Nathan Lambert
37 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
Nathan Lambert argues coding agents will make models superhuman GPU and infrastructure engineers within a few years, accelerating research iteration and raising the value of ideas over execution, without producing a qualitative leap to general superintelligence.
Nathan Lambert on finishing his post-training book for Manning, Reinforcement Learning from Human Feedback, and five things it teaches that are hard to find written down elsewhere.
A 2026 forecast from Interconnects: open weights still have had no Opus-4.5-in-Claude-Code moment, and the real gap measure is whether open models hold up inside agentic harnesses rather than on benchmarks. Also covers compute economics and where the stakes ratchet next.
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.
Interconnects covers new government moves on open-weight AI: a Chinese commerce ministry draft requiring approval to share frontier models abroad, and a DHS order excluding DeepSeek from federal procurement, alongside a roundup of fall open model releases.
Nathan Lambert and Florian on the open-model landscape after Kimi K3 and Qwen 3.8: US-China geopolitics, the economics of open versus closed, distillation, and how wide the open-closed gap really is.
A podcast on Kimi K3.1 Pro's mixed reception despite strong benchmarks, open-weight cyber principles, abliterated APIs and trusted access, and whether earlier open-model risk views held up. Based on show notes only.
Nathan Lambert argues the regulatory window on US open-weight models is closing: reported White House discussions of an executive order, licensing frameworks that treat open releases as an afterthought, and no economic champion to defend them. A policy read for anyone whose stack rests on open weigh
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.
Nathan Lambert on the run of cyberattacks by in-development frontier models, and why the incentives of fast-growing companies and of the federal government are both poorly suited to a technological transition moving this quickly.
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.
A recurring roundup and analysis piece arguing that, contrary to predictions of industry consolidation, more labs (including Thinking Machines, Tencent, Poolside, Moonshot AI, DeepSeek, Meituan, AMD, and others) are training and openly releasing frontier-class models. It surveys specific releases like Thinking Machines' Inkling MoE, Tencent's Hy3 (now under Apache 2.0), Poolside's Laguna-S-2.1, Moonshot's noncommercially-licensed Kimi K3, and DeepSeek's V4 Flash update, discussing their architectures, licenses, and the geopolitical implications of Chinese labs' licensing choices.
Interconnects documents a licensing split among open model makers: Google and Meta move to Apache 2.0 while Kimi K3, MiniMax M3, and now Zhipu's GLM-5.3 add revenue thresholds or security-review clauses for commercial inference and fine-tuning providers.
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.
Nathan Lambert on Z.ai's GLM-5.3, which reaches the frontier of agentic coding benchmarks at roughly 750B parameters, a third of Kimi K3, from the same base model as GLM-5.2 with extended post-training alone.
Nathan Lambert's curated reading list traces open-weight model strategy and economics, from Zuckerberg's and Gurley's open-source arguments to Solaiman's openness gradient and why open models will power custom enterprise agentic workflows.
Nathan Lambert compares open weight models to installed software versions rather than to Linux, and argues that only full training recipes with data and code give the open model ecosystem the self sustaining property people expect from open source.
A paper club discussion of Ai2's Molmo and PixMo, an open VLM family built on human-annotated data instead of distillation from closed models, with notes on Whisper large-v3 turbo.
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.
Nathan Lambert argues that frontier labs' internal anxiety over recursive self-improvement, fueled by thousands of concurrent agents now working inside OpenAI and Anthropic, is amplifying risk narratives beyond current evidence.
Nathan Lambert of Ai2 gives a NeurIPS 2024 talk on the state of reasoning in LLMs, defining reasoning, tying it to reinforcement learning, and outlining where open and closed efforts are heading.