Kai-Fu Lee: AI Superpowers - China and Silicon Valley | Lex Fridman Podcast #27

The following is a conversation with Kai Fu Lee. He's the chairman and CEO of Synovation Ventures that manages a 2 billion dollar dual currency investment fund with a focus on developing the next generation of Chinese high tech companies. He's the former president of Google China and the founder of what is now called Microsoft Research Asia, an institute that trained many of the artificial intelligence leaders in China including including CTOs or AI execs at Baidu, Tencent, Alibaba, Lenovo and Huawei. He was named one of the 100 most influential people in the world by Time magazine. He's the author of seven best selling books in Chinese and most recently the New York Times bestseller called AI China, Silicon Valley and the New World Order. He has unparalleled experience in working across major tech companies and governments on applications of AI. And so he has a unique perspective on global innovation and the future of AI that I think is important to listen to and think about. This is the Artificial intelligence podcast. If you enjoy it, subscribe on YouTube and iTunes, support it on Patreon or simply connect with me on Twitter. Lex Friedman. And now here's my conversation with Kai Fu Lee. I immigrated from Russia to US when I was 13. You immigrated to US at about the same age. The Russian people, the American people, the Chinese people each have a certain soul, a spirit that permeates throughout the generations. So maybe it's a little bit of a poetic question, but could you describe your sense of what defines the Chinese soul?

I think the Chinese soul of people today. Right. We're talking about people who have had centuries of burden because of the poverty that the country has gone through and suddenly shined with hope of prosperity in the past 40 years as China opened up and embrace market economy. And undoubtedly there are two sets of pressures on the people. That of the tradition, that of facing difficult situations, and that of hope of wanting to be the first to become successful and wealthy. So that's a very strong hunger and strong desire and strong work ethic that drives China forward.

And is there roots to not just this generation, but before? That's. That's deeper than just the new economic developments. Is there something that's unique to China that you could speak to, that's in the people?

Yeah, well, the Chinese tradition is about excellence, dedication and results. And the Chinese exams and study subjects in schools have traditionally started from memorizing 10,000 characters. Not an easy task to start with. And further by memorizing his historic philosophers, literature, poetry. So it really is probably the strongest rote learning mechanism created to make sure people had good memory and remembered things extremely well. That I think at the same time suppresses the breakthrough innovation and also enhances the speed execution, get results. And that I think characterizes the historic basis of China.

That's interesting because there's echoes of that in Russian education as well as rote memorization. So you have to memorize a lot of poetry. I mean, there's just an emphasis on perfection in all forms that's not conducive to perhaps what you're speaking to, which is creativity. But you think that kind of education holds back the innovative spirit that you might see in the United States?

Well, it holds back the breakthrough innovative spirit that we see in the United States, but it does not hold back the valuable execution oriented, result oriented, value creating engines which we see China being very successful.

So is there a difference between a Chinese AI engineer today and an American AI engineer? Perhaps rooted in the culture that we just talked about or the education or the very soul of the people or. No. And what would your advice be to each if there's a difference?

Well, there's a lot that's similar because AI is about mastering sciences, about using known technologies and trying new things, but it's also about picking from many parts of possible networks to use and different types of parameters to tune. And that part is somewhat wrote. And it is also, as anyone who's built AI products can tell you, a lot about cleansing the data, because AI runs better with more data and data is generally unstructured, errorful and unclean and the effort to clean the data is immense. So I think the better part of American AI engineering process is to try new things, to to do things people haven't done before, and to use technology to solve most, if not all problems. So to make the algorithm work despite not so great data, find error tolerant ways to deal with the data. The Chinese way would be to basically enumerate to the fullest extent all the possible ways by a lot of machines, try lots of different ways to get it to work and spend a lot of resources and money and time consuming cleaning up data. That means the AI engineer may be writing data cleansing algorithms, working with thousands of people who label or correct or do things with the data. That is the incredible hard work that might lead to better results. So the Chinese engineer would rely on and ask for more and more and more data and find ways to cleanse them and make them work in the system. And probably less time the thinking about new algorithms that can overcome data or other issues.

So where's your Intuition. Where do you think the biggest impact in the next 10 years lies? Is it in some breakthrough algorithms or is it in just this at scale, rigor, a rigorous approach to data, cleaning data, organizing data onto the same algorithms. What do you think the big impact in the applied world is?

Well, if you're really in a company and you have to deliver results using known techniques and enhancing data seems like the more expedient approach, that's very low risk and likely to generate better and better results. And that's why the Chinese approach has done quite well. Now there are a lot of more challenging startups and problems such as autonomous vehicles, medical diagnosis that existing algorithms probably won't solve. And that would put the Chinese approach more challenged and give the more breakthrough innovation approach more of an edge on those kinds of problems.

So let me talk to that a little more. So my intuition personally is that data can take us extremely far. So you brought up autonomous vehicles and medical diagnosis. So your intuition is that huge amounts of data might not be able to completely help us solve that problem.

Right. So breaking that down further, in autonomous vehicle, I think huge amounts of data probably will solve trucks driving on highways, which will deliver significant value and China will probably lead in that. And full L5 autonomous is likely to require new technologies we don't yet know. And, and that might require academia and great industrial research, both innovating and working together. And in that case US has an advantage.

So the interesting question there is, I don't know if you're familiar on the autonomous vehicle space and the developments with Tesla and Elon Musk. I am where they are in fact, full steam ahead into this mysterious complex world of full autonomy. L5, L4, L5. And they're trying to solve that purely with data. So the same kind of thing that you're saying is just for highway, which is what a lot of people share your intuition. Yeah, they're trying to solve with data. So just to linger on that moment further, do you think possible for them to achieve success with simply just a huge amount of this training on edge cases and difficult cases in urban environments, not just highway and so on?

I think it'll be very hard. One could characterize Tesla's approach as kind of a Chinese strength approach. Gather all the data you can and hope that will overcome the problems. But in autonomous driving, clearly a lot of the decisions aren't merely solved by aggregating data and having feedback loop. There are things that are more akin to human thinking. And how would those be integrated and built? There has not yet Been a lot of success integrating human intelligence, or call it expert systems, if you will, even though that's a taboo word with the machine learning. And the integration of the two types of thinking hasn't yet been demonstrated. And the question is, how much can you push a purely machine learning approach? And of course Tesla also has an additional constraint that they don't have all the sensors. I know that they think it's foolish to use lidars, but that's clearly a one less very valuable and reliable source of input that they're foregoing, which may also have consequences. I think the advantage of course, is capturing data no one has ever seen before. And in some cases, such as computer vision and speech recognition high. I have seen Chinese companies accumulate data that's not seen anywhere in the Western world and they have delivered superior results. But then speech recognition and object recognition are relatively suitable problems for deep learning and don't have the potentially need for the human intelligence analytical planning elements.

And the same on the speech recognition side, your intuition that speech recognition and the machine learning approaches to speech recognition won't take us to a conversational system that can pass the Turing Test, which is sort of maybe akin to what driving is. So it needs to have something more than just simply simple language understanding, simple language generation, roughly.

Right. I would say that based on purely machine learning approaches, it's hard to imagine it could lead to a full conversational experience across arbitrary domains, which is akin to L5. I'm a little hesitant to use the word Turing Test because the original definition was probably too easy. We probably do that. Yeah.

The spirit of the Turing Test, that's what I was referring to, of course. So you've had major leadership research positions at Apple, Microsoft, Google. So continuing on the discussion of America, Russia, Chinese soul and culture and so on, what is the culture of Silicon Valley in contrast to China and maybe us broadly? And what is the unique culture of each of these three major companies in your view?

I think in aggregate Silicon Valley companies, we could probably include Microsoft in that, even though they're not in the valley, is really dream big and have visionary goals and believe that technology will conquer all. And also the self confidence and the self entitlement that whatever they produce, the whole world should use and must use. And those are historically important. I think Steve Jobs famous quote that he doesn't do focus groups, he looks in the mirror and asks the person in the mirror, what do you want? And that really is an inspirational comment that says the great company shouldn't just ask users what they want, but Develop something that users will know they want when they see it, but they could never come up with themselves. I think that is probably the most exhilarating description of what the essence of Silicon Valley is. That this brilliant idea could cause you to build something that couldn't come out of focus groups or AB tests. And iPhone would be an example of that. No one in the age of BlackBerry would write down they want an iPhone or multitouch. A browser might be another example. No one would say they want that in the days of FTP, but once they see it, they want it. So I think that is what Silicon Valley is best at. But it also comes with, it came with a lot of success. These products became global platforms and there were basically no companies competitors anywhere. And that has also led to a belief that these are the only things that one should do. That companies should not tread on other companies territory. So that a, you know, Groupon and a Yelp and an open table and the Grubhub would each feel, okay, I'm not going to do the other company's business because that would not be the pride of innovating. Whatever. Each of these four companies have innovated. But I think the Chinese approach is do whatever it takes to win. And it's a winner take all market. And in fact in the Internet space, the market leader will get predominantly all the value extracted out of the system. And the system isn't just one defined as one narrow category, but gets broader and broader. So it's amazing ambition for success and domination of increasingly larger product categories leading to clear market winner status and the opportunity to extract tremendous value. And that develops a practical result oriented, ultra ambitious winner take all gladiatorial mentality. And if what it takes is to build what the competitors built, essentially a copycat, that can be done without infringing laws. If what it takes is to satisfy a foreign country's need by forking the code base and building something that looks really ugly and different, they'll do it. So it's contrasted very sharply with the Silicon Valley approach. And I think the flexibility and the speed and execution has helped the Chinese approach and I think the Silicon Valley approach is potentially challenged. If every Chinese entrepreneur is learning from the whole world, US and China and the American entrepreneurs only look internally and write off China as a copycat. And the second part of your question about the three companies, the unique elements.

Of the three companies, perhaps.

Yeah, I think Apple represents, wow, the user, please. The user and the essence of design and brand. And it's the one company and perhaps the only tech company that draws people with a strong serious desire for the product and the willingness to pay a premium because of the halo effect of the brand, which came from the attention to detail and great respect for user needs. Microsoft represents a platform approach that builds giant products that become very strong moats that others can't do because it's well architected at the bottom level and the work is efficiently delegated to individuals. And then the whole product is built by adding small parts that sum together. So it's probably the most effective high tech assembly line that builds a very difficult product. That and the whole process of doing that is kind of a differentiation and something competitors can't easily repeat.

Are there elements of the Chinese approach in the way Microsoft went about assembling those little pieces and dominating them, essentially dominating the market for a long time, or do you see those as distinct?

I think there are elements that are the same. I think the three American companies that had or have Chinese characteristics and obviously as well as American characteristics are Microsoft, Facebook and Amazon.

Yes, that's right, Amazon, because these are.

Companies that will tenaciously go after adjacent markets, build up strong product offering and find ways to extract greater value from a sphere that's ever increasing. And they understand the value of the platforms. So that's the similarity. And then with Google, I think it's a genuinely value oriented company that does have a heart and soul and that wants to do great things for the world by connecting information and that has also very strong technology genes and wants to use technology and has found out of the box ways to use technology to deliver incredible value to the end user.

We can look at Google for example. You mentioned heart and soul. There seems to be an element where Google is after making the world better. There's a more positive view. They used to have the slogan don't be evil and Facebook a little bit more has a negative tint to it, at least in the perception of privacy and so on. Do you have a sense of how these different companies can achieve? Because you've talked about how much we can make the world better in all these kinds of ways with AI. What is it about a company that can give it a heart and soul, gain the trust of the public and just actually just not be evil and do good for the world?

It's really hard and I think Google has struggled with that. First, the don't do evil mantra is very dangerous because every employee's definition of evil is different and that has led to some difficult employee situations for them. So I don't necessarily think that's a good value statement, but just watching the kinds of things Google or its parent company Alphabet does in new areas like health care, like eradicating mosquitoes, things that are really not in the business of a Internet tech company, I think that shows that there is the heart and soul and desire to do good and willingness to put in the resources to do something. When they see it's good, they will pursue it. That doesn't necessarily mean it has all the trust of the users. I realize while most people would view Facebook as the primary target of their recent unhappiness about Silicon Valley companies, many would put Google in that category. And some have named Google's business practices as predatory also. So it's kind of difficult to have the two parts of a body. The brain wants to do what it's supposed to do for shareholder, maximize profit. And then the heart and soul wants to do good things that may run against what the brain wants to do.

So in this complex balancing that these companies have to do, you've mentioned that you're concerned about a future where too few companies like Google, Facebook, Amazon are controlling our data or controlling too much of our digital lives. Can you elaborate on this concern and perhaps do you have a better way forward?

I think I'm hardly the most vocal complainer of this. There are a lot louder complainers out there. I do observe that having a lot of data does perpetuate their strength and limit competition in many spaces. But I also believe AI is much broader than the Internet space. So the entrepreneurial opportunity still exists in using AI to empower financial, retail, manufacturing, education, applications. So I don't think it's quite a case of full monopolistic dominance that makes that totally stifles innovation. But I do believe in their areas of strength, it's hard to dislodge them. I don't know if I have a good solution. Probably the best solution is let the entrepreneurial VC ecosystem work well and find all the places that can create the next Google, the next Facebook. So there will always be increasing number of challengers. In some sense that has happened a little bit. You see Uber, Airbnb having emerged despite the strength of the Big three. And I think China as an environment may be more interesting for the emergence because if you look at companies between let's say 50 to $300 billion, China has emerged more of such companies than the US in the last three to four years because of the larger marketplace, because of the more fearless nature of the entrepreneurs. And the Chinese giants are Just as powerful as American ones. Tencent Alibaba are very strong. But ByteDance has emerged worth 75 billion. And financial. While it's Alibaba affiliated, it's nevertheless independent and worth 150 billion. And so I do think if we start to extend to traditional businesses, we will see very valuable companies. So it's probably not the case that in five or ten years we'll still see the whole world with these five companies having such dominance.

So you've mentioned a couple of times this fascinating world of entrepreneurship in China, of the fearless nature of the entrepreneur. So can you maybe talk a little bit about what it takes to be an entrepreneur in China? What are the strategies that are undertaken? What are the ways to achieve success? What is the dynamic of VCF funding, of the way the government helps companies and so on? What are the interesting aspects here that are distinct from, that are different from the Silicon Valley world of entrepreneurship?

Well, many of the listeners probably still would brand Chinese entrepreneur as copycats. And no doubt 10 years ago that would not be an inaccurate description. Back ten years ago, an entrepreneur probably could not get funding if he or she could not describe what product he or she is copying from the U.S. the first question is, who has proven this business model? Which is a nice way of asking, who are you copying? And that reason is understandable because China had a much lower Internet penetration and didn't have enough indigenous experience to build innovative products. And secondly, Internet was emerging. Lean startup was the way to do things. Building a first, minimally viable products and then expanding was the right way to go. And the American successes have given a shortcut that if you build your minimally viable product based on an American product, it's guaranteed to be a decent starting point, then you tweak it afterwards. So as long as there are no IP infringement, which as far as I know there hasn't been in the mobile and AI spaces, that's a much better shortcut. And I think Silicon Valley would view that as still not very honorable because that's not your own idea to start with. But you can't really at the same time believe every idea must be your own and believe in the Lean Startup methodology, because Lean startup is intended to try many, many things and then converge one that works. And it's meant to be iterated and changed. So finding a decent starting point without legal violations, there should be nothing morally dishonorable about that.

Yeah, so just a quick pause on that. It's fascinating that that's. Why is that not honorable. Right, Exactly. As you formulated Is it seems like a perfect start for business is to take look at Amazon and say okay, we'll do exactly what Amazon is doing. Let's start there in this particular market and then let's out innovate them from that starting point.

Yes.

Come up with new ways. I mean is it wrong to be. Except the word copycat just sounds bad. But is it wrong to be a copycat? It just seems like a smart strategy. But yes. Doesn't have a heroic nature to it that like. Like a Steve Jobs Elon Musk sort of in something completely. Coming up with something completely. But in you.

Yeah, I like the way you describe it. It's a non heroic acceptable way to start a company and maybe more expedient. So that's the, that's I think a baggage for Silicon Valley that if it doesn't let go then it may limit the ultimate ceiling of the company. Take Snapchat as an example. I think, you know Evan's brilliant, he built a great product but he's very proud that he wants to build his own features, not copy others while Facebook was more willing to copy his features. And you see what happens in the competition. So I think putting that handcuff on the company would limit its ability to reach the maximum potential. So back to the Chinese environment. Copying was merely a way of to learn from the American masters. Just like if we learn to play piano or painting. You start by copying, you don't start by innovating when you don't have the basic skill sets. So very amazingly the Chinese entrepreneurs about six years ago started to branch off with these lean startups built on American ideas to build better products than American products. But they did start from the American idea and today WeChat is better than WhatsApp, Weibo is better than Twitter, Zhihu is better than Quora and so on. So that I think is Chinese entrepreneurs going to step two and then step three is once these entrepreneurs have done one or two of these companies they now look at the Chinese market and the opportunities and come up with ideas that didn't exist elsewhere. So products like Ant Financial under which includes Alipay which is mobile payments and also the financial products for loans built on that and also in education vipkid and in video social network TikTok and in social E commerce Pinduoduo and then in ride sharing Mobike, these are all Chinese innovated products that now are being copied elsewhere. An additional interesting observation is some of these products are built on unique Chinese demographics which may not work in the US but may work very well in Southeast Asia, Africa and other developing worlds that are a few years behind China. And a few of these products maybe are universal and are getting traction even in the United states such as TikTok. So this whole ecosystem is supported by VCs as a virtuous cycle because a large market with innovative entrepreneurs will draw a lot of money and and then invest in these companies as the market gets larger and larger. U.S. mark China market is easily 3, 4 times larger than the U.S. they will create greater value and greater returns for the VCs, thereby raising even more money. So at Sinovation Ventures our first fund was 15 million, our last fund was 500 million. So it reflects the valuation of the companies and US going multi stage and things like that. It also has government support, but not in the way most Americans would think of it. The government actually leaves the entrepreneurial space as a private enterprise, sort of self regulating. And the government would build infrastructures that would around it to make it work better. For example, the Mass Entrepreneur Mass innovation plan built 8,000 incubators. So the pipeline is very strong to the VCs. For autonomous vehicles, the Chinese government is building smart highways with sensors, smart cities that separate pedestrians from cars. That may allow initially an inferior autonomous vehicle company to launch a car without increasing with lower casualty because the roads or the city is smart. And the Chinese government at local levels would have these guiding funds acting as LPs, passive LPs to funds. And when the fund makes money, part of the money made is given back to the GPS and potentially other LPs to increase everybody's return at the expense of the government's return. So that's interesting incentive that entrusts the task of choosing entrepreneurs to VCs who are better at it than the government by letting some of the profit move that way.

So this is really fascinating, right? So I look at the Russian government as a case study where let me put it this way, there's no such government driven large scale support of entrepreneurship. And probably the same is true in the United States. But the entrepreneurs themselves kind of find a way. So maybe in a form of advice or explanation, how did the Chinese government arrive to be this way, so supportive on entrepreneurship, to be in this particular way, so forward thinking at such a large scale? And also perhaps how can we copy it in other countries? How can we encourage other governments, like even the United States government, to support infrastructure for autonomous vehicles in the same kind of way? Perhaps?

Yes. So these techniques are the result of several key things, some of which may be learnable, some of which may be very hard. One is just trial and error and watching what everyone else is doing. I think it's important to be humble and not feel like you know all the answers. The guiding funds idea came from Singapore, which came from Israel and China, made a few tweaks and turned it into a. Because the Chinese cities and government officials kind of compete with each other because they all want to make their city more successful so they can get the next level in their political career. And it's somewhat competitive. So the central government made it a bit of a competition. Everybody has a budget. They can put it on AI or they can put it on bio, or they can put it on energy. And then whoever gets the results, the city shines, the people are better off, the mayor gets a promotion. So the tools. This is kind of almost like an entrepreneurial environment for local governments to see who can do a better job. And also many of them tried different experiments, but some have given award to very smart researchers. Just give them money and hope they'll start a company. Some have given money to academic research labs, maybe government research labs to see if they can spin off some companies from the science lab or something like that. Some have tried to recruit overseas Chinese to come back and start companies and they've had mixed results. The one that worked the best was the guiding funds. So it's almost like a lean startup idea where people try different things and what works sticks and everybody copies. So now every city has a guiding fund. So that's how that came about. The autonomous vehicle and the massive spending in highways and smart cities. That's a Chinese way. It's about building infrastructure to facilitate is a clear division of the government's responsibility from the market. The market should do everything in a private freeway. But there are things the market can't afford to do, like infrastructure. So the government always appropriates large amounts of money for infrastructure building. This happens with not only autonomous vehicle and AI, but happened with the 3G and 4G. You'll find that the Chinese wireless reception is better than the US because massive spending that tries to cover the whole country. Whereas in the US it may be a little spotty. It's a government driven because I think they view the coverage of cell access and 3G 4G access to be a governmental infrastructure spending as opposed to capitalistic. Of course, the state owned enterprise is also publicly traded. But they also carry a government responsibility to deliver infrastructure to all. So it's a different way of thinking that may be very hard to inject into Western countries to say starting tomorrow, bandwidth, infrastructure and highways are going to be governmental spending with some characteristics.

What's your sense? And sorry to interrupt, but because it's such a fascinating point, do you think on the autonomous vehicle space it's possible to solve the problem of full autonomy without significant investment in infrastructure?

Well, that's really hard to speculate. I think it's not a yes, no question, but how long does it take? Question 15 years, 30 years? 45 years? Clearly, with infrastructure augmentation, where there's road, city or whole city planning, building a new city, I'm sure that will accelerate the day of the L5. I'm not knowledgeable enough and it's hard to predict even when we're knowledgeable because a lot of it is speculative. But in the US I don't think people would consider building a new city the size of Chicago to make it the AI autonomous city. There are smaller ones being built. I'm aware of that. But is infrastructure spend really impossible for US or Western countries? I don't think so. The US highway system was built. Was that during President Eisenhower or Kennedy? Eisenhower, yeah. So maybe historians can study how did President Eisenhower get the resources to build this massive infrastructure that surely gave us a tremendous amount of prosperity over the next decade, if not century, If I.

May comment on that, then it takes us to artificial intelligence a little bit because in order to build infrastructure, it creates a lot of jobs. So I'll be actually interested if you would say that you talk in your book about all kinds of jobs that could and could not be automated. I wonder if building infrastructure is one of the jobs that would not be easily automated. It's something you can think about because I think you've mentioned somewhere in the talk or that there might be, as jobs are being automated, a role for government to create jobs that can't be automated.

Yes, I think that's a possibility. Back in the last financial crisis, China put a lot of money to basically give this economy a boost. And a lot of it went into infrastructure building. And I think that's a legitimate way at a government level to deal with the employment issues as well as build out the infrastructure as long as the infrastructures are truly needed and as long as there is an employment problem, which, no, we don't know.

So maybe taking a little step back, you've been a leader and a researcher in AI for several decades, at least 30 years. So how has AI changed in the west and the east? As you've observed, as you've been deep in it over the past 30 years.

Well, AI began as the pursuit of understanding human intelligence. And the term itself represents that. But it kind of drifted into the one sub area that work extremely well, which is machine intelligence. And that's actually more using pattern recognition techniques to basically do incredibly well on a limited domain. Large amount of data, but relatively simple kinds of planning tasks and not very creative. So we didn't end up building human intelligence. We built a different machine that was a lot better than us. Some problems, but nowhere close to us, other problems. So today I think a lot of people still misunderstand when we say artificial intelligence and what various products can do. People still think it's about replicating human intelligence, but the products out there really are closer to having invented the Internet or the spreadsheet or the database and getting broader adoption.

And speaking further to the fears, near term fears that people have about AI. So you're commenting on the sort of the general intelligence that people in the popular culture from sci fi movies have a sense about AI. But there's practical fears about AI, the kind, the narrow AI that you're talking about, of automating particular kinds of jobs. And you talk about them in the book. So what are the kinds of jobs, in your view that you see in the next five, ten years beginning to be automated by AI systems? Algorithms?

Yes. This is also maybe a little bit counterintuitive because it's the routine jobs that will be displaced the soonest. And they may not be displaced entirely, maybe 50%, 80% of a job, but when the workload drops by that much employment will come down. And also another part of misunderstanding is most people think of AI replacing routine jobs, then they think of the assembly line, the workers. Well, that will have some effect. But it's actually the routine white collar workers that's easiest to replace. Because to replace a white collar worker you just need software. To replace a blue collar worker you need robotics, mechanical excellence and the ability to deal with dexterity and maybe even unknown environments. Very, very difficult. So if we were to categorize the most dangerous white collar jobs, they would be things like back office people who copy and paste and deal with simple computer programs and data and maybe paper and ocr. And they don't make strategic decisions, they basically facilitate the process. The software and paper systems don't work. So you have people dealing with new employee orientation, searching for past lawsuits and financial documents and doing reference check.

So basic searching and management of data that's most in danger of being lost.

In addition to the white collar repetitive work. A lot of simple interaction work can also be taken care of, such as telesales, telemarketing, customer service, as well as many physical jobs that are in the same location and don't require.