1. China LLM market of 5.6B RMB (<$1B) is tiny compared to Western countries (US and Europe ~$4-6B each), which highlights a fundamental problem with Chinese AI models - monetization.
2. Generally most Western LLM players suffer from the same problem - Anthropic also burned $5B+ on $1B revenue in 2024, but then in 2025 it should reach ~$9B revenue with $2-3B burn. Revenue should be increasing faster than costs in the future.
3. Totally agree with your analysis on Zhipu, it’s hard to efficiently monetize enterprise software clients, they will keep struggle with scale and market size.
4. On Minimax I slightly disagree. Because 70% of their revenue is not from China and it’s mostly B2C, the 5.6B RMB market size in China is not really relevant to them. They have much higher upside.
5. Do you think China’s government will let Zhipu and Minimax go bankrupt? With the crazy demand for these two companies among domestic (and even foreign) investors, they can keep raising rounds, although valuation and dilution might be an issue.
6. Have you looked at Moonshot? What is the unit economics situation there?
Thanks for the detailed thoughts, Denis. Let me push back on a few points:
On Anthropic’s economics: Sure, their revenue will eventually outpace costs. But look at the context. Anthropic burns $5B chasing a $40-60B enterprise market. Zhipu and MiniMax burn ¥27.6B annually chasing a ¥5.3B market. The competition is completely out of proportion to market reality.
And even if China’s LLM market hits the projected ¥101B by 2030, there’s another problem: ByteDance, Alibaba, Baidu, Tencent are all fighting for this space. They own the compute infrastructure. They control distribution. They can absorb ¥2B losses without blinking. What’s actually left for independent startups after platforms take their share? The addressable market keeps shrinking.
On MiniMax’s overseas revenue: 70% international sounds great until you realize it doesn’t change the fundamental problem. MiniMax spent 1% of what OpenAI spent and still needs emergency funding. Why? DeepSeek’s R1 launch forced every company to accelerate iteration cycles, regardless of where their revenue comes from. There’s no opting out of the compute arms race just because you sell overseas.
On the IPO question: I think we’re talking past each other. My piece isn’t about bankruptcy risk. You’re right the government won’t let them fail, and they can keep raising rounds. My question is different: does going public actually solve anything beyond buying another year or two? As I wrote, continuous dilution and no clear path to profitability make them questionable investments even if they survive as companies.
On Moonshot: The data I’ve seen is concerning. Kimi’s consumer base is dropping fast. They’re pivoting to API services and going international. Fine, but does this change the core challenge? Can they grow revenue faster than the compute investment required to stay competitive? Same question facing every independent model company in China.
Bottom line: when one competitor’s breakthrough forces everyone to match the investment, no moat works. Not government backing, not overseas revenue, not capital efficiency.
Great points. I agree that western labs are in better shape. I’m a software engineer and I swear by Claude Code. The underlying models (Sonnet and Opus) could be twice as expensive and I’d still use them. The productivity boost is just too incredible. Anthropic has managed to differentiate themselves.
This reminds me of one of Buffet’s first investments: a department store that had 3 nearby competitors. He quickly understood the economics were poor because whenever one store spent some money, for instance to install an escalator, the others had to follow suit, even if that wouldn’t increase their sales. Btw I’m surprised the total market is so small (less than $1B). Thank you for the article.
Awesome analysis!
A few thoughts:
1. China LLM market of 5.6B RMB (<$1B) is tiny compared to Western countries (US and Europe ~$4-6B each), which highlights a fundamental problem with Chinese AI models - monetization.
2. Generally most Western LLM players suffer from the same problem - Anthropic also burned $5B+ on $1B revenue in 2024, but then in 2025 it should reach ~$9B revenue with $2-3B burn. Revenue should be increasing faster than costs in the future.
3. Totally agree with your analysis on Zhipu, it’s hard to efficiently monetize enterprise software clients, they will keep struggle with scale and market size.
4. On Minimax I slightly disagree. Because 70% of their revenue is not from China and it’s mostly B2C, the 5.6B RMB market size in China is not really relevant to them. They have much higher upside.
5. Do you think China’s government will let Zhipu and Minimax go bankrupt? With the crazy demand for these two companies among domestic (and even foreign) investors, they can keep raising rounds, although valuation and dilution might be an issue.
6. Have you looked at Moonshot? What is the unit economics situation there?
Thanks for the detailed thoughts, Denis. Let me push back on a few points:
On Anthropic’s economics: Sure, their revenue will eventually outpace costs. But look at the context. Anthropic burns $5B chasing a $40-60B enterprise market. Zhipu and MiniMax burn ¥27.6B annually chasing a ¥5.3B market. The competition is completely out of proportion to market reality.
And even if China’s LLM market hits the projected ¥101B by 2030, there’s another problem: ByteDance, Alibaba, Baidu, Tencent are all fighting for this space. They own the compute infrastructure. They control distribution. They can absorb ¥2B losses without blinking. What’s actually left for independent startups after platforms take their share? The addressable market keeps shrinking.
On MiniMax’s overseas revenue: 70% international sounds great until you realize it doesn’t change the fundamental problem. MiniMax spent 1% of what OpenAI spent and still needs emergency funding. Why? DeepSeek’s R1 launch forced every company to accelerate iteration cycles, regardless of where their revenue comes from. There’s no opting out of the compute arms race just because you sell overseas.
On the IPO question: I think we’re talking past each other. My piece isn’t about bankruptcy risk. You’re right the government won’t let them fail, and they can keep raising rounds. My question is different: does going public actually solve anything beyond buying another year or two? As I wrote, continuous dilution and no clear path to profitability make them questionable investments even if they survive as companies.
On Moonshot: The data I’ve seen is concerning. Kimi’s consumer base is dropping fast. They’re pivoting to API services and going international. Fine, but does this change the core challenge? Can they grow revenue faster than the compute investment required to stay competitive? Same question facing every independent model company in China.
Bottom line: when one competitor’s breakthrough forces everyone to match the investment, no moat works. Not government backing, not overseas revenue, not capital efficiency.
Great points. I agree that western labs are in better shape. I’m a software engineer and I swear by Claude Code. The underlying models (Sonnet and Opus) could be twice as expensive and I’d still use them. The productivity boost is just too incredible. Anthropic has managed to differentiate themselves.
This reminds me of one of Buffet’s first investments: a department store that had 3 nearby competitors. He quickly understood the economics were poor because whenever one store spent some money, for instance to install an escalator, the others had to follow suit, even if that wouldn’t increase their sales. Btw I’m surprised the total market is so small (less than $1B). Thank you for the article.