Kai-Fu Lee has had a front-row seat to the rapid growth of China’s AI industry over the past four decades, playing a central role first in building institutions that have spawned much of the talent now powering the country’s leading companies.
The Taiwanese-American computer scientist helped establish Microsoft Research Asia, which became a vital training camp for China’s leading AI talent, before later heading up Google’s operations in the country. Today, Lee heads Sinovation Ventures, a venture capital firm that invests in AI start-ups and is the founder of 01.ai, a Beijing-based AI start-up building agentic tools for companies worldwide.
In conversation with the Financial Times’ China technology correspondent Eleanor Olcott, he talks about the competition between AI’s two superpowers — China and the US — and why companies need to be more proactive to adopt the changing technology.
Eleanor Olcott: Can you introduce your start-up 01.ai?
Kai-Fu Lee: 0.1.ai makes tools to develop AI agents for companies. We build on open-source models, picking the right model for the company’s application and customising it for each customer. We believe that at an early stage of AI agent adoption, it’s essential to provide a white-glove service where we explain how technology can be applied. Together with the company, we co-create the most valuable applications that generate not just cost savings, but also business outcomes.
EO: How prepared are these companies to adopt AI?
KFL: We work with companies in traditional industries, including banking, insurance, mining and energy, which, compared to technology companies, are unprepared to adopt AI. Some of them haven’t done the digital transformation necessary for AI. In these cases, we won’t work with them because it will take too long and cost too much. The other problem is that some companies are looking in the rear-view mirror in terms of what they want. They might request to build a customer service agent, but that really isn’t where the technology or the best application areas are.
Companies that lack AI expertise must partner with an AI company to co-create their AI strategy. This kind of transformation is CEO-led, and it’s very difficult. Maybe one out of a hundred companies is prepared to do this.
When we partner with a company, we go in deep. They commit. We want them to hire a chief AI officer [CAIO], because the CIO [chief information officer] won’t do. CIOs tend to be very conservative. The CAIOs need to be bold and think big about strategy and the company organisation. They work directly with the CEO to reshape that. When our customers can’t provide a chief AI officer, we provide one for them.
Our business model is Palantir-like in the sense that we have consultants who help shape the strategy and then implementers who build it. We’re paid in accordance with the business outcome we create. We charge a set amount for the strategy development to recover our costs, but if there’s no business outcome, then we don’t get paid any more.
EO: And for the other 99 companies that don’t want to do this, why is that?
KFL: Sometimes it’s because people think of AI as just another piece of software. Sometimes CEOs don’t have a natural understanding of what AI is. Sometimes people think of AI as just another kind of ERP [enterprise resource planning] software. Sometimes they delegate it to the wrong person. And the CIO is often the wrong person if you want to delegate AI strategy because . . . their job is to keep the company’s computers and software running smoothly, not to think about its transformation.
EO: The consensus today is that the Chinese models lag the leading American models by six to 12 months. Why? And do you think this will persist?
KFL: Currently, the US accounts for the great majority of breakthroughs in AI research. The US has most of the world’s top researchers and vast quantities of computing power to come up with advances in large language models. But based on these breakthroughs, talented and engineering-focused Chinese teams will quickly figure out how to build similar technologies, and often make them much faster which led to their own ‘aha moment’ [referring to China’s DeepSeek’s reasoning model released last year, which matched OpenAI’s breakthrough model at a much lower training cost].
It’s difficult for the first country to put a man on the moon. But once that has been done, and even though you don’t know the secret of how it was done, the fact that it was will make it so much easier for the second company or country to do it.
China has very strong engineering and research skills. So Chinese companies have made some inventions themselves, but they’ve also been able to figure out how these American models work, even though the American companies don’t do open source or publish papers. Perhaps the empirical result itself is enough of an inspiration. Perhaps it’s through clever reverse engineering. Perhaps it is through distilling the model. Or perhaps it is figuring out the first principles. Or perhaps, it figured out . . . different first principles, but it got to the result anyway.
So the Chinese models tend to catch up. When DeepSeek came out, it was shortened to like three months, and now it looks like [Google’s] Gemini has taken the lead, and lengthened the gap to perhaps 12 months.
That gap will shorten and lengthen, perhaps with six months as a midpoint. Everyone else will learn from every smart idea and model that’s published because the AI field has attracted many of the smartest people. That is true both in China and the US, and they’re all eager to learn. It’s not all one way. When DeepSeek came out, all the American companies studied it as well.
EO: When DeepSeek released its R1 model in January 2025, there were lots of accusations, including from OpenAI, that it cut corners by distilling its reasoning model. OpenAI then said it took steps to stop that happening. Let’s ignore the accusation of tech theft, which seems unprovable. Is it getting harder for the Chinese companies to learn from the US companies because they are taking more proactive measures to protect their secrets?
KFL: OpenAI feels that they have to keep the models closed, because after all this expensive work training breakthrough models, if they open source it, everyone will learn it much more easily. They feel so much money was put into inventing this IP; they don’t want to share it. This is understandable.
Also, they feel that the future of AGI [artificial general intelligence, a term that refers to a hypothetical future when AI has human-level cognitive abilities] will arrive as a giant step function when one company cracks it, and it will squash every other company in the world, be it American or Chinese. So in that sense, if you believe that the future of AGI is one where the winner takes all, then you have to keep how you arrived at that point secret.
The American companies keep raising hundreds of billions of dollars. They have to tell investors that by building AGI, they will dominate the world and that investing $50bn today is cheap because one day the company will be worth $50tn. So that makes the whole story work for OpenAI, and it’s an understandable, somewhat credible story.
But I think the alternate story is instead of having one genius kid or even four genius kids. In America you have OpenAI, Anthropic, Google, and xAI, each of which believes they’re the genius that will beat everyone else and win the Nobel Prize by solving the ultimate problem of AGI.
But the Chinese approach is different. The approach is more like a study group, where one company publishes a model, and the other looks at and plays with it. Maybe even talks to the company about how they trained it. All the members of the study group are building open source and then sharing it. So the study groups are formed of very smart kids who are all funded by companies that still want to show profit every quarter.
This is very different from the situation in the US, where companies do not care about returns. In China, companies are constrained in how much they can spend. Alibaba isn’t going to lose $10bn the next quarter. But OpenAI can. So, all these reasons cause the Chinese companies to behave the way they do with modest resources, learning and improving, working as a study group, as opposed to the American winner-take-all strategy.
EO: There is a dominant narrative that America’s lead in AI is a strategic geopolitical advantage. I think there’s a world in which, in the future, we see the fact that China’s been behind as an advantage. Because there is a time lag, Beijing can watch how this is evolving in the west. They can see the economic and societal disruption brought by AI and choose to take a different approach depending on the mistakes and pitfalls that they see. What do you make of this?
KFL: It is almost certain that a future bad outcome from AI will come from an American company; if it’s being abused by some bad actors or by some error, a door has been left open that was unintentional. It’s just the way that they operate in this winner-take-all, run fast and break things mentality. It will cause companies to be naturally less conscious. And also they’re playing with more dangerous weapons, because their models and technologies are more advanced. In China, people in general do not believe that AGI will be one company squashing everyone else. I think people believe it’s going to be a linear trajectory where the winner will change.
EO: Surely the Chinese companies want to be the winners?
KFL: They want to, but they don’t want to pay the price; they don’t want to raise $500bn and have the company go bankrupt if they fail. The Chinese companies are more focused on the business results and on building products that make money from the models. Whether it’s Tencent’s WeChat, Alibaba’s Taobao or ByteDance’s Douyin, these companies want to build a competitive model that aligns with their products and can make money.
EO: What do you see happening this year in China’s AI industry?
KFL: I think China will lag the US in terms of enterprise adoption because of the unwillingness of Chinese companies to pay the kind of subscription fees. By contrast, China will lead the US in consumer applications. Both countries have plenty of start-ups working on consumer apps. The time is ready, and the models are good enough. But I think the Chinese giants will, by far, outrun the American giants in building great applications because the Chinese giants have always been tenacious, hungry, and monopolistic. And they see applications as the reason they’re building technology. So they’re going to be more focused. They’re going extend their existing apps with AI. They’re going to build new apps with the AI. It’s already coming out.
I think Chinese internet companies are going to be a source of this app innovation more than their American peers. If you look at the standard American app, whether it’s Instagram, YouTube, or Snapchat, they’re getting very boring. I don’t think the American internet companies have the same kind of approach to hard work, a willingness to reinvent themselves in the same way that the Chinese consumer app companies do. By contrast, the Chinese consumer app companies like ByteDance, Tencent, Alibaba, Meituan, PDD Group, Xiaohongshu, have tenacity and a desire to win and build new and innovative products. Many of them are building great AI technologies, agents, and models. They are already investing heavily in it, more so than the typical American way.
Secondly, 2026 will be the beginning of AI-first devices. This will be the year that we first see, touch, and buy an AI-first device. It may not be the ultimate thing, the format that ends up winning. It will either be the Nokia moment, the BlackBerry moment, or the iPhone moment. We don’t know which one it is, but all three moments were important in the history of mobile development. An AI-first device is needed because humans have always wanted to have a way to delegate to the device using speech and language. So this means telling the device the desired result rather than the steps to get the job done. The smart agent then . . . gets it done.
This is already happening with agent technology. But it needs to have a speech-driven interface, which isn’t a smartphone. The phone is the wrong device because it’s not always on, and it’s not always listening. So you need a device that’s always on, always listening and capturing information throughout your day. It will store everything you have seen and heard, and reason against this. So, it’s a long answer, but I think the key is this ambient AI that’s always on, always listening, infinitely remembering, and invisible.
EO: Reflecting on your career in the Chinese AI industry, if you look back at the beginning, what would you be surprised at about the industry today and what has remained largely the same?
KFL: I think an optimistic belief that AI would change the world has always remained the same. What I was surprised by is the speed at which it grew in the past three years. I thought it would be slower growth over 10 or 20 years, but it came much more quickly and matured very rapidly. We still have a long way to go.
When I started out working in the industry in the 80s, AI was always a bag of things that didn’t work. Whenever it did work, which was infrequent, it got turned into a product and was no longer called AI. People made fun of us or thought we were just a bunch of crazy people who think AI can think like humans. And nowadays, you know, everything calls itself AI. Every IPO calls itself an AI company. So we’ve gone from only the dreamers and wishful thinkers do AI, to now to everybody wants to be a part of it.
This transcript has been edited for brevity and clarity
