasoning model revolution."
"And I think that kind of breakthrough and that beautiful plot with the inference scaling laws is peak OpenAI. They're really good at getting this plot that just changes how everyone thinks about everything."
4. Underestimating the competition:
"The Chinese are so good at building models as well. I think there's a bit of cope where people are like, 'Only we are good at building models.' There are so many companies. There's, like, 20 to 30 companies that are building good models."
5. The AI risks that are already here:
"[The lack of preparation] is kind of the thing that worries me more than, say, RSI is coming and unlocking new risks. There are risks that are here and that are gonna continue to diffuse."
"Figuring out how to get society to prepare is very hard. I'm of the opinion that we need more eyes, we need more science, so that society feels informed."
@natolambert
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在 X 查看被引用的帖子
来源：a16z · x.com