Interconnects AI · 2026-06-19 · notable
Nathan Lambert: 'Banning Open Source AI Would Be A Mistake'
Nathan Lambert and Kevin Xu argue banning open-source AI would hurt US security, education, and competition. Their Interconnects post responds to an executive order to review AI models and a block on foreign access to Anthropic models.
Nathan Lambert and Kevin Xu argue open-source AI is a US asset, not a security risk to ban.
What is it?
Nathan Lambert and Kevin Xu publish a defense of open-source AI on Interconnects. The post responds to a US executive order ordering a review of AI models, a congressional proposal to legislate AI further, and a recent action that blocks foreign nationals from accessing Anthropic's advanced models. They argue restricting open weights would harm American interests.
How does it work?
The piece lays out four reasons restriction backfires. Open source is how students around the world learn to build software at all. Open weights let small teams compete with frontier labs and keep monopolies in check. Public code is safer because more researchers can audit it. And open source has produced more than $8 trillion in worldwide economic value historically.
Why does it matter?
American policymakers face real questions about access to Chinese open models like GLM-5.2 and Qwen. Lambert and Xu push back on framing restriction as the answer. They want more federal support for domestic open work, framing transparency as aligned with US values of competition and innovation.