The faces behind China’s AI rise: Meet the founders of DeepSeek, Z.ai and Moonshot AI
As China emerges as a key competitor in the global AI race, CNA takes a look at the figures leading its top companies.
From left: Moonshot AI founder Yang Zhilin, DeepSeek founder Liang Wenfeng and Zhipu AI co-founder Tang Jie. (File photos: Reuters, Xiaohongshu/Doudou/Xianshuxuezhanglaoshi)
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SINGAPORE: China has caused much buzz in the tech sector recently after one of its artificial intelligence startups launched the world’s largest open-weight AI systems in July.
The country has emerged as a key competitor in the global AI race, with companies releasing models, such as Kimi K3 and GLM-5.2, that hold their own against US alternatives.
As China positions itself as an international leader in AI, CNA looks at the men behind some of its top companies – DeepSeek, Z.ai and Moonshot AI – as well as the direction they’re setting for the country’s development in this sector.
LIANG WENFENG, DEEPSEEK
Hangzhou-based DeepSeek shocked Silicon Valley in 2025 when it released its R1 model, which it claimed stood on par with US rivals but at a fraction of the cost.
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Its founder Liang Wenfeng, 41, hails from the city of Zhanjiang in China’s southern Guangdong province. He graduated from Zhejiang University and co-founded quantitative hedge fund High-Flyer in 2015.
Under his wing, the company grew its assets more than tenfold in four years, and Liang poured its massive profits into over 10,000 Nvidia chips before US AI sanctions on China kicked in.
These early investments provided DeepSeek, High-Flyer’s AI arm, with the computing power it needed to take off after its creation in 2023.
Liang is currently the world’s richest creator of AI models, according to the Bloomberg Billionaire Index. His net worth shot from US$16.7 billion to US$36 billion after DeepSeek’s latest fund-raising round. It now stands at US$37.9 billion.
This places his fortune far ahead of Anthropic co-founder Dario Amodei’s US$7.98 billion net worth and OpenAI president Greg Brockman’s US$25.5 billion.
Bloomberg’s ranking looked at firms whose primary business and majority of revenue come directly from AI models, which ruled out Big Tech founders such as Tesla’s Elon Musk and Meta’s Mark Zuckerberg.
Unlike most Chinese AI startups, Liang had largely bankrolled DeepSeek using his fund, rejecting external financing till recently.
However, as the company faces rising costs and reportedly attempts to develop its own inference AI chip, it has turned to venture capitalists, raising about US$7.4 billion in its first funding round.
Its latest V4 model was released in April this year, and DeepSeek said that it came with “drastically reduced” costs.
DeepSeek’s introduction to the AI world sparked much chatter, but Liang has remained elusive. However, during a rare interview in 2024, he offered a glimpse into his vision for DeepSeek and China’s AI future.
China must move from a beneficiary of technology to a contributor, Liang told Chinese publication Anyong. He believed that his country’s companies had grown accustomed to leveraging overseas technological innovations and commercialising them through apps, barely participating in core inventions in the field.
DeepSeek’s goal is to push the technological frontier forward and nurture the growth of its entire ecosystem, he said.
China must transition to a contributor of technology, rather than continuously riding on others’ achievements, he added.
TANG JIE, Z.ai
A professor at the prestigious Tsinghua University, Tang Jie co-founded Zhipu AI, now known as Z.ai, in 2019 alongside other faculty and alumni.
The company has become a major provider of chatbot tools to Chinese businesses, with its latest GLM-5 model impressing developer communities.
Born in 1977, Tang joined Tsinghua University’s Computer Science department in 2006. His past students include Moonshot AI founder Yang Zhilin.
He has written over 100 articles in computer science journals and invented AMiner, a search system that mines data from online academic publications and identifies connections between researchers, conferences and publications.
He led the development of Wu Dao, China’s first superscale natural-language AI model, which was unveiled in 2021. His team also released China’s first “virtual student”, a personification of the Wu Dao model that could compose music, reason, react to emotions, answer questions and even code.
On his academic website, Tang says he is putting “all my efforts” into artificial general intelligence, aiming to teach machines to think like humans.
In an internal memo to Z.ai staff this month, he told employees that a “great wave” of technological evolution had arrived and the move to artificial general intelligence followed by artificial superintelligence was irreversible.
While other companies focus on commercialisation, Z.ai’s core goal over the next two years is to reach the next high ground in artificial general intelligence technology, said Tang.
The company would allocate all core resources to this, deliberately slowing down its short-term monetisation of apps, he said.
“The wave is here, the trend is irreversible. Zhipu AI must be the one who faces the wave and reaches upwards,” he reportedly wrote.
“If we do not reach the summit, we have failed.”
The letter’s sentiments mirror Liang’s previously stated ambitions for DeepSeek – a focus on innovation rather than commercialisation.
“China’s tech sector has long been very practical – arguably too focused on commercialisation and application, while free-riding on foundational research done elsewhere,” said Lin William Cong, president’s chair professor of Finance, Computing and Data Science at Nanyang Technological University.
The country’s current generation of AI labs is partly a “deliberate correction” for this, an attempt to prove Chinese firms can produce original innovation, not just fast follow, he told CNA.
However, he also cautioned against assuming that this meant the companies were anti-commercial.
“DeepSeek’s aggressive pricing and Zhipu’s enterprise business are commercial acts – they’re just subordinated to the research mission rather than driving it,” he said.
Commercialisation and innovation are not opposing forces and reinforce one other in a well-designed market, added Dr Cong.
“Seen this way, the Chinese labs’ combination of open weights, low prices and a research-first mission isn’t a rejection of commercialisation – it’s a bet that the fastest route to both artificial general intelligence and a large market runs through making the technology cheap enough for a whole ecosystem to experiment on top of it.”
YANG ZHILIN, MOONSHOT AI
Moonshot AI’s founder Yang Zhilin has become the figure behind the latest wave of interest in China’s AI development. The attention came after his company launched Kimi K3, the world’s largest open-weight AI model, in July.
Open-weight models refer to AI systems whose core components are publicly released, allowing anyone to download them.
Yang earned his bachelor’s degree at Tsinghua University, where he was advised by Tang, Z.ai’s co-founder.
He went on to pursue a PhD at Carnegie Mellon University and graduated in 2019. In a LinkedIn post, his professor Ruslan Salakhutdinov described him as “absolutely brilliant”.
Yang previously interned on Meta and Google’s AI research teams.
Though Apple reportedly expressed interest in hiring him, he returned to China and founded Moonshot AI in 2023. According to a Reuters report, the company raised more than US$2 billion in May this year from investors including Meituan, China Mobile and CPE.
Its total historical fundraising stands at over US$5.5 billion and its valuation reached US$30 billion in June, Reuters said.
After its release, demand for Kimi K3 grew too high for the company’s capacity to keep up. Moonshot AI said two days after the launch that it was temporarily pausing new subscriptions due to the strain.
“Kimi K3 has received far more love than we expected, and our GPUs are feeling it,” the company said on X.
Yang’s personal website, dated 2024, sums up his endeavours in a single simple sentence: “I am working on a startup.”
A huge music fan, he has infused his company with the names of his favourite bands.
Conference rooms at its headquarters sport names including Radiohead, Led Zeppelin, Queen and Nirvana. Kimi’s subscription plans are named after the tempo guidelines of classical music: Adagio, Andante and Moderato. The startup was also originally called the Dark Side of the Moon in Chinese, a tribute to Yang’s favourite Pink Floyd album.
Earlier this year, Yang said that Kimi’s team had around 300 people.
He ranked Beijing’s talent pool in the world’s top two, saying that China has cultivated one of the best pipelines for human capital.
“After decades of accumulation, China has built one of the best talent systems in the world – it can cultivate the world’s people with the strongest exploratory capabilities, and it does so in a systematic way,” he said, as reported by Beijing Daily.
His return to China sparked questions about why the US had been unable to retain a talent like him.
Dr Cong told CNA that there were several push and pull factors for Chinese AI experts to return home from the US.
“China produces a huge STEM cohort, and the top labs can build large, young, extremely motivated teams,” he said.
Operating in the country also gives them direct access to its massive consumer and enterprise market, alongside strong policy and funding since China has made AI a national priority, he added.
On the US side, visa precarity and US-China tensions could also play a role in decisions to return home.
Dr Cong added that for someone like Yang, the decision to move back could have a “mission dimension”.
“Building China’s first frontier lab carries a kind of historic significance that a senior role at a US company simply can’t offer,” he said.
CHAMPIONING ACCESSIBLE AI TECHNOLOGY
Despite hailing from different companies, a common thread has emerged in the three men’s conversations about AI: a commitment to open-source models and accessibility.
Open-source AI models are systems that make available their full source code and information required for retraining the model from scratch. This means they can be used, examined, altered and distributed without having to request permission.
The approach is almost an inversion of trends in the US, where leading AI labs keep their best models closed and monetise access, said Dr Cong.
One reason for this is China’s position as the challenger rather than incumbent of AI model creation, he said.
“Giving the model away is the fastest way to win global developer adoption and set de facto standards – it commoditises the layer where US labs charge a premium, which pressures their business model without your having to beat them head-on,” he explained.
The strategy also fits China’s constraints due to US chip export controls, and taps into its massive talent pool.
“China has an enormous developer population, and open weights let all of them build on frontier systems rather than bottlenecking innovation inside a handful of labs,” said Dr Cong.
He added that China’s leading AI companies have been broadly consistent in their commitment to releasing open-source models, but noted that most of the models have been open-weight rather than fully open-source.
For most Chinese AI models, the weights are downloadable, but the training data and full recipe usually stay closed, said Dr Cong.
“And releases are sometimes staggered – API first, weights later – which shows commercial timing still shapes how open they really are.”
By providing high-performance models at far lower costs, China has established itself as a central hub for emerging economies to gain access to affordable AI, said Kong Tuan Yuen, a research fellow at the National University of Singapore’s East Asian Institute.
However, since their core products are essentially free, Chinese AI startups will need to establish completely new types of business models if they want to remain competitive in the long term, he added.
Dr Kong pointed out that one downside of open-source models is safety, since publicly disclosing their core components allows third parties to remove or modify safety features originally included within them.
“This makes it easier for malicious users to misuse the models, as well as increases the difficulty of regulating global AI governance issues such as accountability and data security,” he said.
As companies within and beyond China race to achieve artificial general intelligence, Dr Cong said the more important question was whether work had been done to ensure its safe use by the time they got there.
“My worry is that a competitive sprint between labs and between nations creates exactly the wrong incentives here – everyone has a reason to move faster and cut corners on safety, and no single player wants to be the one who slows down,” he said.
“For something with stakes this high, we owe it to ourselves, and frankly to future generations, to treat getting it right as more important than getting it first.”
Source: CNA/rl(rj)
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