Transcript for Kai-Fu Lee: AI Superpowers – China and Silicon Valley

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 a country has gone through and suddenly shined with hope of prosperity in the past 40 years as China opened up and embraced 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 it's a very strong hunger and strong desire and strong work ethic that drives China forward.

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 the probably the strongest wrote learning mechanism created to make sure people had good memory and remember 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 on China

Well, it holds back the breakthrough innovative spirits 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.

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, error, errorful and unclean. And the effort to clean the data is immense. So I think the Better part of American engineering AI engineering process is to try new things 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 you know error tolerant ways to deal with the data. The Chinese way would be to basically enumerate through 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 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 data and find ways to cleanse them and make them work in the system and probably less time thinking about new algorithms that can overcome data or other issues.

Well, if you're really in the 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 may probably won't solve. And that would put the Chinese approach more challenged and give them more breakthrough innovation approach, more more of an edge on those kinds of problems.

Right. So breaking that down further in autonomous vehicle, I think huge amounts of data probably will solve truck-striving 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 that might require academia and great industrial research, both innovating and working together. And in that case, US has an advantage.

I think it would be very hard. One could characterize Tesla's approach as kind of a Chinese strength approach, right? 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. 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, you know, call it expert systems view well, 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 light ours, but that's clearly a one less very valuable and reliable source of input that they're forgoing, which may also have consequences. I think the advantage of course is capturing data that no one has ever seen before. And in some cases such as computer vision and speech recognition, 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 and the political planning elements.

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 touring tests because the original definition was probably too easy. We probably do that.

I think in aggregate Silicon Valley companies, and 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 first in the mirror, whether you want. And that really is an inspirational comment that says the great companies shouldn't just ask users what they want, but develop something that users will note 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 the focus groups or AB tests. And iPhone would be an example of that. No one in the age of Blackberry would write down the one in iPhone or multi-touch. 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 a lot of success. These products became global platforms and there were basically no 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's a, you know, a group on and a yelp and an open table and the grub hub. Would each feel, okay, I'm not going to do the other companies business because that would not be the pride of innovating, whether 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 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 competitor's 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 entrepreneurs learning from the whole world, US and China and the American entrepreneurs only looking internally and right 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, while 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 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 modes 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 and the whole process of doing that is kind of a differentiation and something competitors can't easily repeat.

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.

Amazon because these are companies that will, um, uh, tenetiously go after adjacent markets build up strong, um, product offering and and 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 user.

It's really hard, and I think Google has struggled with that. First, that 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 healthcare, eradicating mosquitoes, things that are really not in the business of a internet tech company. I think that shows that there's a 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 if they're recent and happiness about Silicon Valley companies, many would put Google in that category. And some have named Google's business practices as a predatory also. So it's kind of difficult to have the two parts of the 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.

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 entrepreneur opportunities 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 challenges. 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 is an environment, maybe more interesting for the emergence. Because if you look at companies between, let's say, 50 to 300 billion dollars. China has emerged more of such companies than the U.S. 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 America once. Tencent Ali Baba are very strong, but White Dance has emerged worth 75 billion. And financial, while it's Ali Baba 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 will still see the whole world with these five companies having such dominance.

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 10 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 US. The first question is, who has proven this business model, which is a nice way of asking for a copy? 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, linked startup was the way to do things, building a first minimally viable product and then expanding or 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 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 link startup methodology. Because the link startup is intended to try many, many things and then converge on that at works. And it's meant to be iterate and changed. So finding a decent starting point with our legal violations, there should be nothing morally dishonorable about that.

Yeah, I like the way you describe it is a non-heroic, acceptable way to start the company, and maybe more expedient. So 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. takes napchat 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 to learn from the American masters. Just like we learned 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 above of six years ago started to branch off with these linked startups built on American ideas to build better products than American products. But they did start from the American idea and today we we chat is better than WhatsApp, way both better than Twitter, Jews better than Korra 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 end financial, under which includes Ali Pay, which is mobile payment, and also the financial products for loans, built on that, and also an education VIP kid and in social video, social network, TikTok, and in social e-commerce, Pindodo, and then in ride sharing, mobile, these are all Chinese-innovated products that now are being copied elsewhere. So, and 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 may be arm universal and are getting traction even in the United States, such as TikTok. So this whole ecosystem is supported by VCs as a virtual cycle, because a large market with innovative entrepreneurs will draw a lot of money and then invest in these companies. So as the market gets larger and larger, US market is easily three, four times larger than the US. They will create greater value and greater returns for the VCs, thereby raising even more money. So, at Sanovation Ventures, our first fund was 15 million, our last fund was 500 million. So, it reflects the valuation of the companies and our 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, so the 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 an interesting incentive. that entrust the task of choosing entrepreneurs to VCs who are better at it than the government by letting some of the profits almost that way.

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 this trial and error and watching what everyone else is doing. I think it's important to be humble and not feel like you know 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, it's kind of almost like an entrepreneurial environment for local governments to see who can do a better job. and also many of them try different experiments. Some have given a word 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 its a clear division of the government's responsibility from the market. The markets should do everything in the private freeway, but there's 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 in the NI, but happens 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 viewed the coverage of sell access and 3G 4G access to be a governmental infrastructure spending as opposed to capitalistic. So that, of course, the state on enterprises also publicly traded, but they also carry a government responsibility to deliver infrastructure to all. So it's a different way of thinking that maybe very hard to inject into Western countries to say, starting tomorrow, bandwidth infrastructure and highways are going to be governmental spending with some characteristics.

Well, that's really hard to speculate. I think it's not a yes, no question, but how long does it take question? You know, 15 years, 30 years, 45 years. Clearly with infrastructure, augmentation where there's road city or host 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 slash autonomous city. There's 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. or that during President Eisenhower or the Kennedy Eisenhower. So maybe historians can study how the President Eisenhower get the resources to build this massive infrastructure that surely gave US tremendous amount of prosperity over the next decade, if not century.

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, a lot of it went into infrastructure building. And I think that's a legitimate way at the government level to deal with the employment issues as well as build out the infrastructure. As long as the infrastructure's are truly needed and as long as there is an employment problem, which we don't know.

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 worked extremely well, which is machine intelligence. And that's actually more using pattern recognition techniques to basically to incredibly well on the 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 on 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.

Yes, this is also maybe a little bit counterintuitive because it's the routine jobs that will be displaced the Sunnis. 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 color workers. That's easiest to replace. Because to replace the white color worker, you just need software. To replace blue color 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, there 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, basic searching and management of data.

In addition to the white-collar repetitive work, a lot of simple interaction work can also be taken care of, such as tell-a-sales, telemarketing, customer service, as well as many physical jobs that are in the same location and don't require high degree of dexterity. So fruit picking, dish washing, assembly line inspection are jobs in that category. So altogether, back office is a big part. And the other, the blue color may be smaller initially, but over time, AI will get better. And when we start to get to over the next 15, 20 years, the ability to actually have the dexterity of doing assembly line. That's a huge chunk of jobs. And when autonomous vehicles start to work, initially starting with truck drivers, but eventually to all drivers, that's another huge group of workers. So, I see modest numbers in the next five years, but increasing rapidly after that.

I think his thinking is generally in the right direction, but his approach as a presidential candidate may be a little bit head at the time. I think the displacements will happen, but will they happen soon enough for people to agree to vote for him? The unemployment members are not very high yet. I think he and I have the same challenge if I want to theoretically convince people this is an issue and he wants to become the president. People have to see how can this be the case when unemployment numbers are low. So that is the challenge. And I think we do, I do agree with him on the displacement issue, on universal basic income. At a very vanilla level, I don't agree with it because I think the main issue is retraining. So people need to be insented, not by just giving a monthly $2,000 check or $1,000 check and do whatever they want. because they don't have the know-how to know what to retrain, to go into what type of a job. And guidance is needed. And retraining is needed because historically, when technology revolutions, when routine jobs were displaced, new routine jobs came up. So there was always room for that. But with AI and automation, the whole point is replacing all routine jobs eventually, so there will be fewer and fewer routine jobs. And AI will create jobs, but it won't create routine jobs because if it creates routine jobs, why wouldn't AI just do it? So, therefore, the people who are losing the jobs are losing routine jobs. The jobs that are becoming available are non-routine jobs. So, the social stipend needs to be put in place is for the routine workers who lost their jobs to be retrained, maybe in six months, maybe in three years, takes a while to retrain on the non-routine job. And then take on a job that will last for that person's lifetime. Now, having said that, if you look deeply into Andrew's document, he does cater for that. So I'm not disagreeing with where he's trying to do. But for simplification, sometimes he just says UBI, but simple UBI within work.

Because an AI system is currently merely optimizing. It's not able to reason plan or think creatively or strategically. It's not able to deal with complex problems. It can't come up with a new problem and solve it. A human needs to find the problem and pose it as an optimization problem, then have the AI work at it. So, in AI, we'd have a very hard time discovering a new drug or discovering a new style of painting or dealing with complex tasks such as managing a company that isn't just about optimizing the bottom line, but also about employee satisfaction, corporate brand and many, many other things. So that is one category of things. And because these things are challenging, creative, complex, doing them creates a higher, high degree of satisfaction, and therefore appealing to our desire for working, which isn't just to make the money, make the ends meet, but also that we've accomplished something that others maybe can do or can do as well. Another type of job that is much numerous would be compassionate jobs, jobs that require compassion, empathy, human touch, human trust. AI can't do that because AI is cold, calculating, and even if it can fake that to some extent, it will make errors and that will make it look very silly. And also, I think even if they did okay, people would want to interact with another person, whether it's for some kind of service or teacher or doctor or concierge or masseuse or bartender. There are so many jobs where people just don't want to interact with a cold robot or software. I've had an entrepreneur who built an elderly care robot and they found that the elderly really only use it for customer service. But not to service the product, but they click on customer service and the video of a person comes up and then the person says, how come my daughter didn't call me? Let me show you a picture of her grandkids. So people, in order for that, people, people interaction. So even the robots improved, people just don't want it. And those jobs are going to be increasing because AI will create a lot of value, 16 trillion dollars to the world in the next 11 years according to PWC. And that will give people money to enjoy services, whether it's eating a gourmet meal. or tourism and traveling, or having concierge services, the services revolving around every dollar of that $16 trillion will be tremendous. It will create more opportunities to service the people who did well through AI with things. But even at the same time, the entire society is very much short in need of many service-oriented, compassionate-oriented jobs. The best example is probably in healthcare services. There's going to be two million new jobs, not counting replacement. Just brand new incremental jobs in the next six years in healthcare services. That includes nurses, orderly in the hospital, elderly care, and also at home care. It's particularly lacking. And those jobs are not likely to be filled. So there's likely to be a shortage. And the reason they're not filled is simply because they don't pay very well. And that the social status of these jobs are not very good. So they pay about half as much as a heavy equipment operator, which will be replaced a lot sooner. And they probably pay probably comparably to someone on the assembly line. And if we ignoring all the other issues and just think about satisfaction from one's job, someone repetitively doing the same manual action and assembly line, that can't create a lot of jobs satisfaction, but someone taking care of a sick person and getting a hug and thank you from that person and the family, I think is quite satisfying. So if only we could fix the pay for service jobs, there are plenty of jobs that require some training or a lot of training for the people coming off the routine jobs to take. We can easily imagine someone who was maybe a cashier at the grocery store, as stores become automated, learns to become a nurse or a home care. Also, to want to point out, the blue color jobs are going to stay around a bit longer. Some of them are quite a bit longer. AI cannot be told, go clean and arbitrary home. That's incredibly hard. Arguably is an L5 level of difficulty. And AI cannot be a good plumber, because plumber is almost like a mini detective that has to figure out where the leak came from. So, yet, AI probably can be an assembly line and ultimate mechanic and so on. So, one has to study which blue color jobs are going away and facilitate retraining for the people to go into the ones that won't go away or maybe even will increase.

I am optimistic if we start to take action. If we have no action in the next five years, I think it's going to be hard to deal with the devastating losses that will emerge. So if we start thinking about retraining, maybe with the low-hanging fruits, explaining to vocational schools why they should train more plumbers than auto mechanics, Maybe starting with some government subsidy for corporations to have more training positions. We start to explain to people why retraining is important. We start to think about what the future of education, how that needs to be tweaked for the era of AI. If we start to make incremental progress and the greater number of people understand, then there's no reason to think we can't deal with this because this technological revolution is arguably similar to what electricity industrial revolutions and internet brought about.

Absolutely and the governments don't have to believe and employment will go up and they don't have to believe automation will be this fast to do something revamping vocational school will be one example and another is if there is a big gap in healthcare service employment And we know that a country's population is growing older, more longevity, living older, because people over 80 require five times as much care as those under 80, then it is a good time to incend training programs for elderly care, to find ways to improve the pay. maybe one way would be to offer as part of Medicare or the equivalent program for people over 80 to be entitled to a few hours of elderly care at home. And then that might be reimbursable and that will stimulate the service industry around the policy.

I don't think it's an easy answer because it's a double-edged sword. The company's end governments can provide better services with more access to data, more access to AI, but that also leads to greater power which can lead to uncontrollable problems whether it's monopoly or corruption in the government. So I think one has to be careful to look at how much data that companies and governments have and some kind of checks and balances would be helpful.

I believe in greater engagement. A lot of the worries about more powerful AI are based on a arms race metaphor. And when you extrapolate into military kinds of scenarios, AI can automate and autonomous weapons that needs to be controlled somehow. and autonomous decision-making can lead to not enough time to fix international crises. So I actually believe a Cold War mentality would be very dangerous because should two countries rely on AI to make certain decisions And they don't talk to each other, they do their own scenario planning, then something could easily go wrong. I think engagement, interaction, some protocols to avoid inadvertent disasters is actually needed. So it's natural for each country to want to be the best, whether it's in nuclear technologies or AI or bio. But I think it's important to realize if each country has a black box AI and don't talk to each other, that probably presents greater challenges to humanity than if they interacted. I think there can still be competition, but with some degree of protocol for interaction, just like when there was a nuclear competition, there were some protocol for deterrence among US Russia and China. And I think That engagement is needed. So of course, we're still far from AI presenting that kind of danger. But what I worry the most about is the level of engagement seems to be coming down. The level of distrust seems to be going up, especially from the US towards other large countries, such as China. And of course, Russia.

I'm not an expert at geopolitics, but I would say that we look pretty foolish as humankind. When we are faced with the opportunity to create 16 trillion dollars for, for, for humanity. And we're not solving fundamental problems with parts of the world, still in poverty. And for the first time, we have the resources to overcome poverty and hunger. We're not using it on that, but we're fueling competition among superpowers. And that's a very unfortunate thing. If we become utopian for a moment, imagine a benevolent world government that has this 16 trillion dollars and maybe some AI to figure out how to use it to deal with diseases and problems and hate and things like that. World will be a lot better off. So what is wrong with the current world? I think the people with more skilled than I should just think about this. And then the geopolitics issue with super power competitions, one side of the issue, there's another side which I worry maybe even even more, which is as the 16 trillion dollars all gets made by US and China and a few of the developed countries. the poorer country will get nothing because they don't have technology and the wealth disparity and inequality will increase. So a poorer country with large population will not only benefit from the AI boom or other technology booms but they will have their workers who previously had hoped they could do the China model and do outsource manufacturing or the India model so they could do the outsource process or call center while all those jobs are going to be gone in 10 or 15 years. So the individual citizen may be a net liability, I mean financially speaking, to a poorer country and not an asset to claw itself out of poverty. So in that kind of situation, these large countries with not much tech are going to be facing a downward spiral. And it's unclear what could be done. And then when we look back and say there's $16 trillion being created and it's all being kept by US China and other developed countries, it just doesn't feel right. So I hope people who know about geopolitics and find solutions that's beyond my expertise.

There's a common belief that full freedom of speech and expression is correlated with creativity, which is correlated with entrepreneurial success. I think empirically we have seen that is not true. China has been successful. That's not to say the fundamental values are not right or not the best, but it's just that that perfect correlation isn't there. It's hard to read the tea leaves on an opening up or not in any country and I've not been very good at that in my past predictions But I do believe every country shares some fundamental value a lot of fundamental values for the long term So you know China is drafting its privacy policy for individual citizens and they don't look that different from the American or European ones so people do want to protect their privacy and have the opportunity to express and I think the fundamental values are there. The question is, in the execution and timing, how soon or when will that start to open up? So, so I so long as each government knows, ultimately people want that kind of protection, there should be a plan to move towards that as to when or how and I'm not an expert.

Yes, I think a lot of people have a misunderstanding that it's okay and possible to just rip all the data out from a provider and give it back to you so you can deny them access to further data. and still enjoy the services we have. If we take back all the data, all the services will give us nonsense. We'll no longer be able to use products that function well in terms of, you know, right ranking, right products, right user experience. So yeah, I do understand we don't want to permit misuse of the data from legal policy standpoint. I think there can be severe punishment for those who have egregious misuse of the data. That's I think a good first step actually China in this side on this aspect has very strong laws about people who sell or give data to other companies and that over the past few years since the that law came into effect pretty much eradicated the illegal distribution sharing of data. Additionally, I think technology is often a very good way to solve technology misuse. So can we come up with new technologies? that will let us have our cake in either tool. People are looking into homomorphic encryption, which is letting you keep the data, have it encrypted, and train our encrypted data. Of course, we haven't solved that one yet, but that kind of direction may be worth pursuing. Also, federated learning, which will allow one hospital to train on its hospitals, patient data fully, because they have a license for that. And then hospitals would then share their models, not data with models to create a super AI. And that also maybe has some promise. So I would want to encourage us to be open-minded and think of this as not just the policy binary as known, but letting the technologists try to find solutions to let us have our cake in either two or have most of our cake in it most of it too. Finally, I think giving each end user a choice is important and having transparency is important. Also, I think that's universal. But the choice you give to the user should not be at a granular level that the user cannot understand. GDPR today causes all these pop-ups. Yes, no, will you give this site this right to use this part of your data? I don't think any user understands what they're saying yes or no to and that's a spec most are just saying yes because they don't understand it. So while GDPR in its current implementation has lived up to its promise of transparency and user choice, it implemented it in such a way that really didn't deliver the spirit of GDPR. It fit the letter but not the spirit. So again, I think we need to think about is there a way to fit the spirit of GDPR by using some kind of technology to can we have a slider that's an AI trying to figure out how much you want to slide between perfect protection, secure security of your personal data versus high degree of convenience with some risks of not having full privacy. Each user should have some preference, and that gives you the user choice. But maybe we should turn the problem on its head and ask, can there be an AI algorithm that can customize this? Because we can understand the slider, but we sure cannot understand every pop-up question.

Yes, absolutely. I would, I sure would hope there is a way we can do long-term projects that really do the right thing. I think a lot of the people who embrace GDPR, they're hearts in the right place. I think they just need to figure out how to build a solution. I've heard Utopian's talk about solutions that get me excited, but not sure how in the current funding environment they can get started, right? People talk about, imagine this, crowdsourced data collection that we all trust and then we have these agents that we ask them to ask the trusted agent to that agent only that platform. So I trusted joined platform that that we all believe is trustworthy that can give us all the close loop personal suggestions by the new social network, new search engine, new e-commerce engine that has access to even more of our data but not directly but indirectly. So I think that general concept of licensing into some trusted engine and finding a way to trust that engine. It seems like a great idea, but if you think how long is going to take to implement and tweak and develop it right, as well as to collect all the trusts and the data from the people, it's beyond the current cycle of venture capital. So how do you do that? It's a big question.

Well, I've been the workaholic my whole life, and I've basically worked nine, nine, six. My name, the 9pm, six days a week, roughly. And I didn't really pay a lot of attention to my family, friends, and people who loved me, and my life revolved around optimizing for work. While my work was not routine, my optimization really made my life basically a very mechanical process. But I got a lot of highs out of it because of accomplishments that I thought were really important and dear and the highest priority to me. But when I faced mortality and the possible death in matter of months, I suddenly realized that this really meant nothing to me, that I didn't feel like working for another minute, that if I had six months left in my life, I would spend it all with my loved ones, and thanking them, giving them love back and apologizing to them that I lived my life the wrong way. So that moment of reckoning caused me to really rethink that why we exist in this world is something that we might be too much shaped by the society to think that success and accomplishments is why we live. But while that can get you periodic successes and satisfaction, it's really in the facing death, you see what's truly important to you. So as a result of going through the challenges with cancer, I've resolved to live a more balanced lifestyle. I'm now in remission. Knock on wood. And I spend more time with my family, my wife travels with me. When my kids need me, I spend more time with them. And before I used to prioritize everything around work. When I had a little bit of time, I would dull it out to my family. Now, when my family needs something, really needs something, I drop everything at work and go to them. And then in the time remaining, I allocate to work. But one's family is very understanding. It's not like they will take 50 hours a week from me. So I'm actually able to still work pretty hard, maybe 10 hours less per week. So I realized the most important thing in my life is really love and the people I love and I give that the highest priority. It isn't the only thing I do but when that is needed, I put that at the top priority and I feel much better and I feel much more balanced and I think this also gives a hint as to a life of routine work, a life of pursuit of numbers. While my job was not routine, it wasn't pursuit of numbers, pursuit of, can I make more money? Can I fund more great companies? Can I raise more money? Can I make sure our VC is ranked higher and higher every year? This competitive nature of driving for bigger numbers And better numbers became an endless pursuit of that's mechanical. And bigger numbers didn't make me happier and faced with death. I realized bigger numbers really meant nothing. And what was important is that people who have given their heart and their love to me deserve for me to do the same.

I would, but it's it's in percentages, right? We're both computer scientists. So I think one balance is one's life when one is younger. You you might give a smaller percentage to family, but you will still give him high priority. And when you get older, you would give a larger percentage to them and still the high priority. And when you're near retirement, you'd give most of it to them and the highest priority. So I think the key point is not that we would work 20 hours less for the whole life and just spend it aimlessly with the family, but that when the family has a need, when your wife is having a baby, when your daughter has a birthday, or when they're depressed, or when they're celebrating something, or when they have a get together, or when we have family time, that is important for us to put down our phone and PC and be 100% with them. And that priority on the things that really matter isn't going to be so taxing that it would eliminate or even dramatically reduce our accomplishments. It might have some impact. But it might also have other impact because if you have a happier family, maybe you fight less. If you fight less, you don't spend time taking care of all the aftermath of a fight. That's right. So it's unclear that it would take more time. And if it did, I'd be willing to take that reduction and it's not a dramatic number, but it's a number that I think would give me a greater degree of happiness and knowing that I've done the right thing. and still have plenty of hours to get the success that I want to get.

I would suggest that people understand technology waves move quickly. What worked two years ago may not work today. And that is very much case in point for AI. I think two years ago, or maybe three years ago, you certainly could say I have a couple of super smart PhDs. And we're not sure what we're going to do, but here's what we're going to start and get funding for a very high valuation. Those days are over because AI is going from rocket science towards mainstream, not yet commodity, but more mainstream. So, first, the creation of any company, to a venture capitalist, has to be creation of business value, a monetary value. And when you have a very scarce commodity, VCs may be willing to accept greater uncertainty. But now, the number of people who have the equivalent of PhD three years ago, because that can be learned more quickly. Platforms are emerging. The cost to become AI engineer is much lower and there are many more AI engineers. So the market is different. So I would suggest someone who wants to build an AI company be thinking about The normal business questions, what customer cases are you trying to address? What kind of pain are you trying to address? How does that translate to value? How will you extract value and get paid through what channel? And how much business value will get created? That's today needs to be thought about much earlier upfront than it did three years ago. The scarcity question of AI talent has changed. The number of AI talent has changed. So now you need not just AI but also understanding of business customer and the marketplace. So I also think you should have a more reasonable valuation expectation and growth expectation. There's going to be more competition, but the good news though is that AI technologies are now more available in open source. TensorFlow, PyTorch, and such tools are much easier to use. So you should be able to experiment and get results iteratively faster than before. So take more of a business mindset to this. Think less of this as a laboratory taken into a company because we've gone beyond that stage. The only exception is if you truly have a breakthrough in some technology that really no one has, then the old way still works. But I think that's harder and harder now.

What is it that differentiates you and me?