Nathan Lambert briefs US Congress on open-weight models in US-China competition
AI researcher Nathan Lambert shared his prepared remarks from a briefing to US Congressional members and staff on the state of open-weight models in the context of US-China competition. The remarks define open-source versus open-weight versus closed models, noting that open-weight models like Meta's Llama and Alibaba's Q are the most common form. The briefing aims to provide an accessible state of the union on open models for a broader audience.
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InterconnectsNathan Lambert
I was recently invited to brief a group of Congressional members and staff on the state of open-weight models in the lens of U.S.-China competition. I’m sharing my prepared remarks as a state of the union on open models that is accessible to a broader audience. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Recap: What is an open source v. open-weight vs. closed model? Open language models are AI models where their weights are publicly available for inspection or downstream use. These are most often contrasted to so-called “closed” AI models. Closed models offer access only through Application Programming Interfaces (APIs) that developers can use to directly query a model, like GPT-4 or Claude Opus 4.5, or through products, like ChatGPT and Claude Code. Open language models primarily are bucketed into two categories, open-weight and open-source models. Open-weight models are the most common form, such as popular models like Meta’s Llama, Alibaba’s Q
