A curated reading list on open-source AI and open models, compiled for public-audience and policy-facing writing. The list covers what open models are, why organizations release them, their business strategy implications, and associated risks.

Key contributions include Bill Gurley on open-source strategy, Mark Zuckerberg on Meta's Llama releases, Irene Solaiman on the gradient of openness, and Nathan Lambert on economic complementarity and adoption trends. Christian Catalini examines open vs. closed AI economics, while Thinking Machines Lab addresses safe open-weight releases.

Additional readings cover societal impact studies, safety debates, data commons decline, Chinese model developments like Kimi K3 and GLM-5.2, and regional adoption data. The list concludes with strategic arguments for U.S. investment in open models amid growing competition from China.

List last updated: 13 Sep. 2026. Readers are encouraged to suggest additions via comments.