 When we talk about the new data economy, we worry a lot about privacy, we worry about questions of, you know, freedom. But, you know, we don't often ask, well, what kind of society are we building? You know, what kind of social order? How is inequality going to look like in that society? So, you know, this is what I'm going to try to present today, and of course a lot of this is speculative because the society hasn't yet fully come into being. The modern digital economy is built upon an implicit Fossian bargain. On the one hand, companies provide services for free, right, tempting us, like first, with universal knowledge at the tip of our keyboard. But in order to access this information, we have to give away our soul, you know, to continue the Fossian metaphor, leaving behind little bits of data that are so many indications of who we really are or what we really do. So, you know, why does this matter? Okay? And as I said earlier, there are legitimate worries about freedom, privacy, democracy, as in the, you know, Cambridge Analytica case. But my own view is that it matters because the Fossian bargain is enabling the rise of a new kind of society, a new kind of social order. We've had this term, the new oil, nearly cross the president of the European Commission, famously referred to data as the new oil in 2012. People are now saying the new electricity. You know, what he meant and what people mean by this is not simply that data is a profitable sector, but that data generation, refinement and use is actually the new fuel powering the entire economy. And, you know, we can legitimately ask ourselves, well, is this oil really new? You know, are we really in a sort of new stage of sort of economic development in which data really becomes, you know, launches us, if you will, in a completely different kind of regime? And to some extent, it isn't. You know, it might be worth posing at this point and ask ourselves, you know, how did we get there? You know, how did we begin to see personal data as the new oil? You know, where did we begin doing this? After all, if we look back, surveillance in the economy, that is, as Oscar Gandhi puts it, the capture or information for the purpose of producing intelligence or strategically useful knowledge is nothing new. Businesses have always had what Gandhi calls a legitimate business interest in collecting data about their consumers, their employees or their competitors. So, you know, when did this legitimate interest in data, you know, sorry, what did this legitimate interest in data look like before Google? And of course, there are many examples. If we look back in history, we can think of the rise of credit, the rise of insurance, the rise of marketing industries as, you know, having been enabled by the emergence of personal profiling in the name of precisely that kind of legitimate interest. So, what I will do is I will actually develop the example of credit because I think, if we think about the history of credit, we see encapsulated in it some very fundamental processes that can help us understand, you know, what our future may be, you know, what is it that, you know, credits, if you will, might stand in for something much bigger. So, we have, if you think about credit, we have moved from a situation in the 1960s where essentially you were inside the credit market or you were outside. If you were inside the credit market, the conditions didn't vary too much across consumers, right? And if you were outside, you didn't have access to credit or you could maybe obtain credit but with sort of loan sharks and sort of more shady kinds of suppliers. By the 1990s and even more than 2000, increasingly, you know, the market expands massively and what we have is we have an increasing differentiation of people on the basis of this numerical information. So, the purpose of a profile now changes and it's not so much about deciding whether the person should be given credit or not. It becomes, should, you know, under which, at which terms should that person obtain credit. So, it is, the purpose is now to assign on the basis of that information, the individual to a prediction. What are the chances that such a person will repay her loan? And you can think about this in every domain, right? What are the person, the chances that such a person has diabetes, you know, that they will have an accident and so on and so forth. So, this is, you know, this is the rise of predictive analytics, okay? So, that, you know, we have the development, as I said, the development of the credit reporting infrastructure, but of course that's true of other kinds of infrastructure allowed for centralization of surveillance, you know. Now, you know, with, armed with this instrument, you could expand your business to all kinds of populations on the strengths of the records held by the credit reporting agencies. And the real important change in the 1980s is the rise of pre-score, a statistical scoring tool using credit bureau data, which was developed by the Fair Isaac and Company in the mid-1980s, which now allowed banks to increasingly seek consumers that were pre-screened, okay? So, scoring, you know, facilitates this assignment of an individual to a class or a category for the purpose of decision-making. And the development of credit scoring was a decisive step, allowing lending decisions to be now largely automated and, of course, this is one of the things that should, the massive expansion of credit and debt in the American economy. But predictive analytics not only enables better risk predictions, it can also be mobilized to harness value. So, in other words, you can actually score conditionally on the risk level. So, the question is now different. Given someone's score or they're fitting into a predicted category, how much value can I possibly extract? Okay? So, there's two questions that are quite different, right? One is, you calculate the risk that an individual represents or the likelihood that a certain outcome will be realized. Two, you know, on the basis of your knowledge of this outcome, how much money can you make? Okay? And, of course, how much value can you possibly extract? You know, depends on a lot of other considerations and behavioral data. So, for instance, you may be scoring on the likelihood that someone may be tempted by a crappy loan offer or, you know, a crappy insurance plan. Now it becomes, you know, about trying to evaluate a lot of other things about who they are. Okay? This is where, you know, the financial behavior meets all kinds of other types of behavior. So, the point now, okay, is very important. The point is to estimate the value or the profits to be made from particular individuals in known that is in predicted situations. So, from an economics point of view, for those of you who know a little bit of economics, this is trying to manage the willingness to pay. How do we think about this process sociologically? Which is really what interests me here, you know? What do we have here? You know, what kind of society are we preparing ourselves for? We can think of the totality of one's interactions with the digital economy, if you will, as a sort of form of capital in the sense of bourgeois, that is, as it calls it, accumulated labor, which has a potentiality to produce profits. We call this urban capital, okay? And the reason is that it is a form of capital that results from everything, right? So, we have to find a trend that sort of captures that sort of status, right? It overlaps with the traditional forms identified by Bourdieu, like it overlaps, for instance, with your cultural capital, but at the same time, it departs from them. You know, it has a clear materiality and it could take in principle a numerical form. It is accumulated over the long history of a person's recorded action built up from traces left on everything, from social media to credit bureaus to shopping websites and fidelity programs, courthouses, pharmacies and the contents, of course, of your emails and chats. It incorporates your social ties, which are now measurable, right, through the value of your social network and, you know, some measure of your moral worth, okay? So, you can think about this as a potentiality, right? So, we call it urban capital. We have a larger term for it for the computer scientist among you, which is Eigencapital. And, you know, it's a little bit Eigencapital. It's a little bit as if each system in your life was doing something like the credit scoring system, right? So, on the financial side, you know, you have a credit score. But your social network data can be also subject to the same kind of scoring processes, right? And maybe your health data can do the same thing, you know, measuring your sort of your fitness and so on and so forth. So, you can imagine this as a sort of, as a vector, right? In a vector space among the thousands of dimensions of data that all of these companies are keeping about you, okay? And so, if you think of a God's, you know, of a God's view on you, that would be urban capital. If you think more about the individual protections, that's the, you know, that's the Eigencapital. So, like cultural capital, urban capital may exist in three, under three principle forms. The first form is that it is embodied. You know, it is expressing some durable dispositions about yourself, your fitness, your sociability, your social influence, your character. You know, that's the Foucailian truth. At the same time, it is also, it is objectivated or objectified. It is realizing the forms of access to goods and services. So, when you have the capital, right, a certain kind of measured worth, if you will, you get access to certain goods and services, better social consideration, better prices. You know, you bought early on the plane. You don't have to wait when you call customer service, right? And it is finally institutionalized in the sense that it may exist as a quantity that is widely used and, you know, the kind of labor use that I mentioned earlier, where, you know, it goes from credit to housing, to insurance and so on and so forth. It can circulate, okay? It can circulate also in, you know, very far away corners. So, you know, dating websites today increasingly are using your credit score, okay? So, the way that I'm thinking about Uber capital is, if you will, as a potentiality, right? The technology is driving us in a particular direction, right? To the creation of sort of these increasingly measured qualities at the, you know, of the individual on different markets. But it doesn't exist as a fully coherent institutionalized thing. It doesn't exist as a number I couldn't tell you. Uber capital is like 596. Of course, people, you know, worry about the political consequences of this big brother type of system. But perhaps more profound and more interesting are the consequences in terms of social stratification and inequality, okay? So, you know, the road to increasing efficiency and profits will be now to match, right, individuals to what algorithm determines they deserve, right? That will be, you know, the process of value extraction, if you will, will capitalize on people's behavior, their dispositions, their habits, you know, refracted, of course, through the very particular classificatory architecture of the digital economy. Now, this is not new. Inequality has always been moralized, right? All form of dominations have been buttressed by distinctions between the deserving and the undeserving. And of course, this is the base that we have constantly about the welfare state. You know, do people deserve the social benefits that they have access to, right? This is how systems of power always legitimate themselves. But in this particular case, you can see that this is a system that is going to be harder to contest politically. The reason is that first mobilization in this kind of system doesn't come about naturally, right? Because rather than, you know, we will have a graded, you have a sort of graded scale rather than groups, you know, say workers versus managers or, as Mark said, you know, since it was his birthday, you know, the proletariat versus the bourgeoisie, right? The social collectives that actual practices produce are just sort of aggregations of people. Right now, you know, there's no natural solidarity among them, right? They're not solidaristic communities bound by categorical statues or by voluntary choice. That's the first point. You know, it is hard to develop a politics in this kind of system. But second, in this system, outcomes appear to be legitimate. In China, sesame credit is quite well accepted because it is seen as increasing trust, as Shao Jinqin, and I'm quoting from an article from the Zhechou Statue, actually, as Shao Jinqin from the Shanghai Municipal Commission of Economy and Informatization, which is in charge of the Honest Shanghai app, put it in a response to an interviewer. It is all about bringing order to the market. And ultimately, it's also about social order. So that's, you know, and this more, this remoralization of the whole system comes from the fact that differences in outcomes are seen to emanate only from behavioral differences, right? Rather than some other sort of difference, like categorical differences, you know, men and women, you know, discrimination, whatever, which are protected by law. In this case, it's just you. It's your fault, right? And therefore, those who, whether individuals or corporate entities who are outside, are really truly outside. They are truly outside because the principle of their exclusion seems to lie truly within them, within them. And, you know, as an article in Wired puts it in the end, it's just you. So you can see now the social force of this kind of system, right? And of course, it's not lost to the designers in China. In the city of Rongcheng, oh, sorry, the other quote was from the Wired article. This is from the Zhechou Statue. In the city of Rongcheng, a civil servant tells a journalist from the Deutsche Zeitung who has come to inquire about his city's pioneering role in this domain. We want to civilize people. He proudly cites the founding document of the Office of Honesty of the City of Rongcheng, allow the trustworthy to roam everywhere and are heaven while making it hard for the discredited to take a single step. Now, of course, whether the system will work the way it's supposed to, you know, whether it will be something totally fictional, you know, garbage numbers that people ignore, or whether it will be something in between, like it kind of works, but not exactly how it's supposed to, we don't know yet. But if the Chinese situation is a guide to the potential appeal of universal scoring and remembering that similar if more decentralized tendencies artwork on this side of the world, there is little reason to think that this kind of design will not be part of our future. The way the FICO score is already embedded in our present. I will stop here and let you meditate on this. Thank you very much.