John Thornhill writes:
Viewed from abroad, it seems only a matter of time before China emerges as the AI superpower of the 21st century.
Here in the West, our initial instinct is to focus on America’s significant lead in semiconductor expertise, its cutting-edge AI research, and its vast investments in data centers. The legendary investor Warren Buffett once warned: “Never bet against America.” He is right that for more than two centuries, no other “incubator for unleashing human potential” has matched the US.
Today, however, China has the means, motive, and opportunity to commit the equivalent of technological murder. When it comes to mobilizing the whole-of-society resources needed to develop and deploy AI to maximum effect, it may be just as rash to bet against.
The data highlights the trends. In AI publications and patents, China leads. By 2023, China accounted for 22.6% of all citations, compared with 20.9% from Europe and 13% from the US, according to Stanford University's Artificial Intelligence Index Report 2025. As of 2023, China also accounted for 69.7% of all AI patents. True, the US maintains a strong lead in the top 100 most cited publications (50 versus 34 in 2023), but its share has been steadily declining.
Similarly, the US outdoes China in top AI research talent, but the gap is narrowing. According to a report from the US Council of Economic Advisers, 59% of the world’s top AI researchers worked in the US in 2019, compared with 11% in China. But by 2022 those figures were 42% and 28%.
The Trump administration’s tightening of restrictions for foreign H-1B visa holders may well lead more Chinese AI researchers in the US to return home. The talent ratio could move further in China’s favor.
Regarding the technology itself, US-based institutions produced 40 of the world’s most notable AI models in 2024, compared with 15 from China. But Chinese researchers have learned to do more with less, and their strongest large language models—including the open-source DeepSeek-V3 and Alibaba's Qwen 2.5-Max—surpass the best US models in terms of algorithmic efficiency.