 The people who actually, who are more worried about concern about privacy are precisely the type of people they use that needs to experience more. So what does that mean then? What is the nature of the privacy paradox? Really, it says that there's two kinds of the welfare related to the data from a customer's point of view. One is that they want their privacy to be protected. The other one is that they want to share some information to get better services. So we have to satisfy both needs. That means to deal with the issue of privacy is not to lock down data. Because if you lock down data, you refuse to exchange your information. You don't get the service out of the society, out of a lot of economic activities. So the right approach then is to encourage the data exchange. But in the meantime, we protect the privacy in this process. This is Rob Johnson, president of the Institute for New Economic Thinking. I'm here today with Dr. Wang Chen, the director of the Lujan Academy and who's been with me as a guest once before. Today we're here to talk about a fascinating new report that the Lujan Academy has put out in conjunction with many famous economists, including Mike Spence, Brent Coldstrom, Patrick Bolton, Chris Pizzerides, Eric Masken, and numerous others. At any rate, Dr. Chen, thank you for joining me. The name of your report, Understanding Big Data. Data Calculus in the Digital Era came out in February. It's available on your website and it's in my own reading. It's an extraordinary report. I would encourage all kinds of people who think they know what's going on with big data and its influence on society. Everybody will learn from reading this report. It's very fresh, very deep, very thoughtful. So congratulations, but please tell me, what inspired you to bring this report to life and then what did you find in doing so? Well, thank you very much, Rob, for your kind words. I think we globally, so a lot of people, we understand data plays a crucial role in the digital age. And so, but in the meantime, people are increasingly worried about their privacy. And so the question is, how should we, so there are several reasons that kind of motivate us to write this up. People are worried about privacy, but then they don't, and they feel they're losing control of their privacy and they want to control it. But on the other hand, we see there's a lot of power and a lot of digital benefits out of using the data. So we need a good way to understand what is privacy anyway, and what is the nature of data. But let me start, I think about a couple of years ago, at Lohan Academy's annual meeting, we had this debate about the privacy related issues. And so we have some guests from Europe. And their opinion is that people's privacy were somewhat invaded on the platforms because they had no choice. So that actually motivate us to study what people would do if they do have choice. And there's such a thing called privacy paradox. Basically, and it's really globally in every country, you can observe that people, when you ask them, do they care about, worry about privacy? And almost all of them will say, most of them will say yes, globally, in any country, including China. But then they turn around to kind of publish a lot of stuff about themselves to a lot of information, personal information, seems to look like they don't care much at all. So this is kind of paradox. Now, some people claim that that's because they have no choice. Let's say you are on Facebook, you're kind of your old friend or your friends on that. You cannot just take yourself off it. So you're kind of forced to do it. But then that is strange if you think about that there's billions of people who actually do exactly the same thing. They worry about privacy, but then they share a lot of information. So it comes down to what is privacy and what is information? What is the benefit of information and data? And unless we put things together, we won't be able to understand what's going on. And it's possible that we might even hurt the users in the name of protection because there seems a clear need for them to share information. Another thing that motivated us to write this report with some of the best information economists is that actually nowadays we call this same data, but in old days we call them information. And so information economics in a sense is the data economics in this age. It's really very similar. So we've been thinking about the value of information for a long time. And so many economists and authors in this report, they won Nobel Prize because of that deep thought about the role of information. And for very, very long time, the efforts in Syria and in practice is to overcome the obstacles to the block information obstacles of asymmetric information. So we've been promoting spreading information for a very long time because we realized that's one of the crucial thing that separates human being from animals. But then somehow now we care so much about the downside about spreading information personal information. So then, so in a sense, we have to, of course, we care about privacy, but we need to understand the value of information and data. So in a sense, we have to borrow the old lessons, the principles we have learned by so many economists who have spent so much time and so many institutional arrangements trying to that are made to overcome the asymmetric information. So we have what trying to bring all those things together to help us understand the nature of data and privacy. And so that sometimes reminds me of Paris. It's called, I think this came from India, it's called the elephant and the blonde man. What he says is that once upon a time, there were I think five, six blonde men, they were first, for the first time they were led to an elephant and people asked them, what does the elephant look like? And one of them touched the trunk and they feel like elephant is a trunk. Another guy touched the ear of the elephant and feel like the elephant is really a fan, a big fan that can wave around. And another one touched the body of the elephant and he feel like the elephant is really a war. Of course, in a sense that they're all right. But unless you put things together, you really won't be able to understand what is an elephant. In a sense, I think data is like that. So that motivates us and it's been an interesting journey. So we were working together to try to understand what is the nature of data and what is the nature of privacy and how should we deal with it to take the best at leverage on the strengths of digital technology and data. But in the meantime, we can protect privacy and other stuff. Well, I'm curious about one dimension when you talk about, say the person on Facebook who puts things on there. Is the problem that they don't know what would be done with that information? In other words, if I'm tailoring what I want to put on my page, if I know everything you're going to be doing with it, I can exercise my own judgment. But if I think the only people seeing this are my 12 best friends from high school, but in fact all kinds of other people and agencies and others are seeing it, then perhaps I'm not conscious of protecting my own privacy. So I guess where do you want the locus of responsibility to be? Should it be with the person who joins a system or puts their data on, but knowing where it's going to go? Or should it be, how do I say, something imposed from above, like by a government agency or whatever, about what I'll call the rules of fair play? How do you see that architecture working? So I think we, firstly, we probably need to remind us that there has been a long tradition that we share personal information in the public. And I'll give you one example is the yellow page. In the United States for a long time, for more than 100 years in most cities and towns, you can get the name, address and telephone number from every family in the town. So we are very, very used to sharing information. And because I think for a long time we realized that it's very crucial for everybody to be connected to the society, if they trust the society. And another angle I can think about is the economic activity. So a lot of time when we think about economic activity, we think about trades or collaborations. But actually beneath that economic activity is the information flow, is exchange of information. We have to know each other. A producer has to understand the preference of the customer to serve them well. This is a basic, I have to give the supply of information such that they can give me the service, provide me stuff. So really beneath any economic activity, so it is beyond two people, more than two people, then we have to exchange information. So that I think has been going on for a long time. And that's why I think as a society for people that are used to exchange your information or always been doing that or making a lot of arrangement to spread information. But it comes down to, I think, of course this other side is the privacy. It comes down to what you feel comfortable or you do not feel comfortable. And so in most circumstances that we do feel comfortable, but sometimes it might be abused in some way. So I think that's why if we go back to the 1970s, when the fifth, it's a fair information practice standard when it came out from in the United States. Essentially it asks how to make good use of information. So if that information is being used by some other people, some other institution. But so long as you set some boundaries, for example, you have to acknowledge them. You have to get some approval in general. You set the boundaries. But otherwise I think exchanging information is essential and we've been used to it for a long, long time. Well, you do a very nice job really in the report of tracing essentially the history of economic thought about information. With Hayek, the coast, Ronald Coase, famous for the Coase theorem. The people like Stiglitz, Akerlof and Spence who shared a Nobel Prize focused on asymmetric information and many others. But you also talked about something you called the three Vs, velocity, variety and volume of information. Can you describe a little bit how you came to frame it with the three Vs and how does that encompass what you're trying to illuminate? Yeah, so we've been trying to promote the flow, the exchange of information as we just mentioned. And so those economists, information economists, they spend a lot of time thinking about how to overcome the problems of blocking the information. But what's special in the digital age is really is make the data digital so that can be easily produced, exchanged at very, very little cost. So that's why we see an explosion of the data and information ever since the digital technology, what I have a century ago. Now, but if we want to describe what's the nature, it's still information, still data, so what changed? I think that's the three Vs we're trying to use to describe it. Why is the volume, that's the big data, the size, the amount of data we are using. And then the second is the variety. So there's so much more information that can be recorded, observed, recorded and can be exchanged because they are all digitized. So that's the variety. But I think the last thing, which is really, really crucial, but people kind of underestimate its power is the velocity. The speed while producing information is like the, it's instant. So that means we have a lot more instant ability than before. One example maybe is the, let's think about is the, it's a car. It's the new car that's the Tesla and the other country in China is coming up. So if you think about the type kind of explosion of the information. And now, if you have a silent cause, if you have the, you have the, if you drive it without the driver, you know, so if you use the AI it drives by itself. So amount of information it kind of produce a day. It's kind of somewhat equivalent to one years search amount of information in China. That's through Baidu, which is China's version of the Google. So that's really explosion of information. And we use that information instantly. That's the velocity side. So you can see it's the variety. It's amount of information volume and the, an instance, a timely nature of that using that that distinguishes this age from the industrial age. I remember just as I'm listening to you, I was inspired at one time when Ben Bernanke was the chairman of the Fed. There was a group that included Google credit card companies and so forth, because he was saying he has to make decisions about the course, the trajectory of the macro economy. But the data from the US employment reports and so forth is compiled and comes out something like 45 to 60 days after the time of what was being measured. And so the credit card companies and the search engines and so forth started looking to see and they kind of said, we could replicate that awareness with great confidence. 72 hours after it happens, we can put something on your desk for the Federal Reserve Board. So instead of waiting for six or eight weeks, you waited for three days to feel like you understood what the state of macroeconomic vitality was, whether you were ahead of the curve, behind the curve, what have you. And they also could do things that gave you a much more textured sense of region rather than national aggregation by zip code or county or congressional district. They could see the variations they could see by sector. And I just remember listening to Bernanke talking one evening about like, he felt like the way I think he described it was like we went from the era of radio to the era of television. All of a sudden I could see the economy, whereas I was just dreaming and guessing with long lag times beforehand. And obviously to him that was a great benefit. And I didn't see any way in which the Fed was snooping on an individual's credit card account to see what they were buying. They were pulling the aggregate information together to have a much more sensitive and timely understanding. And I imagine there are thousands of applications like that and the data, just like you described with Tesla, the data is getting faster and better, more variations. And so I find this fascinating potential and perhaps if people understand your report, they might be more conscious of what the boundary should be so that they don't eliminate this potential. On the other hand, maybe officials become more mindful of where the danger zones are for violating personal privacy. And how you say you're allowing us with this report and the findings to go on a trajectory where we can get more from data and be less scared. And I think I think that's very productive. That's a very constructive thing to offer to society. Thank you so much for your kind words. But let me follow up what you said. Precisely, so you mentioned the risk, financial risk. So, and that is something we're really seeing this in China. In the financial sector, sometimes you talk about the credit risk. So essentially, we, if you think about a bank, it lends the money. And but then the bank, you see, it's kind of static in the sense that whether the borrower will return the money back, you won't be able to see much after at least several months. But nowadays, there's so much more real time, daily data, when the economy is being digitized. So actually, you can track credit risk daily. So that really changes the definition of the risk management. Traditionally, we think about what we think about, wow, we have to understand the probability of default and the last given default since those kind of concept. But those kind of static, you're assuming there's a stable distribution, then there's nothing else can do it. So you could have some reserve to prepare for that. But how about you can't know much more real time, what's going on the risk, you can actually change your risk. You can change your risk exposure, change your products. So that changed the whole dynamic of that thing. So I'm pretty sure Nanke would be much happier if he had those tools. And that's precisely during the COVID-19 in early 2020. Actually, in February 2020, we actually at Lohan Academy, we actually used the big data to construct a daily economic activity index across China. And so that covers more than 300 regions. So we actually can track the economic activity daily. And that's an almost very precise. And then we extended that globally to many more than 190 countries and regions. So why do we do that? Because as you, many people can experience. Now, if you want to recover back to work, now, should we speed up the recovery or should we contain the COVID first? If you restart slowly, then people get hurt financially. Economy gets hurt really bad. But if you speed up too quickly, then the COVID-19 is coming back. So we have to strike some kind of balance because they are really going up the directions. Now, you need some kind of measurement to see how we kind of know every day how the COVID is spreading. Because we know how many people get kind of unfortunately died or have confirmed cases. But then we do not know enough about how the economy is suffering daily. So that combination is something at my expense and as we thought, we call this the pandemic economy. Because we have to combine the two sides to make the right decision. The first thing I think I want to remind is that's something we feel interesting in the report. So as I already mentioned that there has been a very long tradition for the human being societies to spread information sharing. But now we care much more about privacy. So what exactly is a trade-off? And I mentioned that there's this privacy paradox. So people in every country worry about privacy, but in the meantime they share a lot of information. So what's going on? Now, sometimes people say, well, maybe people in China do not care about this much about privacy. And that's why the digital spending is going up. China is leading the world in the e-commerce, for example. So e-commerce accounts for about 27% of the retail sales in China's economy, which leads the world. And China has transformed itself into a mobile-based country. So sometimes people suspect that maybe Chinese do not care about privacy. What they do, actually, we had a survey. It's more than 90% of the people say they really worry about privacy. So human beings are the same. Now the questions, what exactly is the nature of the thing that they care about? Do they really care about? If they do, why do they share personal data? So sometimes then it comes down to another argument that says that maybe that's because they have no choice. That's like, if you're on Facebook or maybe it's in China, if we are on WeChat or Alipay, it's a big app. Then if you don't use it, that's really a trouble. So that's why we did this, I think. It's one of the largest scale. It's a big data on big data. It's one of the large-scale experiments and tests to see what people would do if they do have choice. So in this particular setting on Alipay, so we have more than 800 million users, so it's a big app. But in a sense that you don't have a choice. It's kind of, your life will be inconvenient if you don't use it. But then on Alipay and like on many other apps these days, we have something called mini programs, which is kind of the temporary version of the app on Alipay. But those apps, you can see that we have hundreds of thousands of those apps and some of them are very young, new apps. Some of them are very mature, big service providers. So each of them on Alipay, when it's kind of propped up to the potential users, they have to ask their approval. You have to tell them which kind of information are you willing to share. If you want to really share, you can get this kind of service, things like that. So it's like a restaurant. Okay, if you want to use that order in the restaurant, you are there. But then you have to tell me some of your information, you know. So we have kind of connect to you, so you know you are really going to pay the money later, things like that. So are you willing to do this? If you do not, then you just pay cash, that's okay. But then people might want to do it. So there's a lot of apps. There's a huge variety of those apps. And so they vary by their necessity. So you can choose not to use it, because some of them are kind of trivial. Some of them are essential. They're also varied by the information sensitivity. Some of them ask for your financial information, your credit score, your address, some very more private things. Some of them ask a very general, it's just your name, your nickname seems like that. So they vary by the necessity, by the sensitivity of information. Then on the other hand, we have people vary by their risk aversion, how much trust they want to place on those apps. So that becomes one of the largest scale of the study on how people, when they do have choice, how they are going to trade off. And so we have some interesting findings. For example, we find that about three quarters of the people, when they first time pop up, they are going to accept the service. And they don't really care as much about whether it's a new service or some big names, the trust, or some new apps. They're going to try three quarters of the people. And then later, they actually, they seldom regret because about 0.1% of the people will kind of delete the app, you know, so the information later. So it's very small, that means they don't really regret. So, fascinatingly, this is very consistent with what we observe in Europe, the United States, and globally. So there was this privacy index study. So that conclusion is also about three quarters of people. They're kind of willing to accept the data, the kind of pragmatic regarding how much they want to share the personal information. So that's very consistent. And also about in the both United States, Canada and Europe, about 0.1 or 0.2% people delete the log off. They'll kind of refuse to use those apps later. You can see that that means people don't really regret. We also find that when people use, they have more digital experience. So sometimes we suspect that that's because they don't know that they could be cheated, abused. But then we actually find that people when they have more digital experience, they actually are open, more open to use them. So that really the bottom line is this. And we actually had a separate academic study with my friend Wei Xiong and his student from Princeton and with a couple of fellows at the Lohan Academy. What we find is that the people who actually, who are more worried about concern about privacy are precisely the people, type of people that use the digital experience more. So what does that mean then? What is the nature of the privacy paradox? Really, it says that there's two kinds of the welfare related to the data from customer's point of view. One is that they want the privacy to be protected. The other one is that they want to share some information to get services, get better services. So we have to satisfy both needs. That means to deal with the issue of privacy is not to lock down data. Because if you lock down data, you refuse to exchange your information. You don't get the service out of the society, out of a lot of economic activities. So the right approach then is to encourage the data exchange. But in the meantime, we protect the privacy in this process. So that's the one thing we learned. So that naturally proposes to ask a question. There must be a lot of the benefits in sharing information. So what's the value of data anyway for us to share? So we also look at the empirical evidence. We summarize going back to the literature of the information economics. Essentially, we can say that the three types of benefits from sharing information. One is the connectivity. So nowadays you can really connect to people from very far away. For example, traditionally any small shop, the customers come from around within 10 kilometers. Because once further, there's no trade between them. So that's called the gravity model. It's like the longer the distances, very smaller gravity. But then nowadays on a typical trading platform. I think in the United States, maybe Amazon or eBay or in China at Alibaba. Alibaba's platform, the average distance between the bar sellers is about 1,000 kilometers. So that gravity model is essentially strong. But that really reminds us of the power of market. So the power of market is to let more people to participate. So that we have specialization or improve the productivity, etc. But then if you do not have information, then this will not happen. So connectivity means opportunity. So nowadays on Alibaba's platform, we have like more than 10 million SMEs. They are serving hundreds of millions of people. And so for those entrepreneurs of SMEs, half of them are women. So they would never have opportunity without the digital platforms. And without some kind of exchange of information. And so that means opportunity. Now we also tried to ask a question. So what happens if sometimes we said, okay, you can connect but you don't need much of my information. But now another interesting thing is you see on the platform nowadays, you actually for the customers, it's very hard for them to choose. For example, on Alibaba's Taobao, you have more than a billion items, commodities. How are you going to browse through them? It's impossible. So you need recommendations. So a natural question is what happens if those recommendations do not contain any personal preference? We do not know anything about you. So we did a natural experiment here. So we kind of shut down a huge number of users when doing the recommendations. We shut down their personal preference. So the recommendation is based on the industry level kind of data. It's the standard data. You can see that those recommendations quickly converge to the top 1% of the brands. So the kind of multi-genre standard, like what we are used to in the industry age. Then you can see that the actually the sales, the browsing just dropped by like 80%. So it's shocking. So because customers do not find those recommendations interesting. And especially those SMEs, the smaller brands, the small brands, they were hurt most. So you can see that's the power. But anyway, so that's the part of the connectivity and the value of the based on personal information. And those help us to be smarter as we know. And then finally, it builds a trust system. So on the typical platform, you could have the hundreds of millions of the users. You could have the tens of millions of merchants and every part of the service of the products, and including those SMEs, they will be rated by the customers. By doing that, they build the trust system. As if they have hundreds of millions of people that see each other directly. And that really overcome the traditional lemon problem. Like actually a worry about because there's no information. There's no trust. There's no market. There's no exchange. There's no economic activity. You can see those things. But let me stop here for now. These are many, many interesting things here. You know, I think about the fact that I use search. And if the search knows that I'm 64 years old, I just took up surfing. I'm a beginner or this, you know, all these kind of things that. It'll help me buy a better life jacket or the kind of surfboard that can support my height and weight and all these kind of things that. How would I say it would take a lot of going to stores and asking questions and a lot of time to learn the same thing that can be distilled very quickly. So I can see how by revealing myself, I can be helped by these services. And so I guess the nature of what you want to keep private. You have to be conscious of when you're playing with these powerful tools. But there's huge advantages. But let me, that's just kind of a reflection. If you see more nuance to it, please share with me. But the other piece that I think is also very interesting is and you were kind of alluding to the structure of markets. Does this information power this aggregating power and everything else. Foster highly concentrated large monopolies who then can be monopsiness or monopolists and extract wealth in ways that a more competitive marketplace couldn't. Or is there something that says all kinds of small firms can participate because they can reach so many people over a broader footprint beyond a thousand kilometers. And now they can play at scale through the access to these platforms. And that creates a more competitive marketplace. It used to be the guy down the street who knows that you can't afford a car can raise the price on your groceries. Now if you know you can order them and have them delivered from nine different places, there's a competition there. So I'm just curious where the balance is on this question between the power that leads to monopoly platforms and extracting of rents either from the suppliers or from the buyers. And on the other side facilitating a more competitive marketplace. Yeah, so you see, if we only discuss this serratically, we can discuss this for a very long time without a conclusion, because they all sound right. So that's why we have to respect reality empirical evidence. And then that we can see that really varies by country and by industry. So we have to acknowledge this we don't pretend this one way it has to be true. But now let me give you some examples for example in China. So in China you see we Ali Baba is a typical example, Ali Baba as in about five years ago it still accounts for about 80% of the e-commerce market because it has the first mover advantage. But then it has come down to about 50%. It's really going down very steadily. So that's one example. Another is like what I'm used to is the AliPay. So it's a mobile payment. AliPay was the first to because the e-commerce took off first in China. It has more than again 80% of the market share, but now it's down to a lower than 50%. And another, so if you think about that, another one player is the TikTok people in United States are kind of very familiar now. TikTok and it took off very quickly. And another one is the it's called the Pinduoduo. It's e-commerce. It actually took this company only about four years to accumulate more than 400 million new users. So what we really observe really is that your power, presumably the dominant power is fading quite fast. And if we look at the history of the internet to nowadays, really, I think whoever claims it's a big data is actually dying very fast in general. And there are very few examples. And so my point is that I think the competition is dynamic. A lot of the advantage are trends. New technologies are coming up. And if we ask how come the big data doesn't necessarily leads to cleaner takes all because really if we think about data is really one dimension of the of the of the business model. So but when you compete, you're competing based on your products, your business model, how much you can satisfy your customers. And so that combined with a lot of new technologies coming up. So it's very dynamic. Now acknowledge. So, and also a nice interesting things about the data is that I think the power of data is that there's a very big limit. For example, there are studies actually that found that the advantage of they do not last more than let's say half a year. So it's not like you if you have a certain years of data, you're going to know the human being so well, you're going to serve the customer better. Actually, you do not. You what really start is that you have a new product, people like it. So you have some kind of data to understand how much they like it how much you can improve your product you serve me even better. So that can take off. But then there's a limited how long of history of the data that uses and normally is is no more than one year. So the bottom line here is that if we want to understand how they data or big data is contributing to the the competition, we have to acknowledge that it is getting more and more important. We have to use make good use of it to understand your customer. But that doesn't mean the dominant because this numerous examples globally to show the opposite that you seemingly to have the first mover at the vantage you presumably build some data somehow it doesn't really leads to your dominance for a long time. But I also acknowledge that because it is one dimension of the competition, then that it is getting important so that it is possible in some industry it can become more concentrated, but the many industries is definitely not the case. And finally, I think, especially on platforms because you have to look at which kind of platform it is. Now, if it is a platform that connects the suppliers with the customers, the platform actually has a lot of incentive to promote that connection to use the data to empower the the demand side and supply side, including SMEs. So that's why I think we really needs to, especially in the digital age, we really needs to promote that everybody to participate in the to get the benefits of the sharing the data, especially for the SMEs to get that benefits. When we when we think about the digital divide, one of the big divide is information divide. So we really have to empower the smaller average Joe's small companies startups to get things to get the digital digitize the business infrastructure, such that a new start up. That consists of only several people that can serve the serve the customers 1000 kilometers away, they can prosper. So I think it has the both side of this. I don't think that hypothesis of witness takes a while is general, but it does happen on the dice downside to we really need to promote that innovation and more inclusive side of the market structure. I'm coming and listening to you and I'm thinking about how the information got much more fast and perfect and transparent and costless. But now I'm seeing that if I were setting up a hedge fund today, I would be looking for people who are very sophisticated in analyzing big data to find which companies to invest in. But moments ago, about the data is only good for about a year. You really got to become gifted in diagnosis and pattern recognition in big data, whose half life is very short. But I imagine there's some people that are just beating the market by leaps and bounds now, because they've mastered the kind of skills that you're describing are inside of the knowledge systems. Yeah, so yes or no. So, I think there's this huge benefits of using the, the 3v of the information, not only the volume but really the variety and the instant part of this but the so it's in practice. This is really crucial. What we're really observing is that it's really changing the business models. Now the business much more customer driven so called C2B customer to business because now you have so much more instant customers response. Now, so that's why when you have the new on the on the your products upgrade revision of your products is much more customer driven have much more activity. So business models are really changing because of instant quality and risk assessment. What I will observe in China, I look at on financial, they are doing things because of the disparity of the data. And so, so those are the things we are doing, but I'm not exactly sure in the let's say in the capital market, the secondary market that the very few hedge funds that can brag about the big data, maybe they don't know enough about it. I don't know, but yeah, but I observed very few hedge funds or the funds, they claim and big data funds, but actually they're making a super profit. So I think that's still very illusive and challenging. Well, I'm going going a little bit further with what you're saying. I remember I worked with a very brilliant man at Soros fund management in Stanley Druckenbauer. And he said to me, we have to study what the world thinks, then we have to have our own idea that differs from that consensus. And then we have to understand the process by which they will come to understand that we're right and then prices will move. And I think what you're saying partly is this data is so fast now, the volume and velocity parts are so fast that you got to be real quick to diagnosis what's going on because the time when people who also understand these systems will catch on and change their view. It's much more rapid than the old days, which makes it harder to capture that arbitrage. But I do wonder if there aren't people who are mastering the use of these platforms in a way, but they can't be as confident that everybody else will catch on because they know not everybody else knows how to use these information systems. So they may have to be patient, take longer time horizon or understand structural things that will not reverse or be just transient in order to make money. But I would imagine there are some young, very smart computer scientists who might become very good hedge fund managers in the years to come. Yeah, I think I always suspect that, but it's coming slower than I thought. But what I'm observing more generally, I think it's that in a lot of the business, many of this, a lot of sectors nowadays we have to, we have opportunity to get better use of the data we're having. And on platforms, we can see that those kind of data services are coming up, make it the use of that much more inclusive. So if we do not think too fancy about those things, it's really about the average company. Nowadays, the cost of starting a business becomes so much lower nowadays because instantly they can connect it to globally, if you think about it. And if they have good products and they had a lot of instant response and these were not imaginable like the 20 years ago. So I think those are the lot of things that are happening and make us very excited. When you look through data, the inspiration has come through stimulus provided by patterns, puzzles, anomalies revealed by the systematic gathering of data, particularly when the prime need is to break our existing habits of thought. So what you're doing in this report is breaking down what I will call the echoes of past patterns of thought that came from a different structure. And this structure has to be looked at with fresh eyes and with the data to illuminate what is happening. I thought that paragraph described the essence of the gift that the Luton Academy and you and your team are giving us all. Well, thank you again for your kind words. So, indeed, so a lot of time, we cannot distinguish what is our, we believe is what really seeing what we're worried about, and what we're really can understand. So we are really affected by what we're worried about and our experience or inspired by that. So I think if we really wants to understand things, we really have to respect the evidence. For example, privacy. Of course, we care about privacy, but really it's what's the nature of privacy. And if we observe everybody's what's going on, what exactly how do we share information, then we see that we share information all the time. And in the meantime, we'll care about privacy. And so those are something. And for example, ownership. Sometimes we feel like, well, for example, let's say Rob, we were talking to each other. This fact. Does that belong to you? Does that belong to me? The fact is that it belongs to both of us. And we actually have a different version of information. For example, I probably noticed that you wear glasses. You have you have mirror in background and you might not observe what's going on in my background. So we actually. So that's another the nature of the data of information that it has is non rivalries. It can be produced and reused unlimited times without consuming the subjects, the loss here. So that means there's a lot of the different versions of ownership. So that also means that it's not about define who owns it because it's unlimited version of it. But it's really more about how to properly use it, how to not abuse the information. So we really need to understand the value of the how data, where does the value come from? It comes from exchanging from the and it also the proper way is to protect it in the right way. But anyway, so there's a lot of things. It goes beyond our instant immediate concerns. Then that that requires us especially serious economics serious. They it's very because it's very easy to come up with a beautiful model, but with the wrong assumption. And you and and you are trying to use the mathematics to come to convince the world I'm right, but really sometimes it's terrible. And so so we need needs to have the academic rigor. But really that's going back to the Rona course words. As you mentioned, we we need to be inspired by the better by the reality. Well, as you said, at the conclusion of your forward, when these new things are coming onto the radar, many people are afraid. Many of people are afraid of change until they become acclimated and see its possibilities. But what you say at the end is you don't want to dismember the goose that laid the golden egg. And I think the prospects of this platforms and technology, particularly for the emerging countries, which can constitute a profound transformation in Southern Asia, Africa, Latin America. We don't want to dismember that goose. And I think that you and the Lujan Academy, I can say that I met we're very proud to work with you and we're very excited to learn from you. And we're very interested in how they say how you insights can particularly be applied to the people most in need and who are suffering most on this planet. So thank you very much for the work that you do for your partnership with INET. And I look forward for for many future episodes to continue to learn from you. Thanks so much.