Open-source and proprietary AI models represent two distinct strategies for distributing AI capability, each with different incentive structures, time horizons, and tradeoffs. Open-weight models publish learned parameters—and sometimes code, data, and training procedures—enabling researchers and developers to inspect, fine-tune, run locally, and redistribute them.