HackerPapers

arXiv:2606.15999econ.GN

U.S. Technological Containment and the Rise of China's Open AI Ecosystem

Wang Jin, Nadav Kunievsky, Bowen Lou, Tianshu Sun, James Evans

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

Over the past decade, U.S. policies have increasingly aimed to preserve artificial intelligence (AI) leadership by promoting domestic free-market policies while controlling global technological chokepoints, particularly advanced semiconductors and computational infrastructure. These measures raised the cost of Chinese AI development, but they also increased the strategic value of open and locally adaptable AI systems. Before raising export controls on high-performance chips, both the U.S. and China promoted policies that included support for open-source AI. During the period following major U.S. export-control shocks, China increasingly embedded open-source AI into national technology strategy through proposed ecosystem building, standards coordination, and resilience-oriented deployment. Moreover, Chinese developers increased engagement with open-source large language model repositories substantially more than U.S. developers did, consistent with a shift toward open infrastructure under geopolitical constraints. Subsequently, Chinese-origin open models diffused widely through open-source communities and scientific research. Even though such models remained largely absent from U.S. patent disclosures, American commercial entities use them in open-access research, suggesting their undermeasured importance within the foundation of U.S. commercial activity. These findings suggest that technological containment can shape not only the direction of AI development, but also the ecosystems through which AI is developed, improved, and diffused.

arXiv comments
Journal reference
DOI
Subjects
econ.GNGeneral Economics cs.CYComputers and Society

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Jul 2026 to Jul 2026. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.