Junde Wu's experience reflects the tightening of visa and immigration pathways for Chinese students in the U.S. since the late 2010s through administrative measures like PP10043. Similar policies have previously led to multiple rejections of offers from top institutions like Stanford. The long wait times faced by applicants born in China in categories like EB-1A are a well-known structural issue.

In terms of capital pathways, Chinese institutions are directly competing for talent through high salaries, housing, and research funding, rapidly reallocating resources to overseas high-level PhD return projects. Meanwhile, the U.S. is losing some AI talent due to policy barriers, similar to the trend of several Chinese scientists returning to develop in China in recent years.

In the ongoing talent competition between Silicon Valley and Chinese AI laboratories, the global flow of AI talent is currently in a transitional phase characterized by the U.S. tightening immigration and China actively attracting talent.

This fundamentally represents a reconstruction of talent capital: U.S. policy restrictions accelerate the concentration of top Chinese AI talent domestically, with the mechanism being the contrast between visa backlogs and return incentives, prompting a shift of R&D resources and innovation momentum from the U.S. to China, thereby altering the global distribution of AI capabilities and pricing power.

ABAB News · Cognitive Law

The higher the policy barriers, the lower the cost for other countries to poach talent.
With a four-year backlog, the opportunity window has closed, with three to four offers from China each week.
Talent is not loyal to a flag but flows to where the opportunities best match.