Nathan Lambert

I’m departing the Allen Institute for AI (Ai2), where I got the great privilege to work on the Olmo models, to grow, to learn, and to have broad lasting impacts. This post is an attempt to reflect on why what we…

Nathan Lambert argues that open-weight and closed AI models are improving at different compounding rates, with user willingness to pay premium prices as the central unresolved economic question.

Nathan Lambert shares forward-looking ideas on the next phase of AI development as model capabilities, labor market effects, and policy stakes all intensify simultaneously.

Nathan Lambert surveys a dense wave of new open-weight model releases—Gemma 4, DeepSeek V4, Kimi K2.6, MiMo 2.5, GLM-5.1—alongside a CAISI government evaluation.

Nathan Lambert argues that open model ecosystems create compounding advantages over time as community contributions, tooling, and fine-tuning build on each other.

Nathan Lambert shares firsthand observations from visiting Chinese AI labs, offering rare insight into their research culture, engineering focus, and technical priorities.

Nathan Lambert argues "distillation attack" is an overblown term for what is primarily a policy question about API access and fair use of model outputs.

Nathan Lambert analyzes the open-vs-closed model performance gap, arguing it is more nuanced and dynamic than any single benchmark number suggests.

Nathan Lambert shares his mid-2026 predictions on which open model approaches — fully-open weights, fine-tuning ecosystems, or hybrid licensing — will gain the most traction.

Nathan Lambert shares updates on the ATOM Report, an upcoming post-training course, his book on RLHF, and current active research threads.

Nathan Lambert argues that the open-source AI community will eventually need a formal industry consortium — similar to the Linux Foundation — to fund and coordinate truly open foundation model development.

Nathan Lambert argues that the anti-open-weight backlash following Claude Mythos's cybersecurity benchmark results is exaggerated and strategically opportunistic.

Nathan Lambert reviews Google's Gemma 4 and argues that an open model's success depends less on raw capability than on licensing clarity, tooling support, and community adoption.