Nathan Lambert is focused on publishing his book and a complementary course on Reinforcement Learning from Human Feedback (RLHF), while also analyzing trends in open model adoption, capabilities, and sustainability through his work with Interconnects.
Recent Activity
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
@natolambert.bsky.social
Nathan Lambert·Apr 9, 2026
Another dance around fears of open-source.
Highlights: The post critiques the 'Claude Mythos' narrative that overstates risks of open-weight AI models, arguing it's a form of fearmongering that distracts from more substantive discussions. It suggests this pattern reflects recurring anxieties in open-source debates rather than new, evidence-based concerns.
Worth reading: It offers a critical perspective on current AI discourse, challenging common assumptions about open-source risks and encouraging more nuanced evaluation of model accessibility.
Nathan Lambert·Apr 11, 2026
And yes, I hate consortia too.
Highlights: The article argues that despite general skepticism toward consortia, the AI field urgently requires an open model consortium to ensure transparency, collaboration, and ethical standards. This collective approach is framed as essential for addressing the rapid, often opaque advancements in AI development.
Worth reading: It offers a pragmatic perspective on overcoming industry fragmentation and highlights the critical role of open collaboration in shaping responsible AI innovation.
Nathan Lambert·Apr 14, 2026
What I've been up to!
Highlights: The post offers a personal update on Nathan Lambert's multifaceted contributions to AI/ML, including the ATOM Report for technical insights, a post-training course for practical education, and his book for broader dissemination of knowledge. It highlights the importance of bridging research, education, and community engagement in advancing the field.
Worth reading: It provides a concise overview of current projects from an active researcher, useful for those interested in AI/ML trends, educational resources, or community contributions.
Nathan Lambert·Apr 15, 2026
What I expect to come next and why, focused on the open-closed gap.
Highlights: The author predicts that by mid-2026, the gap between open and closed AI models will significantly narrow, with open models achieving performance parity in key areas. This shift is expected to be driven by advancements in training efficiency, data curation, and collaborative development within the open-source community.
Worth reading: It offers a forward-looking perspective on the evolving AI landscape, grounded in technical trends, making it valuable for developers, researchers, and anyone interested in the future of accessible AI technology.