Former Google China president Kai-Fu Lee is betting heavily on artificial intelligence; his investment firm Sinovation Ventures has invested more than $600 million in computer vision, machine learning and other forms of automation. And he’s confident that soon this technology will dramatically change the job landscape.

“If we look at what AI cannot do, there are really two main things,” Lee said on the latest episode of Recode Decode, hosted by Kara Swisher. “One is creative jobs. Jobs like scientists, storytellers, artists and so on. And the other are the compassionate people who really have created a human-to-human connection, trust.”

And what about the jobs that require low creativity and compassion?

“All those jobs will be taken by AI,” he said.

On the new podcast and in his new book “AI Superpowers,” Lee predicts that we’ll have to rethink professions like doctors and teachers, tilting them toward those skills that machines won’t have.

“The medical diagnosis will become very, very good through AI, and then the doctor is more of a human connector,” he explained. “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?’”

“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,” Lee added. “But most healthcare, more doctors could be employed, but not the same kind of doctors [as] today.”

You can listen to Recode Decode wherever you get your podcasts, including Apple Podcasts, Spotify, Google Podcasts, Pocket Casts and Overcast.

Below, we’ve shared a lightly edited full transcript of Kara’s conversation with Kai-Fu.

Kara Swisher: Today in the red chair is someone I’ve known for a long time, Kai-Fu Lee, the CEO of Sinovation 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 Recode Decode.

Kai-Fu Lee: Thank you, Kara — 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 when you were hired. I think I wrote a story in the Journal at the time you were hired.

Yes.

So, talk a little about your background so people can get a sense of where you’ve come from.

Sure. I grew up in the U.S., Columbia, Carnegie Mellon PhD, and then I ran multimedia at Apple, followed by SGI, Microsoft, where I started Microsoft research in Asia, back at 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, because you had obviously a very storied career. You had a lot of great spots. Multimedia at Apple was a critical job. That was back in the ’90s or ’80s?

In the ’90s, ’90 to ’96.

’90s, yeah, which was their recovery period, really, when they were ...

A difficult period, yeah.

Difficult, yeah. Kind of tough. So, you had a long time at Silicon Valley. 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.

What was your goal there to do? It 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 bit about that? Because it’s gonna be relevant to what we’re talking about later.

Sure. Well, the Chinese laws 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.

Right, at the bottom.

At the bottom, and also providing an uncensored search, and also not stor[ing] personal information in China.

Right, which they didn’t. They didn’t allow people to register, essentially.

That’s right.

Correct. 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, yeah.

That was the idea. Now, that was a Chinese government idea or Google idea?

Both were okay with it.

Both were okay with it. Now, you were there until how long?

For four years.

Four years. Were you there during the pullout, or no?

No. I left three months before that, and I had no idea that was happening.

Oh, you didn’t? Really?

No. No.

No? You weren’t aware of that?

Well, you saw the later reports that they saw things in November and decided to leave in December.

Right. There was meddling by the Chinese government.

Well, that was the allegation.

That was the allegation. Okay.

Allegation. Yeah, yeah.

That was their allegation.

I left earlier, in September.

Mm-hmm.

Yeah. No idea.

Why did you do that?

Well, I saw that the entire entrepreneurial landscape was just burgeoning.

Right, in China.

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 taking off.

It really was right then.

So, it was an exciting thing. I thought that I wanted to be a part of that.

So, you wanted to escape, too, at the same time?

Well, I didn’t quite use the word “escape,” but ...

Yeah. Yeah, but Google, it was a big job, running Google China.

It was a tremendous opportunity, but also frustrating, at times. I compared that with having the freedom to invest in companies and help young entrepreneurs. That seemed more fun.

So, let’s set the table then, because before that, China wasn’t seen as the entrepreneurial engine that it is today, correct, or it was just ... That was right around when it was really becoming clear.

I think that’s very 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 copiers, correct? That was the generalized feeling, that they copied U.S. innovation.

That’s not an inaccurate statement.

Exactly. Yeah, at that time.

At that time.

At that time.

Right.

So, there was an Alibaba that was like an Amazon. There was everything that was in the ... There was Baidu, that was like a Google, and various things like that, and many people had felt that that was the way it was gonna be for China. They were gonna 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 were having troubles operating in China. Can you talk about why that was? eBay had a disastrous run. Yahoo was only successful because it bought a China company, or it had a stake in a Chinese company.

I think the core reasons are numerous. First, the U.S. headquarters thought of China as just another market, so “just take the product, it should work. It worked in Europe and Japan, should work in China.” But 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 ...

That they placed in China, yeah.

Obviously, there were exceptions, but generally speaking, were no match for the local entrepreneurs. The entrepreneurs, they owned 80 percent of the company. This was their one thing in their life that was gonna make or break their whole career and future. They worked 12 hours a day, seven days a week. They did whatever it took to win, and then the multinationals had a normal professional lifestyle ...

We’re gonna come in. We’re gonna ... Business development.

Yeah, do business, do it a standard way, do it the 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 trade it. They had success going into Europe or Germany or wherever in those ways. What was the difference of the market?

So, 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 U.S. will fail. For example, if you’re an app, you would expect to promote using Facebook, Snap and so on in the U.S., but in China, none of those worked. The U.S. is very well-segmented companies with Google, Amazon, Facebook, each having clearly what they did as a separate ...

Their lane.

Yeah, their 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 [ecosystem]. That would seem like a phenomenal choke point, but all of a sudden, in one year, Tencent took almost half of that way from them.

It’s what I call in my book a 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 gonna win. Things change so much.

So, you can’t treat it like it’s like a U.S. market. It’s its own market rather than a subsidiary market.

That’s right. And also, another huge difference is the China companies go heavy. Right? The American companies like lightweight tech platforms. Chinese companies are willing to hire 600,000 people to lower the cost of something. For example, compared with Yelp, OpenTable, all very light platforms, Meituan in 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. Meituan 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.

Competition can be very tough, dealing with challenges in the press, and also, users were unhappy, and false rumors being spread, and those are all part of doing business in China.

Right, and what about the government?

Well, the government ...

That’s what they always point to, “The government’s not gonna 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 way more license, it seems.

Maybe.

Facebook is trying. Maybe, yeah. 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 was at 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. We had our arguments and then we had some success. We gained market share, from 9 percent to 24 percent, and [were] on the way to becom[ing] a billion dollar 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 Didi.

Right, right. When they say the government is ... One of the arguments that Silicon Valley companies make is that the government advantages 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, well, China’s part of WTO. I think at this point it’s 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 gonna succeed anyway so they don’t have to do anything. But in any case, I think the main issue today ...

Was how they competed.

... is just that it’s too hard.

It’s too hard for ...

I would also say the Chinese company coming to America would be equally hard.

Right. It’s a different ...

It’s just that the two ecosystems are so different, they’re bound to continue to live in their independent parallel universes.

All right. Let’s get to AI and your book. Talk about the premise of your book, what you were trying to do. Now, since then you’ve been investing. Give some examples of what you’ve been investing in at your venture company.

Okay. Well, we manage a total of about $2 billion, and AI is our largest portfolio. About a third or so is in AI.

Mm-hmm. When did you start doing that investing?

Four years ago.

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 gonna 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 we’re ...

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 have.

Well, explain why, though. I want you to give me a deeper ... Why did you think that was the bet to make?

Okay. Okay. So, deep learning was the single biggest breakthrough in AI that made 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’s gonna be ... Somewhere, it’s going to tip.

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 very high prices for display versus AI, and that high level of margin I think leaves room for local competition.

To compete with 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, 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 this 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 continue to think that will be the largest disruption. Might take a little bit of time, so we made four ...

Right. I agree with you.

... 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, these two companies, 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.

Yeah, absolutely. Yeah, they’re catching up really quickly.

So, they’re catching up very fast. The next two years may be harder, but in two years they’ve caught up six years. Then finance is the lowest-hanging fruit because finance is a numbers game, and if AI is an objective function that optimizes profitability, lowers cost, improves margin for loans, credit card frauds, banks, insurance companies, that seemed like a no-brainer because you didn’t have warehouses, manufacturing plant[s]. You just plug in the algorithm and money comes out. You’re printing money. So, we backed a couple of companies in that one.

We’re here with Kai-Fu Lee. His new book is called “AI Superpowers: 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 the U.S. 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.

Yeah. So talk about the first thing. One of the things that I’ve been talking to a lot of people about is this idea of China moving forward rather quickly in AI, well past the U.S., a lot having to do with data. Someone was asking me why Google is going to China, or I asked someone that, and they said data. That’s it. Data is the problem. They don’t have enough, China has more, and we’ll 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 U.S. is going to decline in that area pretty quickly because of the lack of data.

Yeah. So I clearly a believe in the power of data because deep learning simply works with more data.

Yes.

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’s 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 fifteen 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 what and you can suddenly do much better inventory prediction, sales forecasts and so on.

That’s just one example. But if you move forward to — China has 10 times more takeouts in the U.S. 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 there’s also going offline because a number of health clinics and autonomous stores, autonomous fast-food shopping malls, of course airports, 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 picked up this product and frowned and didn’t buy it.

People have ... 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 where that’s not the case in China. And so they can suck up so much data about people’s movements, their faces, their activities, that they couldn’t do here, that 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 rates, 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.

Which would hinder the West from collecting data in a good way. The people in the West think that.

Well, I think the people in the West will generally not adopt the widespread sensors used by government. Therefore also, the private companies would have a hard time putting it in stores. I don’t know how Amazon Go is perceived, because they have those cameras.

Yes, they do ... People are wary of it. People are wary of Nest. 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.

Talk about that.

Well, in hospitals, we can prevent sick people, elderly people, from falling down, we can call alarms for them. Crime rate, we’ve talked about. Autonomous stores, you can essentially turn an offline store into an online store by capturing user preferences. And schools, I think parents in China are likely to give consent for cameras at school, not for surveillance, but for giving teachers feedback 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 have a hard time getting launched here.

Right. And the mentality is definitely different, which I think it 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 in investments in this area, as have many others. Where are the trends going in AI?

Well, we see four waves of AI all really just at 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 visual data were discarded. They became transient. But now they could be captured and something could be done, you know, smart cities and so on.

And then lastly, we see autonomous AI, and that is when AI gets arms and legs, 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 nonpublic 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 and seventh wave, which we just don’t know what they are.

What are you thinking that might be?

Well, I think a delegation interface with the smart assistant that has an infinite memory to enhance us without any of this hardware intrusion, but just as an augmentation of our physical limitation, that could be ...

That means not something ... Remember, Google had the eyeball thing and the ear thing? Not that, but it would be part of your ...

Have you seen the “Black Mirror” episode?

Oh yeah, yeah, the eyes being ...

That one, that remembers everything that you saw and heard ...

Yeah, through your eye.

… and can 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. 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 to somebody the other day, there was something, “I wish I could have remembered this.” My son was asking about it. It’s 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 an interesting discussion I ended up having with 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 forward.

Explain that.

Well, AGI means having AI be like humans, have the common sense across domain, ability to reason and plan, and then one step further, maybe even with self-awareness and emotions.

Cyborgs.

Cyborgs, right. So some people, like Ray Kurzweil, project that’s coming in 2029.

It’s not.

I think not. I think the number of breakthroughs that would be needed would be probably another dozen deep learning level breakthroughs. And if you look at the last 60 years, we’ve had one deep learning level breakthrough. So when will the 12th come? Maybe 720 years.

So of the ones you’re talking about, all these different AI waves, give me time frames 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 and less so in the U.S. The spoken is happening in both China and the U.S. Amazon Alexa is a perfect example, the Chinese equivalent of that. Autonomous vehicles, I think, are already happening in nonpublic roads, shuttles, forklifts, smart robots, Kiva inside Amazon, and the Chinese equivalent.

Kiva. People never pay attention. I pay a lot of attention to Kiva.

Sure.

Absolutely. When they bought that, I was like, “Oh, well that’s an interesting change.”

And I think the natural next step is to have another robot pick out the item.

100 percent.

Yeah, 100 percent.

I was at the Amazon warehouse in Kent. They were using the Kiva robots and there was a guy, the Kiva robot would bring over that stuff and then the guy would pick it out. I said, “You’re finished.” And he’s like, “What?” And I’m like, “I kinda don’t want to explain what’s about to happen to you.”

That’s right.

Before we get to the issue of work, how do you look at U.S. companies right now, having worked for all of them? What do you think about 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’ve worked for. I also think China is emerging as the kind of a new way to build companies, of 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 take all.

That used to be Silicon Valley. Correct or not? Was it? Did you not think? We’re too soft, right?

I think there were days when Microsoft was pointed and considered that way.

And then?

And then Microsoft became kinder and gentler, right?

Right. Yeah, they had to. They were forced into it by government.

Yes, right.

Is that a bad thing?

I think it’s not good or bad. It’s just different.

Yeah.

Because China’s 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 of 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, absolutely.

So I think all of those pointed — is almost a system constructed with just the right elements, and that’s what in the last 10 years turned it from a copycat to an innovative country.

Are U.S. tech companies now too soft or too rich or have too many private planes?

I don’t think that’s the issue. Maybe that’s an issue. I think the Chinese tycoons buy just as many private jets. 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?

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, “I look in the mirror and that’s my user” understanding.

That’s really interesting.

It’s brilliant, but it’s hard.

But you know, the kombucha from the Silicon Valley entrepreneurs would be better and the various foods and things like that. I’m teasing.

Yeah, 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 were just listening to. But one of the things that has been in this discussion, the future of work. So what does AI mean for that from your perspective?

I think there are a lot of simple kind of one-sentence answers, none of which I think are right.

Okay.

Some people said “it’s all purely 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. Jobs like yours, jobs like scientists, storytellers, artists and so on.

Yep, I agree.

And the other are the compassionate people who really have created a human-to-human connection, trust.

Right, until they get the robot eyes right and then maybe.

Well, I think the robots will always mess up, and when robots mess up, they mess up badly.

See, I think we should put robotic things in people and then not ... Instead of making robots try to be like people, we should try to make people be like robots. It’s a big thought. Just think about it. No, think about it. We spend all our time trying to get a robot to open a door and everyone goes, “Wow, it opened the door.” Why bother? Just put robotic things in people.

That’s right. My favorite example is elderly care. All these people building robots to take care of old people. We should be able to take care of our parents.

Yeah, you just can’t get ...

And then if we don’t, then we should hire a person to. 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’re saying.

Then put robotic things in the caregivers. Anyway. It’s a bigger idea. It’s too genius for you. I’m too genius.

No, I get it. You want the human caregiver, but maybe with super strong arms that can prevent the fall.

Yes, instead of spending our time trying to get a robot to open a door. Come on, why bother? A man or a woman can open the door. So that’s already solved. In any case, so now we give them eyes and ears and things like that. Next you’re lifting an exoskeleton and things.

I see, I see. Right. So coming back to my thoughts. So the human connection is hard. So what will happen? So 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.

Name those jobs. Those are factory jobs.

Oh, beginning with, yeah, factory jobs, starting with inspection going into assembly, starting with dishwashing, going into flipping hamburgers and simple ...

There’s a robot hamburger place here in San Francisco now. The Creator.

Right. Absolutely. And we invested in the Chinese Noodle, a robot too. And then there are also white-collar jobs that will be replaced.

So what sector is that? That’s in the same sector.

Yeah. Basically traders, they’re already gone, right? And then Citibank said they were going to remove 10,000 of their operational staff. And even before that, telesales, telemarketing, customer reps, the jobs currently outsourced to India, and the current manufacturing jobs are outsourced to China. Both sets of those will be challenged. I would argue white-collar ones first because that’s software only.

Right. So name those jobs. Lawyers, possible doctors.

Not yet, no. The lower left corner, those are more the inspection, assembly line, telemarketing and 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 doctor’s job will probably change because the medical diagnosis will become very, very good ...

Through AI.

Through AI, 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?”

Because diagnostics should be done by — like radiology, for example. I use always use the radiology example. Like you don’t need a radiologist, they’re not accurate compared to the AI.

That’s right. Well, it takes time, but eventually they’ll be displaced in terms of ...

All of diagnostics, it seems.

Eventually, take 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.

Talk about that.

I think a lot of what 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 a teacher 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 a public school, but one-to-one ratio. So teacher numbers could blossom. And also, I think teacher and doctors are more ... require 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 a very difficult-to-fill job 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 telemarketing jobs, they can move into either an elderly care type of a job or a teacher type of a job, depending on their aptitude.

Right.

And then the right side are the creative jobs. So that’s a sigh of relief. We’re 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 these countries, including China? There’s a lot of rote manufacturing jobs there. There’s a lot, and you’ve thrown people at it, which is why it’s lower cost, which is why Apple and others have moved manufacturing there. What happens? There is a social crisis that can result in this.

I think the bigger crisis is 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 the work and when the work has gone, so is the meaning. So I think it’s imperative that governments start to understand how to redistribute the money so that there is 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. They don’t know how Facebook works.

I think very few governments have that capability. I think we can start with something small. For example, rather than you 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 [way] ... 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. 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 distribution.

Yes, get more control.

Chinese government has historically been effective in pushing one segment to another. The Chinese agriculture-to-manufacturing shift was done faster, with a lot of chaos, but still more effectively than probably any other country. So there might be a different example.

How do you assess the U.S.’ commitment to this? I don’t think there’s any.

I don’t think the current administration acknowledges that this is happening. I’ve seen some top officials say the AI job displacement is 50 years away, and that would be worrisome.

That was the treasury secretary.

That was.

That was the treasury secretary, but he’s an imbecile. So what do you do then? Does that bring, again, the U.S. 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’ll be some profession that’s suddenly disrupted and millions of people are out of their jobs, and then that will wake up the government. That’s my guess.

Which one?

I don’t know, but possibly the danger is it might be one of the outsourced jobs. So the pain is in India, not in China.

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 as 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 advocated UBI. I do not.

Universal Basic Income.

Yes.

Why is that?

I do not because I think that doesn’t solve the meaning problem. I think you’re just giving people money as an anesthesia for pain, and it doesn’t get them over that really true problem. And I think all of us are thinking of ideas, and 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 investments, we could look more at investments that create jobs and 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.

The individual companies. Do you think they have that commitment?

I’m optimistic. I’m optimistic because in Silicon Valley, I think all the talks about UBI suggest that people want to do something. Whether we think that one will work or not, it’s still very respectable that they are thinking ahead.

And when you look forward 20 years, what are the jobs do 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 hundred 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 gonna put you on the spot. If you had to pick one U.S. company and one Chinese company that you find — a large one or small one, we’ll do each of those — that you’re most impressed with right now, what would they be?

Most impressed U.S. company would be Alphabet/Google.

Because?

Because of its aspiration, because it tries to stick with its values. And we may or may not agree with it, but it the 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.

Okay.

The Chinese company would be a tie between Alibaba and Tencent. I think Alibaba has demonstrated that a 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 the 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 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 in 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 that.

What about startups?

There are many great startups. We fund the VIPKID, 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, VIPKID has really proven 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 poorer kids in the group. And I think as a company that thinks far ahead and more likely to disrupt education than the sum 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. Kai-Fu, thank you so much for talking.

Thank you.

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