Liang Wenfeng, the founder of Chinese AI lab DeepSeek, has revealed an unconventional hiring philosophy that prioritizes creativity and passion over industry experience. In a rare 2023 interview with Chinese tech publication 36KR, Liang explained why his company deliberately recruits fresh graduates rather than seasoned professionals from AI giants like OpenAI and Meta’s AI Research division.
DeepSeek has recently captured global attention by developing cost-efficient AI models that reportedly match the capabilities of leading US competitors while using less advanced semiconductor chips. This achievement has sent shockwaves through the tech industry, triggering a market sell-off that temporarily impacted AI stocks like Nvidia before they stabilized.
When asked about recruiting overseas talent from established AI companies, Liang offered a contrarian perspective: “If you are pursuing short-term goals, it is right to find people with ready experience. But if you look at the long-term, experience is not that important. Basic skills, creativity, and passion are much more important.” He emphasized that China has an abundant pool of suitable candidates who meet these criteria.
Liang’s reasoning challenges conventional wisdom in the tech industry. He argues that experienced professionals tend to apply familiar solutions, while inexperienced workers “will repeatedly explore and think seriously about how to do it, and then find a solution that suits the current actual situation.” DeepSeek’s core technical positions are primarily filled by fresh graduates or those who graduated within one or two years.
In a July 2024 interview, Liang elaborated that his company’s selection criteria “have always been passion and curiosity.” He noted that many candidates “thirst for the opportunity to do research — and that desire far exceeds their need for money.”
The impact of DeepSeek’s approach has reverberated across the AI industry. Tech giants including Meta and Microsoft are now facing difficult questions about their massive AI infrastructure investments in light of DeepSeek’s more efficient methodology. Sam Altman, CEO of OpenAI, responded by announcing his company would accelerate the release of “better models.” OpenAI has also launched a review to determine whether DeepSeek “inappropriately” replicated advanced US models, adding a layer of controversy to the competitive landscape.
Key Quotes
If you are pursuing short-term goals, it is right to find people with ready experience. But if you look at the long-term, experience is not that important. Basic skills, creativity, and passion are much more important.
Liang Wenfeng explained his hiring philosophy in a 2023 interview with 36KR, articulating why DeepSeek focuses on fundamental capabilities rather than industry experience when building its AI team.
When doing something, experienced people will tell you without hesitation that you should do it one way. But inexperienced people will repeatedly explore and think seriously about how to do it, and then find a solution that suits the current actual situation.
Liang elaborated on why he believes inexperienced workers can be more innovative, suggesting that lack of preconceived notions allows for more creative problem-solving approaches tailored to specific challenges.
So many people have some unique experiences, which are very interesting. And a lot of people thirst for the opportunity to do research — and that desire far exceeds their need for money.
In a July 2024 interview, Liang described the motivation of DeepSeek’s workforce, emphasizing that intrinsic passion for research drives his team more than financial compensation.
Our core technical positions are mainly filled by fresh graduates or those who have graduated one or two years ago.
Liang revealed the composition of DeepSeek’s technical team, demonstrating his commitment to hiring junior talent for even the most critical roles in AI model development.
Our Take
DeepSeek’s success with a predominantly junior workforce challenges the talent arms race that has defined AI development in recent years. While Silicon Valley companies have competed fiercely for experienced researchers with multi-million dollar compensation packages, Liang’s approach suggests that intellectual curiosity and first-principles thinking may be more valuable assets. This philosophy aligns with DeepSeek’s technical achievements—their ability to build competitive models with constrained resources mirrors their unconventional talent strategy. However, questions remain about sustainability and scalability. Can this approach work as the company grows? Does it risk reinventing solutions that experienced professionals would recognize? OpenAI’s investigation into potential model replication also raises important questions about whether DeepSeek’s efficiency comes partly from building on existing work rather than purely from innovative thinking. Regardless, this story forces the AI industry to reconsider assumptions about what drives breakthrough innovation.
Why This Matters
DeepSeek’s hiring strategy and technical achievements represent a paradigm shift in AI development that challenges Silicon Valley’s dominance and conventional talent acquisition wisdom. By demonstrating that cost-efficient AI models can be built with less advanced chips and younger, less experienced teams, DeepSeek has fundamentally questioned the necessity of massive capital investments and veteran talent pools that have characterized Western AI development.
This story matters because it reveals alternative pathways to AI innovation that don’t require cutting-edge hardware or expensive talent wars. For businesses worldwide, it suggests that fresh perspectives and creative problem-solving may be more valuable than established expertise when tackling novel challenges. The approach also has significant implications for AI accessibility and democratization, potentially enabling more countries and smaller companies to compete in the AI race.
The broader impact extends to workforce development and education policy. If DeepSeek’s model proves sustainable, it could reshape how companies recruit and train AI talent, potentially creating more opportunities for recent graduates while challenging the premium placed on experience in the tech industry. The geopolitical dimension is equally important, as China demonstrates it can achieve AI breakthroughs despite US chip export restrictions.
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