I think our whole society needs to rethink how much we depend on jobs. The job displacement is coming. If you ask someone, "Tell me about yourself," they pretty much tell you about their job. It's extremely dangerous as we look at jobs being turned upside down by AI. We really need to reinstill in people that a job isn't everything. There's more to it than that in life.
Kai-Fu Lee, who's played a central role in the tech boom in the U.S. and China for 40 years. In the long run, do you think that the Chinese companies will be the ones which are more likely to make a profit?
No, I do not. Anthropic and OpenAI have built the iPhone. The Chinese companies are more like Android. iPhone makes by far the most money, but Android has the larger market share.
From Bloomberg weekend, this is The Mishal Husain Show. From the very early days of this podcast on making sense of the world, AI has often been part of the conversation. We have wanted to understand its ever-expanding impact. And so I have talked to pioneers and evangelists, like Mustafa Suleyman, as well as realists and critics like Meredith Whittaker and Karen Hao. It's a field dominated by immense American companies, the giants behind ChatGPT, Claude, Gemini, and others. But this week we really wanted to dig into what's coming out of China: the AI-embedded products, devices, and apps that are already serving a huge population, that are popular in many other countries and may potentially end up winning the global AI race. These advances are especially notable because the United States has for some years been restricting Chinese access to the most advanced chips, the components that essentially make AI possible. Who can really explain this landscape, we thought? And the person we turned to is himself an AI pioneer. Kai-Fu Lee was born in Taiwan, but at the age of 11, his parents sent him to live in the United States. He learned English at school in Tennessee, and in the 1980s, he was doing his PhD on how computers could recognize speech. He went to work for Apple, then Microsoft, then Google, which poached him with a multimillion-dollar pay deal that showed his value to them back in 2005. He was at the forefront of American tech going into China, and Beijing is where he lives today. He's invested in dozens of tech startups, which means there are a number of Chinese billionaires who owe their fortunes to him. And today he has his own AI firm, 01.AI. So Kai-Fu Lee has seen a lot, and as a tech CEO, he remains part of a landscape which will further change our lives. I think he is able to connect the transformative power of technology to our lives in a pretty unique way. He has a new book coming out called AI Native: The Mandate to Transform Your Company, but really, he thinks and talks about our changing world in the broadest sense. Here's how our conversation began.
Hello! Dr. Kai-Fu Lee, can you hear me?
Thank you very much for taking the time. Have we kept you later than usual in your office in Beijing to talk to us?
No, no. You know we're 996, right?
9:00 AM to 9:00 PM, six days a week.
Oh, wow. Okay. You still work like that?
Actually, I do not, but that's the way people talk about working in China.
Okay, I see. Let me set the scene broadly for you about why we wanted to turn to you and to hear your perspective right now. It's because there is so many headlines and news of developments in AI in the U.S. and China. Both these countries you have seen, in fact, the entire trajectory of the development of this technology over 40 years. So let me just start by asking you this: What do you think today is still underappreciated about the power of AI?
I think there are two things. One is the speed of improvement and the reduction of costs. Most people do not realize that AI is solving tasks 10 times longer than it was one year ago. So, if I solve a four-minute task a year ago, now I can solve a 40-minute task, measured by how many minutes a human would need to solve it.
I see, okay. So it's getting better and better at longer, more complex tasks.
Right, right. And if you want to look at it with a skeptical view, it is displacing 10 times more human work potentially. So the acceleration of the much faster intelligence and improvement and the much lower costs is going to drive adoption like no technology ever has before—so much more than the steam engine, so much more than the internet, so much more than Moore's Law. The second is a consequence of AI improving in that sense, means that the fabric of our companies, enterprises, society, government will need to consider the drastic changes that will bring about. And in my view, the CEO is currently one of the least aware of how important and transformative this AI technology is because the companies affect everything, right? We all have jobs, and the jobs are going to change. In five years, the typical successful company's organizational chart will be quite different from today. The people who occupy the most important places, their qualifications will look quite different than today because AI workers are becoming better and cheaper, faster at a pace that people cannot fathom. And in order to make that work effectively in an organization, it cannot be retrofitted into an organization with a hierarchy intended to manage people. AI workers don't need hierarchies. All the information is connected. What they need is people who will know how to design the right problem to solve, organize AI to solve it, and also, very importantly, willing to be accountable if anything goes wrong, because AI can't be accountable.
So what are the qualifications then that would put people in these positions? What are you advocating in terms of what people study in order to make the most of this revolution?
I suggest they study one thing, which is how to solve hard problems and come up with new problems, work with AI to solve them, and to command the armies of AI to massively parallel solve complex problems. But this is not coding. This does not require any engineering background. A humanities student can easily do this. This does require a willingness to work on complex problems, ability to fuse multiple sources of information. So these are kind of the hard requirements. They can be learned even for an older non-tech background person.
What's more important is what kind of a person one is. And if that doesn't match, then no chance. And this position has been called by Steve Jobs and Jack Dorsey as the DRI: Directly Responsible Individual. And these soft traits are more important than the hard traits. The soft traits include singular responsibility—that means anything goes wrong, the buck stops here. Second is willingness to do whatever it takes to accomplish something, with the tenacity and ownership of a project. And the third is ability to work with other people and processes.
Can I put a real-world example to you? Our team right here? We are a small team who work together to produce this podcast: myself, an executive producer, a handful of other producers, video editors working on different aspects of this. I would hate to think of a future where it's me and essentially a version of an AI team, that I might be the directly responsible individual and everyone else around me is an AI colleague. I hope that's not the future that you're trying to prepare me for?
I am not saying the group is one person and all AI. It's as many people as is needed to ensure that people connection part is worked out. I don't know enough about your business, so I'm using my business as an example. So maybe a unit of 20 people and 100 AI to begin with. Then over time, if the business is flat, then probably fewer people and more AI. If the business is growing, there may be more people and more AI. So I'm not saying there will be no people left. There are some tasks—you mentioned video editing. So if I want to set up a one-person company that automatically edits videos, and actually I know a company like this, is a one-person company. They make videos for handymen and gardeners in the U.S. And basically the handyman and gardeners submit their pictures and videos, and AI chops them up, puts on music and background, and then promotes them on TikTok, Instagram, and gets them referrals of deals, and it also promotes their own service to the communities of handymen and gardeners. So this would have been a team of probably 20 people, but now it's one person.
I mean, the implications of that are obvious, aren't they? And I don't just mean in this field, in so many other fields. Look at the protests that have just happened in India with so many young people. Their frustrations, I mean, they were partly about the exam process, but there were also more general frustrations at the kind of entry-level jobs they would have had in the past in places like call centers and others aren't there anymore. So there are very serious social implications, unless you're saying there will be enough jobs just in other fields for all of those people?
Well, I think there will be jobs in certain new industries, new segments created, and I think our whole society needs to rethink how much we depend on jobs, because AI will generate a lot of wealth. And I think we can, as countries that can afford it, find ways of redistribution so people can work fewer hours and be paid for activities that were not economically important. But I think this is all very hard to communicate to someone who couldn't find a job or lost his or her job. So this has been something since my first AI book in 2018 I've talked about: the job displacement is coming, let's start the training, the preparation. And now I think it's just recently rapidly risen to very high levels.
Another thing on my mind that I'd love to get your insight on is what is going on at American tech, and particularly AI companies and Chinese ones. And you're uniquely placed to tell us about both those worlds because you've moved within them. You worked for Apple, Microsoft, and Google, and for Microsoft and Google, you were at the forefront of their foundations in China. Let's take companies like OpenAI and Anthropic. How much of a challenge do Chinese versions like DeepSeek and Moonshot represent to them?
I think they represent a significant challenge, especially if they continue to keep up at the recent levels, because OpenAI and Anthropic always stayed number one or two by most metrics in the AI quality. But their models are closed and not open source. The Chinese models have been largely open source. The models trailing them trail between anywhere between 3 and 15 months. Right now, it's probably six months behind because everyone's pushing on new models. So what this means is, if you were OpenAI or Anthropic, you would have a product you sell for a very high price that is deeply discounted with an open-source version that is equivalent to your best model six months ago. So it's like, would you pay $50,000 for a Tesla this year or a, you know, $5,000 Tesla that's six months old? Obviously the second is a strong value proposition.
So in the long run, do you think that the Chinese companies will be the ones which are more likely to make a profit?
No, no, I do not. I think the American companies will make more money. Anthropic and OpenAI have built the iPhone. The Chinese companies are more like the way Google felt. It's like, okay, you got the best product, we'll build something that's almost as good and sell it very cheaply—in this case, at very, very low costs, sometimes near zero—and just win the larger share, and the rest may come later or may not, but for now, let's gain market share. So like Android, the open-source models will have more share, more footprint, more usage, but people will pay very little. In some cases they just copy the model and they pay for the service on which it's run, but they don't actually pay the Chinese companies anything, and that's being sacrificed to gain share. Whereas Anthropic and OpenAI, they have the American enterprise system selling enterprise software products that are very highly prized, and large companies are getting good value, so they pay a lot of money for the software. So iPhone makes by far the most money, but Android has a larger market share.
That is a really useful analogy, I think, and obviously I'm thinking about all those countries in the world where iPhones are unaffordable and people are using Android phones. Do you think then that DeepSeek and the Moonshot models like Kimi, for example, which Moonshot recently brought out, in most of the world in developing countries these will be AI to people, that China will win that global race, if you like?
I think that's a likely outcome. It presumes a few things. One is that Chinese companies continue to trail not too far. It's possible, because of the talent depth in the Silicon Valley companies, that some company comes out with something that takes much longer to match, let's say two years, three years, in which case that may not hold. The second really is who's going to build the apps for the people in these developing countries. So there's not a lot of money to be made in these countries, and the Chinese model companies already don't make much money on what they sell. So who's going to build that nice interface that OpenAI and other companies have done and cater to the languages for all of these developing countries? So we'll have to see how that plays out. Right now, there's not enough carrier for the Chinese products to conquer the consumers of developing countries.
How has Chinese advance in AI been possible to this extent, given that the US for several years now, from the time of President Biden, has restricted access to the most powerful semiconductors?
I think that set of export controls undervalued the Chinese companies and their engineers' tenacity and willingness to do whatever it takes to get things to work despite a lack of resources. And so we've probably had 1%, 2%, 3% of the GPU power of the top American rivals, and they were able to produce almost comparable results. And that's through the kind of dirty work, if people can imagine the dirty work, right? The dirty work it takes to, you know, fix your car, polish your car, and things like that. So the Chinese people basically view it as a challenge, and they'll work hard on things that are not glorious, not interesting, but gets the job done. So it's maybe 10 times more engineering work to deliver that exceptional result. So I think that was an underestimation. And the other really is that I think the speed at which the Chinese companies learned the technology also went through a very fast accelerating pace. I think the moment ChatGPT came out, I would argue the US was three to four years ahead of China, and I think today we're talking six months. So the speed at which the gap narrowed in technology and the degree to which hardworking engineering overcame the hardware deficiency, I think those were surprises to the policymakers.
So it's an underestimation of human ingenuity and capacity for hard work, I guess. I'm also curious about the fact that the Chinese models tend to be open source, and that other companies can take them, develop them, modify them, do their own products because that does allow your competitors to come in and do a better version of your product. So why have the Chinese companies gone down that open-source street rather than make them closed source like OpenAI and Anthropic, who obviously don't want to give away the tech that they've spent money developing?
I think it's because they don't think they're more likely to win that. I mean, every company makes a choice that's more likely to have a successful outcome. So the Chinese companies are kind of like a study group. So if the Silicon Valley companies are like a genius who feels destined to win a Nobel Prize one day, the Chinese companies are—each one is a very good student, but not so good they feel they're destined for a Nobel Prize, so they better figure out some way not to be kept way behind by the genius kids so that they decide to form a study group. They don't physically really sit down and study together, but because they write papers, they open models, and people move from company to company. So I would say the Chinese companies have moved somewhat in tandem. They're quite aware of each other's innovations. You'll see DeepSeek reference Kimi and vice versa. So while the American companies have stopped publishing, the Chinese companies continue to publish. So the study group is conducted in a fishbowl for the world to see.
