https://x.com/i/article/2113330179271491584

【X 长文】China's Young AI Power Brokers

They seized influence through technical skill. Now they must use it to protect the freedom that produced it.

Editor’s Note

On October 19, Chinese business outlet LatePost published a feature, China AI’s Young Power Brokers, on how a generation of high-profile AI researchers is gaining influence inside their companies. The companies include AI labs Zhipu AI and Kimi, as well as Chinese technology giants Xiaomi, Alibaba and Tencent.

The report was edited by LatePost founder Song Wei. Cheng Manqi, the outlet’s technology coverage lead, also contributed reporting.

The original English translation ran to roughly 5,500 words. This version has been selectively condensed and lightly adapted to make it more accessible to English-language readers.

Four months before Kimi planned to release K3, its flagship model, young researchers asked Moonshot’s CEO Yang Zhilin to change its architecture. The change could improve training efficiency but delay training and the launch. With thousands of GPUs about to run, Yang had to decide whether the potential gains were worth the risk.

For two decades, power at China’s internet companies followed commercial success: Build a hit product, win a market, make more money—and gain more influence. Now technologists in their late 20s and early 30s are taking control of R&D decisions before their products have proved commercially successful.

Yang, 33, has made Alibaba and Tencent both rivals and investors in Moonshot AI. At Xiaomi, 31-year-old Luo Fuli leads a team of dozens training a trillion-parameter model. At Tencent, 28-year-old Yao Shunyu joined the office of the president and took over Hunyuan.

Their choices direct hundreds of researchers and enormous computing resources. In September, Luo showed Xiaomi’s training dashboards for two models; the displayed rates put their combined cost at $30,800 an hour.

These leaders are building teams with fewer management layers and more freedom to test ideas. As one tech lead at an AI model company told us: “Hire talented people and give them GPUs.” Their power rests not only on technical skill, but on the ability to spot promising ideas, direct scarce resources and persuade an organization to take a bet. How they do so will help shape China’s increasingly expensive AI race.

China’s Young AI Leaders Decide How Its Top Labs Deploy Their Most Valuable Resources

At Kimi, Yang Zhilin Chooses to Wait

In March 2026, several young researchers at Kimi wanted the company to alter the plan for K3, its next flagship model, just four months before its expected release. Their proposal, Attention Residuals, could improve the model’s training efficiency. But changing the architecture so late could delay training and the launch. Before thousands of GPUs began running, they needed CEO Yang Zhilin’s approval.

The idea came from Su Jianlin, a Kimi researcher known to peers as “God Su（苏神）.” He lives in Guangzhou and works remotely with Kimi’s Beijing team. For years, he had written about mathematics and model research on his blog, attracting readers who later became his colleagues.

Around the Lunar New Year, Su read a DeepSeek paper on ways to improve how a large language model moves information between its layers. His idea was simple: apply Attention—a technique models use to determine which parts of an input matter most—not just to text tokens but across layers. That would let each layer choose which information to draw from earlier ones.

Su quickly built a prototype. Zhang Yu, a young doctoral researcher, and Chen Guangyu, then a 17-year-old high school student, joined him to test it on bigger models. Zhang had studied efficient model designs during his Ph.D. at Soochow University and later became the first author of Kimi’s Linear paper. Chen, despite still being in high school, had also spent years on the same problem. More complicated versions mostly performed worse than the first, simpler one.

The problem was cost. The full design required the model to retain information from every layer and move it between devices. The inference team thought it could be optimized, but the training team concluded Kimi did not have enough computing resources. “Put simply, we couldn’t afford it,” Su later wrote.

Kimi’s traffic was also growing quickly, putting pressure on the company to reduce both response times and inference costs. Its infrastructure team asked for a design that would keep related data closer together, reducing the information that had to move between devices. Zhang and Chen came up with a block-based version in a single night. Rather than retain every layer’s information, it grouped layers and applied Attention between the groups. It added no more than 5% in overhead, according to the paper and the team’s review data, while improving training efficiency by about 20%.

The work was possible because Kimi’s algorithm and infrastructure teams trusted each other’s constraints. Yaoyu Wang, an AI infrastructure engineer, later described an internal meme: infrastructure engineers say a mathematically sound design should be buildable; algorithm researchers reply that if it is not, the algorithm has not been thought through.

Once Kimi’s training, inference and infrastructure teams agreed the design could work, the company pushed to add it to K3. Yet K3 was about to begin training, and the new architecture still needed testing. Yang agreed to delay the start of training, giving the team time to validate it.

Kimi published the research on March 16, naming Su, Zhang and Chen co-first authors. Four months later, it released K3, a 2.8-trillion-parameter model that used Attention Residuals as a key part of its design. The model performed well in several independent benchmarks.

The story shows how a remote researcher’s idea, a teenage student’s work and collaboration across algorithm, training and infrastructure teams can reshape a company’s most important model. More important, it shows how young AI leaders gain power: not only through their own technical skill, but by recognizing others’ ideas and making the institutional bet to pursue them.

At Zhipu, Gen Z Researchers Push the Company to Bet on Coding

In early 2025, Zhipu needed a breakthrough. DeepSeek’s R1 had raised the bar for Chinese AI companies, while a price war was squeezing the industry. Tang Jie later wrote in an internal letter that Zhipu’s own models were not meeting expectations.

Around the Lunar New Year, Tang called together a small group of young technical leaders to decide where the company should put its people and computing power. The meetings had no regular schedule. One person at Zhipu said Tang convened them whenever he was unsure which way the company should go.

Zeng Aohan, born after 2000 and still working toward his Ph.D., argued that Zhipu should focus on coding. Zheng Qinkai and other researchers agreed. They believed the company should make models better at writing and working with code.

The proposal was far from a consensus. After OpenAI released o1-preview and DeepSeek followed with R1, mathematics, logic and complex reasoning had become the main focus for much of the industry. Some researchers, including Hou Zhenyu, wanted Zhipu to keep pursuing that path. At the time, coding was not a priority for many Chinese AI companies.

Inside the company, people described the choice as a contrast between the “scientist” approach associated with OpenAI and the “engineer” approach associated with Anthropic. One pushed the limits of model reasoning and new training methods; the other focused on real-world tasks, such as helping programmers write code. The debate was not only about research taste. It would determine where Zhipu put hundreds of researchers and scarce computing resources. Researchers backing coding thought the company should put more resources into the latter. “We had to do something new and different,” one person at Zhipu said.

Tang did not settle the argument at the meeting. He asked teams to test both directions: Zeng, Zheng and their colleagues worked on coding, while Hou’s group continued with reasoning.

The coding camp began to win support in April 2025, when Zhipu released several 32-billion-parameter models. One had stronger capabilities in engineering code and function calling. Internal tests and user feedback made coding look more promising, and Zhipu began to invest more in it.

In July, GLM-4.5 brought Zhipu renewed attention, with coding among the features users liked most. In September, the company launched GLM Coding Plan, allowing developers to pay a monthly fee to use GLM through tools including Claude Code and Cline. By December, Zhipu CEO Zhang Peng said the business had passed RMB 100 million in annual recurring revenue.

Betting on coding became one of Zhipu’s biggest decisions since its founding. It was not one person’s decision, but a judgment formed by young researchers and tested across teams before it shifted the company’s resources and strategy.

At MiMo, Luo Fuli Wants to Go From 7B to 1T—Fast

In April 2025, Xiaomi released its first MiMo model, with 7 billion parameters. The team had fewer than 20 people in January and only 50 to 60 by May and June. Most had never trained a large language model. Even the researchers with experience had often worked only on 7-billion- or 14-billion-parameter models.

MiMo initially used its 7B model to...

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