What you need to know about the latest models and AI research trends, from @natolambert

Interconnects AI retweeted
Hearing AI regulation is in the air, especially with a misguided tone of “open dangerous, closed safe” so it felt timely to re-up this one. There are ways to make the AI industry safer without kneecapping transparency, education, and competition. interconnects.ai/p/banning-o…
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Interconnects AI retweeted
The biggest disagreement JSD and I had in this podcast was on the impact of distillation. In our research for Interconnects, @xeophon and I agree that there's no hard evidence that distillation is a massive impact for the Chinese labs. At the same time, the gossip mill in SF has been doubling down on the Chinese labs getting massive gains from distilltion. The argument where distillation is a huge impact is something along the lines of distilled traces go into mid training and make RL work far more easily. My argument, that distillation is a 1-2 month pull ahead in capabilities closer to the frontier, is that Chinese labs already have sufficiently strong models, where this mid-training setup can be done on their own models, and most of the capabilities gains are from scaling RL environment training, which doesn't link cleanly to the distillation data pathway. We feel like the recent RL dashboard from @XiaomiMiMo supports this claim. A lot of it comes down to a gut call on if you can believe it that the Chinese labs are really great at building LLMs, maybe even better than focusing than OpenAI/Ant etc, as their current ambitions are a bit narrower (catching up), rather than transformative products/inventions
New podcast with @datagenproc of @EpochAIResearch digging into the open questions determining the future of frontier AI! We cover: 00:00 Predictions for RSI 18:15 The role of robotics in an AI acceleration 24:20 How far behind are Chinese models? 27:39 Does distillation explain the gap? 40:58 What Chinese job postings reveal about their labs 48:13 Are open or closed models safer? 58:10 How Epoch AI ticks 1:00:55 What a frontier post-training recipe looks like He's one of the people who gives the best feedback on my writing, so I was stoked to have him on.
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Interconnects AI retweeted
I was asked by Congressional staff to share my views on open models performance, adoption, and competition vis a vis China. Here’s my briefing with all our latest data and predictions for what comes next. I am very happy to spend time on broader audience content on open models!
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We hide a lot of fun open model data at dashboard.interconnects.ai/e… 🤓
Here's what the historical open vs closed data here looks like. Would be useful to have volume too (not just %)!
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Interconnects AI retweeted
If you are looking for an alternate viewpoint today, which assumes AI models accelerate the process of AI research, but it doesn't result in rapid RSI and an explosion of near term risks: interconnects.ai/p/lossy-sel…
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When will average people feel AI’s impact? We’re <5 years into a compounding revolution which could take a century, and how the AI industry should manage this. interconnects.ai/p/when-will…
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Artifacts 24: Motif-3, GLM-5.3, Hy4-preview and open model licenses. In this issue, the 25 models from july/aug/sept you should be aware of, from: InclusionAI @TheInclusionAI (4) Qwen @alibaba_qwen (3) Z ai @Zai_org (2) DeepSeek @deepseek_ai (2) Motif Technologies @motif_tech Dots Studio @dotsstudioai Tencent @TencentGlobal NVIDIA @NVIDIAAI IBM @IBM Institute of Foundation Models @IFM_AI Trillion Labs @TrillionLabs SenseTime @SenseTime_AI Meta @AIatMeta Liquid AI @liquidai Cohere @Cohere_Labs Superwhisper @superwhisper Google @GoogleAI MiniMax @MiniMax_AI Read the issue below.
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Check out our latest open model data -- which models are mentioned in every arXiv ML paper since ChatGPT.
Over the weekend I had Codex parse 500K arXiv AI/ML papers since ChatGPT to understand which open models are used for research. In 2024, ~30% of papers mentioned an American open model and only 10% a Chinese model. Today, ~40% of papers mention a Chinese (open) LLM, and only 25-30% an American one. Chinese models are the default for research. Chinese mentions are still growing while American open models are stagnating. When looking at this data it's important to remember that papers substantially lag model releases, as research takes a long time. Qwen's steady growth is reflective of this, but so is Llama's lasting power. Some more observations: 1. Qwen has been steadily growing, and today 1/3 of papers which mention any LLM mention qwen. OpenAI's closed models are the highest overall, at ~37%. 2. Llama peaked around April of 2025 at 30% of papers which mention any LLM (including ChatGPT etc). Llama 4 was released at about the same time, and Llama has been declining since. 3. Gemini and Claude are less common than the leading open models, mentioned in 10-15% of papers puts them behind all of Qwen, Llama, and DeepSeek. Open models should be and are the foundations of open research. The % of papers mentioning any LLM have been steadily climbing since 2023. | Year | January | April | July | October | | 2023 | 10.43% | 15.39% | 18.69% | 32.18% | | 2024 | 29.70% | 33.93% | 35.70% | 44.25% | | 2025 | 39.23% | 45.28% | 44.94% | 53.52% | | 2026 | 55.49% | 57.26% | 53.14% | TBD Now over 50% of AI papers, from 10% in 2023. Other notes: - Gemma and Mistral hover around 5-10%. - Our beloved fully-open Olmo models have been ~1% since the first release in Jan. 2024. - DeepSeek has a clear jump after R1 in Jan. 2025 - Data derived from the most popular ML arXiv categories: cs. AI, cs. CL, cs. CV, cs. LG, stat. ML Just like our downloads and derivative model data, this is updated daily on the Interconnects Open Model Dashboard.
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Interconnects AI retweeted
Personal milestone: 1000 true fans of @interconnectsai ! Hitting a very long term goal feels great. I’m very happy to get to be an independent voice in AI. Cultivating a paid base helps me commit to that longer term, and scale Interconnects’ impact.
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Interconnects AI retweeted
With prodding from @xeophon I added a crucial detail. Data industry go brrr in China. Our interconnects group chat has on many occasions been discussing the data industry in China recently, a huge change from when we visited in April.
GLM 5.3 notes and why we should stop being so surprised about these very strong Chinese models (most of this is talking myself through some of my denial -- yes, these models are the real deal). interconnects.ai/p/glm-53-ho…
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Interconnects AI retweeted
My latest piece is a little verbose, but I think it is really a nice example digging into a sort of knowledge work (and arguably science) that the models really aren't close to solving today -- writing something like a AI textbook in a well-known field. interconnects.ai/p/i-wrote-a…
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RLHF book is in print, so we threw it a party in Seattle. Being in a room full of people who actually care about open post-training was really cool. Thanks @radixark for making the night happen, @ManningBooks for the book, and everyone who came out!
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