OpenAI and Anthropic Lead AI Revenue by a Wide Margin
The latest analysis from US-based Rhodium Group shows that OpenAI and Anthropic, two leading American AI companies, generate approximately ten times the annual recurring revenue (ARR) of all major Chinese AI models combined. According to figures from mid-2026, OpenAI reported $40 billion in ARR, while Anthropic’s revenue reached $65 billion, dwarfing the Chinese firms’ totals.
Chinese AI companies such as ByteDance and Alibaba posted ARR figures of $4 billion and $2.4 billion respectively, with other prominent players including Z.ai at $1.8 billion and Moonshot at $1 billion. Despite rapid user growth in China, these numbers highlight a significant revenue gap that underscores differing market dynamics between the US and China in AI monetization.
Valuation Disparities Highlight Investor Expectations
The Rhodium report points out that valuations of Chinese AI startups are disproportionately high relative to their revenue. Moonshot and DeepSeek, two leading Chinese AI firms, have estimated valuation-to-revenue multiples of 50x and 163x, respectively. These ratios far exceed the 34x multiple for OpenAI and 21x for Anthropic.
This discrepancy raises concerns about the sustainability of current valuations, especially given the lower revenue base in China. While Moonshot and DeepSeek are reportedly preparing IPOs in Hong Kong, the steep multiples indicate investor optimism may be driven more by growth potential than current financial performance, increasing market risk.
IPO Plans Signal a Critical Phase for AI Companies
Anthropic is expected to list on a US stock exchange next month, with OpenAI postponing its IPO plans until 2027. On the Chinese side, Moonshot has filed confidentially for a Hong Kong IPO, and DeepSeek is also preparing for a public offering. These moves suggest both US and Chinese AI firms are entering a crucial phase of raising capital from public markets amid intense competition.
Market reactions have been mixed; for example, Z.ai’s shares on the Hong Kong exchange fell earlier in the week after a strong summer rally but rebounded following news of new fundraising. Minimax’s shares have also struggled to sustain gains post-IPO. These fluctuations reflect investor caution amid uncertainties about AI technology development and monetization strategies.
Revenue Models and Market Dynamics Differ Sharply
Chinese AI models are often open-source, allowing third parties to run the software independently, which limits direct revenue capture by developers. In contrast, US AI companies like OpenAI and Anthropic operate mostly closed models with higher costs per task, translating into greater direct revenue. This fundamental difference affects the financial scale and growth strategies of AI firms in each country.
Rhodium’s analysis notes that Chinese AI firms face challenges scaling sustainably due to this revenue gap. Government funding has supported infrastructure like AI chips and servers—over 60% of equity investment in Chinese AI hardware comes from state-affiliated sources—but direct funding for frontier AI labs remains limited. This funding environment adds uncertainty to long-term growth prospects for Chinese AI startups.
Market Risks Amid Rapid AI Development
US tech stocks experienced volatility as executives from leading American AI companies warned of risks tied to rapid AI development, emphasizing caution. Chinese AI leaders have yet to publicly address these concerns, but the sector’s sensitivity to market conditions is evident in recent share price movements and fundraising activities.
Rhodium’s partner Logan Wright highlighted the difficult equity market climate in China, which could impact the ability of Chinese AI companies to secure sustained investment. The contrasting regulatory and funding environments between China and the US will likely influence how these companies evolve and compete on the global stage.
Takeaway: Despite rapid adoption, Chinese AI firms generate only about 10% of the revenue of US leaders OpenAI and Anthropic, reflecting divergent business models, valuation gaps, and funding challenges.