Nathan Lambert publishes open-models reading list

Nathan Lambert has published a curated reading list of open-weight AI writing, gathering his own briefing materials into a single Interconnects post marked "List last updated: 13 Sep. 2026."

"Hey all! I've been prepping for some public-audience and policy-facing writing on open models, so I figured I would share my research materials," Lambert writes at the top. He frames the collection as "my list of the best writing on open models in the last few years," split into three sections: Foundation ("What open models are, why people release them, how they relate to business strategy, and what the risks are"), US-China Competition ("Who is leading in open models, how this has changed over time, how China maintains its leading position, and relevant history"), and Technical Details, which asks "how much does it help Chinese labs" and "how far are open models behind the closed frontier?"

Named entries include Mark Zuckerberg's July 2024 "Open Source AI is the Path Forward," Irene Solaiman's February 2023 paper on the gradient of generative AI release, Bill Gurley's May 2026 essay on open-source strategy, Helen Toner's April 2025 "Nonproliferation is the wrong approach to AI misuse," an Anthropic misuse report dated September 2026, and Panfilov et al.'s 2026 paper "Stealing Reasoning Traces from Proprietary LLM APIs." Lambert singles out distillation as "the single most eventful debate around open models in 2026." Two of the researchers we follow shared the post the day it went out.