Open Weights and American AI Leadership was shepherded by Microsoft on July 24th and signed by 235 AI-adjacent companies including NVIDIA, Amazon, Y Combinator, The Linux Foundation and OpenAI.
The letter counters any instincts by the current US government to ban or limit open weight models over safety concerns. That is a reasonable consideration given what happened to Claude Fable 5.
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.
The one surprising note in the letter is that it comes out in support of distillation, where models train on output from other models.
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.
Notably absent from the signatures: Anthropic, who published their own response three days later. CEO Dario Amodei doubled down on the risk of authoritarian governments building AI models that are more powerful than those built by the US, and models being misused to carry out cyberattacks or biological attacks, and called for a crack down on industrial-scale distillation operations, while also stating that Anthropic has never advocated for a ban on open-weights models.
Then on July 28th Pacing the Frontier was published, featuring signatures from 1,324 employees of frontier AI companies – with names like Jakub Pachocki (Chief Scientist, OpenAI), Ilya Sutskever (Safe Superintelligence Inc, previously OpenAI), Dario Amodei (Anthropic), Jack Clark (Anthropic) and more. Their core message:
We request that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.
Their concern is intense competitive pressure combined with accelerated AI progress caused by automated AI research. Given that Anthropic produce 80% of their code with Claude Code, OpenAI had Sol reduce their end-to-end serving costs by 20%, and Kimi K3 designed a chip to serve a nano model built on its own architecture, you can see why people are taking that risk more seriously right now.
What it means
Developers and researchers now face a clearer path to inspect and improve models without relying on vendor black boxes. At the same time, the industry acknowledges that automated research tools are speeding up progress so much that some teams worry about losing control over the pace of development.
