Open Weights and American AI Leadership
Open Weights and American AI Leadership
开放权重与美国人工智能的领导地位
July 24, 2026 2026年7月24日
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. 在20世纪80年代，早期的开源软件先驱们挑战了一种普遍的观念，即软件只有在公司严格控制其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能够研究、修改和改进软件。
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. 由开源社区开发的软件如今支撑着互联网的大部分，并构成了全球大型科技公司所使用系统的基础，同时也支持着美国军方和联邦机构进行科学研究、网络安全及其他关键任务。开源不仅仅降低了软件成本，它还创造了一个共享的知识基础，几代美国工程师和企业家正是以此为基石，建立了他们的机构主权。
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. 如今，美国在人工智能领域也面临着类似的选择。我们的人工智能领导地位将不再由单一的前沿人工智能模型来衡量，而是取决于美国是否能构建一个强大的、开放的、并能渗透到各个行业的生态系统。这对于在全国范围内创造创新和繁荣的机会至关重要。它需要扩大对人工智能的访问权限，鼓励竞争，建立稳健的应用层，并让美国人对自己所依赖的技术拥有更大的控制权。
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. 开放权重模型——即任何人都可以下载、检查、修改并在自己的基础设施上运行的人工智能模型——是这一基础的重要组成部分，因为它们使先进的人工智能更易于获取、更具适应性且更广泛可用。开放权重扩大了对人工智能经济的参与。初创企业、成熟企业、大学和公共机构可以在先进模型的基础上进行构建，而无需从零开始训练模型，也不必为每项任务支付前沿模型的高昂费用。
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. 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，将前沿规模的能力留给真正的前沿问题，而在其他所有领域运行高效、专业的模型。这种纪律性将使人工智能在扩展到数十亿日常任务时，在经济上保持可持续性。美国赢得人工智能时代的途径，在于将其渗透到工厂、医院、农场、教室和普通商业的工作流程中。
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. 开放权重也加强了竞争，而竞争正是确保人工智能的收益被广泛共享，而不是集中在少数人手中的关键。通过允许许多组织构建、调整和部署先进模型，开放权重不仅在模型开发者之间，而且在云芯片、应用程序和服务领域创造了竞争。这种竞争激发了创新，降低了成本，并将人工智能的红利广泛分配到我们的经济中。
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. 开放权重也赋予了客户更大的控制权。随着组织在人工智能上的投入，他们希望确保自己不会被锁定在单一供应商身上，也不会失去随着时间推移所积累的知识和能力。开放权重模型通过允许组织控制自己的数据、评估并根据自身需求调整模型，以及根据业务需求在任何地方部署模型，从而提供了这种保障。当组织利用人工智能创造价值时，开放权重使他们能够通过自我改进的模型、专业能力和积累的知识来拥有这些价值，从而推动美国的自主权和繁荣。
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. 诚然，开放权重确实存在真实且明显的风险。一旦发布，权重就超出了原始开发者的控制范围，且修改后的版本难以追踪或逆转。但应对这一风险的正确方法并非禁止开放权重。在一个网络安全攻击者使用先进人工智能的世界里，防御者需要能够使用具有同等能力模型，以便他们能够检测、模拟并应对新兴威胁。
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. 开放模型拓宽了防御能力，增加了透明度，并允许跨多个团队发现和修复漏洞。事实上，开放性可能是实现人工智能安全与保障的最重要途径之一。仅仅依赖封闭模型并非天生安全：它们可能被入侵、滥用，或以局外人无法察觉的方式失效。将先进的人工智能能力集中在少数几个封闭模型背后会加剧这种风险。这会导致少数几个单点故障，削弱竞争，并将关键技术掌握在少数供应商手中。
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. 另一方面，开放权重模型允许广泛的研究人员和开发者社区检查其行为、识别漏洞、开发防护措施并随着时间推移对其进行改进。正如开源软件证明了透明度可以比隐蔽性更安全一样，人工智能的安全可能取决于让更多人有能力测试和加强社会所依赖的模型。它允许进行严格的基准测试和评估、红队测试，以及针对真实且已证实的危害进行保护，而不是默认封闭系统更安全。
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. 强大的AI生态系统并非必然。政策制定者拥有采取行动的重要机会。这包括为初创企业和研究人员扩大计算资源的获取渠道，投资于共享训练资产（数据集、工具、评估框架），并通过避免对开放模型进行过早的限制来保持前沿领域的多样性，以免扼杀竞争或将创新推向海外。这些措施还必须考虑强大的应用层如何能够扩大人工智能在整个经济中的自主使用。
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 va 在塑造这一生态系统时，政策制定者应注意不要将合法的模型开发技术与盗用行为混为一谈。蒸馏（即利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、评估和验证的技术。它反映了学习、构建和改进现有技术的悠久传统，这一传统自开源软件运动兴起以来一直推动着创新。相比之下，非法提取价……（原文在此处中断）