Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.

Moonshot AI, the company behind Kimi, has four core founders. Their backgrounds are unusually strong: - Founder and CEO Yang Zhilin studied computer science at Tsinghua before earning his PhD from Carnegie Mellon. He was the first author of Transformer-XL and XLNet, and previously worked at FAIR and Google Brain. - Co-founder and CTO Zhang Yutao earned his PhD in computer science from Tsinghua. His earlier work covered knowledge graphs and AMiner, and he previously co-founded Recurrent AI with Yang. - Co-founder Wu Yuxin studied at Tsinghua and CMU before joining FAIR. He worked with Kaiming He on Group Normalization and also created Detectron2. - Co-founder Zhou Xinyu studied computer science at Tsinghua and later joined Megvii, where he worked on turning research algorithms into production systems and co-authored ShuffleNet. They all share one root: Tsinghua University. Tsinghua is widely regarded as one of China’s top universities. In the latest U.S. News Best Global Universities ranking, it reached No. 6 worldwide. Its influence on China’s AI industry extends well beyond Moonshot. the company behind the GLM models, also grew out of Tsinghua. Its co-founder and chief scientist, Tang Jie, was once Yang Zhilin’s teacher. There is also a more personal connection. Yang and Zhou formed a rock band together at Tsinghua. Moonshot AI’s Chinese name, 月之暗面, comes from Pink Floyd’s album The Dark Side of the Moon, one of Yang’s favorites. Kimi may look like a young AI company. Behind it is a much older network of classmates, teachers, research labs, and friendships.