Open-Source AI and Open Models Reading List

Nathan Lambert published an open-source AI and open models reading list on Interconnects, last updated 13 Sep. 2026, compiling research materials for public-audience and policy-facing writing on open models. The list spans foundational pieces including Mark Zuckerberg's July 2024 'Open Source AI is the Path Forward' post on why Meta releases open models, Irene Solaiman's February 2023 paper on viewing open models on a gradient, and Lambert's own Interconnects essays on open models in perpetual catch-up and open versus closed models on different exponentials.

Hey all I’ve been prepping for some public-audience and policy-facing writing on open models, so I figured I would share my research materials. There’s lots of wonderful stuff in here. This is my list of the best writing on open models in the last few years. If someone decides they want to get up to speed on the area, reading this will be a comprehensive overview of the state of affairs. Please comment pieces to consider adding below, and I’ll update this over time. List last updated: 13 Sep. 2026 Foundation What open models are, why people release them, how they relate to business strategy, and what the risks are. - On open source AI strategy, a walkthrough of how open-source software has been used by businesses and early signs of what that means for AI — From Open Source Software to Open Source Strategy https://p3institute.substack.com/p/from-open-source-software-to-open , Bill Gurley May 2026 . - One of the clearest articulations is Mark Zuckerberg’s comments around Llama 3’s release as to why Meta releases open models — Open Source AI is the Path Forward https://about.fb.com/news/2024/07/open-source-ai-is-the-path-forward/ , Mark Zuckerberg Jul. 2024 - Why you should view open models on a gradient, rather than binary open/closed, based on factors such as licenses, cost of running the model, data access, etc. — The Gradient of Generative AI Release: Methods and Considerations https://arxiv.org/abs/2302.04844 , Irene Solaiman Feb. 2023 . - The role open models will play in the economy of the future, as a complement to strong closed models. Why open models will be used to create custom agentic workflows in enterprises across the world – What comes next with open models https://www.interconnects.ai/p/the-next-phase-of-open-models , Nathan Lambert / Interconnects Mar. 2026 - A position on how open models will capture value by providing a complementary tool to large swaths of the existing economy, drawing on the history of IP and current debates on open vs. closed models e.g. distillation — Some Simple Economics of Open versus Closed AI https://www.a16z.news/p/some-simple-economics-of-open-versus , Christian Catalini Aug. 2026 - Why open models will constantly be behind closed models in performance — Open models in perpetual catch-up https://www.interconnects.ai/p/open-models-in-perpetual-catch-up , Nathan Lambert / Interconnects Feb. 2026 - Where adoption differs for open and closed models — Open and closed models are on different exponentials https://www.interconnects.ai/p/open-and-closed-models-are-on-different , Nathan Lambert / Interconnects Jun. 2026 - A clear articulation on how to balance releasing powerful open-weight models while taking safety seriously — A Safe Path to Open Weights https://thinkingmachines.ai/blog/a-safe-path-to-open-weights/ , Thinking Machines Lab Jul. 2026 . - Early paper on marginal risks that showed text-focused LLMs very marginally increased documented potential risks of models — On the Societal Impact of Open Foundation Models https://arxiv.org/abs/2403.07918 , Sayash Kapoor, Rishi Bommasani et al. Feb. 2024 . - Closed models safety guardrails are regularly bypassed causing a plethora of real AI-risks before hypothetical risks of open weight models have emerged — The Myth of unsafe Open Source AI https://florianbrand.com/posts/open-model-safety , Florian Brand Jun. 2026 . - The mass reduction in open data, which is a crucial factor that has hampered truly open AI research — Consent in Crisis: The Rapid Decline of the AI Data Commons https://arxiv.org/abs/2407.14933 , Shayne Longpre et al. Jul. 2024 . - Recent examples on how strong Chinese models impact the AI ecosystem — Kimi K3: The open-weights escalation https://www.interconnects.ai/p/kimi-k3-the-open-weights-escalation , Nathan Lambert / Interconnects Jul. 2026 / GLM-5.2 is the step change for open agents https://www.interconnects.ai/p/glm-52-is-the-step-change-for-open , Nathan Lambert / Interconnects Jun. 2026 . - A summary of the story of open models in 2025: Nathan Lambert on China’s AI Ecosystem and the Open Model Gap | The Curve 2025 https://www.youtube.com/watch?v=VpYU4VOicI0 , Golden Gate Institute for AI Nov. 2025 . - Optional Latest data on open model adoption: A general summary on US vs. China model adoption — The ATOM Report https://arxiv.org/abs/2604.07190 Apr. 2026 , The latest data on model downloads, derivatives, and research adoption by region — Interconnects Adoption Dashboard https://dashboard.interconnects.ai/ , and The most important models to know about in the ecosystem — Interconnects Artifacts Hub https://artifactshub.ai/ US-China Competition Who is leading in open models, how this has changed over time, how China maintains its leading position, and relevant history. - Why the U.S. needs to invest in open models for fundamental R&D / innovation in the face of growing competition from China – The ATOM Project http://atomproject.ai , Nathan Lambert Aug. 2025 - The lens as to why open models help spur research innovation and beneficial outcomes for AI — Why I build open language models https://www.interconnects.ai/p/why-i-build-open-language-models , Nathan Lambert / Interconnects Oct. 2024 - Why open models foster education, innovation and competition, three core American values — Banning Open Source AI Would Be A Mistake https://www.interconnects.ai/p/banning-open-source-ai-would-be-a , Nathan Lambert & Kevin Xu Jun. 2026 - Why the recent “vibe regulation” / vague federal oversight mechanisms set us up for a clash and-or ban of frontier open models in the near future — 6 months to live for open models https://www.interconnects.ai/p/6-months-to-live-for-open-models , Nathan Lambert / Interconnects Jul. 2026 - Optional Fully open language model technical reports to illustrate the start of the art in understanding: Pythia https://arxiv.org/abs/2304.01373 EleutherAI, 2023 , Olmo https://arxiv.org/abs/2402.00838 2024 , Olmo 2 https://arxiv.org/abs/2501.00656 2024 , Olmo 3 https://arxiv.org/abs/2512.13961 2025 - Chinese open-source history leading up to AI — Chinese Open Source: A Definitive History https://interconnect.substack.com/p/chinese-open-source-a-definitive , Kevin Xu Mar. 2026 . - China’s structural advantages in open-source — China’s Structural Advantage in Open Source AI https://interconnect.substack.com/p/chinas-structural-advantage-in-open , Kevin Xu Jun. 2025 . - How Chinese labs themselves discuss building models, and how the Chinese industry differs from the U.S. — Notes from inside China’s AI labs https://www.interconnects.ai/p/notes-from-inside-chinas-ai-labs , Nathan Lambert / Interconnects May 2026 . - Why Chinese labs are so good at keeping up with American competition e.g. American open weight labs struggle to compete with Chinese labs on fair performance comparisons — GLM-5.3: How Chinese labs keep stride with the frontier https://www.interconnects.ai/p/glm-53-how-chinese-labs-keep-stride , Nathan Lambert / Interconnects Aug. 2026 . - Prominent uses of Chinese models by Western companies have prompted meaningful regulatory attention more https://x.com/CharlesRollet1/status/2091929539433890088 discussion https://x.com/kyleichan/status/2092065399701471396?s=46 - Lawmakers have probed the following companies over using Chinese models: DoorDash CNBC https://www.cnbc.com/2026/07/31/us-lawmakers-doordash-chinese-ai-models.html , Jul. 31 2026 , Airbnb Bloomberg https://www.bloomberg.com/news/articles/2026-04-29/us-house-probes-airbnb-anysphere-s-use-of-chinese-ai-models , Apr. 29 2026; Semafor https://www.semafor.com/article/04/29/2026/house-committee-probes-cursor-parent-airbnb-over-chinese-ai , Apr. 29 2026 , Anysphere / Cursor Bloomberg https://www.bloomberg.com/news/articles/2026-04-29/us-house-probes-airbnb-anysphere-s-use-of-chinese-ai-models , Apr. 29 2026; Semafor https://www.semafor.com/article/04/29/2026/house-committee-probes-cursor-parent-airbnb-over-chinese-ai , Apr. 29 2026 , Apple Reuters https://www.reuters.com/world/china/trump-administration-is-concerned-by-deal-put-alibabas-ai-iphones-nyt-reports-2025-05-17/ , May 17 2025 - Other western companies have very publicly shifted the models they use from American, closed labs to Chinese open models to save costs. Examples include Perplexity prominently and rapidly adopted DeepSeek R1 Forbes https://www.forbes.com/sites/luisromero/2025/01/28/deepseek-now-in-perplexitys-ai-search-us-ai-dominance-challenged/ , Jan. 28 2025 and Thomson Reuters building on Qwen to move off Claude Business Insider https://www.businessinsider.com/thomson-reuters-builds-ai-model-rely-less-on-anthropic-2026-8 , Aug. 24 2026 Technical Details What is distillation and how much does it help Chinese labs, how do open models impact frontier AI risks like cybersecurity, and how far are open models behind the closed frontier? - The open-closed model gap has reduced in recent years, and is now at roughly 4-6 months. The leading open models have all come from Chinese labs since ~2024. - SemiAnalysis article which ran independent evaluations, concluding that open models have been getting closer to the closer frontier of performance over time — Are Open Models Catching Up? https://newsletter.semianalysis.com/p/are-open-models-catching-up , SemiAnalysis Aug. 2026 - Open models are on the Pareto cost frontier, while not at the absolute performance frontier. E.g. DeepSeek V4 Flash, see evaluation and cost on Artificial Analysis https://artificialanalysis.ai/articles/deepseek-v4-flash-0731-scores-50-on-the-artificial-analysis-intelligence-index-10-points-above-previous-deepseek-v4-flash?utm source=chatgpt.com . - Data sources from Epoch AI https://epoch.ai/benchmarks/eci?view=graph&tab=release-date&subset-view=graph&subset-tab=Software+engineering&showFrontierTrend=true&colorCategorization=Accessibility and Artificial Analysis https://artificialanalysis.ai/trends progress-in-open-weights-vs-proprietary-intelligence and U.S. v China https://artificialanalysis.ai/trends?country=us%2Ccn frontier-language-model-intelligence-by-country-over-time , related showing the open-closed gap over time. - An independent analysis of the open-closed gap across a mix of public and private evaluations — How far behind are open models? https://www.lesswrong.com/posts/rJcCrXyEsJKmmDpWG/how-far-behind-are-open-models , Håvard Tveit Ihle May 2026 - E.g. in 2025, the product lead of Z.ai http://z.ai said with respect to their release time “Get it out fast. We open source it within a few hours.” — The Z.ai Playbook https://www.chinatalk.media/p/the-zai-playbook , ChinaTalk Nov. 21, 2025 - Cyber, risks & open models I plan to develop this further - Why we cannot effectively ban open models as used by bad actors for cyber capabilities they will always have access — The OpenAI/Huggingface incident; how we should manage the imminent arrival of autonomous hacking too cheap to meter https://joshuasaxe181906.substack.com/p/the-openaihuggingface-incident-how , Joshua Saxe Jul. 2026 - What the government should do to observe, orient, decide, and act with respect to emerging cyber threats versus blocking models based on in-house capability assessments — We urgently need a coherent national AI cybersecurity policy https://joshuasaxe181906.substack.com/p/we-urgently-need-a-coherent-national , Joshua Saxe Aug. 2026 - Why you cannot expect to control access to AI at a certain threshold e.g. open weight models and need to prepare society to tackle risks downstream of available intelligence — Nonproliferation is the wrong approach to AI misuse https://helentoner.substack.com/p/nonproliferation-is-the-wrong-approach , Helen Toner Apr. 2025 - Distillation – the process of training on output tokens from another model – is the single most eventful debate around open models in 2026. - For basic background, see a textbook chapter https://rlhfbook.com/c/12-synthetic-data on synthetic data & distillation generally, from Reinforcement Learning from Human Feedback post-training textbook published in 2026 - How distillation helps the Chinese labs, but doesn’t take away from their innovation — How much does distillation really matter for Chinese LLMs? https://www.interconnects.ai/p/how-much-does-distillation-really , Nathan Lambert / Interconnects Feb. 2026 - A very transparent documentation of how Chinese company use Anthropic’s products and circumvent the terms of service or intended use. The report details at-scale usage of Anthropic’s products by banned parties, as a mix of technical distillation mentioned via SFT data and extensive routing of Claude into their products and services without telling users — Detecting and countering misuse of AI: September 2026 https://www.anthropic.com/threat-intelligence-report-september-2026 . - A recent paper that showed that the frontier labs had implementations in their APIs that made systematic extraction of reasoning traces the crucial part of modern training through clever tricks. Recent distillation paper, my writing on it — Stealing Reasoning Traces from Proprietary LLM APIs https://arxiv.org/abs/2608.09867 , Panfilov, Schmotz, Shumailov et. al 2026 more on X https://x.com/kotekjedi ml/status/2087147042888114428 . Anthropic confirmed https://www.anthropic.com/threat-intelligence-report-september-2026 illicit-distillation-sep-26 this technique was used by Chinese labs. - Why the political panic over distillation, claiming that distillation is the only reason Chinese models are close to the frontier, is not grounded in the evidence — The distillation panic https://www.interconnects.ai/p/the-distillation-panic , Nathan Lambert / Interconnects May 2026 - How labs can use distillation to improve models in an era of scaling RL environments across agentic behaviors — How distillation is used today and what performance uplift it gives to open models https://natolambert.substack.com/p/how-distillation-is-used-today-and , Nathan Lambert Jul. 2026 - Optional More history: In 2024, I wrote Frontiers in synthetic data https://www.interconnects.ai/p/frontiers-in-synthetic-data where the key points were that synthetic data, primarily in “distilling” models by training with SFT on outputs from a stronger model, was the dominant form of distillation. Frontier labs had been shifting the logit-based, knowledge distillation, confirmed earliest in Gemini and continuing to this day. In early 2025, there was substantial debate on if DeepSeek-R1 was distilled from OpenAI’s o1 model. There is no clear evidence suggesting that they did, and in Apr. of 2025 I wrote confidently https://www.interconnects.ai/i/156272283/did-deepseek-distill-openais-o1-model-hint-no that DeepSeek did not distill. At the time of R1, it is more possible than I gave it credit to that DeepSeek did distill some o1 traces to make it easier for them to train their R1 model – based on the above reasoning trace extraction methods. This does not take away from the innovation of it, but it’s worth being realistic and is a way that distillation could accelerate China closing the gap to American labs.