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China’s path to AI domination has a problem: loss of talent to the US
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China’s path to AI domination has a problem: loss of talent to the US

A new analysis shows that the number of Chinese AI researchers has increased tenfold over the last decade, but the majority of them live outside the country.

Superpower dreams: China has put forth a concerted effort to grow into a leading AI powerhouse over the last few years. Beijing deemed the discipline in need of special attention as early as 2012, and in 2017 it released a detailed national strategy for advancing and harnessing the technology.

Home-grown army: In a new analysis, Joy Dantong Ma, the associate director of MacroPolo, a Chicago-based think tank focused on China’s economic growth, showed how this top-down push has affected AI talent. The report analyzed the authorship of papers accepted to NeurIPS, one of the most prestigious international AI conferences, and found a nearly tenfold increase in the number of authors who did their undergraduate studies in China over the last decade. Whereas there were only around 100 Chinese researchers in 2009, accounting for 14% of the total number of authors, there were nearly 1,000 in 2018, accounting for a quarter. The largest increase happened between 2017 and 2018 after the release of the national strategy, primarily driven by the rush of second-tier universities that have opened up AI specialization and degree programs.

Brain drain: Despite the country’s success in cultivating domestic talent, however, it has struggled with retention. Roughly three-quarters of the Chinese authors in the study currently work outside China, and 85% of those work in the US—either at tech giants like Google and IBM or universities like UCLA and the University of Illinois Urbana-Champaign.

Why it matters: Among the four major inputs into a country’s AI ecosystem—talent, data, capital, and hardware—the first has the greatest impact. The concentration of expertise determines whether practitioners will direct their energy more toward AI research or applications, for example. It’s also the main driver of innovation in algorithms and hardware, which will likely be more important in advancing the technology in the long run than, say, the availability of data.

The analysis shows that China’s investments in the field could be insufficient in building up its long-term capacity for AI leadership. The government is aware of this problem and recently began taking steps to address it: in the 2017 AI national strategy, it committed to luring top scientists back home with competitive compensation packages and other incentives. In the meantime, the US’s position as an AI leader has benefited greatly from an influx of Chinese scientists—even though that goes against the current presidential administration’s push to minimize collaboration on AI development.

“It’s very unfortunate,” says Ma. “Because of the AI race mentality, people see this as a zero-sum game.” A more fluid movement of scientists would benefit both the US and China, she says, building up both countries’ AI ecosystems while making it easier to create much-needed global standards for AI ethics. A lot of Chinese researchers, for example, receive a PhD in the US, return to China for the first part of their career, and then move back abroad back to the States to continue it. “It’s that type of movement that lets researchers start to coauthor papers in different places,” says Ma, “and that opens the door for people to have discussions on best practices.”

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