A New Framework for Open Source AI

What happened

A system can be open on weights while remaining closed on training data, or open on code while offering little documentation on how it was evaluated. But as that debate plays out in Washington, Brussels, and beyond, the harder questions are still very much up for discussion.

The convening was the start of a years-long effort to turn a room full of perspectives on openness into something durable and useful for developers, builders, and policymakers alike. Instead, it breaks the AI stack down into its component parts – data, code, model weights, documentation, and more – and looks at how openness at each layer shapes who actually gets to inspect, use, and build on a system.

A lot of the current policy debate treats open source AI as a monolith, when in reality the details of what gets shared (and how) are what determine whether openness delivers on its promise of safety, competition, and accountability. A few of the framework’s ideas are worth pulling out.