Chinese AI developer Zhipu trades at a 1280x multiplier: analysis of the AI market bubble
The artificial intelligence market continues to show surprising anomalies in company valuations. This is especially evident in the case of Chinese developers of large language models (LLMs), whose market multiples have become detached from real financial indicators by dozens of orders of magnitude.
According to my analysis, Zhipu (Z.ai), the creator of the GLM-5.2 model, is trading at a multiple of about 1280 times annual revenue. For comparison, this is dozens of times higher than giants like OpenAI and Anthropic. Recall that Zhipu listed on the Hong Kong Stock Exchange on January 8, 2026, becoming the first public company among fundamental LLM developers. After the release of GLM-5.2, shares soared: on June 22, its market capitalization exceeded $118 billion, with 2025 revenue of about $107 million. The net loss for the same period was 4.7 billion yuan.
Why multiples have become detached from reality
For Zhipu to trade at 50 times annual revenue, it would need to boost sales to $2.7 billion per year—26 times its current figure. For a multiple of 20, it would require $6.9 billion, or a 65-fold increase. A similar picture is seen with MiniMax: with a market cap of about $23 billion and revenue of $79 million, the multiple reaches 290 times revenue. Alibaba, with its Qwen model, looks more modest: a market cap of $245 billion and revenue of $151 billion for fiscal year 2026 gives a multiple of just 1.6. However, Alibaba is not a pure AI company, so the comparison is conditional.
American "labs" are valued significantly more modestly relative to revenue. OpenAI, with annual revenue of $25 billion and a latest private valuation of $852 billion, trades at roughly 34 times annual revenue. Anthropic, with an annual result of $47 billion and a valuation of $965 billion, trades at about 21 times.
Where Chinese developers can generate revenue
I see the main reason for the gap in the fact that Chinese companies give a significant portion of their income to third-party inference providers (services that run other people's AI models on their servers and sell access via API)—such as OpenRouter, Venice, and BaseTen. Users want to work with these models but are not willing to send data directly to China, so they turn to intermediaries.
To turn the situation around, Chinese developers will have to prove that they do not store user data and offer lower prices than competitors. This is difficult to achieve due to cultural and social reasons. An alternative scenario: Chinese companies could take stakes in American inference providers and enter into agreements for early access to top models in exchange for a share of revenue. Then, money would flow to model developers—through providers on a percentage basis, but with growth in the overall market volume.
My conclusion: Zhipu's current valuation is a classic example of a speculative bubble, fueled by the hype around Chinese AI projects. Until fundamental indicators catch up, such multiples remain a zone of extremely high risk for investors. The market is clearly pricing in future growth, but how justified it is remains a big question, especially given the regulatory and cultural barriers to monetization.