Open-Source AI & Open Models Reading List

SubscribeSign inOpen-Source AI & Open Models Reading ListHow to get up to speed on open models and their implications.Nathan LambertSep 11, 202682315ShareHey 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. 2026ShareFoundationWhat 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, 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, 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, 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, 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, Christian Catalini (Aug. 2026)Why open models will constantly be behind closed models in performance — 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, 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, 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, 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, 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, Shayne Longpre et al. (Jul. 2024).Recent examples on how strong Chinese models impact the AI ecosystem — Kimi K3: The open-weights escalation, Nathan Lambert / Interconnects (Jul. 2026) / GLM-5.2 is the step change for open agents, 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, 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 (Apr. 2026), The latest data on model downloads, derivatives, and research adoption by region — Interconnects Adoption Dashboard, and The most important models to know about in the ecosystem — Interconnects Artifacts HubInterconnects AI is a reader-supported publication. Consider becoming a subscriber.SubscribeUS-China CompetitionWho 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, 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, 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, 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, Nathan Lambert / Interconnects (Jul. 2026)[Optional] Fully open language model technical reports to illustrate the start of the art in understanding: Pythia (EleutherAI, 2023), Olmo (2024), Olmo 2 (2024), Olmo 3 (2025)Chinese open-source history leading up to AI — Chinese Open Source: A Definitive History, Kevin Xu (Mar. 2026).China’s structural advantages in open-source — China’s Structural Advantage in Open Source AI, 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, 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, Nathan Lambert / Interconnects (Aug. 2026).Prominent uses of Chinese models by Western companies have prompted meaningful regulatory attention (more discussion)Lawmakers have probed the following companies over using Chinese models: DoorDash (CNBC, Jul. 31 2026), Airbnb (Bloomberg, Apr. 29 2026; Semafor, Apr. 29 2026), Anysphere / Cursor (Bloomberg, Apr. 29 2026; Semafor, Apr. 29 2026), Apple (Reuters, 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, Jan. 28 2025) and Thomson Reuters building on Qwen to move off Claude (Business Insider, Aug. 24 2026)Leave a commentTechnical DetailsWhat 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?, 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.Data sources from Epoch AI and Artificial Analysis (and U.S. v China, 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?, Håvard Tveit Ihle (May 2026)E.g. in 2025, the product lead of 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, 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, 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, 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, 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 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?, 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. 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, Panfilov, Schmotz, Shumailov et. al 2026 (more on X). Anthropic confirmed 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, 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, Nathan Lambert (Jul. 2026)[Optional] More history: In 2024, I wrote 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 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.82315SharePreviousDiscussion about this postCommentsRestacksInterconnects AI reply rulesMax Körbächer10hLiked by Nathan LambertCan approve, very helpful list, I had a few of them already bookmarked!ReplyShareCeline Nguyen10hLiked by Nathan Lambertthank you so much for this!! really fortuitous timing bc I was just trying to collect my own reading list on open-weight models, but this is a much better starting point—really appreciate you sharingReplyShare1 more comment...TopLatestDiscussionsNo postsReady for more?Subscribe© 2026 Interconnects AI, LLC · Privacy ∙ Terms ∙ Collection notice Start your SubstackGet the appSubstack is the home for great culture

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The landscape of open-source artificial intelligence and open models involves complex considerations spanning strategy, economics, safety, and geopolitical dynamics. Understanding this area requires examining why open models are released, their relationship to business strategy, and the associated risks. Some key perspectives articulate that open models should be viewed on a gradient rather than a binary open or closed distinction, considering factors such as licensing, running costs, and data access. Furthermore, open models are anticipated to play a role in the future economy by serving as complementary tools to strong closed models, particularly in creating custom agentic workflows across enterprises.

The evolution of open models is intertwined with data access and societal impact. There is concern regarding the mass reduction in open data, which hampers truly open AI research, prompting discussions about consent in the context of the AI data commons. Safety remains a critical concern; while closed models feature safety guardrails that are often bypassed, the hypothetical risks associated with open weight models have emerged, leading to calls for a safe path toward releasing powerful models while maintaining rigorous safety oversight. Early research also indicated that text-focused large language models marginally increased documented potential risks, suggesting that the absence of strong safety guardrails in open systems could lead to numerous real AI risks before hypothetical ones materialize.

Geopolitical competition significantly shapes the trajectory of open models. The dynamic between the United States and China in the open models space is a focal point, addressing who is leading in open models, how that leadership has shifted over time, and the rationale for fostering open models for fundamental research and innovation against growing competition. Research into this competition highlights China’s structural advantages in open-source AI and how this contrasts with the United States’ push for open models, which is framed as fostering education, innovation, and competition. This competition is evident in how Western companies have shifted from American closed labs to Chinese open models to manage costs, prompting regulatory scrutiny from lawmakers regarding the use of these models by entities such as DoorDash, Airbnb, and Apple.

Technically, the performance gap between open and closed models is evolving. Open models are currently positioned on the Pareto cost frontier rather than the absolute performance frontier, with recent independent evaluations suggesting that the open-closed gap has narrowed, with leading open models increasingly drawing from Chinese labs since around 2024. Distillation, the process of training on output tokens from another model, is central to this evolution. While distillation is seen as a factor accelerating the gap closure, there is a debate surrounding the political panic claiming that distillation is the sole reason Chinese models are closing the frontier. Research suggests that distillation can be a method by which Chinese labs accelerate progress by extracting reasoning traces from proprietary models, which may be a means for them to close the performance gap with American labs. Furthermore, open models are perpetually behind closed models in performance, although recent analysis suggests that this gap is shrinking. Ethical and technical concerns also extend to cybersecurity, where the lack of control over open models raises questions about their use by bad actors, necessitating the development of coherent national AI cybersecurity policies.