Kai-Fu Lee on China-US AI Race - Q&A Transcript from a Bloomberg Interview

Kai-Fu Lee, Chairman of Sinovation Ventures and author of AI Superpowers and AI 2041: Ten Visions for Our Future on the China-US AI Race

Q: There's still a lot of challenges when it comes to the whole monetization of AI, according to his perspective. In the last three years, though, how have you looked at this whole evolution of China AI versus the US, and what are the advantages and challenges for China?

A: Yeah, so firstly, I disagree on the prospects for the USA. USA because yes, you look at OpenAI, it's making half billion spending 40 billion. It looks like a bad balance sheet, but most of that 40 billion is spent for future revenues. And if you believe there are 2x, 3x, 5x growth for the next three years, it's going to justify that valuation at some point. The bubble is merely that it's gotten ahead of itself, not the likelihood of growth in the future. Not saying it's worth its price, right, but there's absolute substance under the thumb.

Now back to the China AI. The fundamental difference is actually closed source versus open source. The US model is necessarily propelled by this ambition and dream of one company reaching AGI first and squashing everyone. Winner take all worth trillions of dollars. That's what OpenAI, Anthropic, Google and others are betting on. And it's like a genius kid who thinks they'll - he or she - will win the Nobel Prize.

The Chinese approach is, "Hey, let's all work together and share." We compete, but let's open source our models. So A learns from B, B learns from C. It's more like a study group of smart kids. Maybe not genius kids.

Interviewer: But is open source the reason why a lot of Chinese AI startups cannot monetize?

Kai-Fu Lee: Well, I think there's a belief, there's a common belief in in US and China: AI is the most important technological revolution ever. There is a belief that this monetization will happen, is happening first in the US and it will happen later in China. So there is a bet that if all of China kind of pseudo-collaborates through open source then builds a great platform, then everyone can benefit because the knowledge of the platform - or if one company comes out ahead, there's a chance to leap ahead and be the next great company as well. So there's tremendous optimism that AI is changing the world and these models - closed source approach is a belief one company will squash everyone else. Open source is, "Hey, let's share for now and we'll see what happens."

Q: Has the open source model in some ways discouraged competition? Because I simply have to look across the classroom and see who has the best hands and simply build on that classmate.

A: That is the business of my company, 01.AI. We've pivoted ourselves to build on open source. Whoever has the best model, we'll use it. If it's DeepSeek or Qwen, we are agnostic. So we're in the position not so dissimilar from NVIDIA. Obviously NVIDIA's much better, but they don't care who wins and we don't care either. We only care that open source moves forward. And its benefits are that open source is free. It's much, much, much lower cost when it's basically free and we can build on it. It's ours. We take it, we do whatever we want. We never have to pay DeepSeek or Alibaba. And also enterprises, they can take the model in-house, run everything in-house. If they have secret data they don't want to send to the cloud, well, they don't have to.

Q: But someone needs to pay for the innovation though. Who hooks into that? I'll just take the OpenAI example. But you know, there's completely a difference in the Chinese context where you have CapEx of a trillion and people are trying to figure out how are they going to pay for that. And then people, to your point longer term, will then have to worry about how they then make that back. In China's case where you don't have to pay for it, where it's open source, how is the innovation getting funded and how do you justify to those asset allocators that they will get a return for their investment?

A: Well, I actually like the Chinese model better. While the likes of Alibaba, ByteDance, Baidu, they're paying a lot of money, they're willing to subsidize because it's fundamental to their core business, whether it's e-commerce or search or social. And I think they're smart business people. They'll do the balancing. They're spending a tiny fraction of what OpenAI is spending right now. OpenAI's spending is a big bet with big return. It's betting that it will win. Whereas I think Alibaba, Baidu, Tencent, ByteDance, if they're spending more prudently, which they are, they will be able to look at the whole balance sheet and deliver a reasonable result. We don't see any of these people reporting huge losses, right? So I think the Chinese model is more prudent for a financial investor. The US is a winner take all, so you pick your winner. If you win, you win big. If you lose, you lose big.

Q: How do you see it, though, play out in China, where you have the big giants like Alibaba, like ByteDance, who have the deep pockets to continue on with this sort of AI war in China? How does it stack up against some of these startups like Minimax or Moonshot?

A: Well, I admire the courage of the smaller companies who choose to compete. I feel that it's more prudent for a smaller company to build either applications, either enterprise or consumer, and bet on the fundamental foundation models of the big companies, because I don't think it's very likely that any startup can raise money the way OpenAI does, or even one-tenth of OpenAI. And with that, with less money, it is going to be difficult. The talent is obviously very good in the Chinese startups as well as the large companies, but the lack of spend and the lack of a huge number of talent and talent density makes it an uphill struggle for the startups that try to do that.

Q: The DeepSeek moment - well, let's call it two moments, right? Because the R1 moment in January this year was where it became public to the world, although it was this time last year where people had started to already talk about the DeepSeek. And a lot of people are now looking for what's the next one. You're connected and well connected into the industry. What innovations are taking place right now? What should we be prepared for going into next year?

A: Well, I think in terms of deep fundamental foundation models, US is ahead. But China is very rapidly figuring out the great things the US is doing, plus a few of its own good things to basically trail by three to six months. I've been saying this for a year and a half and it still remains the case. So I don't think there's going to be a huge breakout change-the-world technology in China, that's not likely.

What is more likely is there may be great consumer apps. And what's even much, much more likely is that Chinese hardware - there's a company called Plaud. It is basically an NB(?) AI that listens all the time and gives you summaries of all the meetings and tells you what to do and gives you reminders. And that's just the beginning. That is so much cheaper to build in China. Yeah. And it iterates that much faster and it's getting smaller. And I think the Chinese VCs are hesitant to invest in large language models for the reasons we discussed, but they are going all out, including my own Sinovation Ventures. I'm betting on hardware.

When people talk hardware, people think always embodied robotics. It's, you know, the humanoid robots, but those are also a bit overpriced. It's much more prudent to invest in small devices because when AI revolution moves forward at the next step, we will see that the best place, the best device on which to use AI is not the phone. Whether it is glasses or a watch or wristband or a little necklace or almost invisible, are all possible because AI-first means it's always on, always listening, always thinking. And it's your external hard drive that's smart and remembers everything you see and did. I know you're going to talk about privacy, but I know that's a separate - it gets a bit creepy, I might be listening to you. Oh, my goodness.

Q: There's a lot of hot air around agents. Tell me what's all the buzz around it and how long before it becomes pretty mainstream, you think?

A: It's real already, but it's not yet mainstream. An agent is built on the large language model. The large language model is the brain. And there may be multiple large language models, each doing special things. But the two most important things wrap around that. One is memory. It knows who you are, what you did, who's your company, what your policies are. So it's relevant to what you do as opposed to a general chatbot. But the other even bigger thing is that it executes tasks. So instead of giving you a travel itinerary, it books your travel - your airline, your hotel. Instead of suggesting what's the strategy, it starts executing. Your ads are placed as opposed to suggesting what ads you might do.