Nathan Lambert
13 Intel
Nathan Lambert is a machine learning researcher who earned his PhD from UC Berkeley working at the intersection of machine learning and robotics. He spent over two years at the Allen Institute for AI (Ai2) as a senior research scientist and post-training lead, contributing to open language models including OLMo and the Tulu series, after earlier helping build the RLHF research team at Hugging Face; he left Ai2 in mid-2026. He writes the widely read AI newsletter Interconnects and authored a reference book on reinforcement learning from human feedback (RLHF).
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Intel
An analytical article examining the release of Moonshot AI's Kimi K3, a 2.8T parameter open-weights MoE model, and its implications for the global AI ecosystem — including China's open-source AI strategy, capital efficiency advantages, and the shifting balance between open and closed frontier models.
A roundup of recent open model releases showing the ecosystem diversifying well beyond the handful of mostly Chinese labs that dominated it a year ago, with Zyphra, Cohere and Poolside among the newer entrants.
An analytical essay examining China's high-participation, open-first AI ecosystem and how sharing research reduces development costs across labs. It argues that most compute in frontier models goes to R&D rather than final training, making open collaboration a structural advantage.
An opinion essay from the Interconnects AI newsletter arguing against conflating legitimate model distillation with 'distillation attacks' by Chinese labs, and warning that hasty policy responses could harm the U.S. open-weight AI ecosystem.
An opinion essay arguing that economic pressures will eventually force AI companies to form a funding consortium to sustain near-frontier open-weight models, as individual labs increasingly abandon fully-open releases in favor of profitable closed products.
An opinion article analyzing Anthropic's fictional 'Claude Fable 5' model release and its safety measures, arguing that some silent model manipulations amount to competitive entrenchment rather than genuine safety. It uses the release as a cautionary fable about AI control and makes the case for open-source AI ecosystems.
An analytical blog post about GLM-5.2, an open-weight AI model from Z.ai, arguing it's the first open model to credibly compete with frontier closed models like Claude in agentic coding workflows. The piece examines the closing capability gap between US closed labs and Chinese open-weight labs and its economic and regulatory implications.
An analytical blog post from Interconnects AI examining the capability gap between open-weight and closed AI models, forecasting economic and geopolitical dynamics shaping the open-source LLM ecosystem through 2026-2027.
A podcast interview and technical summary tracing how large language model post-training recipes have evolved from InstructGPT to 2026-era frontier models. It explains the shift toward Multi-teacher On-Policy Distillation (MOPD) and compares recipes across DeepSeek, Llama, Tülü, OLMo, MiMo, Nemotron, Kimi, and GLM.
A firsthand essay from Interconnects AI documenting a trip to visit most of China's leading AI labs, exploring the cultural, organizational, and industry differences that shape how Chinese researchers build language models.
An analysis essay from the Interconnects AI newsletter arguing that closed frontier labs (OpenAI, Anthropic) and open model builders are on divergent economic trajectories, with closed models monetizing high-end knowledge work while open models diffuse broadly across the economy.
An analytical essay from the Interconnects AI newsletter examining why the perceived capability gap between open and closed language models is more nuanced than a single benchmark number suggests. It explores how benchmark relevance shifts over time, the role of RL environments and data in keeping fast-follower labs competitive, and the economic pressures driving frontier labs to constantly reinvent the 'frontier.'
A comprehensive book and free lecture series teaching post-training techniques for language models, from RLHF foundations to reinforcement learning algorithms. Aimed at helping practitioners transition from beginner to expert in modern LLM alignment methods.