重磅！Meta、英偉達、谷歌等科技巨頭聯合發聲明呼吁：美國應當牽頭打造開放權重的AI的未來，避免過早限制開放模型

投資者內參丨壽紫薇

7月24日，Meta、英偉達、谷歌、微軟、IBM等多家美國領先科技企業聯合發布了一份題為《Open Weights and American AI Leadership》——《開放權重與美國人工智能領導力》的文件。該文件系統闡述了開放權重模型對于擴大AI經濟參與、強化競爭、保障用戶自主權以及提升整體安全性的戰略意義，并提出了具體建議。

文章稱，上世紀80年代，開源軟件運動打破了“閉源才能進步”的迷思，構建了全球開發者共同參與、持續改進的透明生態。當下人工智能行業正迎來相似抉擇，開放權重模型是這套生態的核心基石 —— 任何人都可下載、檢視、修改、自主部署模型，大幅降低先進 AI 的使用門檻，提升技術適配性與普及度。

文章闡述了開放權重模型的三重核心產業價值：

開放權重模型拓寬全社會參與 AI 經濟的渠道。初創企業、傳統企業、高校與公共機構無需從零訓練大模型，也不必為各類業務承擔前沿模型高昂使用成本，可按需匹配輕量化專用模型，僅將頂級算力留給前沿科研難題，實現 AI 規?；逃玫慕洕沙掷m性。依托開放權重，AI 能夠深度融入工廠、醫院、農田、校園與線下中小企業，讓全社會共享 AI 發展紅利。

開放權重還能強化市場競爭，而充分競爭能夠讓 AI 發展紅利普惠大眾，而非被少數企業壟斷。各類主體均可自主開發、改造、部署先進模型，由此催生全方位競爭，競爭范圍不僅覆蓋模型研發廠商，還延伸至云端芯片、應用產品與配套服務領域。競爭持續驅動技術創新、壓縮使用成本，讓 AI 發展紅利廣泛惠及各行各業。

開放權重也賦予使用者更強的自主掌控能力。各類機構布局人工智能時，都不希望被單一廠商綁定，或是丟失長期積累的技術經驗與能力。開放權重模型恰好能打消這一顧慮：機構可自主管控自有數據，結合自身業務需求調試、改造模型，并根據業務場景自由部署。企業依托開放模型創造產業價值的同時，還能通過持續迭代優化模型、打造專屬行業能力、沉淀技術經驗，完整持有 AI 帶來的收益。

文章也提到，開放權重天然存在管控短板：權重對外發布后原始開發者無法收回，第三方修改版本難以追蹤溯源，但明確反對一刀切禁止開放模型。

閉源模型并非天然安全，同樣存在泄露、濫用、隱性故障風險，且外部無法排查；少數閉源頭部模型會形成單點故障隱患，削弱市場競爭。開放權重可匯聚全球研究者共同校驗模型行為、挖掘安全漏洞、搭建防護機制，依托大規模紅藍對抗、標準化評測，基于真實危害制定防護規則，印證 “透明化比信息隔絕更安全” 的開源底層邏輯。

最后，文章指出，一個強大的AI生態系統并非必然到來，政策制定者應抓住機遇：

擴大算力獲?。簽槌鮿撈髽I和研究人員提供計算資源支持；

投資共享基礎設施：構建公共數據集、工具和評估框架；

保持前沿多樣性：避免過早對開放模型施加限制，以免扼殺競爭或把創新推向海外；

支持強大應用層：使AI主權能力廣泛滲透經濟各個領域。

同時需注意，不應將合法的模型開發技術（如蒸餾——利用模型輸出來訓練或改進另一模型）與不當挪用混為一談。蒸餾是模型改進、評估和驗證的常用技術，繼承自開源運動以來“學習、借鑒和改進”的優良傳統。對于非法竊取閉源模型價值的行為，應通過針對性的法律和商業框架處理，而非對蒸餾等核心創新技術施加全面限制。

附上原文如下：

Open Weights and American AI Leadershi

July 24, 2026In 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 frontiermodel 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.