White House science advisor Michael Kratsios said that Moonshot, the Chinese company behind Kimi K3, the largest available open-weight LLM, built its model by copying Anthropic’s Fable LLM while using chips that aren’t cleared for export to China.
“Large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable,” Kratsios wrote, amid reported discussions about banning Chinese open-weight models that have roiled the AI sector. Moonshot did not respond to questions about its training process, and Kratsios did not share more details about the sources of his allegations.
Kratsios’ tweet echoed comments from Treasury Secretary Scott Bessent that “we are finding watermarks of our U.S. large language models on many of the Chinese models, and that that’s unacceptable.” It’s not clear what those watermarks consist of, and the Treasury Department did not respond to a query.
However, experts are skeptical that distillation — the process of querying an LLM to determine its inner workings and copy its capabilities — is responsible for the advanced capabilities that Kimi K3 displays.
“I don’t think you get a model this strong and this quickly on the heels of Fable doing strictly distillation,” Braden Hancock, a researcher at the Laude Institute and co-founder of Snorkel AI, told TechCrunch. “There’s just not even frankly time, right? Fable’s only been publicly available since July 1st. You can’t distill that much data, train a model, and release it in two weeks.”
“I’ve been of the opinion that distillation is becoming less and less impactful over time as the Chinese models get closer to the frontier and the training regime shifts to [reinforcement learning],” Nathan Lambert, an AI researcher at the Allen Institute for AI, said in a podcast released yesterday. “[I]f it were the case, everyone would be easily able to catch up to a GLM or to a K3 by using its data for distillation. But we have not, or we won’t see this, from supervised fine-tuning alone.”
Performing distillation requires a lab to systematically query its target model in order to generate data that can be used for post-training. Sometimes this explicitly involves asking the model to articulate its chain-of-thought to understand how it solves problems. Other times, the prompts and responses from a model are used to train a new model in a process called supervised fine-tuning, or SFT.
It’s this fine-tuning process that can result in a model ostensibly created by a third party claiming that it is Claude. Fine tuning is where, in Lambert’s view, the “model picks up its manners.”
But Lambert says that the benefits of SFT are becoming less important as models become more complex. To distill Fable-like capabilities would likely require reinforcement learning techniques. In many cases, that means having an agent of the larger model grade the smaller model’s responses, and adjusting based on the grade.
The more advanced techniques also require more significant infrastructure. Large reinforcement learning runs can require tens of millions of agents. Using a frontier lab’s API to do that “would be insanely expensive and potentially it would probably be a time bottleneck because these models are pretty slow and to be frank might not even give you a performance uplift.”
It seems likely that previous frontier models might have contributed to Kimi; Anthropic publicly accused Moonshot, DeepSeek, and MiniMax of systematically distilling its models earlier this year. Anthropic said it discovered millions of exchanges between its models and users it identified at those companies through IP addresses and other meta data. Those queries were “distinct from normal usage patterns, reflecting deliberate capability extraction rather than legitimate use.” Anthropic didn’t respond to TechCrunch’s queries about Fable distillation.
However, distillation is seen as common among AI companies, not just in China. Elon Musk testified earlier this year that his company SpaceXAI distilled OpenAI models to develop Grok, and that the practice was common in the industry. The line between distillation and developing synthetic datasets, for example, can be fairly blurry.
“[I]n general, Americans are understating the technical expertise of these Chinese teams,” Hancock said. “One of the founders of Moonshot was a CMU PhD student. These are legitimate researchers and engineers doing solid work. …if American models ground to a halt, I think China’s progress would slow, but would still continue. They’re not just riding coattails here.”
It’s also hard to disentangle distillation from the second part of Kratsios’ comment — that Moonshot had obtained advanced Nvidia Chips, Grace Blackwell 300s, and also accessed GB300 equipped-servers in Thailand. Those chips are banned from export to China, but a black market exists, according to Sam Bresnick, a research fellow at Georgetown’s Center for Security and Emerging Technology. In May, the founder of Supermicro, a U.S. server builder, was indicted for smuggling advanced chips into China.
“I am a proponent of know your customer laws for data centers across the world,” Bresnick said. “If you are letting a company conduct huge training runs on your state-of-the-art hardware, there needs to be a reporting mechanism for who that company is and what they’re doing.”
President Joe Biden’s Department of Commerce proposed federal know-your-customer rules for data centers in 2024, but no further progress appears to have been made under Donald Trump. Exporters shipping advanced chips abroad, however, are supposed to ensure they are only used for approved purposes.
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