China’s artificial intelligence industry is attracting rapidly growing user adoption, but the revenue generated by its leading AI models remains a fraction of that of US rivals, raising questions about whether soaring valuations are supported by underlying business performance.
US research firm Rhodium Group estimates that all major Chinese AI models combined generate only about 10% of the revenue reported by OpenAI and Anthropic. The figures, published Thursday, use annual recurring revenue, or ARR, an industry metric that annualizes a recent monthly revenue figure to capture the pace of rapidly growing businesses.
The gap is striking given the attention Chinese AI companies have received from investors this year.
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DeepSeek had the lowest estimated ARR among the major Chinese AI companies at about $500 million, according to Rhodium. MiniMax was estimated at $800 million, while Moonshot stood at about $1 billion.
Z.ai told investors on Wednesday that its latest ARR had reached $1.8 billion, according to a transcript seen by CNBC. The company now expects its ARR to reach $3 billion by the end of the year, up from an earlier forecast of $2.4 billion.
Even after including larger technology companies with AI operations, however, China’s revenue base remains considerably smaller. Rhodium estimated ByteDance’s ARR at $4 billion and Alibaba’s at $2.4 billion, compared with $40 billion for OpenAI and $65 billion for Anthropic.
The disparity becomes more significant when revenue is compared with valuations.
“Valuations relative to revenue appear exorbitant for Moonshot and DeepSeek at present,” Rhodium said, estimating revenue multiples of about 50 times for Moonshot and 163 times for DeepSeek.
By comparison, the report put OpenAI’s valuation-to-revenue multiple at 34 times and Anthropic’s at 21 times.
The figures point to a significant disconnect between how investors are pricing China’s emerging AI leaders and the amount of revenue those companies are currently generating. That disconnect could become an issue as several major AI companies move toward public markets, where investors will have greater access to financial disclosures and will be able to compare valuations against revenue growth more directly.
Anthropic is reportedly expected to list in the US next month, while OpenAI has pushed its IPO plans to next year. Moonshot has reportedly filed confidentially for a Hong Kong listing, while DeepSeek is also reportedly preparing for an IPO.
Moonshot said it does not comment on market rumors or speculation when asked about the reported confidential filing. DeepSeek and Anthropic did not respond to requests for comment.
Open-Source Models Create A Different Revenue Challenge
The revenue gap is not necessarily a straightforward measure of the technological progress being made by Chinese AI companies.
Rhodium acknowledged that its analysis relies on the latest available figures from this summer, while usage of Chinese AI models has increased sharply from relatively low levels earlier in the year. The acceleration means current revenue figures could change considerably if adoption continues to translate into paid usage.
Z.ai’s revised year-end ARR forecast illustrates that trajectory. Its new $3 billion target would represent a substantial increase from the $1.8 billion figure it reported this week.
Chinese AI companies also face a different monetization environment because several of their leading models are open source. Developers and businesses can download models and operate them independently if they have sufficient computing infrastructure, meaning the company that develops the model does not necessarily capture revenue every time that model is used.
Rhodium said Chinese AI labs are therefore exploring ways to capture a larger share of the revenue generated by third parties providing access to their models.
The contrast with US companies is significant. OpenAI and Anthropic largely operate closed models and directly control access to their leading systems. That gives them greater ability to monetize usage, although it also leaves them carrying substantial costs associated with training and operating increasingly powerful models.
According to AI comparison firm Artificial Analysis, the cost per task for leading OpenAI and Anthropic models is substantially higher than for Chinese models. Lower pricing can help Chinese models gain users quickly, but it can also make converting that usage into equivalent revenue more difficult.
This results in a central challenge for China’s AI sector: rapid adoption does not automatically translate into equally rapid monetization.
“The financing gap means it will be far more difficult for Chinese frontier AI labs to scale sustainably,” Logan Wright, a partner at Rhodium Group and co-author of the report, told CNBC.
Wright said Chinese AI companies would be heavily dependent on favorable equity-market conditions, adding that relying on China’s equity market has historically been difficult. He also said government support has been useful for expanding computing infrastructure but suggested that direct government financing for frontier AI laboratories could face limits.
The contrast between infrastructure and AI-model funding is important. Rhodium estimated that more than 60% of equity investment in Chinese AI chips and servers came from state-affiliated sources. That indicates a substantial role for government-linked capital in building the hardware infrastructure needed to support the country’s AI ambitions.
But funding the physical infrastructure required for AI development is different from guaranteeing the commercial success of individual AI laboratories. Companies still need to turn computing capacity and model capabilities into recurring commercial revenue.
Investors Are Already Testing The Valuations
The market’s response to China’s listed AI companies has demonstrated how quickly investor enthusiasm can change.
Z.ai shares rose more than 5% in Thursday morning trading, recovering from an earlier decline following news of its second major fundraising round in two months. The Hong Kong-listed stock has fallen back toward levels seen in the spring after briefly more than tripling during the summer.
MiniMax has faced a similar pattern. Its shares have struggled in recent months to maintain gains above their IPO-day levels after a sharp rise earlier in the year.
The volatility indicates that investors are already grappling with the distinction between AI adoption, technological capability and financial performance.
China’s AI sector has demonstrated that its models can attract users and compete with leading US systems, while companies such as DeepSeek have shown that capable models can emerge with different cost structures. The unresolved concern rests on how that technological progress can produce enough recurring revenue to justify the valuations being assigned to some of the industry’s most closely watched startups.