DeepSeek has achieved remarkable employee stability in its core research team while competitors across the industry struggle with talent exodus. During the development of DeepSeek V4, only 10 employees departed from the company's research and engineering team of approximately 270 people, resulting in a turnover rate of less than 4%. This stands in sharp contrast to OpenAI, which lost more than 25% of its key research talent over the past two years, with many departing researchers joining competitors like Meta or launching their own ventures.

Why Is DeepSeek's Employee Retention So Unusual in AI?

The Chinese AI startup's ability to hold onto talent comes at a time when the large language model field has become intensely competitive. Since early 2025, when DeepSeek R1 demonstrated its capabilities, several high-profile researchers have left the company for positions at major tech firms. Notably, Guo Daya joined ByteDance's seed team, while others moved to Xiaomi and Tencent. Yet despite these departures and the intense competition for AI talent, DeepSeek has maintained remarkable stability in its core team.

The company's founder, Liang Wenfeng, has taken a deliberate approach to talent management that differs from the typical playbook of fast-growing tech companies. Rather than aggressively expanding headcount immediately after early success, DeepSeek has prioritized retention and team stability. This philosophy reflects Wenfeng's belief about what actually matters in building long-term AI capabilities.

"Most of DeepSeek's developers are fresh graduates or those with short-term AI work experience. If pursuing short-term goals, it is of course right to recruit experienced people. But in the long run, basic skills, creativity, and enthusiasm are more important," stated Liang Wenfeng.

Liang Wenfeng, Founder of DeepSeek

This philosophy has proven effective. DeepSeek's research team includes many young people who recently graduated or are still studying at top domestic universities, with a high proportion from Peking University and Tsinghua University. Rather than viewing youth and inexperience as liabilities, Wenfeng has positioned them as assets that bring fresh perspectives and sustained motivation to the work.

How Is DeepSeek Securing Employee Loyalty?

• Equity Restructuring: In late April 2026, Liang Wenfeng increased his direct shareholding ratio from 1% to 34%, making his controlling interest more visible and transparent to employees and potential investors. This move signals commitment to the company's future and provides clearer equity structures for internal talent.
• First Financing Round: DeepSeek announced its first external financing at a valuation exceeding 10 billion RMB, with some reports suggesting a pre-investment valuation of 300 billion RMB. This financing is partly designed to give employees definite valuations for their equity stakes, addressing a key concern that had driven some departures.
• Competitive Compensation Strategy: The company is preparing to offer employees concrete financial rewards as the financing round progresses, reducing the incentive for them to seek opportunities elsewhere where they might receive immediate payouts from other investors.

The timing of DeepSeek's financing announcement is particularly strategic. Rather than seeking investment when the company was at peak visibility following R1's release in early 2025, Wenfeng waited until the competitive landscape had intensified and multiple rivals had emerged. This counterintuitive timing suggests that stabilizing internal talent through a financing round became more important than capturing market momentum.

What Does DeepSeek V4 Reveal About the Team's Stability?

The release of DeepSeek V4 in late April 2026 provides concrete evidence of the team's cohesion. The technical report's author acknowledgments list approximately 270 people in the research and engineering team, with only 10 departures during the entire development period. For context, this represents a turnover rate substantially lower than typical tech industry standards, especially in the high-stakes AI sector where poaching is rampant.

The V4 model itself demonstrates the team's technical capabilities. The Pro version features up to 1.6 trillion parameters and offers competitive pricing at 1 RMB per million input tokens with cache hits, or 12 RMB without cache hits, with output costing 24 RMB. The Flash version is even more affordable at 0.2 RMB, 1 RMB, and 2 RMB respectively. Both versions come standard with a million-token context window, allowing the model to process roughly 100,000 words at once.

Perhaps most significantly, DeepSeek has confirmed adaptation to domestic Chinese chips. While the model training still likely uses NVIDIA processors, the technical report lists Huawei Ascend and NVIDIA side by side on the verification platform. This represents a meaningful step toward reducing dependence on American semiconductor technology and opens possibilities for further cost reductions once Huawei's Ascend 950 super-node reaches mass deployment in the second half of the year.

What Does This Mean for the Future of AI Development?

DeepSeek's approach challenges conventional wisdom about how to build world-class AI companies. The typical trajectory for successful tech startups involves rapid scaling, aggressive talent acquisition, and quick market expansion. Instead, Wenfeng has chosen a path focused on team stability, measured growth, and long-term capability building.

This strategy appears to be working. Despite losing some high-profile researchers to competitors, DeepSeek has maintained the core team needed to develop competitive frontier AI models. The company's ability to release V4 with a nearly intact research team suggests that institutional knowledge, team cohesion, and shared vision may matter more than individual star researchers in modern AI development.

The contrast with OpenAI's experience is instructive. While OpenAI has achieved significant commercial success, the loss of over 25% of key research talent over two years indicates that even market-leading positions do not guarantee employee retention in the competitive AI landscape. DeepSeek's sub-4% turnover rate in its core research team suggests that alternative approaches to compensation, equity, and company culture can effectively compete for talent even against well-funded rivals.

As the AI industry matures and competition for talent intensifies, DeepSeek's retention success may become a case study for how startups can build sustainable, high-performing teams without the massive cash reserves of established tech giants. The company's willingness to move slowly on financing, focus on team stability, and invest in younger talent with growth potential offers a different model for building frontier AI capabilities.