A Safe Path to Open Weights

Abstract: Safe open-weight models are public goods, as they put AI development and safety work in many hands and make training choices inspectable. Open models also carry real misuse risks, and release is irreversible. To release safely, we must consider both the model and the ecosystem it enters. For the model, we conduct robust safety testing and research whether dangerous capabilities can be decoupled from general intelligence. For the ecosystem, we invest in its readiness through staged releases, supporting defenders, and collaboration with safety researchers. By iteratively choosing the most open option the evidence supports while building up the ecosystem's resilience, we can move toward openness safely.

The mission of Thinking Machines is to build AI that extends human will and judgment. In pursuit of that mission, we recently released Inkling and Inkling-Small, two open-weight language models that anyone can own, run, and customize.

Strong open-weight models put AI development in many hands. We believe that this is a good thing: the knowledge of what AI should do lives with the people doing the work, so they should be able to shape their models directly. All models, including our own, carry the assumptions and biases of those who trained it; open weights make those choices inspectable and revisable. Without strong open models, training and deployment expertise risks becoming concentrated in a few labs, leaving everyone else less able to understand, adapt, or govern the technology.

Open-weight models carry misuse risks

Once weights are public, anyone can use them, including bad actors. Cybersecurity shows what this means. In April 2026, Anthropic reported that Claude Mythos Preview found thousands of previously unknown vulnerabilities across every major operating system and browser and wrote working exploits without human guidance. Systems like this could substantially lower the time, cost, and expertise required for offensive cyber operations. Will our…