Why China's Race For AI Dominance Depends On Math

Why China's Race For AI Dominance Depends On Math

Forget about “AI” itself: it’s all about the math, and America is failing to train enough citizens in the right kinds of mathematics to remain dominant.


Moreover, China is just as aggressively buying leading mathematicians abroad. The “Thousand Talents” program attracts both native and foreign researchers in key fields back to China. These individuals bring with them the fruits of their labors in U.S. universities, corporations, and research institutes, and transfers them to Chinese public and private research institutes. According to the Asia Times’ David Goldman, Huawei alone employs 50,000 foreigners, many of them highly-trained engineers lured away by huge salaries and bonuses.

It is little surprise then that China’s mathematical capability is letting the country challenge America for AI dominance. From the U.S. chief technology officer to the Boston Consulting Group, a diversity of voices are warning that China’s aggressive public and private investment in AI is threatening America’s lead, along with the siphoning off of knowledge originated in the United States. As one leader of a small San Francisco-based AI firm recently complained, “If I don’t employ Chinese nationals, then I lose half my engineering staff. But I have no idea if those whom I employ are security risks or not.”


THE SPECTER of two global technological systems emerging—one Chinese, one American, one open and one closed—is a theme of much contemporary concern. Beijing is exporting its technologies of control across the globe, primarily to repressive regimes in Asia and Africa, but also to democratic states like Great Britain. Already over a dozen countries are using Chinese surveillance technology, while nearly three dozen are being trained in CCP models of censorship, according to Freedom House. The increasing global ubiquity of surveillance cameras means that only the nature of the political regime and society employing them gives a modicum of hope for some level of protection of individual rights and for ameliorating government or corporate abuse of power.

Facing America’s slipping competitiveness in this field, foreign governments and companies will likely be pushed to let advanced Chinese systems into their societies, with the commensurate shaping of a repressive or intrusive AI-driven technological environment, not to mention the possible passing of national and personal information to China. And despite the assurances of Chinese companies that they do not share the information they collect with the Chinese government, Beijing’s national security, cybersecurity, and national intelligence laws mandate that private corporations in China cooperate with authorities when ordered to, including with providing private data.

While investors like Kai-fu Lee see China quickly catching up to the United States in AI, few comment on the math ability that drives such development. This, combined with China’s lead in developing 5G networks, and its government-funded attempts to become a leader in semiconductor chip production, means it is possible to envision a future where not only do U.S. companies become mere consumers of Chinese technology, surrendering their ability to shape the digital economy, but so does the U.S. government, leading to a collapse in its ability to maintain the global military balance of power.

The best way to prevent this is by focusing on the basics. America needs a major all-of-society push to increase the number of U.S. students being trained in both the fundamentals of math and in the more advanced, rigorous, and creative mathematics. Leadership in implementing this effort will have to come from the U.S. government and leading technology companies, and through the funding of ambitious programs. A few ideas come to mind: talent-spotting schemes, the establishment of math centers, and a modern successor to the post-Sputnik National Defense Education Act, which would provide math scholarships to promising students along with guaranteed employment in either public or private enterprises.

Assuming such an approach leads to an increase in Americans’ math abilities, there will then have to be a national reckoning about the unwillingness of private American corporations to protect their intellectual property from China, as well as a push to keep U.S.-trained mathematicians and engineers at home instead of selling their services abroad through programs like China’s “Thousand Talents” plan.

Winning the AI competition begins by acknowledging how poorly we do in attracting and training Americans in math at all levels. Without getting serious about the remedy, the AI race may be lost as clearly as two plus two equals four.

Michael R. Auslin is the Payson J. Treat Distinguished Research Fellow at Stanford University’s Hoover Institution and the author of Asia’s New Geopolitics: Essays on Reshaping the Indo-Pacific.

Anonymous is involved in the tech industry.

Image: Reuters.