Policymakers Overlook How Open Source AI Is Reshaping Global Power

For the past several years, policymakers have been preoccupied with an AI competition narrative defined by closed “frontier model” development in the United States and China. Regulation, strategy documents, and public debate revolve around which country’s corporations can train the most capable proprietary system, who controls the most compute, and how national champions stack up against one another.

Yet when we examine empirical evidence of how AI is actually being adopted and integrated into products and services, such as in our study on longitudinal data on global model downloads from the Hugging Face ecosystem, a different reality quickly becomes apparent. This open layer of development captures a far wider range of applications and often anticipates where the broader AI market is headed.

Work from the nonprofit research institute Epoch AI has shown that the performance gap between open and closed models is narrowing, even as open models remain dramatically cheaper to build and deploy. The real contest for influence is not unfolding behind closed doors, but in the open-source ecosystem. Open source and open-weight models form the substrate on which millions of developers, startups, and public institutions now build. They shape the languages supported in downstream applications, the assumptions embedded in everyday tools, and the way AI capabilities diffuse across borders. Because they are widely accessible, they also provide a rare empirical window into the global dynamics of AI adoption. By examining model download data, we can observe, rather than merely speculate, how influence in AI is shifting across nations, companies, and communities.