It’s becoming a fad to claim that China has “caught up to the U.S.” every time a Chinese AI lab releases an impressive new model. It wasn’t true the last time and it’s still not true today. But there are important lessons to take from the stories around Chinese AI, only some of which I’ve covered before.
Alongside a speech by Chinese President Xi Jinping, today’s big story is the release of Moonshot AI’s Kimi K3. With 2.8 trillion parameters, it is the largest AI model with open weights (anyone on the internet can download it). It’s large even by closed-weight standards, similar in size to (though likely not larger than) most leading American AIs. Models this size run on hardware worth hundreds of thousands of dollars, but if you have access to that hardware, the marginal cost to run is pretty low.
Laurie Chen of Reuters has a pretty good analysis, but uncritically shares some claims from the Chinese lab that deserve a closer look. Chen repeats Moonshot’s claims of outstanding hardware performance, claims which are likely true but only somewhat related to a model’s capabilities in practice. She also reports Kimi K3’s high performance on several AI benchmarks (it often did better than any model except Anthropic’s Fable), but neglects to mention that such benchmarks are often noisy, cherry-picked, and easily gamed.
Chen draws an ominous conclusion:
Companies including Moonshot, Z.ai and MiniMax are releasing increasingly powerful models at sharply lower cost, challenging long-held assumptions in the West that Chinese developers trail their American peers by months.
But by most accounts, Chinese developers do trail their American peers by months. Reporting by Jamie John of the Financial Times strikes me as more grounded, citing recent analysis from the UK AI Security Institute that puts the U.S. lead at 4 to 7 months — lower than in 2025, but still substantial.
Citing the same institute, John points out the reason open-weight models can be more dangerous than private ones: Once an AI model can be downloaded by anyone over the internet, there’s no taking that decision back. And bad actors with access to a model’s weights can easily strip away built-in protections.
Like many AIs in America and China, Kimi K3 was probably trained (or “distilled”) at least in part using the outputs of some leading American models. Christopher Mims of the Wall Street Journal notes that this is common industry practice, though often irritating to leading AI developers when done at scale.
Mims observes that high-performing, open-weight models like Kimi K3 threaten to undercut the profits of American AI labs. This checks out: People will often choose a cheap, good-enough AI model over the expensive best-in-class.
I think he’s wrong, though, when he goes on to say that access to electricity is “the moat that will matter most” for AI companies. Electricity does matter, and since attempts to build new American power plants are often smothered by environmental lawsuits, China may be on track to power its own datacenters more cheaply.
But I think there’s still an immense difference between having the world’s smartest AI and the next best thing. I haven’t heard any stories of American companies distilling Chinese AIs to copy their scary capabilities. Due to a combination of talent, chips, and other factors, American AI companies are still driving most of the progress in AI. As my colleague Mitch put it, it’s hard to outrun your own shadow.
I also think there’s an immense difference between jockeying for market share and racing to build the next generation of AI. Most leading American labs are explicitly trying to create vastly superhuman systems, and if the prospect that their creations might extinguish our species won’t stop them, neither will losing some customers to China.
The analyses and opinions expressed on AI StopWatch reflect the views of the individual contributors and the sources they cover, and should not be taken as official positions of the Machine Intelligence Research Institute.