Strategic Shift for China’s AI Talent Development
As the global competition in artificial intelligence intensifies, particularly between the United States and China, the need for a strategic shift in talent development becomes increasingly evident. The challenge lies not only in training a large number of AI professionals but also in creating an environment that can attract and retain top-tier talent. This transition is crucial as the geopolitical landscape evolves, with the U.S. integrating AI more deeply into its military and national security systems.
According to Dai Mingjie, a researcher at the Institute of Public Policy at the Guangzhou-based South China University of Technology, many Chinese researchers working in the U.S. are facing difficult choices due to tightening security reviews and identity conflicts. These challenges have led them to “choose a side,” highlighting the need for China to develop regionally embedded talent ecosystems that allow top talent to integrate into the domestic innovation system.
China has long been recognized for its ability to train a significant number of AI professionals. However, this strength has not been matched by a robust development scene. Data from MacroPolo, a think tank of the Paulson Institute, indicates that in 2019, 27% of top-tier AI researchers at U.S. institutions were from China. By 2022, this figure had risen to 38%. Furthermore, the 2024 Global AI Talent Tracking Report by MacroPolo noted that China trained about half of the world’s AI talent, compared to 17.5% by the U.S.
Despite these numbers, retention in the U.S. remains strong. A 2025 sample from the Carnegie Endowment for International Peace showed that 87 out of 100 leading Chinese-origin AI researchers still worked in the U.S., with only 10 returning to China. This trend underscores the importance of creating a compelling environment that can retain talent.
The Pull of the U.S. and the Challenges in China
The allure of the U.S. is multifaceted, encompassing higher salaries, better research opportunities, and a more flexible academic-industry collaboration. For instance, Meta’s Superintelligence Labs, which focuses on R&D for artificial general intelligence, has a cohort where half of the initial “AI super talent” had Chinese educational backgrounds.
John Zhu, a data science graduate from southern Guangdong province currently pursuing a master’s degree in AI in the U.S., shared his perspective on the pull of studying abroad. He noted that while he was aware of potential visa issues and political pressures, the pay and development prospects in the U.S. were significantly better.
Beyond financial incentives, non-financial factors such as a sense of participation in technological change, research autonomy, and social influence are also critical. According to a recent article in the MIT Technology Review, more than 60% of top talent prioritize these non-financial incentives.
Dai highlighted the advantages of the U.S. system, including flexible academia-industry collaborations, evaluation systems focused on problem-solving rather than formal credentials, and a less bureaucratic innovation culture. In contrast, China faces challenges such as fragmented industry-academia-research linkages, the intense “996 schedule,” and “involution” or excessive internal competition that can exhaust talent and stifle originality.
Building Sustainable Ecosystems for Talent
Wang Yifang, a prominent Chinese physicist from the Chinese Academy of Sciences, acknowledged the difficulties in competing with top U.S. institutions. He emphasized that while China has plenty of ordinary researchers and results, the top-tier talent remains largely abroad.
“The most attractive point for talent overseas is its research environment. It has a circle of like-minded peers with whom one can discuss ideas. Through these discussions, one can gain inspiration,” Wang stated. He also highlighted the importance of external conditions such as funding, but stressed that the research environment and peer circle are the most critical factors for growth.
As the U.S.-China tech competition intensifies, an effective talent strategy could prove decisive. Local officials in China’s eastern Jiangsu province, involved in AI development initiatives, noted the challenges in recruitment. They mentioned that while there is pressure to attract AI investment and talent, it is unclear what kind of people or businesses should be targeted.
Dai called for a shift from short-term “high-salary recruitment” tactics to building sustainable, regionally embedded talent ecosystems. She suggested focusing on areas such as the Guangdong-Hong Kong-Macau Greater Bay Area, breaking administrative barriers to foster open innovation communities where talent is fully integrated into the research-production chain.
This structural reform, Dai argued, would address the question of why China struggles to nurture original innovators within its education system and retain top talent, moving the country from an AI “training powerhouse” towards a true “AI development highland.”
