China’s leading technology companies have been steadily releasing artificial intelligence models that perform nearly as well as the best systems in the world. But they are all facing the same problem: overcoming the punishing economics behind the technology.

Chinese A.I. companies have tried different approaches to bring in enough money to sustain the enormous expense of building A.I. systems.

Start-ups like DeepSeek and Moonshot have raised billions from investors. Alibaba, China’s A.I. heavyweight, has started charging users to access its best models. ByteDance, the parent company of TikTok and an A.I. powerhouse itself, launched a tiered pricing system, hoping to get people to pay more for using its most advanced models.

To building state of the art A.I. models, A.I. companies constantly need to buy powerful computer chips — enough to build the models, more to test improvements and still more to ensure they can perform for users all over the world. The companies also need to build or rent space in data centers that house all this computing power.

Chinese A.I. companies are not the only ones confronting these tough economics. Their giant rivals in Silicon Valley, from OpenAI and Anthropic to Google, are also investing more in A.I. than they are earning from it.

For Chinese companies, the challenge is compounded by a central paradox of the country’s approach to A.I. development. Most of China’s leading A.I. systems are open source or open weight. That has accelerated their development — the entire industry gain when everyone shares their work in public.

China’s open source approach has spawned a crowded field of innovative and intensely competitive start-ups all offering systems at low cost.

So attempts to increase revenue by charging more to access certain models can scare away customers. Price conscious Chinese consumers — businesses and individuals alike — are quick to hop across platforms in search of inexpensive A.I. tools.

While China’s A.I. companies are searching for sustainable business models, spending is high and revenue is low, said Richard Lin, a vice president at the Silicon Valley company Datastrato.

“In two or three years we will still be trying to figure out how large models can earn money,” he said.

Offering low prices has helped the Chinese firms gain users, including in Silicon Valley, where many companies depend on the more affordable systems. Yet, Chinese companies have struggled to translate massive numbers of global users into profits.

Last month, the Chinese start-up Z.ai released a model called GLM-5.2 that it said was nearly as powerful as Anthropic’s best. Many software developers and start-ups in Silicon Valley quickly started using it, in part because it was cheaper than the American systems.

Z.ai’s models improved and became more popular, and the company’s revenue more than doubled last year. But Z.ai lost nearly $700 million.

When it went public in Hong Kong, Z.ai said it planned to use a majority of the money to improve its models, largely by acquiring more chips. A few weeks after the stock listing, Z.ai said it was looking for partners to share computing power resources and apologized after users complained about slow service.

“Open models are a powerful distribution strategy, but they are not a complete business model,” said Wei Sun, a principal A.I. analyst at Counterpoint Research in Beijing. “An I.P.O. can finance the next training cycle but it cannot by itself create sustainable economics.”

This month, the Chinese start-up Moonshot AI released a new model called Kimi K3, which the company said performed better than models from OpenAI and Anthropic did on some tasks. But within two days the company was forced to announce that it needed to stop accepting new users because it couldn’t get enough computer chips to serve them.

China’s A.I. industry has faced years of U.S. trade restrictions that confine its ability to buy the world’s most powerful chips. To get around these limitations, many Chinese companies rent remote access to data centers outside of China stocked with advanced chips.

But Chinese A.I. start-ups have far less money to buy computing power than their deep-pocketed American rivals.

Last month, DeepSeek held one of China’s most anticipated funding rounds, raising money from investors including the internet giant Tencent, the battery maker CATL and the country’s state investment fund for artificial intelligence. DeepSeek raised $7.5 billion. In May, Moonshot raised $2 billion.

By comparison, Anthropic raised $65 billion in May alone.

Chinese tech firms have far less capital, but U.S. tech executives and investors worry that China’s open-source push threatens to upend the economics of A.I. production.

Chinese A.I. start-ups say that years of export controls have pushed them to use chips as efficiently as possible, potentially challenging the idea that has motivated much of global A.I. investment: that making cutting edge A.I. systems will always require increasing investment in more chips and data centers.

Now, a debate is raging in the United States that could make it even harder for Chinese A.I. companies to earn money.

Leading Silicon Valley companies including Anthropic and OpenAI have claimed that Chinese firms improperly harvested data from their A.I. systems to accelerate the development of the Chinese models. Some American tech companies and investors want Washington to limit access to Chinese open source models, saying they be a threat in the wrong hands.

Kevin Xu, the founder of Interconnected Capital, a hedge fund that invests in A.I. technologies, said, “Many Silicon Valley start-ups rely on open-weight models to both customize their product and also not have to pay OpenAI and Anthropic their high prices.”

These American tech companies have been an important source of revenue for many Chinese A.I. firms. If they get cut out of it, they risk losing that.

Xinyun Wu contributed research from Taipei.