OpenAI is scared of open-weight models. Should the US be?

OpenAI 畏惧开源权重模型，美国也该如此吗？

The impressive capabilities of Chinese lab Moonshot’s Kimi K3, the biggest open-weight large language model, has kicked off a debate that conflates two things: the economic possibilities of American AI giants and the future of LLMs as a technology. 中国实验室月之暗面（Moonshot AI）推出的 Kimi K3 是目前最大的开源权重大型语言模型，其令人印象深刻的能力引发了一场辩论。这场辩论混淆了两个问题：美国 AI 巨头的经济前景，以及作为一项技术的大语言模型（LLM）的未来。

OpenAI’s head of strategic futures, Dean W. Ball, went so far as to argue that the US government should find a pretext to create regulatory fear, uncertainty, and distrust around the new models, since open-weight models must necessarily deter capital spending by the frontier labs. People freaked out, with tech luminaries like Yann LeCun and Martin Casado arguing that open software can accelerate innovation and coexist with proprietary projects. OpenAI 战略未来负责人 Dean W. Ball 甚至主张，美国政府应寻找借口，针对这些新模型制造监管层面的恐惧、不确定性和不信任感，因为开源权重模型必然会抑制前沿实验室的资本支出。此言一出引发哗然，Yann LeCun 和 Martin Casado 等科技界领袖纷纷反驳，认为开源软件能够加速创新，并能与闭源项目共存。

Ball soon retracted his claims that a regulatory crackdown was the White House’s “best strategy” and that open-weight models necessarily slow down advances in the technology. However, Axios reports that the Trump administration is considering banning K3 and other advanced Chinese models at the behest of American frontier labs. Another report from Politico said that the Department of Commerce would not take that step anytime soon. Ball 随后收回了“监管打压是白宫最佳策略”以及“开源权重模型必然拖慢技术进步”的言论。然而，据 Axios 报道，特朗普政府正在考虑应美国前沿实验室的要求，封禁 K3 及其他先进的中国模型。Politico 的另一篇报道则称，美国商务部短期内不会采取这一举措。

The benefit for major AI companies is clear: Open-weight models, running on independent infrastructure or inside major enterprises, offers cheaper intelligence than Anthropic or OpenAI’s class-leading models. If users increasingly spend more outside the closed labs, that means smaller return on their massive investments in model training. That view extends far beyond OpenAI. 对大型 AI 公司而言，利益诉求显而易见：在独立基础设施或大型企业内部运行的开源权重模型，比 Anthropic 或 OpenAI 的顶级模型提供了更廉价的智能服务。如果用户越来越多地在闭源实验室之外进行消费，就意味着这些公司在模型训练上的巨额投资回报率会降低。这种观点远不止 OpenAI 一家持有。

“Strong, frontier-caliber open source models will place a squeeze on the margins and will bring down the prices of the frontier companies,” Braden Hancock, the co-founder of Snorkel AI and a research partner at the Laude Institute, told TechCrunch. “It will not necessarily mean that the amount of AI usage goes down a little bit. You know, obviously, quite the opposite.” Snorkel AI 联合创始人兼 Laude Institute 研究合伙人 Braden Hancock 对 TechCrunch 表示：“强大的前沿级开源模型将挤压利润空间，并拉低前沿公司的定价。但这并不意味着 AI 的使用量会下降，恰恰相反。”

That’s not a problem for people without shares in Anthropic and OpenAI. AI will still proliferate. So what’s the justification for the government to block Americans from purchasing something in our ostensibly free markets? 对于那些没有持有 Anthropic 和 OpenAI 股份的人来说，这并不是问题。AI 仍将普及。那么，政府在表面上自由的市场中阻止美国人购买某些产品，其正当性何在？

Concerns over Chinese models come in several flavors. One is protecting US data from the Chinese government; the US banned the import of modern Chinese EVs over concerns about their data gathering. But experts tend to think that open-weight models run on US servers are unlikely to leak data back to China, although it’s not impossible that such a thing could be done. 对中国模型的担忧有多种表现。其一是保护美国数据免受中国政府获取；美国曾因数据收集担忧而禁止进口现代中国电动汽车。但专家倾向于认为，在美方服务器上运行的开源权重模型不太可能将数据泄露回中国，尽管这种可能性并非完全不存在。

Another is that the models may have implicit bias toward the PRC — but it’s not clear what that might mean for, say, coding tasks. A third common worry is that Chinese models lack the guardrails that the US government has mandated (through an opaque process), which aim to prevent leading US LLMs from being used to exploit closed computer systems or create weapons. 其二是模型可能对中国存在隐性偏见——但对于编程等任务而言，这具体意味着什么尚不明确。第三个常见的担忧是，中国模型缺乏美国政府（通过不透明流程）强制要求的护栏，这些护栏旨在防止领先的美国 LLM 被用于攻击封闭计算机系统或制造武器。

However, those same guardrails may make US companies more vulnerable: David Sacks, the venture capitalist and Trump adviser, has been sharing cases of US companies turning to Chinese LLMs to close security gaps when US frontier models refuse to do the tasks. 然而，这些护栏反而可能使美国公司更加脆弱：风险投资家兼特朗普顾问 David Sacks 一直在分享案例，显示当美国前沿模型拒绝执行任务时，美国公司转而求助于中国 LLM 来填补安全漏洞。

But the most significant motivation for restricting the models is that fear that China will be able to outpace the US if the frontier labs slow down. Sam Bresnick, a China-focused research fellow at Georgetown’s Center for Security and Emerging Technology, says the growing importance of AI to the US military operations gives the US a reason to support continued investment in AI at the frontier labs. 但限制这些模型最核心的动机在于担心：如果前沿实验室发展放缓，中国将能够超越美国。乔治城大学安全与新兴技术中心（CSET）专注于中国问题的研究员 Sam Bresnick 表示，AI 对美国军事行动日益重要，这给了美国支持前沿实验室持续投资 AI 的理由。

But the whole question, he says, is fraught. “Why should the weight of the U.S. government be aimed at protecting these companies from competitors that are being locked out from the U.S. market based on their origins?” Bresnick asks. 但他认为，整个问题充满了争议。“为什么美国政府的力量要被用来保护这些公司，去抵御那些仅仅因为出身就被拒之于美国市场门外的竞争对手？”Bresnick 问道。

Advocates for open AI say that the frontier companies are creating a false binary between innovation and closed models. “The bigger impact of having these open source models come from China is less that they’re sneaking in back doors, and more that they are owning the innovation,” Hancock told TechCrunch. 开源 AI 的倡导者认为，前沿公司在创新与闭源模型之间制造了一种虚假的二元对立。“这些来自中国的开源模型带来的更大影响，不在于它们是否植入了后门，而在于它们正在掌握创新主导权，”Hancock 对 TechCrunch 说道。

“You end up with, effectively, an expanded workforce on your model. PyTorch became the industry standard because it was open source, and so the whole community could contribute to it rather than just one company, and it grew and grew, and all the rest of the deep learning libraries kind of died in comparison.” “最终，你实际上拥有了一支为你的模型工作的庞大劳动力队伍。PyTorch 之所以成为行业标准，是因为它是开源的，整个社区都可以为其做出贡献，而不仅仅是一家公司。它不断壮大，相比之下，其他所有的深度学习库都逐渐消亡了。”

Hancock and other advocates fear that Chinese LLMs will become the locus of international research. Already, US graduate programs mainly build on open-weight Chinese models, and Hancock says that half of the papers students study are coming from Chinese institutions, with American frontier labs increasingly reticent about sharing their work widely. Hancock 和其他倡导者担心，中国 LLM 将成为国际研究的中心。目前，美国的研究生项目主要基于中国的开源权重模型，Hancock 指出，学生们研读的论文中有一半来自中国机构，而美国前沿实验室则越来越不愿广泛分享其研究成果。

“Restricting open models wouldn’t make AI safer,” said Clem Delangue, the CEO of Hugging Face, a platform for open AI collaboration. “It would simply hide the risks, concentrate power in the hands of a few and make it harder for the next generation of builders, researchers, academia, non-profits, governments to participate in making AI safer and more beneficial for all.” “限制开源模型并不会让 AI 更安全，”开源 AI 协作平台 Hugging Face 的首席执行官 Clem Delangue 表示，“这只会掩盖风险，将权力集中在少数人手中，并使下一代的建设者、研究人员、学术界、非营利组织和政府更难参与到让 AI 更安全、更造福全人类的工作中来。”

Bresnick says that the real way to slow China would be to focus more on chip export controls. A better way to preserve US AI leadership would be to stop selling Nvidia H200 processors to China. “That,” he says, “could potentially keep us out of this thorny debate about banning open source technologies that huge numbers of US companies want to use.” Bresnick 认为，减缓中国发展的真正途径是更加关注芯片出口管制。保持美国 AI 领先地位的更好方法是停止向中国出售英伟达 H200 处理器。“那样，”他说，“或许能让我们避开这场关于封禁大量美国公司想要使用的开源技术的棘手辩论。”

Part of the problem is that uncertainty around AI economics. “The open business model, the proprietary business model — neither one is figured out. AI companies are struggling to figure out how to make money on their tools, especially as training costs need to go up and up,” Bresnick points out. 问题的一部分在于 AI 经济的不确定性。“开源商业模式和闭源商业模式——目前都没有定论。AI 公司正在努力寻找如何通过工具盈利，尤其是当训练成本需要不断攀升时，”Bresnick 指出。

The same challenges that play out in the US are also playing out in China, where AI companies are also struggling to generate revenue and access compute power, and the government is seen as encouraging open releases for policy reasons despite the challenge in capital. 同样的挑战也在中国上演，那里的 AI 公司同样在为创收和获取算力而挣扎。尽管面临资本挑战，但中国政府被认为出于政策原因正在鼓励开源发布。