OpenAI and Anthropic generate ten times the revenue of all Chinese AI models combined, Rhodium Group finds

A new analysis by research group Rhodium Group says U.S. firms OpenAI and Anthropic are producing roughly ten times the revenue of Chinese AI models taken together, and that lower sales figures do not necessarily mean lower market valuations.

Findings from Rhodium Group

Rhodium Group's latest study comparing U.S. and Chinese artificial intelligence companies found that two U.S. firms, OpenAI and Anthropic, are generating roughly ten times the revenue of Chinese AI models combined. The research highlights a substantial disparity in near-term commercial returns between leading American model-makers and their Chinese counterparts.

Revenue and valuation diverge

The analysis also noted a disconnect between revenue and valuation: companies or models with relatively low current sales can still command high market valuations. Rhodium attributes this divergence to investor expectations about future growth and strategic importance, rather than to present-day cash flows alone.

Explanations and context

Rhodium's report suggests several reasons for the gap, including earlier commercial launches and broader international market access for some U.S.-based offerings, differing regulatory and data environments, and the timing of product rollouts. The research emphasizes that revenue is only one metric among many when assessing the competitive position of AI developers.

Implications for competition and policy

The findings add nuance to debates about the global AI race, indicating strong near-term commercial advantages for certain U.S. firms while underscoring that valuations reflect expectations about future capabilities and market shifts. Observers say the divergence between current revenue and market value may influence investment flows, partnership choices and policy discussions around innovation and competition in the AI sector.

Limitations and next steps

Rhodium's report is presented as an analysis of available data and market signals; it cautions that the landscape is rapidly evolving as new models and commercial offerings emerge. The group recommends continued monitoring of revenue trends, deployment patterns and regulatory developments to better understand how commercial performance and valuation interact over time.