Deepseek Founder: "We're done following. It's time to lead"
Interview with the founder of DeepSeek
Editor’s Note: Please find below our translation of an interview conducted by Chinese media outlet Anyong, originally published in Chinese.
How Was the First Shot in the Price War Fired?
An Yong (Interviewer): After the release of the DeepSeek V2 model, it quickly triggered a fierce price war in the large model industry. Some say you are a disruptor in the market.
Liang Wenfeng (DeepSeek Founder): We never intended to be a disruptor; it just happened by accident.
An Yong: Were you surprised by this outcome?
Liang Wenfeng: Very surprised. We didn’t expect pricing to be such a sensitive issue. We were simply following our own pace, calculating costs, and setting prices accordingly. Our principle is neither to sell at a loss nor to seek excessive profits. The current pricing allows for a modest profit margin above our costs.
An Yong: Five days later, Zhipu AI followed suit, and soon after, ByteDance, Alibaba, Baidu, and Tencent joined the race.
Liang Wenfeng: Zhipu AI lowered prices for an entry-level product, while their flagship models remain expensive. ByteDance was the first to truly match our price for a flagship model, which then pressured others to follow. Since large companies have much higher model costs than us, we never imagined anyone would operate at a loss. It ended up mirroring the internet era’s subsidy-driven logic.
An Yong: From an outsider’s perspective, price cuts seem like a tactic to grab users—typical of internet-era competition.
Liang Wenfeng: Grabing users wasn’t our primary goal. We reduced prices because, first, while exploring next-generation model structures, our costs decreased; second, we believe that both AI and API services should be affordable and accessible to everyone.
An Yong: Before this, most Chinese companies simply copied the Llama model structure to develop applications. Why did you choose to focus on model structure instead?
Liang Wenfeng: If the goal is to develop applications, adopting Llama’s structure to quickly launch a product is a reasonable choice. However, our goal is AGI (Artificial General Intelligence), which requires us to explore new model structures to achieve superior capabilities within limited resources. This is foundational research for scaling up. Beyond architecture, we’ve studied data curation and human-like reasoning—all reflected in our models. Also, Llama’s training efficiency and inference costs lag behind cutting-edge global standards by about two generations.
An Yong: Where does this generational gap come from?
Liang Wenfeng: First, there’s a gap in training efficiency. We estimate that China’s best models likely require twice the compute power to match top global models due to structural and training dynamics gaps. Data efficiency is also half as effective, meaning we need twice the data and compute for equivalent results. Combined, that’s four times the resources. Our goal is to continuously narrow these gaps.
An Yong: Most Chinese firms pursue both models and applications. Why is DeepSeek focusing solely on research?
Liang Wenfeng: Because we believe the most important thing right now is to participate global innovation. For years, Chinese companies have been accustomed to leveraging technological innovations developed elsewhere and monetizing them through applications. But this isn’t sustainable. This time, our goal isn’t quick profits but advancing the technological frontier to drive ecosystem growth.
An Yong: The prevailing belief from the internet and mobile internet eras is that the U.S. leads in innovation, while China excels at applications.
Liang Wenfeng: We believe that with economic development, China must gradually transition from being a beneficiary to a contributor, rather than continuing to ride on the coattails of others. Over the past 30 years of the IT revolution, we barely participated in core tech innovation.
We’ve grown accustomed to Moore’s Law “falling from the sky”—waiting 18 months for better hardware and software. Scaling Law is treated similarly. However, these advancements are the result of generations of relentless effort by Western-led technology communities. Because we haven’t been actively involved in this process, we’ve come to overlook its significance.
The Real Gap Lies in Originality, Not Just Time
An Yong: Why did DeepSeek V2 surprise many in Silicon Valley?
Liang Wenfeng: Among the daily innovations in the U.S., this is quite ordinary. Their surprise stems from seeing a Chinese company join their game as an innovator, not just a follower—which is what most Chinese firms are accustomed to.
An Yong: But in China’s context, prioritizing pure innovation seems almost a luxury. Developing large models is capital-intensive. Not every company can afford to focus solely on research without commercializing first.
Liang Wenfeng: Innovation is undoubtedly costly, and our past tendency to adopt existing technologies was tied to China’s earlier developmental stage. But today, China’s economic scale and the profits of giants like ByteDance and Tencent are globally significant. What we lack isn’t capital but confidence and the ability to organize high-caliber talent for effective innovation.
An Yong: Why do Chinese companies, even well-funded giants, often prioritize rapid commercialization?
Liang Wenfeng: For three decades, we’ve emphasized profit over innovation. Innovation isn’t purely business-driven; it requires curiosity and creative ambition. We’re shackled by old habits, but this is a phase.
An Yong: But DeepSeek is a business, not a nonprofit research lab. If you innovate and open-source your breakthroughs—like the MLA architecture innovation releasing in May—won’t competitors quickly copy them? Where’s your moat?
Liang Wenfeng: In disruptive tech, closed-source moats are fleeting. Even OpenAI’s closed-source model can’t prevent others from catching up.
Therefore, our real moat lies in our team’s growth—accumulating know-how, fostering an innovative culture. Open-sourcing and publishing papers don’t result in significant losses. For technologists, being followed is rewarding. Open-source is cultural, not just commercial. Giving back is an honor, and it attracts talent.
An Yong: How do you respond to market-driven views like those of Zhu Xiaohu (who advocates prioritizing immediate commercialization over foundational AI research, dismisses AGI as impractical)?
Liang Wenfeng: Zhu’s logic suits short-term profit ventures, but the most enduringly profitable U.S. companies are tech giants built on long-term R&D.
An Yong: But in AI, pure technical lead isn’t enough. What larger goal is DeepSeek betting on?
Liang Wenfeng: We believe that China’s AI cannot remain a follower forever. Often, we say there’s a one- or two-year gap between Chinese and American AI, but the real gap is between originality and imitation. If this doesn’t change, China will always be a follower. Some explorations are unavoidable.
NVIDIA’s dominance isn’t just its effort—it’s the result of Western tech ecosystems collaborating on roadmaps for next-gen tech. China needs similar ecosystems. Many domestic chips fail because they lack supportive tech communities and rely on secondhand insights. Someone must step onto the frontier.
To read the full article, please visit: https://thechinaacademy.org/interview-with-deepseek-founder-were-done-following-its-time-to-lead/