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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.
Minor technical nitpick: I think something "open source" is carrying a bunch of connotations which do not apply to LLMs. It is a bit like if I called a CC-BY-SA photograph "open source".
To the degree that LLMs are like traditional software, the source code -- the human-readable inputs which decide what a program does -- would be a neural network framework plus the training data (most of which is crawled/pirated rather than open source licensed).
Compiling would be the process of training.
In normal open source software, almost all of the effort goes into creating the code base. Compilation is basically free, and you compile your code a zillion times in the process of building your codebase. With LLMs, training is really expensive. Nobody downloads your sources, everybody just takes your binary, the weights.
With a normal open source project, you can easily git clone the sources and compile. If you run into a problem or need the program to do something differently, you just edit the sources and compile again, and if you think your changes might be generally useful, you make a pull request upstream to start the process of getting them into the official version.
With LLMs what you git clone are giant inscrutable matrices. If you are really good, you might be able to tweak the weights a bit so that the LLM will talk about the Golden Gate Bridge all the time. But this is a gimmick, not a general improvement. If you want to actually make the model more useful for purposes you have in common with others, you need RLHF, which is computationally expensive again.
This is an important difference between how traditional open source software interacts with the users and how "open source" LLMs interact with the users. I would thus propose to use the name "open weights" for LLMs, which carries none of the connotations of "users will contribute bug fixes".
I mean it's certainly possible to release your training code as well as the resulting weights for an LLM -- now I'm curious as to whether this company is actually doing that or not?
If not, agreed that "OS" is a big misnomer here -- there are certainly lots of individuals floating around who might like to train their own version of this and could afford to do so (FIRE startup retirees spring to mind) and "you can use our weights" is quite different from "you can try to make improvements on our process". More like free beer than free speech.
There are tiers to this, from just weights release to full data+code+weights. Chinese labs mostly release weights and tech report with a reproducible (given some effort) recipe, sometimes code, rarely some or all of the data (more often parts of post-training data, though in these cases it's typically just links to datasets that have already been open).
I think nitpicking about open source is uninteresting when the recipe is available. This is a very dynamic field of applied science, rather than labor-intensive programming exercise. The volume of novel code in a given LLM project is comparable to a modest Emacs package, what matters is ideas (derisked at scale). Specific implementations are usually not that valuable – DeepSeek's GRPO, as described in their papers, has been improved upon in the open multiple times by this point. Data composition is dependent on your own needs and interests, there are vast open datasets, just filter them as you see fit.
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