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黄仁勋开通社交账号，首封公开信揭示AI路线之争（中英全文）

来源：互联网 2026-07-26 06:10:14

人工智能资讯合集

英伟达CEO黄仁勋在社交平台发布联合公开信，为开放权重AI模型站台。联署方包括微软、Meta等25家科技企业。信中指出，开放权重能强化安全、加速创新、实现技术主权，不应盲目封禁，世界需要顶尖闭源与开放模型双轨并行。

先说几个核心判断。

7月24日晚间，英伟达CEO黄仁勋在社交平台X上发布了个人账号的第一条推文。内容不是推介自家芯片，而是直接附上了一封联合公开信，标题是《开放权重与美国在AI领域的领导地位》。信的核心主张，是为开放权重AI模型站台。

长期稳定更新的攒劲资源： >>>点此立即查看<<<

这条推文，瞬间引爆了全球科技圈和资本市场。

这次联署的阵容相当豪华，包括英伟达、微软、Meta、IBM等25家美国顶级科技企业、投资机构与开源基金会。这应该是今年立场最鲜明、影响也最深远的AI行业宣言了。

这封公开信直指当下美国AI监管的争议核心，而且直接推翻了一个市场上固有的认知：

一个国家的AI竞争力，不在于能不能垄断最强、最闭源的大模型，而在于能不能搭建一个开放、可扩散、可自主掌控的全民AI生态。

信里还拿80年代的开源软件革命做历史参照，论证开放权重模型才是技术普及、产业竞争、网络安全与数字主权的核心基石。

公开信的表态非常明确：盲目封禁开放权重AI，只会扼杀创新、把产业优势拱手让人、制造技术垄断风险。相比之下，开源体系反而更安全——因为全球开发者可以共同审查、迭代修复漏洞，这种安全韧性，是封闭模型单点防护比不了的。

同时，开放模型大幅降低了AI创业和产业落地的门槛。初创企业、高校、传统行业不用从零训练模型，就能实现技术普惠。

信的最后给出了一个时代定论：AI未来必须双轨并行——顶尖闭源模型保障极致性能，顶尖开放模型保障生态活力。

以下是公开信的完整英文原文与权威中文精译，全文无删减。

黄仁勋 X（原Twitter）

英文原文

For my first post, I'm sharing a letter signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models.

中文译文

这是我的第一条帖子，我分享一封英伟达参与联署的公开信，阐释开放模型为何至关重要。人工智能将重塑各行各业，赋能每家企业，并由世界各国共同建设。开放模型能够强化安全与网络防御，加速创新与技术普及，实现技术主权。世界既需要顶尖闭源模型，也需要顶尖开放模型。

中文完整译文（严谨直译）

开放权重模型——即任何人都可以下载、审查、修改并在自有基础设施上运行权重的人工智能模型——对维持美国在人工智能领域的领导地位至关重要。

目前华盛顿正在展开一场辩论：是否应当限制开放权重模型。许多人担忧开放权重可能遭到滥用，这类顾虑值得严肃对待。但限制开放权重将会损害美国的创新能力、市场竞争、网络安全与数字主权，反而让海外竞争对手获得优势。

历史提供了一条清晰的参照系：上世纪80年代开源软件的兴起。早期开源先驱打破了一种观念——软件进步只能依靠严格管控的专有代码。他们搭建起透明生态，让全球开发者能够学习、修改、改进共享技术。如今，开源软件支撑着互联网绝大部分基础设施，成为各大科技巨头的底层底座，在美国创造了数百万就业岗位。

开放权重模型将同样的发展逻辑带到了人工智能领域。

初创企业、高校、公共机构与工业主体，不再需要从零训练前沿大模型，就能基于顶尖AI开展研发。这扩大了经济参与机会、降低了行业准入门槛、充分激发了竞争，创新不再局限于少数资金雄厚的实验室。

评判美国的AI领导力，不应以能否掌控单一前沿模型作为标尺，而要看美国能否搭建一套渗透各行各业、稳健开放的人工智能生态。开放权重模型正是这套生态的基石。

批评者提出的风险客观存在：模型对外发布后，开放权重可以被第三方修改，脱离原始开发者管控。但全面禁止并非正确的解决方案。限制措施无法消除风险，只会促使风险向不透明的闭源体系转移，同时倒逼相关产业流向海外。仅仅依靠闭源模型，并不能天然保障安全。闭源系统同样面临黑客入侵、滥用、运行故障等问题；将先进AI能力集中在少数封闭体系中，反而制造了单点重大风险。

开放体系能够强化安全水平。当成千上万的研究者可以审查模型权重，漏洞能够更快被发现与修复。开放权重同时推动数字主权建设：各国与企业能够依托自有硬件运行AI，不必受制于境外服务商。

政策制定者应当制定针对性的防范滥用保障机制，而非大范围封禁开放权重。监管规则需要区分合法对模型进行适配改造与非法窃取知识产权这两类行为。监管机构应当鼓励分层安全机制、信息透明原则与自愿安全标准落地。

想要充分释放人工智能价值，世界既需要顶尖闭源模型，也需要顶尖开放权重模型。限制开放权重，会削弱美国竞争优势；拥抱开放权重，才能持续维系美国创新活力、强化网络安全，守住全球人工智能竞赛中的领先地位。

Open Weights and American AI Leadership

《开放权重与美国人工智能领导力》

Open weights models — AI models whose weights anyone can download, inspect, modify, and run on their own infrastructure — are essential to sustaining American leadership in artificial intelligence.

Today, a debate is underway in Washington about whether to restrict open weights models. Many fear open weights could enable misuse. These concerns deserve serious consideration. But restricting open weights would undermine U.S. innovation, competition, cybersecurity, and national sovereignty. It would hand an advantage to competitors overseas.

History offers a clear parallel: the rise of open-source software in the 1980s. Early open-source pioneers challenged the idea that software progress depended solely on tightly controlled proprietary code. They built transparent ecosystems where developers worldwide could learn, modify, and improve shared technology. Today, open-source software powers most of the internet, underpins every major tech company, and created millions of jobs across the United States.

Open weights models bring the same dynamic to artificial intelligence. They let startups, universities, public agencies, and industrial operators build on state-of-the-art AI without training frontier models from scratch. This expands economic participation, lowers barriers to entry, and fuels competition. Innovation is no longer confined to a small set of well-resourced labs.

American AI leadership should not be measured by control over a single frontier model. It should be measured by our ability to build a robust, open AI ecosystem deployed across every industry. Open weights models are the foundation of that ecosystem.

Critics rightly note risks: once released, open weights can be modified and used outside the original developer's control. But prohibition is the wrong remedy. Restrictions will not eliminate risk; they will shift risk toward opaque, closed systems and push development offshore. Reliance only on closed models does not guarantee safety. Closed models can be hacked, misused, or fail. Concentrating advanced AI capability in a small number of closed systems creates single points of failure.

Open systems strengthen security. When thousands of researchers can inspect model weights, vulnerabilities are found and fixed faster. Open weights also advance digital sovereignty: nations and companies can run AI on their own hardware, independent of foreign providers.

Policymakers should pursue targeted safeguards against misuse rather than broad bans on open weights. Rules should distinguish between legitimate adaptation of models and unlawful theft of intellectual property. Regulators should encourage layered security practices, transparency, and voluntary safety standards.

The world will need both frontier closed models and frontier open models to maximize AI benefits. Restricting open weights weakens America's competitive edge. Embracing open weights will sustain U.S. innovation, strengthen cybersecurity, and preserve American leadership in the global AI race.

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