Hi, I'm Karis Wisher, editor at Large of Recode. You may know me as a super advanced AI that is displacing the jobs of 20 other tech journalists in San Francisco, but in my spare time I talk tech and you're listening to Rico Decode from the Box Media Podcast Network. Today in the red chair is someone I've known for a long time, Kai Fu Lee, the CEO of Cinnovation Ventures, and former president of Google China. He's also the author of a very important new book about artificial intelligence called AI Superpowers, China Silicon Valley, and the New World Order.
Kai Fu, welcome to Rico Decode. Thank you, Karis, or AI should I say? So there's lots of things I want to talk about with you, but let's get people up to speed on who you are. I've known you for a long time, and I remember you think I wrote a story in the journal at the time.
You were hired. So talk a little bit about your background, so people can get a sense of where you've come from. Sure, I grew up in the US, Columbia, Carnegie Mellon PhD, and then I ran multimedia at Apple, followed by SGI, Microsoft, where I started Microsoft Research in Asia, back at the headquarters, worked for five years in Redmond, and then I went to start Google China in 2005. Right, so talk about how you got there.
So you had a very story career, you had a lot of great spots, multimedia at Apple was a critical job. That was back in the 90s or 90s or 90s? In the 90s, 90s, 90s. Yeah, which was their recovery period really.
A difficult period. Yeah, kind of stuff. So you had long time still time. Why did you do China for Google?
You went over there in what year? In 2005. 2005, which is early in Google's, when they were involved in China. It was the initial entrance.
But I was at Microsoft Research China in 98, so that gave me the experience. And that's presumably why Google tapped me. And what was your goal there to do? What was at the time when they were entering, and then they exited?
But talk about your goal. What was the goal for Google there? Well, the goal was to build up a local presence, win as much market share as we could, and stay true to the corporate values, and all three of which we accomplished. Right, and you located servers outside of China.
There were all kinds of different things you did. Can you talk a little about that? Because it's going to be relevant to what we're talking about later. Sure.
Well, the Chinese law has required some servers to be present in China. So we had that. The majority of the servers were outside. And then there were certain commitments that Google made in order to do censorship and be present in China.
Things like providing an explanation whenever something was removed at the bottom, and also providing an instance of search, and also not store personal information in China. Right, which they didn't allow people to register, essentially. That's right. And one of the interesting parts was putting that, saying this was if someone was doing a search at the bottom saying things were left out of the search due to laws of China, correct?
Something like that. Yes, and actually all search engines ended up doing that. Right, right. And it was because the idea was that you didn't pretend that you weren't censoring things, correct?
That was the idea. That was the idea. No, it was a Chinese government idea or Google idea. Both were OK with it.
Both were OK with it. Now, so you were there until how long? Four years. Four years.
Were you there during the pullout? No. I left three months before that. Right.
And I had no idea how long. Oh, you didn't? Really? No, you weren't aware of that?
Well, you saw the later reports that things, they saw things in November and decided to leave in December. Right. There was meddling by the Chinese government. That was the allegation.
Allocation, OK. I was really angry. And I left earlier in September. And why did you do that?
Well, I saw that the entire entrepreneurial landscape was just burgeoning. Right. And I lost all of my young, super smart staff. They were all into startups, all doing very well.
VC industry was starting the Chinese internet market as an independent market was making off. It really was right then. So it was an exciting thing. I thought I wanted to be a part of it.
You wanted to escape too. At the same time. Well, I didn't quite use the word escape. But Google was a big job running Google China.
It was a tremendous opportunity. But also frustrating at times. But I compared that with having the freedom to invest in companies and help young entrepreneurs. Right.
That seemed more fun. So let's set the table then. Because before that, China wasn't seeing as the entrepreneurial engine that it is today, correct? Or it was just that was right around when it was really becoming close.
I think that's a reasonable to say. Actually China's entrepreneurial energy started in the late 90s with the portals. And then later, the search engine Alibaba were launched in the early 2000s. Initially, they were thought of as copies, correct?
That was the generalized feeling that they copied US innovation. And that's not an inaccurate statement. At that time. At that time.
And so there was an Alibaba that was like, in Amazon, there was everything that was in the, there was a good buy-do that was like a Google and various things like that. And many people felt that that was the way it was going to be for China. That we're going to be fast followers, essentially. I think that's the assumption.
Because in Silicon Valley, copying is frowned upon. And it's viewed that once you copy, you always copy. But I think those turned out to be wrong assumptions. Yeah, absolutely.
And then at the same time, Silicon Valley companies are having troubles. We're operating in China. Can you talk about why that was? eBay had a disastrous run.
Yahoo was only successful because it bought a Chinese company. It had a stake in a Chinese company. I think the core reasons are numerous. First, the US headquarters thought of China as just another market.
So just take the product. It should work. It worked in Europe and Japan. It should work in China.
The China was substantially different. Secondly, some of the companies wanted to make money too early and too soon. And I think thirdly, the heads of these organizations, generally speaking, obviously there were exceptions. But generally speaking, we're no match for the local entrepreneurs.
The entrepreneurs, they own 80% of the company. This was their one thing in their life. It was going to make or break their whole career in the future. They worked 12 hours a day, seven days a week.
They did whatever it took to win. And then the multi-nationals had a normal professional lifestyle. When they come in, we're going to business development. Do business, do a standard way, do it a corporate way.
Don't offend the headquarters. Do what headquarters wanted. And don't contradict the headquarters. And hopefully get a promotion back to the headquarters in three years.
And that mentality just had no chance of success. So talk about why the Chinese market was different. Because they did treat it. They had been had success going into Europe or Germany or wherever in those ways.
What was the difference of the market? Well, there's a difference then and difference now. The difference now is even more dramatic. It's almost like a parallel universe.
So all the practices and assumptions you have in the US will fail. For example, if you're a nap, you would expect to promote using Facebook's nap and so on in the US. But in China, none of those worked. The US is very well-segmented companies with Google, Amazon, Facebook, each having clearly what they did as a separate, yeah, they're lay of the land, their piece.
In China, everybody was competing with everyone. No one had any market for sure. And you had to know the dynamics of what was happening and make the right bets. For example, Alibaba had the entire payment that would seem like a phenomenal choke point.
But all of a sudden, in one year, Tencent took almost half of that away from them. So it's what I call in my book, Gladiatorial, kind of competition. So if you want to play in that game, first you have to be a gladiator, then you have to know how to work with the other gladiators and read the tea leaves on who's going to win. Things change so much.
You can't treat it. It's a US market. It's like its own market rather than a subsidiary market. That's right.
And also another huge difference is the Chinese companies go heavy. The American companies like lightweight tech platforms. Chinese companies are willing to hire 600,000 people at to lower the cost of something. For example, compared with Yelp, OpenTable, all very light platforms, May 20 China, they brought in 600,000 people to ensure the delivery of a takeout order goes down to something like 70 cents per delivery.
And that completely changed the way Chinese people eat. So that led to a very different model than what OpenTable and Yelp did. Those companies left the restaurant industry alone. May 20 basically disrupted the restaurant offline industry.
So how does an American company learn to play in that kind of tough, tenacious, disrupting market that left nothing alone? And go to any length, in other words. And competition can be very tough, dealing with challenges in the press and also users who are unhappy and false rumors being spread. And those are all part of doing business in China.
And what about the government? That's what they always point to. The government's not going to let us succeed here. Yeah.
Well, the government actually plays, I think, at this point, a very modest part of difficulty of American companies going in. Now, obviously, some companies need to get a license. But as we can see, Google's now got a way more license. It seems Facebook is trying.
Maybe. And I think it's not impossible to get a license. But my question is, even if you get a license, can an American company really learn to thrive in that environment? In that environment.
Now, when I went as Google, the environment wasn't that tough and tenacious. And I was, I would say, a little different from the typical multinational leader. I disagreed with headquarters at times and made decisions that I thought was good for the company. And we had our arguments.
And then we had some success. We gained market share from 9%, 24%, and on the way to become a billion dollars subsidiary. So the numbers were going the right direction. And I thought I was going to be the only one who may have a chance to have at least a significant minority share.
And then after Google, Uber, I think had a chance. I think Travis and his team were tenacious also. And that fit the Chinese spirit. But ultimately, dealing with all the local issues, they ended up still losing to D.D.
Right. When I say the government is that the government, one of the arguments that Silicon Valley companies make is that the government advantage is Chinese companies. How would you answer that? I think that's very minimal.
Because there are obviously licenses. They can grant or not grant. Beyond that, what China's part of the WTO, I think at this point is also, I don't really see anything that they have done in the recent 10 years that would show this. I guess you could argue that American companies weren't going to succeed anyway.
So they don't have to do anything. But in any case, I think the main issue today is just that it's too hard. It's too hard. And I would also say that Chinese companies coming to America would be equally hard.
It's just that the two ecosystems are so different. They're bound to continue to live in their independent parallel universes. Let's get to AI in your book. Talk about the premise of your book.
What you were trying to do. Since then, you've been investing. Give some examples of what you've been investing in at your venture company. OK, well, we managed a total of about $2 billion.
And AI is our largest portfolio. About a third or so is an AI. When did you start doing that investing? Four years ago.
I think most of China caught the AI fever about two years ago. So we were ahead, because we saw deep learning was going to start making headways. So we have about 45 investments in AI. And we have five unicorns that are totally valued at about $23 billion.
So what were you looking for? Why were you ahead of the curve on that? Well, we saw, for example, deep learning was going to make a big difference. We were very big on deep learning computer vision very early.
And then we were among the first to go into autonomous vehicles. We saw that AI for finance was going to be a big segment. And then hardware and semiconductors was going to be an important area for China. So those were our fundamental bets and the makeup of the five unicorns that we had.
Explain why, though. I want you to give me a deeper question. Why did you think that was the best to make? OK, so deep learning was the single biggest breakthrough in AI that made a machine learning possible on huge amounts of data with minimal human intervention.
And it didn't need humans to tell features. It would discover them as long as there was enough data. And China had so much data. So there was going to be somewhere that's going to tip.
And we also saw that it's the people in computer vision that invented deep learning. So it's likely that computer vision would be the first area to tip, not speech recognition or something else. So we made big bets in that area. The semiconductors, well, we saw the NVIDIA pricing.
And we knew that the Chinese companies would want alternatives. And there are ways. High prices, you mean? Well, they sell the same product for a very high prices for display versus AI.
And that high level of margin, I think, leaves room for local competition. Too convenient, NVIDIA. Right. I think it's hard to compete completely because NVIDIA is a powerful company.
But if you take one segment of the compute, let's say the inference, not the training, or make it cheap in cell phones devices, toys, where China is strong. So those were our investments in semiconductors, in AI acceleration. Autonomous vehicles, I think, was an area. There was a large number of people who decided to bet in the space.
And we found a couple of really, really good teams. And we actually made four investments in autonomous vehicles, not counting the sensors. And I just continue to think that would be the largest disruption. It might take a little bit of time.
So we made four very good investments, one of which has become a unicorn. And the speed at which progress is made in that space is phenomenal. I think two years ago, three of the four companies started. And one could say they were eight years behind Google.
Today, I think they're about two years behind Google. So they're catching up very fast. The next two years may be harder. But they've caught in two years, they've caught up six years.
And then finance is the lowest hanging fruit, because finance is a numbers game. And if AI is an objective function that optimizes profitability, lowers costs, improves margin for loans, credit card frauds, banks, insurance companies, that seem like a no-brainer, because you didn't have warehouses manufacturing plant. You use a plug in the algorithm, and money comes out. You're printing money.
So we backed a couple companies in that. All right. We're here talking with Kai Fu Lee. He is an investor in Chinese, the CEO of Cinevation Ventures.
His new book is called AI Superpowers. And we get back. We're going to talk more about what that means. We're here with Kai Fu Lee.
He is the CEO of Cinevation Ventures. He also ran China for Google for many years, and worked for a number of other big tech companies in the US. His new book is called AI Superpowers. And at the bottom part is China, Silicon Valley, and a New World Order.
Talk about what you mean by superpowers. You've done investments. You talked about your investments in the area. And you did it early and often.
Talk about what you mean by superpowers, and why that's important. Yeah, I actually meant three things. Primarily, I meant China and US will be, by far, the world's AI superpowers, because they'll possess the greatest value companies, people, data, and IP. I also mean the companies such as Google, Facebook, Amazon, Alibaba, Tencent will be superpowers, because they started early and they benefit from the virtuous cycle of AI.
And the third meaning is that AI itself is a superpower in that it will create a wealth generation engine that we've never seen, but also potentially a job displacement engine that we have to deal with. So talk about that first. One of the things that a lot of people, I've been talking a lot of people about, is this idea of China moving forward rather quickly in AI well past the US. A lot of times you do with data.
Someone was asking why Google's going to China, or I asked someone that, and they said data. That's a data is the problem. They don't have enough. China has more.
And some of that will talk about the surveillance and facial recognition things, where they just have data coming out of their eyeballs in China, essentially comparatively. So talk about that competition, because a lot of people feel the US is going to decline in that area pretty quickly because of the lack of data. Yeah, so I clearly believe in the power of data, because deep learning simply works with more data. You take any three or four variants of the algorithm.
One thing for sure is you pump a lot more data at it. It works better. That's the primary reason that speech recognition vision and others have improved a lot. And China has so much more data, not just in terms of number of people in market.
For example, China is fully connected with mobile payment. So 700 million people, most of the Chinese population, can pay each other with two buttons on the phone, with almost no commission, and as little as 15 cents. And that level of universality of transaction will create so much data that can be used by Tencent and Alibaba for mining, insights, targeting, and so on, but can also be used by individual merchants or apps who have transactions. If you had a retail store before you had faceless people who bought stuff, now you know who bought one.
And you can suddenly do much better, inventory prediction, sales, forecasts, and so on. So that's just one example. But if you move forward to China has 10 times more takeouts in the US for food delivery, it has 300 times more in shared bicycle rides. It has I think four times more in shared car rides.
And all these standard numbers are larger, more than the ratio of the internet population, simply because the usage of mobile was stronger, more sticky in China. But it's also going offline because a number of health clinics and autonomous stores, autonomous fast food, shopping malls, and of course, airport train stations are hooked up with all kinds of sensors. And the sensors might track motion, heat, cameras, microphones. And these will send up data.
Lots of data. Yeah, they will send up data, not raw data, but data that's relevant, right? Such as a user pick up this product and a frown and didn't buy it. Well, people, there's more nefarious uses of that data, obviously.
But one of the things that they were talking about is that in this country, they wouldn't, the allowance of sensors and facial recognition is going to see a much rougher road because of consent, all kinds of things. That's not the case in China. So they can suck up so much data about people's movements, their faces, their activities that they couldn't do here, that the companies like Google and others are hindered. Most people feel it's a good thing that they're hindered.
Talk about that. Well, I think the Chinese users have a stronger willingness to exchange the capture of certain data if, in return, there is value to be provided, for example, greater security, lower crime rate or possibly convenience. I do think the Chinese people care about privacy. There are people raising awareness, but I think at the current level of deployment, people accept the trade-off that is being offered.
And I think the worries about the government observing people is well-founded, correct or not? I think that's a very popular feeling in the West. Yes. And which would hinder the West from collecting data?
In a good way. People in the West think that. Well, I think that people in the West would generally not adopt the widespread sensors used by governments. Therefore, also, the private companies would have a hard time putting it in stores.
I don't know how Amazon Glow is perceived because they have those cameras. Yes, they do. People are wary of it. People are wary of it.
People are wary of all the different products that they are trying to insert in the home and everywhere else. And there's always some complaint about them. Yeah, well, I think there's also a lot of good that could come from this deployment. All right, talk about that.
Well, in hospitals, you can prevent sick people, elderly people, from falling down. You can call alarms for them. Crime rate, we've talked about autonomous stores. You can essentially turn them offline store into an online store by capturing user preferences and at schools.
I think parents are likely to give consent for cameras at school, not for surveillance, but for giving teachers feedback on how to improve the kids' performance, where they might be getting lost. So hospitals, clinics and elderly homes and so on. So there are applications I think the West would find useful as well. But I think those would be, we have a hard time getting launched here.
Right, and the mentality is definitely different, which I think depends on what you think about it. So talk a little bit about where AI is going. And so here you have all these major investments. You've made billions of dollars investments in this area, as many others.
And where are the trends going in AI? Well, we see four waves of AI all really just had the very beginnings. One is using AI and internet. The other is using it in businesses.
So for business intelligence, banks, insurance companies, and basically all corporations to automate the middle person out of the system. The third level, third wave, we think is adding eyes and ears. What we were just talking about, the sensors. And we think that will create a lot of new applications that didn't exist before.
Because previously those data, visual data, were discarded, they became transient. But now they could be captured, and something could be done. Smart cities and so on. And then lastly, we see autonomous AI.
And that is when AI gets arms and legs. They mean that they do maybe wheels, but they can move around, manipulate in factories, manufacturing, in farms, for picking fruits, in commercial applications, like washing dishes. And also eventually in the home for education, toys, and also eventually there will be housework robots. All of these will happen, along with autonomous vehicles, that will begin in non-public roads, then going into highways, and then going into all the roads, and then going from L3 to L4.
And that will lead to another wave of changes. So we see great investment opportunities in all four waves, and no doubt there will be a fifth, sixth, seventh wave, which we just don't know where they are. What are you thinking that might be? Well, I think a delegation interface with a smart assistant that has an infinite memory to enhance us, without any of this hardware intrusion.
But this is an augmentation of our physical limitation. That could be a... That means not something, remember Google had the eyeball thing, and the ear thing, not that, that it would be part of your... Have you seen the black mirror?
Yes, yes. Yeah, the eyes being... That one, that remembers everything we saw in her index it. Obviously that has also dystopian and worrisome outcomes.
Yeah, the guy took it out at the end. Yes, but it may not be that instantiation. But it's a... I mean, we people are limited in our ability to remember that's our faultiest part of our brain.
And computers are perfect. I was just saying it was somebody that day, something I wish I could have remembered this. My son was asking about it, something and I was like, I just don't remember it. It happens all the time.
I have a vague memory of it, or if at all, if it's the correct memory or if it's not the correct memory, it was really... It was an interesting discussion. I ended up having my son. Right.
And beyond that? Well, beyond that, whether we believe in AGI, general AI or not, I'm in the camp that feels that's very distant. Many decades... Well, in that...
Well, AGI means having AI be like humans, have the common sense, cross-domain, ability to reason and plan. And then once that further, maybe even with self-awareness and emotions, I feel... Cyborgs, right. Right.
So some people, like Raker Swell, project that's coming in 2029. It's not. I think not. I think the number of breakthroughs that will be needed would be probably another dozen deep learning level breakthrough.
Right. And if you look at the last 60 years, we've had one deep learning level breakthrough. So when will the 12 come? Maybe 720 years.
So what's the one you're talking about? All these different AI waves? Which give me timeframes on a lot of them, on the first four? The first three have already happened.
Internet AI is all around us. Business AI is being implemented, but requires a large amount of data. So it's going to be the large banks that start and so on. The visual is, I think, starting in China, less so in the US, the spoken is happening in both China and US, Amazon, Alexa is a perfect example.
Chinese equivalent of that. Autonomous vehicles, I think, are already happening in non-public roads. Shatos, forklifts, smart robots, Kiva inside Amazon and the Chinese equivalent. Kiva's people never pay attention.
I pay a lot of attention to Kiva. Sure. I absolutely. When they bought that, I was like, oh, wow, that's an interesting change.
And I think the natural next step is to have another robot pick out. The 100%. 100%. I was at the Amazon warehouse in Canton.
They were using the Kiva robots. And there was a guy, the Kiva robot would bring over the stuff, and then the guy would pick it out. And I said, you're finished. And he's like, what?
And then the guy would tell him what I'm going to explain what's about to happen to you. That's right. Before I get to the issue of work, how do you look at US companies right now, having worked for all of them? What do you think, where they are?
Well, I think the Silicon Valley style of entrepreneurship is still the world's leading way of building companies. It's vision driven, focused on tech, tends to go deeply to solve problems and has strong culture and value that makes them are built to last. So I have a lot of respect for all the companies I work for. I also think China is emerging, is a new way to build companies, running as fast as you can, and always assume someone's going to eat your lunch.
And so you better eat theirs first. And it's a very scrappy, tenacious competition winner. I used to say, I'm in the con value, correct or not? It was.
Did you not think? We're too soft, right? I think there were days when Microsoft was pointed and considered that way. And then Microsoft became kinder and gentler.
Right? Yeah, they had to. They were forced into it by the government. Yeah.
Was that a bad thing? I think it's not good or bad. This is different. Because China is such a big market and there's so much capital going in, and the people who've made the right investments have made so much money, and the market kept growing, at least up to now, all those things incentivize this behavior.
Also keep in mind Deng Xiaoping about 40 years ago said he will change the economic system by letting some people get rich first. So there's a rush for the Chinese in the last 40 years to be among the first, because otherwise you may not be among those. Right, right. So I think all of those pointed is almost a system constructed with just the right elements.
And that's what in the last ten years turned it from a copycat to an innovative country. Are you a company? Are you a company now too soft? Too rich?
Any private planes? I don't think that's the issue. Maybe that's an issue. I think the Chinese tycoon's buying just as many private jets.
Yeah, that's her point. Yeah. But let's say we found an internet population in Mars, and we're going to land the two top entrepreneurs in Silicon Valley and two top from China and let them compete to see who can win the Martians. I would bet on the side of the Chinese entrepreneurs.
Because they're faster, more tenacious, more understanding of user needs and willing to build products for user needs rather than what Steve Jobs says. You know, look in the mirror and that's my user understanding. Right, that's really interesting. Really?
You know, the kombucha from the Silicon Valley entrepreneurs would be better. And the various foods and things like that. That could be. So let's talk about the impact of AI on jobs because I think this is something that American companies or tech companies are facing.
This idea that what they're doing right now they're in the midst of, did they kill democracy and they were just having hearings for example, we're just listening to. But one of the things that's been this discussion of the future of work. Right. So what does AI mean for that from your perspective?
Well, I think there are a lot of simple, simple kind of one sentence answers, none of which I think are right. Okay. Some people said it's all purely a human amplifier. Some people said, oh, it's just going to make us better.
Others said, no, it's going to take all the jobs away. I think it'll be different depends on what jobs you have. If we look at what AI cannot do, there are really two main things. One is creative jobs.
The job's like yours, job's like scientists, storytellers, artists and so on. And the other are the compassionate people who really create a human to human connection. Right. Until they get the robot eyes right.
And then they're like, well, I think the robots will always mess up. And when robots mess up, they mess up badly. I think we should put robotic things in people and then not, instead of making robots, try to be like people, we should make people try to be like robots. It's a big thought, actually.
Just think about it. No, think about it. We spend all our time trying to get a robot to open the door and everyone goes, wow, it opens the door. Why bother?
Just put robotic things in people. That's right. My favorite example is elderly care. All these people building robots, they care about people.
We should take care of our parents. Yes, we just can't get people. And if we don't, then we should hire a person too. None of our parents want to be taken care of by a robot.
So we should, yeah, I agree with you. I get what you say. But I think that robotic things in the caregivers. Anybody can do a good idea.
I'm too genius for you. I get it. No, I get it. You have to.
No. No. No. No, not at all.
Why bother? A man or a woman can open a door. So that's already solved. So in any case, now we get our eyes and ears and things like that.
Next to lifting and active skeletons. I see, I see. Coming back to my thoughts. So the human connection is hard.
So what happens if we make a quadrant of four types of jobs? I think the lower left quadrant is the low compassion, low empathy, low creativity. All those jobs will be taken by AI. Erm.
Name those jobs. Back to. Beginning with factory jobs. Starting with inspection.
Going into assembly. Hamburg or place here in San Francisco and all the creator. And we invested in a Chinese noodle robot too. And then there are also white collar jobs that will be replaced.
So we're sectors that's in the same sector? Yeah, basically traders are already gone, right? And then a city named said they're going to remove 10,000 of their operational staff. And even before that, tell us how the marketing customer reps the jobs currently also are in India.
And the current manufacturing jobs are also in China. Both sets of those will be challenged. I would argue white collar ones first because that's software only. So that's a lower left.
And the lower jobs, lawyers, possible doctors. Not yet. The lower left corner, those are more the inspection assembly line, telemarketing, customer service. Those are in danger because they were low human touch and low creativity.
And data rich. Data rich, exactly. And routine in nature. And then the upper left quadrant would be lower creativity, but lots of compassion.
I think those jobs will flourish. And in fact, I think migration needs to go from lower left to upper left. So for example, the doctors job will probably change because the medical diagnosis will become very, very good. And then the doctor is more of a human connector.
And then maybe just four years of college is enough. Maybe nurse practitioners can become doctors. Maybe there's more training about how to comfort and how to tease out from the patient, what are you really feeling? What is the...
This diagnostic should be done by a... like radiology, for example. You always use the radii. Like you don't need a radiologist.
They're not accurate compared to the AI. That's right. Well, it takes time. But eventually there'll be displaced in terms of...
All of the diagnostics, it seems. Eventually, it takes 20, 30 years. But one segment at a time. So doctors can become this compassionate profession.
And we might have 10 times more doctors because the cost of medical care will go down. Poor people can access it. And then you can still have real super experts that you pay a lot of money for. But most healthcare, so more doctors could be employed.
But not the same kind of doctors today. The same could be applied to many other areas like professionals, wealth planners, teachers in particular. You probably want that? I think a lot of our teachers do are routine.
So grading homework, grading exams, giving us exams, giving the same lecture again and again. Those can be done by AI or MOOC. And what the teachers should do is one on one, targeted, finding out what your passion is, guiding you, coaching you, become your mentor for life. And that could be one to one.
That could be homeschooling. That could be public school, but one to one ratio. So teacher numbers could blossom. So I...
and also I think teacher and doctors are more... ...required a lot of training. There will be other less trained jobs. For example, elderly care.
We're going to have a lot more older people. People over 80 requires five times as much care. And we want people to take care of them. And elderly care is very difficult to fill jobs because it's not paid well, nor does it have a high social status.
And I think those need to be changed so that when people come off the assembly line and tell them marketing jobs, they can move into either an elderly care type of a job or a teacher type of a job, depending on their aptitude. And then the right side are the creative jobs. So that's a side of relief, okay? The creative jobs without too much compassion, empathy needed.
They can use AI as a tool. Scientists can find more drugs with AI filtering for them. And their power will be amplified, symbiotic combination. And then the upper right will be high empathy and high creativity.
And those are what will make humans shine. What does that mean politically and for the countries, these countries and good in Chinese. There's a lot of road manufacturing jobs there. There's a lot.
And you throw people at it. And that's why it's lower cost and which is why Apple and others have moved manufacturing. What happens? But there is a social crisis that can result in this.
I think this is the bigger crisis social more than financial because it's not just a question of losing a job and getting some social welfare UBI to pay for you. It's that people have attached the meaning of their lives to their work. And when the work is gone, there's so is the meaning. So I think it's imperative that governments start to understand how to redistribute the money so that there's enough money to take care of this set of people who need to make a transition.
And then there needs to be retraining and incentives put in place so the migration can happen. Do you think government has that capability? I don't. I think Facebook works.
I think very few governments have that capability. I think we can start with something small. For example, rather than give everybody a tax break, give it to those who homeschool, give it to those who are doing volunteer work, give it to those who take early retirement, but put their time in a socially meaningful. So there can be smaller steps.
Vocational training should change. We should have fewer auto mechanics courses, but maybe more plumber courses, because plumber is not a job that robots can do soon. And we already know which jobs are going to be on the decline. So the vocational schools should follow this projection of job increase or decrease based on automation.
So those are things that can be done by any government. I think potentially governments like China may be able to make bigger steps with this position. Chinese government has historically been effective in pushing one segment to another. The Chinese agricultural manufacturing shift was done faster with a lot of payoffs, but still more effectively than probably any other country.
So there might be a special role. How do you assess the US's commitment to this? I don't think there's any. I don't think the current administration acknowledges that this is happening.
I think some top officials say the AI job displacement is 50 years away. And that would be worrisome. That was the Treasury Secretary. That was the Treasury Secretary.
He's an imbecile. So what do you do then? Does that bring again the US behind again? Because we'll be losing these jobs because that's the way it's going to go without any preparation for the future.
Well, my guess is that when there will be some profession that suddenly disrupt it and millions of people are out of their jobs, and then that will wake up the government. That's the way. I don't know, but possibly the danger is it might be one of the outsource jobs. So that the paying is in India, not in China.
Right, right, absolutely. So finishing up, let's talk a little bit about what that means for the creators of these technologies. Do you have a responsibility, someone who funds them to figure that part out? I think I do.
And all of us try to contribute differently. There are those who advocate the UBI. I do not. Universal based income.
Why is that? I do not because I think that doesn't solve the meaning problem. I think you're just giving people the money as a anesthesia for paying. That's really good.
And it doesn't get them over. That's really true problem. And I think all of us are thinking of ideas. That's good.
And I think all of us are willing to contribute, whether it's by taxation or by donation or by foundations. And I think those of us in investment, we could look more at investment that create jobs. Still make money, but maybe not as much as the AI companies. So I think it's imperative for all of us to do what we can without expecting the government to do it all, which is why I wrote this book.
So that the awareness would be there, but the call to action is up to the individuals. You know, the individual companies. Do you think they have that commitment? I'm optimistic.
I'm optimistic too, because in Silicon Valley, I think all the talks about UBI suggest that people want to do something. Yeah. Whether we think that one will work or not, it's still very respectable that they are thinking ahead. And when you look for 20 years, what are the jobs you think will be the most important?
I think the creative jobs will be the most important. I think a lot of people who could have been creative were stifled over the last 100 years, because maybe they weren't the highest paying jobs. They were forced into some relatively more routine job that paid more. But I think now we can really have a chance to release our potential in creativity.
And I think the empathetic jobs, human jobs will also be important, because that's the only job type that can absorb the exodus and the displacement that will happen in the routine jobs. Yeah. Last question, I'm going to put you on the spot. If you would pick one US company and one Chinese company that you find a large one or a small one, we'll do each of those that you're most impressed with right now.
What would they be? Most impressed US company would be Alphabet Google, because of its aspiration, because that it tries to stick with its values. And we may or may not agree with it, but it tries to do that. And also because of the phenomenal creativity from the company.
And their most important part, Waymo, what? Cloud? I think Waymo is most interesting to me. But I think a lot of the new healthcare initiatives are interesting as well.
The Chinese company would be a tie between Alibaba and Tencent. I think Alibaba has demonstrated that the company can grow so big and still top level people feel empowered like they own the company. And that kind of cultural strength will probably give it legs to go to the next level. I don't see that in other companies.
Alphabet obviously is trying to do that with different segments. But Alibaba, if you really go in, each of the 25 CEOs really feels like a CEO and they're still working like startups. So they maintain the culture and that's expandable. That doesn't fall apart with size.
I also respect Tencent and a great deal, because they're one of the very few companies that could build a product to disrupt itself. They had QQ, which was the dominant messenger. And then they allowed WeChat to be built and to disrupt QQ. But QQ didn't die.
QQ kind of became the Snapchat of China, appealing to the younger generation. So a company that can tolerate two camps to compete and both actually continue to be successful, that I think is really rare to see. What about startup that you find? There are many great startups.
We fund VIP Kid, which is an aspirational education company that connects American English teachers to Chinese students. It's essentially the Uber for education. And when everyone thinks education is not going to create unicorns, VIP Kid has really proved them wrong. And now they're using technology and AI.
They're also using Pro Bono to give the English teachers a chance to give some hours to teach poor kids in a group. And I think it's a company that thinks far ahead and more likely to disrupt education than some of the American education companies that have a big aspiration. But it's too difficult to implement. Absolutely.
That's really interesting. All right, this has been fascinating. Qifu, thank you so much for talking. Qifu Lee's book is called AI Superpowers China Silicon Valley and the New World Order.
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