Last year, the titans of China’s technology industry received a rare invitation to meet with Xi Jinping, the country’s leader, in the ceremonial Great Hall of the People.

But when the entrepreneurs — including the heads of national stars like the telecommunications giant Huawei and rising companies like the robotics start-up Unitree — assembled in neat rows before Mr. Xi, an unexpected figure was among them: Jack Ma.

Mr. Ma, who founded the Alibaba e-commerce empire in a cramped Hangzhou apartment and became a rock star of China’s internet era, had all but vanished from public view since Beijing torpedoed the planned $34 billion public offering of one of his companies after he criticized regulators in 2020.

Now, however, Alibaba has re-emerged as an A.I. powerhouse, creating one of the world’s most widely used A.I. systems.

Alibaba made its most popular A.I. models open source, allowing others to use and modify them freely. That made its technology much cheaper to use than proprietary systems from U.S. competitors like Anthropic and OpenAI, helping the company attract users around the world.

But it also raised a difficult question — how to turn that global popularity into a profitable business. It’s a challenge that many companies with open-source technologies face and one that is dividing Alibaba’s A.I. team.

Alibaba became one of China’s most valuable companies on the strength of its globe-spanning e-commerce business. But in recent years, it has been outmaneuvered by lower-cost rivals at home and has struggled to expand overseas. After Mr. Ma faded from public view, a new generation of executives took over, adopting a lower profile while emphasizing Alibaba’s role within Beijing’s broader technology policies.

“Alibaba is quite aware of its position as part of this team of national champions that are leading China’s charge in A.I.,” said Kyle Chan, a fellow at the Brookings Institution. “But also they have this huge responsibility to not repeat what they did before.”

In hindsight, Alibaba had gradually laid the groundwork to become an A.I. heavyweight. As its online shopping and logistics businesses expanded, the company built data centers capable of processing vast volumes of customer data worldwide.

It also borrowed a page from Amazon, parlaying its e-commerce success and technological infrastructure into a major cloud computing business, giving Alibaba two essential ingredients for building artificial intelligence systems: data and computing power.

“Alibaba in many ways has been ahead of its time,” Mr. Chan said. “It made early bets in a lot of these areas that are now the hot topics.”

Alibaba introduced its family of A.I. models, Qwen, in 2023 and quickly made them open source. When DeepSeek, an A.I. start-up, burst into the spotlight with claims that it had built a powerful model for a small fraction of the cost of Western competitors, global investors rushed to capitalize on the excitement around China’s open-source A.I.

China has made open-source technology a pillar of its drive to become an A.I. superpower. Nearly all of its leading A.I. systems are open source.

By January, Qwen had become the world’s most downloaded open-source A.I. system. Its models were being downloaded about one million times a day, according to data from Hugging Face, which hosts many open-source A.I. projects.

But that reach has not translated into big money. In the first three months of this year, Alibaba reported $1.3 billion in revenue from A.I.-related products — less than 4 percent of its total revenue. That pales in comparison with the company’s plan to spend more than $55 billion by the end of next year to build out its A.I. infrastructure.

The push to turn its open-source success into a profitable business has quietly fractured the team behind Qwen.

In March, Lin Junyang, Qwen’s lead engineer, announced that he was leaving Alibaba. Several other key engineers left around the same time. Two people familiar with the team said it had become divided by disagreements over how best to commercialize Qwen.

Alibaba had long kept its largest, most advanced models proprietary, while releasing leading open-source models alongside them. Now, the company is signaling a broader shift away from widely used open-source models and toward closed ones that customers must pay to use. In April alone, Alibaba released three proprietary models within days of one another.

The Qwen team remains focused on keeping pace with the leading models coming out of Silicon Valley, but there is a growing recognition that technological leadership will not be enough if the company doesn’t make money, said a member of Alibaba’s A.I. research lab, which includes the Qwen team and others, who spoke on the condition of anonymity because the person was not authorized to discuss internal matters.

The challenge for Alibaba is apparent in its stock price. While A.I.-related stocks have surged in markets worldwide, Alibaba’s stock has fallen 37 percent this year in Hong Kong, where the broader market has declined 12 percent.

Building a top-performing A.I. model is expensive, requiring enormous investments in hangar-size data centers filled with computer chips that consume copious amounts of electricity.

Chinese A.I. companies face an additional hurdle: U.S. export controls limit their access to the most advanced chips. China’s leading A.I. start-ups and researchers routinely say shortages of computing power are the biggest thing holding them back, and they are spending heavily to secure it.

Those exorbitant costs are heaping financial pressure on Chinese A.I. companies. Start-ups like MiniMax and Z.ai have gone public to raise money from investors, and Alibaba is increasingly steering customers toward proprietary models even if that strategy upsets some of its top talent.

“It is a tough choice to make, and one that every open-model lab has to make at some point,” said Kevin Xu, the founder of Interconnected Capital, a hedge fund that invests in A.I. technologies.

Alibaba’s A.I. business faces mounting external challenges. The Pentagon recently placed the company on a blacklist of firms that it says support the Chinese military, a designation that Alibaba argues has already harmed its business. In a statement, Alibaba said it was not affiliated with the Chinese military.

Anthropic and OpenAI have also accused Chinese companies, including Alibaba, of improperly harvesting data from their A.I. systems to accelerate the development of their models.

Last month, Anthropic sent a letter to Senators Tim Scott, Republican of South Carolina, and Elizabeth Warren, Democrat of Massachusetts, accusing Alibaba of “brazenly” and “illicitly” trying to copy its technology using 24,000 fraudulent accounts. Alibaba declined to comment on the allegations.

Many in the technology industry compare today’s A.I. boom to the early days of the dot-com era, when internet companies were flourishing but no one had yet figured out how to build durable businesses around the technology.

Richard Lin, a vice president at the Silicon Valley company Datastrato who has long been involved in China’s open-source community, said China’s leading A.I. companies were all fighting for survival.

“There isn’t an A.I. company with a sustainable business model right now,” he said. “It’s not a healthy industry.”

The American companies building artificial intelligence systems are loudly complaining that their Chinese competitors are unfairly copying their technology, and they are pleading with officials to do something about it.

On June 10, Anthropic sent a letter to Senators Tim Scott and Elizabeth Warren, accusing the Chinese tech giant Alibaba of surreptitiously copying its A.I. technologies using a technique called distillation.

Like other Chinese companies, Alibaba tapped into Anthropic’s technologies through tens of thousands of unauthorized accounts, according to the letter, which was viewed by The New York Times. Then it used the data it collected to train its own A.I. systems. Anthropic asked the lawmakers, who lead a Senate committee that was about to hold a hearing on A.I., to explore ways of curbing China’s distillation.

“These distillation attacks are carried out illicitly, systematically and at industrial scale to harvest U.S. A.I. capabilities across frontier labs and repackage them as their own,” Anthropic told the two senators, referring to companies on the frontier of A.I. development.

Experts say China trails the United States in A.I. development by just six months. Anthropic and other U.S. companies argue that without help from distillation, China would be much further behind, which could affect major A.I. uses like business planning, drug research, mass surveillance and military weapons.

Their complaints have new urgency now that the Chinese start-up Z.ai has released an A.I. model, GLM-5.2, that is nearly as powerful as the top American systems. It rivals them when used for cybersecurity, an area that American A.I. companies and the Trump administration have singled out as vitally important to geopolitics.

But what exactly is distillation, and are Chinese companies the only ones doing it? Here is an explanation.

Is distillation a new concept?

Not at all. Distillation has been common in the tech industry for more than a decade. A small team of Google researchers first developed the technique in the early 2010s as a way of building more efficient A.I. systems.

Through distillation, researchers can collect data from a particularly powerful system and use that data to build a system that can run on less expensive hardware.

The first A.I. model essentially shows the second model how to behave, said Geoffrey Hinton, a former Google researcher who helped develop the technique. “Think of one model as the teacher and the other as a student,” he said.

Distillation is a way to copy your own A.I.?

Correct. But some companies used distillation to mimic technologies built by other A.I. labs. They often copied the behavior of open source technologies — systems that anyone can use, modify and copy for free and largely without restriction.

That is what labs hope to encourage when they open source their systems. The idea is that everyone benefits because A.I. is developed more quickly.

When is distillation a problem?

Anthropic, OpenAI and other A.I. labs get annoyed when companies use distillation to mimic the behavior of their proprietary systems — technologies that are not open source. These are typically their most powerful systems.

Anthropic and OpenAI do not allow distillation for their leading systems under their terms of service. But distilling these systems is still common.

In April, while testifying in a federal trial in Oakland, Calif., Elon Musk acknowledged the practice at his A.I. company, xAI. When a lawyer asked if xAI had ever distilled technology from OpenAI, Mr. Musk replied: “Generally A.I. companies distill other A.I. companies.”

Is that illegal?

That’s not clear, said Sarah Tishler, a partner at the law firm Beck Reed Riden who specializes in trade-secret litigation.

Some legal scholars argue that the practice violates the Defend Trade Secrets Act, a 2016 law that allows businesses to sue over the theft of trade secrets, but courts have not explicitly decided that.

Copyright law does not necessarily apply because distillation is an effort to copy the behavior of the system, as opposed to copying text verbatim.

Are the Chinese doing something similar?

It is also not completely clear what Chinese companies are doing. They have likely distilled proprietary models in much the same way that American companies like xAI have done.

Chinese distillation efforts, however, have caused far more concern among Anthropic, OpenAI and the other U.S. companies.

About 18 months ago, the Chinese start-up DeepSeek shocked Silicon Valley when it showed that it could build effective A.I. far more affordably than many of its American counterparts. OpenAI soon accused DeepSeek of distilling its technologies.

In February, Anthropic accused DeepSeek and two other Chinese start-ups of improperly harvesting large amounts of data from its systems. Anthropic said the start-ups had used about 24,000 accounts to generate over 16 million conversations with its Claude chatbot that could be used to teach skills to their own chatbots.

How does Anthropic know this?

Anthropic closely monitors how people use its systems. Certain repeated behavior, the company said, showed that accounts linked to China were lifting data from its proprietary models.

Anthropic claimed that various Chinese companies had used a network of accounts to gain access to its systems. Each Chinese company, Anthropic said, uses this data to help train its own technologies.

Can Anthropic prevent this?

Anthropic, OpenAI and Google are sharing information that they can all use to combat the practice, they said. But it can be difficult to stop. If Anthropic shuts down too many accounts, it may end up barring legitimate users.

Even if U.S. law did bar illicit distillation, Ms. Tishler said, it would most likely have little effect on behavior in China.

“So much of this conduct is happening outside the United States,” she noted. “It would be very challenging to address it through a U.S. court.”

(The Times sued OpenAI and Microsoft in 2023, claiming copyright infringement of news content related to A.I. systems. The two companies have denied those claims.)

What else can U.S. companies do?

Anthropic called on Congress to pass legislation that would allow “deeper collaboration to combat distillation attacks, both between the U.S. government and leading frontier labs as well as between the frontier labs themselves.”

The company also said the U.S. government should extend its efforts to limit China’s access to the specialized computer chips needed to train A.I. technologies. The world’s most powerful chips are designed by American companies, and the federal government has used export controls to stem the flow of those chips to China. It is difficult to do distillation without those chips.

Alibaba declined to comment on Anthropic’s letter to the two senators. Ms. Warren, Democrat of Massachusetts, also declined to comment. Mr. Scott, Republican of South Carolina, did not respond to a request for comment.

Would a distillation crackdown have an impact?

Many experts believe that a crackdown on Chinese distillation would have little effect, and that distillation alone cannot build a top A.I. system as Z.ai did.

Others believe that distillation will become less important as companies build systems, like GLM-5.2, that are designed to serve as A.I. agents. Training these agents — digital assistants that can use other software to perform tasks — is much harder to duplicate through distillation.

Distillation “won’t matter as much for the next era of A.I.,” said Sara Hooker, chief executive of Adaption, an A.I. research lab.

Ryan Mac contributed reporting from Los Angeles, Eli Tan from San Francisco and Steve Lohr from New York.