真是没想到，Kimi K3发布，在美国硅谷引发的反响，远比比上一次DeepSeek R1更甚。
白宫和美国商务部等机构已经启动调查，焦点是 Moonshot 是否窃取了美国知识产权、是否违规使用了受限的英伟达芯片。硅谷内部也出现了明显的分裂——在“是否应当限制中国开源模型”这个问题上，美国科技公司罕见地站成了两队，而且队伍的大小悬殊得有些尴尬：Anthropic 和 OpenAI，几乎成了仅有的两家主张强硬应对的公司。
公开信全文
Open Weights and American AI Leadership
July 24, 2026
In the 1980s, early open-source software pioneers challenged the prevailing belief that software would advance only if companies kept tight control over their code. This movement pushed for a transparent ecosystem where developers around the world could study, modify, and improve software. Software developed by the open-source community now supports most of the internet and underlies systems used by the world’s largest technology companies, as well as the U. S. military and federal agencies conducting scientific research, cybersecurity, and other critical missions. Open source did more than lower the cost of software; it created a shared foundation of knowledge on which generations of American engineers and entrepreneurs built their institutional sovereignty.
The United States now faces a similar choice with artificial intelligence. Our AI leadership will be judged not by one frontier AI model, but by whether the United States builds a strong, open ecosystem that diffuses into every sector. This is essential for creating opportunities for innovation and prosperity across the country. It requires expanding access to AI, encouraging competition, robust application layers, and giving Americans greater control over the technology they rely on. Open-weight models—AI models that anyone can download, inspect, modify, and run on their own infrastructure—are an important part of that foundation because they make advanced AI more accessible, adaptable, and widely available.
Open weights expand access to the AI economy. Startups, established businesses, universities, and public institutions can build on advanced models without training one from scratch or paying frontier-model prices for every task. Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient, specialized models everywhere else. That discipline is what will make AI economically sustainable as its use scales into the billions of everyday tasks. America wins the AI era by diffusing it into the workflows of factories, hospitals, farms, classrooms, and main street businesses.
Open weights also strengthen competition and competition is what keeps the gains of AI broadly shared rather than concentrated in a few hands. By allowing many organizations to build, adapt, and deploy advanced models, open weights create rivalry not only among model developers but across cloud chips, applications, and services. That competition spurs innovation, drives down costs, and distributes the benefits of AI broadly across our economy.
Open weights also give customers greater control. As organizations invest in AI, they want to know that they will not become locked into a single provider or lose the knowledge and capabilities they build over time. Open weight models help provide that assurance by allowing organizations to control their own data, evaluate and adapt models to their own needs, and deploy them wherever their business requirements demand. And as organizations create value with AI, open weights allow them to own that value through self-improving models, specialized capabilities, and accumulated knowledge that drive American sovereignty and prosperity.
To be sure, open weights carry real and distinct risks. Once released, the weights are beyond the original developer’s control, and modified versions are difficult to trace or reverse. But the right response to this risk is not to prohibit open weights. In a world where cybersecurity attackers use advanced AI, defenders need access to models with comparable capabilities so they can detect, simulate, and respond to emerging threats. Open models broaden defensive capability, increase transparency, and allow vulnerabilities to be discovered and remediated across many teams.
In fact, openness may be one of the most important paths to AI safety and security. Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers. Open weight models, on the other hand, allow a broad community of researchers and developers to examine their behavior, identify vulnerabilities, develop safeguards, and improve them over time. Just as open-source software demonstrated that transparency can be more secure than obscurity, AI safety may depend on giving more people the ability to test and strengthen the models on which society relies. It allows for rigorous benchmarking and evaluation, red teaming, and protections tied to real and demonstrated harms rather than assuming that closed systems are safer by default.
A strong AI ecosystem is not a foregone conclusion. Policymakers have an important opportunity to act. This includes expanding access to compute for startups and researchers, investing in shared training assets (datasets, tools, evaluation frameworks), and keeping the frontier plural by avoiding premature restrictions on open models that stifle competition or drive innovation overseas. These measures must also look at how strong application layers can expand sovereign use of AI across the economy.
In shaping this ecosystem, policymakers should be careful not to conflate legitimate model-development techniques with misappropriation. Distillation, or the practice of using one model’s outputs to help train or improve another, is a widely used technique for model improvement, evaluation, and validation. It reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement. By contrast, unlawful efforts to extract value from closed models raise legitimate concerns. Those concerns should be addressed through targeted legal and commercial frameworks rather than sweeping restrictions on techniques that play an important role in AI innovation.
The age of AI can be one of prosperity. With the right choices, open weight AI can expand opportunity, strengthen competition, extend American technological leadership, mitigate risk, and ensure that the benefits of this extraordinary technology are shared broadly across our economy. That future is worth building, and the United States should lead in building it.
签署机构： American Innovators Network · Andreessen Horowitz · Arcee AI · Arena · Black Forest Labs · Box · CrowdStrike · Dell Technologies · Emergence Capital · Hugging Face · IBM · The Linux Foundation · Mariana Minerals · Meta · Microsoft · Mistral · Mozilla · NVIDIA · Palantir · Perplexity · Reflection · Replit · ServiceNow · Telnyx · Y Combinator
附：公开信参考译文
《开放权重与美国 AI 领导力》
2026 年 7 月 24 日
上世纪 80 年代，早期开源软件先驱挑战了当时的主流观念——即软件只有在公司严格控制代码的情况下才能进步。这场运动推动建立了一个透明的生态系统，让世界各地的开发者都能研究、修改和改进软件。如今，开源社区开发的软件支撑着互联网的大部分，也是全球最大科技公司、美国军方以及从事科研、网络安全等关键任务的联邦机构所用系统的底层基础。开源的意义不止是降低了软件成本；它创造了一套共享的知识地基，一代又一代美国工程师和企业家在这块地基上建立了自己的体系性主权。
今天，美国在人工智能面前面临同样的选择。我们的 AI 领导力，不取决于某一个前沿 AI 模型，而取决于美国能否建立一个强大、开放、能扩散到每个行业的生态系统。这对在全国范围内创造创新与繁荣的机会至关重要。它要求扩大 AI 的可及性、鼓励竞争、建设健壮的应用层，并让美国人对所依赖的技术拥有更大的掌控力。开放权重模型——任何人都可以下载、检查、修改并在自己的基础设施上运行的 AI 模型——正是这一地基的重要组成部分，因为它们让先进 AI 更易获取、更可适配、更为普及。
开放权重扩大了 AI 经济的准入。创业公司、成熟企业、大学和公共机构都可以在先进模型之上构建，而无需从零训练，也无需为每个任务支付前沿模型的价格。开放权重让每个组织都能以合适的成本为合适的工作匹配合适的模型——把前沿级能力留给真正的前沿问题，在其余场景运行高效、专用的模型。正是这种纪律性，才能让 AI 在向数十亿日常任务扩展时保持经济上的可持续。美国赢得 AI 时代的方式，是把 AI 扩散进工厂、医院、农场、课堂和街边小店的工作流之中。
开放权重也强化竞争，而竞争正是让 AI 的收益被广泛分享、而非集中于少数人之手的保障。通过让众多组织能够构建、适配和部署先进模型，开放权重不仅在模型开发者之间，也在云、芯片、应用和服务之间制造了竞争。这种竞争激发创新、压低成本，并将 AI 的红利广泛分配到整个经济体。
开放权重还赋予客户更大的控制权。组织在投资 AI 时，希望确保自己不会被锁定在单一供应商身上，也不会丢失随时间积累的知识与能力。开放权重模型让组织掌控自己的数据、按自身需求评估和改造模型、在业务需要的任何地方部署，从而提供了这种保障。随着组织用 AI 创造价值，开放权重让他们通过自我改进的模型、专业化的能力和积累的知识真正拥有这份价值——这驱动着美国的主权与繁荣。
诚然，开放权重有其真实而独特的风险。权重一旦发布，便超出原始开发者的控制，修改版本也难以追踪或撤回。但对这一风险的正确回应，不是禁止开放权重。在一个网络攻击者使用先进 AI 的世界里，防御者需要获得能力相当的模型，才能侦测、模拟和应对新出现的威胁。开放模型拓宽了防御能力、提高了透明度，也让漏洞能被众多团队发现并修复。
事实上，开放可能是通往 AI 安全最重要的路径之一。只依赖闭源模型并非天然安全：它们同样可能被攻破、被滥用，或以局外人无法察觉的方式失灵。而把先进 AI 能力集中在少数闭源模型背后，会放大这种风险——制造少数单点故障、削弱竞争，并把关键技术留在少数供应商手中。相反，开放权重模型让广大的研究者和开发者社区能够检视其行为、发现漏洞、开发防护手段并持续改进。正如开源软件证明了透明可以比隐蔽更安全，AI 安全或许正取决于让更多人有能力测试和加固社会所依赖的模型。它使严格的基准评测、红队演练成为可能，并让防护措施对应真实、已被证明的危害，而不是默认假定闭源系统更安全。
强大的 AI 生态并非理所当然。政策制定者拥有重要的行动机会：扩大创业公司和研究者的算力获取渠道；投资共享的训练资产（数据集、工具、评估框架）；避免对开放模型施加扼杀竞争、把创新驱向海外的过早限制，保持前沿的多元格局。这些措施还应关注强大的应用层如何在整个经济中扩展对 AI 的主权化使用。
在塑造这一生态时，政策制定者应谨慎，不要把正当的模型开发技术与窃取行为混为一谈。蒸馏——即用一个模型的输出来帮助训练或改进另一个模型——是一种广泛用于模型改进、评估与验证的技术。它体现了学习、借鉴并改进既有技术的悠久传统，这一传统自开源软件运动兴起以来一直在驱动创新。相比之下，以非法手段从闭源模型中榨取价值的行为确实会引发正当关切，但这些关切应通过有针对性的法律和商业框架来解决，而不是对在 AI 创新中发挥重要作用的技术施加一刀切的限制。
AI 时代可以是一个繁荣的时代。只要做出正确的选择，开放权重的 AI 能够扩大机会、强化竞争、延展美国的技术领导力、缓释风险，并确保这项非凡技术的红利在整个经济体中被广泛分享。这样的未来值得去建设，而美国应当引领它的建设。
签署机构： 美国创新者网络、Andreessen Horowitz（a16z）、Arcee AI、Arena、Black Forest Labs、Box、CrowdStrike、戴尔科技、Emergence Capital、Hugging Face、IBM、Linux 基金会、Mariana Minerals、Meta、微软、Mistral、Mozilla、英伟达、Palantir、Perplexity、Reflection、Replit、ServiceNow、Telnyx、Y Combinator
本文基于公开报道与公开信原文整理，观点供参考。