As the United States government debates new artificial intelligence rules and regulations, Anthropic PBC and Nvidia Corp. are drawing a line against blanket bans on open-weight models, urging regulators to focus instead on specific risks and misuse.

An open-weight model is one where the developer releases its trained parameters, the “weights,” which are the numerical values and vectors learned during training that determine how a model behaves. These weights act like dials that can be adjusted to shape a model’s outputs and performance. Because they’re accessible, weights can be fine-tuned with proprietary data, letting developers customize the model for specialized purposes and use cases.

Open-weight models are released so that users and enterprise firms can run them locally or in their own cloud, eschewing the need to access frontier closed-source models built and deployed by big AI model developers such as OpenAI PBC or Anthropic.

Well-known examples of open-weight models include Meta Platforms Inc.’s Llama, Mistral AI SAS’s models, Alibaba Group Holding Ltd.’s Qwen and DeepSeek’s models. All of these models let developers download, run and tune the weights directly.

According to Axios, last year, the U.S. Department of Commerce considered adding multiple Chinese AI labs to the “Entity List.” An action that would have effectively banned U.S. companies from accessing their models without a license. More recently, U.S. Treasury Secretary Scott Bessent said the White House could sanction open-weight Chinese frontier labs.

In a long treatise, Anthropic Chief Executive Dario Amodei stressed that the company has never advocated for a ban on open-weight models.

“Open-weights models that don’t have dangerous capabilities are a public good: they don’t cost anything besides the compute needed to run them, and they provide value to businesses, developers, and researchers,” Amodei said.

He centered his discussion on controlling the infrastructure and behavior around models, not the models themselves. He stated that the focus should be around controlling the export of powerful AI training and inference silicon, preventing industrial-scale distillation – when a larger model is used to improve a smaller model – and requiring safety testing of sufficiently powerful models, irrespective of whether they’re open or closed.

Anthropic’s concerns over distillation follow on from fears that its largest and most powerful frontier models, Mythos and Fable, have been surreptitiously used to train foreign AI models. Last week, Director of the Office of Science and Technology Policy Michael Kratsios accused Beijing-based Moonshot AI of using distillation to train Kimi K3, the company’s most recent large language model and the largest open-weights model in the world. Although distillation is strongly suspected at industrial scales, there is no evidence that K3 received any training from Mythos.

Nvidia CEO Jensen Huang weighed in on X, formerly Twitter, sharing an open letter signed by the AI chip supergiant and stating, “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.”

The open letter, signed by Nvidia, IBM Corp., Mozilla Corp., Microsoft Corp., Meta and numerous other technology industry leaders, argued that open-weight models support innovation, competition, customer control and sovereignty. The signatories stated that this type of model should not face additional restrictions that would curtail their usefulness within the larger software and AI industry.

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