DeepSeek Reportedly to Appoint 90s-Born Investor Yan Wentao as CFO Ahead of STAR Market IPO

DeepSeek is reportedly on the verge of appointing a new Chief Financial Officer — Yan Wentao, a post-90s generation investment veteran — according to recent market chatter. The news has sparked considerable discussion, given that founder Liang Wenfeng is already within striking distance of becoming China's richest person. With a seasoned professional who understands both industry and capital now by his side, expectations for Liang and DeepSeek's future trajectory have grown.

By: Fang Fang, Special Correspondent for Financial Gossip Queen

From Quantitative Trading to Large Language Models: A Deliberate Path

Liang Wenfeng was not born into privilege. Growing up in an ordinary family, his standout trait was exceptional mathematical ability. At Wuchuan No.1 Middle School in Guangdong, he was a top student. By junior high school, he had already completed high school mathematics and begun exploring university-level math. Yet he was no mere "study machine" — he balanced work and play, simply learning with remarkable efficiency.

In 2002, at age 17, Liang achieved the top理科 (science) score in the college entrance exam at his school and enrolled in Zhejiang University's Electronic Information Engineering program. When his homeroom teacher suggested he apply to Tsinghua, Liang chose instead to follow his own interests.

This decision reflected his later approach: rather than chasing the most popular path, he prioritized what he genuinely wanted to study.

At Zhejiang University, Liang continued deepening his technical expertise. In 2007, he entered the graduate program in Information and Communication Engineering, focusing on machine vision. A conventional route would have led to a job at a major tech firm after graduation — but Liang chose differently.

In 2008, while still a graduate student, he began researching quantitative trading with senior student Xu Jin and others, aiming to use machine learning and algorithms to automate investment decisions. At that time, quantitative investing was far from mainstream; most investors relied on experience, judgment, and market intuition. The idea of letting computers make investment decisions seemed almost fantastical to many.

But Liang was willing to experiment.

After earning his master's degree, he moved to Chengdu, where he continued developing quantitative models in a rented apartment. As index futures were introduced, quantitative investing gained momentum. Liang's team leveraged algorithmic advantages to refine their strategies, growing their own capital from tens of thousands of yuan to hundreds of millions — securing their first major fortune.

However, accumulating wealth was never his endgame.

His experience in quantitative trading taught Liang an early lesson: the future competition would not be about data alone, but about computing power.

Quantitative trading is an industry heavily dependent on algorithms, data, and computational resources. The more complex the model, the more information it must process, and the greater the demand for computing power.

Thus, when High-Flyer Quant later made massive investments in computing infrastructure, it was not a sudden pivot to "playing with AI" — it was a continuation of thinking that had evolved over more than a decade.

In 2019, High-Flyer invested nearly 200 million yuan to build "Firefly No.1," deploying 1,100 GPUs. In 2021, it invested close to 1 billion yuan in "Firefly No.2," using approximately 10,000 A100 GPUs.

At the time, many questioned why an investment firm would spend so heavily on chips.

But for Liang, computing power was never just hardware expenditure — it was an integral part of research capability.

It was precisely because of these accumulated resources that Liang's shift toward AI in 2023, when the large language model wave arrived, was not abrupt.

That year, ChatGPT exploded globally, and companies rushed to join the LLM race — fundraising, expanding teams, scrambling for GPUs. DeepSeek, by contrast, maintained a relatively low profile.

Liang did not rush to scale the company. Instead, he focused on model development and technical optimization.

In 2025, DeepSeek-R1 was released and attracted attention. Observers noted that it did not simply follow the "big investment, massive compute" route, but explored new approaches to algorithmic efficiency and training methodology.

Traditional LLM training typically requires feeding the model大量 demonstrations — showing it human-summarized problem-solving steps. Liang took a different approach: instead of telling the model exactly what to do at each step, he let it explore solutions on its own.

If the result was correct, the model received a reward; if wrong, it adjusted. Through continuous trial and error, the model gradually learned to check its own answers and spend more time reasoning through complex problems.

In simple terms: rather than manually teaching the model every "solution method," the model learned to find solutions through training.

This approach also had notable cost implications. DeepSeek disclosed in its paper that the total cost for the post-training phase of the R1 series was only approximately $294,000 — a figure that remains a talking point in today's AI industry.

Over the past few years, the LLM industry has continuously escalated computing power investments. Models grow larger, training requires more GPUs, and data centers expand. Often, the competition boils down to who can secure more chips, more data, and more funding.

Liang's message: the scale of computing power matters, but how you use it matters just as much.

From this perspective, his journey from quantitative trading to AI carries a certain inevitability. The difference is that he once faced data and strategies in financial markets; now, he confronts a larger, faster-changing AI industry. The next challenge for DeepSeek is to continue refining its proven technical approach and bring its models to more real-world applications.

Why Liang Wenfeng, Who Didn't Need Money, Is Now Turning to Funding

DeepSeek's valuation has now reached hundreds of billions of yuan, with Liang holding approximately 84.29% of the company's equity (direct and indirect combined). If the company's market cap reaches 1 trillion yuan in the future, his paper wealth could surpass Zhang Yiming's, making him China's richest person.

Of course, paper wealth is just that — on paper.

But the shift behind these numbers is noteworthy: Liang has moved from his "three no's" principle (no fundraising, no commercialization, no roadshows) toward engaging with capital.

In its early days, DeepSeek had little need for capital markets. By the end of 2025, Liang had achieved an average annual return of 114.35% over five years at High-Flyer, giving him ample confidence to pursue research at his own pace. For a young tech company, this was a comfortable position.

However, competition in AI models requires continuous investment in computing power, talent, and infrastructure. Relying solely on Liang's personal funds was no longer sustainable. Fundraising became a natural next step.

In June 2026, DeepSeek completed its first external fundraising round since inception, raising approximately 51 billion yuan at a post-investment valuation of about 338 billion yuan. Liang personally committed 20 billion yuan. Tencent, CATL, and "national team" investors also participated. Market rumors later suggested DeepSeek planned a new round at a valuation of around 500 billion yuan, though that round was reportedly delayed and has not yet been finalized.

By September, foreign media reported that DeepSeek had hired CITIC Securities to prepare for a STAR Market IPO, with plans to file within the year.

Typically, founders set direction while investors provide capital, and both work together to grow the company. Liang's approach is different: external capital is welcome, but he continues to invest personally, and the company's original technical roadmap and R&D pace will not fundamentally change due to fundraising.

In other words, Liang needs capital — but that does not mean he will let capital dictate the company's direction.

Still, the AI industry faces an unavoidable issue: burning cash.

In the first seven months of this year, Liang spent approximately 11 billion yuan on AI infrastructure, including leasing AI chip servers and purchasing computing equipment. In all of 2025, that figure was only about 1.2 billion yuan. In just over a year, spending surged nearly tenfold. As for R&D, it is not a matter of investing today and seeing results tomorrow — many projects may take years to bear fruit.

As the company grows, how to spend money has become an unavoidable question for Liang. Should he continue scaling computing power, or expand the team? Should he shore up infrastructure, or reserve funds for future R&D? In the past, Liang was more comfortable focusing on technology, R&D, and efficiency. But no one, no matter how capable, can handle everything alone.

Liang can still firmly control technical direction and R&D pace. But financial management, capital planning, and other capital-market-facing tasks require professional hands.

Against this backdrop, a previously low-profile position has become increasingly important: CFO.

On the Path to Becoming China's Richest Man, Liang Needs Someone Who Understands Capital

If Yan Wentao joins DeepSeek, it would signal that the company is filling a critical gap in capital and management expertise.

Born in 1991 and a graduate of Fudan University, Yan previously worked at Tencent Investment and H Capital. In 2020, he joined Hillhouse Capital's venture arm, becoming a representative figure among the firm's younger generation of investors. In 2025, he was named to the Nova New Investor TOP 50 by Touzhong.

Foreign media have described Yan as a "dealmaker" — someone skilled at spotting opportunities and closing transactions. He has publicly stated that the core of tech venture investing is identifying the era's biggest technological challenges and the people capable of solving them.

Yan's focus on AI is not recent. He began researching the LLM space in 2020, and by 2023, his attention had shifted to embodied intelligence. In his view, the window of opportunity for investors in each technological wave is extremely short — the key is finding the companies and entrepreneurs who can seize the moment.

Notably, two of his portfolio companies — MiniMax and Zhipu AI — have both initiated STAR Market IPO processes, signing advisory agreements in May and February of this year, respectively. This means Yan is well-versed in the full journey of AI LLM companies from early growth to capital market readiness.

It is worth noting that Hillhouse is not an investor in DeepSeek. Therefore, if Yan ultimately joins DeepSeek, it would not be a traditional case of an investor "betting on a project." Rather, the two sides likely share a common understanding of tech industry trends and capital market logic.

DeepSeek has been searching for a CFO for some time.

As early as February 2025, the company publicly posted a CFO job listing. In June 2026, DeepSeek announced plans to at least double the size of all departments, and the finance team was actively recruiting on its website, with CFO remaining a core position.

However, DeepSeek clearly needs more than a traditional financial officer.

According to the company's official job requirements, the CFO must be familiar with accounting standards and tax policies, hold a CPA qualification (preferred), and be able to establish a complete compliance and risk control system. Yan's strength, however, lies not in financial expertise but in his long-term involvement in tech investing and his deep understanding of AI industry development and capital market operations.

In a sense, he would help DeepSeek complete a kind of "translation": converting technical advantages into a business logic that capital markets can understand, and transforming the potential of a technology-driven company into a clearer development path.

Over the past year, as DeepSeek has rapidly advanced in AI, Liang Wenfeng's personal wealth has soared — from approximately $1 billion to $39.9 billion, ranking 51st globally.

But rising wealth figures are only the surface. How far Liang can ultimately go — and whether he can challenge for the title of "China's richest person" — depends on whether DeepSeek can truly convert its technical advantages into commercial value.

At least for now, he has found a key partner who can help the company navigate the intersection of capital, management, and industry in its next phase of growth.

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