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Periodic Open-Source AI Update: Kimi K2 and China's Cultural Shift
(yes yes another post about AI, sorry about that). Link above is to the standalone thread, to not clutter this one.
Two days ago a small Chinese startup Moonshot AI has released weights of the base and instruct versions of Kimi K2, the first open (and probably closed too) Chinese LLM to clearly surpass DeepSeek's efforts. It's roughly comparable to Claude Sonnet 4 without thinking (pay no mind to the horde of reasoners at the top of the leaderboard, this is a cheap-ish capability extension and doesn't convey the experience, though is relevant to utility). It's a primarily agentic non-reasoner, somehow exceptionally good at creative writing, and offers a distinct "slop-free", disagreeable but pretty fun conversation, with the downside of hallucinations. It adopts DeepSeek-V3’s architecture wholesale (literally "modeling_deepseek.DeepseekV3ForCausalLM"), with a number of tricks gets maybe 2-3 times as much effective compute out of the same allowance of GPU-hours, and the rest we don't know yet because they've just finished a six-months marathon and don't have a tech report.
I posit that this follows a cultural shift in China’s AI ecosystem that I've been chronicling for a while, and provides a nice illustration by contrast. Moonshot and DeepSeek were founded at the same time, have near-identical scale and resources but have been built on different visions. DeepSeek’s Liang Wengeng (hedge fund CEO with Masters in engineering, idealist, open-source advocate) couldn't procure funding in the Chinese VC world with his inane pitch of “long-termist AGI research driven by curiosity” or whatever. Moonshot’s Yang Zhilin (Carnegie Mellon Ph,D, serial entrepreneur, pragmatist) succeeded at that task, got to peak $3,3 valuation with the help of Alibaba and Sequoia, and was heavily spending on ads and traffic acquisition throughout 2024, building a nucleus of another super-app with chatbot companions, assistants and such trivialities at a comfortable pace. However, DeepSeek R1, on merit of vastly stronger model, has been a breakout success and redefined Chinese AI scene, making people question the point of startups like Kimi. Post-R1, Zhilin pivoted hard to prioritize R&D spending and core model quality over apps, adopting open weights as a forcing function for basic progress. This seems to have inspired the technical staff: "Only regret: we weren’t the ones who walked [DeepSeek’s] path."
Other Chinese labs (Qwen, Minimax, Tencent, etc.) now also emulate this open, capability-focused strategy. Meanwhile, Western open-source efforts are even more disappointing than last year – Meta’s LLaMA 4 failed, OpenAI’s model is delayed again, and only Google/Mistral release sporadically, with no promises of competitive results.
This validates my [deleted] prediction: DeepSeek wasn’t an outlier but the first swallow and catalyst of China’s transition from fast-following to open innovation. I think Liang’s vision – "After hardcore innovators make a name, groupthink will change" – is unfolding, and this is a nice point to take stock of the situation.
I was worried when I saw that it had a custom license, but it turns out it's just a slightly modified MIT license, requiring credit if your project gets big enough. This truly is open source.
I remain of the opinion that it is likely (but not guaranteed) that courts will find "training models" to not be a sufficiently creative endeavour to merit copyright protection. "Throwing a bunch of data into the GPU blender and doing massive least squares" isn't IMO more creative than scanning a painting, compressing the works of Shakespeare with gzip, or having a monkey press the camera shutter.
Well, I don't really understand American law but it seems to me that Anthropic has set the precedent of LLM pretraining corpora being essentially immune to copyright claims. Anthropic's models are, ironically, the most paranoid about reproducing copyrighted material.
The Anthropic case there is focused on "Is it a copyright violation to train models on copyrighted data without licensed distribution?", which is an interesting question, but my comment is more on the separate "Is the resulting model I've trained something I can claim copyright over?" question.
Sorry, misunderstood you. I don't think we've seen anyone seriously defend having stolen or distilled someone's model. My bet is the precedent will depend on who/whom and lawyer muscle rather than fundamentals of the situation.
The closest I'm aware of is the nominal academic license of Facebook's llama models that seems to have been largely ignored once they were out in the wild. At the time, Meta was trailing a bit, and it probably helped their mindshare overall, but they didn't bring any court cases that I'm aware of either.
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