A coalition of artificial intelligence companies—including Hugging Face, Meta, Microsoft, Mistral, and Nvidia—has signed an open letter urging policymakers to avoid imposing broad, “premature restrictions” on open-weight AI models. This appeal arrives as Washington debates how to respond to allegations that Chinese AI labs are stealing intellectual property from their American counterparts while rapidly advancing their capabilities.

Although the letter never mentions China directly, it follows reports that the Trump administration has considered banning Chinese open-weight models and potentially imposing sanctions on AI companies from that country. The White House has specifically accused Moonshot AI of distilling Anthropic’s Fable model to train its newly released Kimi K3 model, which has been widely praised for its performance. The letter appears aimed at discouraging a total ban on Chinese models and ensuring that any response to alleged distillation does not lead to broader restrictions on open-weight AI or common techniques like distillation.

“Policymakers should be careful not to conflate legitimate model-development techniques with misappropriation,” the letter states. “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 letter also pushes back against arguments within the industry that open-weight models are inherently dangerous because they expand access to powerful tools that could be used in cyberattacks or other nefarious activities without oversight. “The right response to this risk is not to prohibit open weights,” the letter reads. “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.”

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Last week, OpenAI disclosed that while testing GPT-5.6 Sol and another unnamed model, one of the systems exploited a weakness in its testing environment to access a Hugging Face repository containing a solution to a coding benchmark. Some argue the model’s goal was not malicious—it was effectively cheating on a test to achieve the highest score. However, the incident sparked debate about the risks of concentrating advanced AI technology within a handful of closed providers. Hugging Face said it could not defend itself against the attack using commercial frontier AI models because their guardrails blocked its efforts; the closed AI models it used were unable to distinguish between being asked to build exploits for an attacker versus a defender trying to detect them. The company instead had to pivot to using Chinese AI firm Z.ai’s GLM 5.2, a powerful open-weight model, to defend itself against the attack.

The letter highlights a growing divide in the AI industry. Companies like OpenAI and Anthropic have urged the administration to respond to alleged IP theft by Chinese AI firms as open-weight models rapidly increase in capability. The outcome could have major implications for their business models, which are being threatened by the spread of cheap, highly capable, and accessible AI models. These companies, along with other closed-source AI developers like Google DeepMind and SpaceX, are notably absent from the letter’s signatories. Those who signed have an obvious economic stake in seeing open AI models flourish. Companies like Nvidia, Microsoft Azure, and other infrastructure providers benefit from commoditized models: if models are interchangeable, people will buy more GPUs, rent more cloud capacity, build more applications, and use more routing layers.

The letter encourages policymakers to expand access to compute for startups and researchers, invest in shared training assets like datasets, tools, and evaluation frameworks, and “keep the frontier plural by avoiding premature restrictions on open models that stifle competition or drive innovation overseas.”