Cheryl Wu is an economics PhD student at Yale and a research scholar at the Centre for the Governance of AI. She studies technology adoption, growth and the economic effects of AI.[1][2][4][5]
Start with How do Chinese AI companies make money? →
X · cherylwoooo Background
Wu earned degrees in mathematics and economics at the University of Chicago from 2017 to 2021 and began her Yale economics doctorate in 2023. Her CV records research work on growth and trade and a 2026 research-scholar position at GovAI. Her broader research studies how new technologies are adopted and how they change economic activity.[1][2]
Contributions
Different paths from AI capability to revenue
Her report with Anson Ho separates consumer applications, APIs, enterprise services, licensing and indirect monetization through existing products. Each has different constraints. Open weights can reduce pricing power for API access, while customized deployments bring maintenance costs that may limit scaling. The report uses those distinctions to examine Chinese AI firms’ strategies.[3]
Job descriptions reveal another part of the strategy
With Jean-Stanislas Denain and Ho, Wu analyzes Chinese AI job postings as evidence of what firms want to build and whom they need to hire. This complements papers and announcements with an operational signal. The authors compare requested skills, research and deployment priorities, and experience requirements, while recognizing that postings reveal demand rather than the final composition or performance of a team.[4]
Tools, writing & talks
Writing, research & talks
Start here
A report on AI revenue streams, competitive constraints and company strategy.
Begin with the five revenue categories and the difference between model-focused firms and conglomerates. No specialist economics training is required to follow the examples.
Coauthor with Anson Ho[3]Epoch AI lays out five revenue routes for Chinese AI labs: consumer apps, API access, enterprise deals, licensing, and cloud complements. A GLM 5.3 Flash case study shows open weights quickly pushing first-party token share down.
Catalog workOriginal sourceAn analysis of hiring requirements as evidence about AI-company priorities.
Coauthor with Jean-Stanislas Denain and Anson Ho[4]