AI公司的护城河究竟在哪里——从Jensen Huang的倡议书一瞰

昨夜，NVIDIA的CEO Jensen Huang开通了自己的社交媒体，并且同时放出一份由多家科技公司和机构联署的政策倡议书Open Weights and American AI Leadership。它主张美国应支持开放权重模型——即用户可以下载模型参数在自己的设备运行的模型。并且在文章中阐述了原因和理由，那我们就先一睹为快！

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（独立自主）.

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（弥漫） 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（从零开始） 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（reserve A for B：表示把A留给B使用）, 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（刺激） innovation, drives down costs（降低成本）, 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（部署） 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（仅仅） 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（使...加重） 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（不透明）, AI safety may depend on giving more people the ability to test and strengthen the models on which society relies. It allows for rigorous（严格的） 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（多元的） by avoiding premature restrictions on open models that stifle（扼杀） competition or drive innovation overseas. These measures must also look at how strong application layers can expand sovereign use of AI across the economy.

Policymakers should be careful not to conflate（混为一谈） legitimate model-development techniques with misappropriation. Distillation（蒸馏）, 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. 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.

Open Weights and American AI Leadership的目的是影响监管者和市场的判断：不要过早限制开放权重模型、模型蒸馏和企业私有部署。

事实上，倡议书表面上在谈开放。它说开放权重能降低 AI 成本、增强竞争、减少供应商锁定、提高安全性，并让 AI 进入工厂、医院、农场和中小企业。

但这封倡议书真正争取的是另一件事：未来 AI 的价值不能只集中在少数闭源模型公司的 API 上。企业不应只能租用智能，还应拥有部署、改造和切换模型的能力。

这本质是一场利润分配的讨论。模型权重是否开放，会影响硬件公司卖多少算力，模型公司能否维持高价，应用公司能否降低成本，也会影响企业数据、工作流和客户关系最终由谁掌握。

下面我们将从三个角度来讨论 AI 的故事。

但是事实上，这并不容易。按结果收费意味着先定义结果，还要明确模型出错时谁承担损失。很多公司会宣传自己卖的是结果，实际仍主要按账号或调用量收费，因为它们暂时无法承受全部业务风险。

这四家公司放在一起，可以看出不同层的根本区别。

硬件公司看的是供给稀缺和资产回报；AI 云公司看的是利用率、融资成本和长期合同；模型公司看的是推理毛利能力差距；应用公司看的是客户续费率和业务价值。

因此，开放权重倡议书若逐渐影响监管和企业采购方向，影响也不会一样。对NVIDIA 和 CoreWeave 这类硬件与算力公司，它更可能扩大总推理需求；对 Anthropic 这类模型公司，它会扩大市场但压低普通能力的价格；对 ServiceNow 这类应用平台，它提供了更多模型选择和更低成本，但也让客户更容易要求真实 ROI，而不是只为AI 功能买单。

但最佳模型之间的能力差距已经缩小。Stanford 的数据中，最强美国模型对最强中国模型的优势约为 2.7%。中国模型还具有成本更低、发布更快、开放权重比例更高的特点。

BCG 的比较显示，2026 年 5 月，中国头部模型 Kimi K2.6 的价格约为每百万 token 1.71 美元，美国头部 GPT5.5 的价格约为 11.25 美元。不同模型不能只按价格比较，但这种价差足以改变许多成本敏感任务的采购选择。