Copyright Copyright © 2018 by Viktor Mayer-Schưnberger and Thomas Ramge Hachette Book Group supports the right to free expression and the value of copyright The purpose of copyright is to encourage writers and artists to produce the creative works that enrich our culture The scanning, uploading, and distribution of this book without permission is a theft of the author’s intellectual property If you would like permission to use material from the book (other than for review purposes), please contact permissions@hbgusa.com Thank you for your support of the author’s rights Basic Books Hachette Book Group 1290 Avenue of the Americas, New York, NY 10104 www.basicbooks.com First Edition: February 2018 Published by Basic Books, an imprint of Perseus Books, LLC, a subsidiary of Hachette Book Group, Inc The Basic Books name and logo is a trademark of the Hachette Book Group The publisher is not responsible for websites (or their content) that are not owned by the publisher Library of Congress Control Number: 2017963910 ISBNs: 978-0-465-09368-7 (hardcover), 978-0-465-09369-4 (ebook) E3-20180131-JV-PC CONTENTS COVER TITLE PAGE COPYRIGHT REINVENTING CAPITALISM COMMUNICATIVE COORDINATION MARKETS AND MONEY DATA-RICH MARKETS COMPANIES AND CONTROL FIRM FUTURES CAPITAL DECLINE FEEDBACK EFFECTS UNBUNDLING WORK 10 HUMAN CHOICE ACKNOWLEDGMENTS ABOUT THE AUTHOR ALSO BY VIKTOR MAYER-SCHÖNBERGER PRAISE FOR REINVENTING CAPITALISM IN THE AGE OF BIG DATA NOTES INDEX –1– REINVENTING CAPITALISM IT SHOULD HAVE BEEN A VICTORY CELEBRATION BY THE time eBay’s new CEO, Devin Wenig, climbed the stage for the online marketplace’s twentieth-anniversary event in September 2015, goods worth more than $700 billion had been traded on eBay’s platform, and active eBay users had reached 160 million The company Pierre Omidyar had started in 1995 as a small side-business turned into what looked like a perpetual money-maker EBay had taken an old but highly successful idea, the market, and put it online Because eBay’s market was no longer a physical place, it never closed And thanks to the Internet’s global reach, pretty much everyone connected to it could buy and sell on it Through eBay’s unique rating system, it created a way to trust market participants without knowing them Together that made the new virtual marketplace tremendously attractive, resulting in what economists call a thick market, a market with lots of buyers and sellers Thick markets are good markets, because they increase the likelihood of finding what one is looking for EBay also took a feature of traditional markets and improved on it: it replaced fixed prices with an auction mechanism, a far better way to achieve optimal price, as economics students learn in their first semester A marketplace with global reach that’s always open and makes transacting simple, easy, and efficient—that’s the recipe for eBay’s meteoric rise It not only ushered in the Internet economy but also seemed to reconfirm the preeminent role markets play in our economy But to journalists attending the celebration, Wenig looked more like “a general rallying the troops of a beleaguered army,” and his speech felt like a pep talk—with good reason The world’s largest marketplace had lost some of its mojo Analysts on Wall Street even labeled eBay “due for a reset.” With so much going for it, some may see eBay’s recent troubles as a bout of bad management, aggravated by bad luck But to us it’s an indication of a much larger, structural shift Just months before eBay’s twentieth anniversary, Yahoo, another early Internet pioneer, was suffering its own market woes Yahoo owned a substantial chunk of Chinese online marketplace Alibaba, and based on Alibaba’s share price, its holding of Alibaba’s shares was more valuable than Yahoo’s total market capitalization So sellers of Yahoo’s shares were essentially paying buyers to take on their stock and shares of Yahoo were trading at an effectively negative price That doesn’t make sense, of course, because the value of a share of common stock can’t be negative But stock prices, economists tell us, should reflect the collective wisdom of the market; so they ought to be right Something was wrong—terribly wrong EBay’s surprising troubles and Yahoo’s crazy share price aren’t random events They signify a fundamental weakness of existing marketplaces, a weakness, as we’ll explain, that is tied to price Because the flaw is linked to price, not all marketplaces are suffering In fact, some markets, less reliant on price, are outright thriving Just about the time eBay and Yahoo got into trouble, a more recent Internet start-up, BlaBlaCar, was doing amazingly well Founded in Europe by a young Frenchman bitten by the Internet bug during graduate studies at Stanford, BlaBlaCar, much like eBay, operates an online marketplace, albeit a highly specialized one It is in the business of helping people share car rides by matching those offering a ride with those looking for one And it does so very well, matching millions of riders every month and growing quickly Whereas eBay’s original focus was on price-based auctions, BlaBlaCar’s marketplace offers participants rich data about each other, ranking details such as driver chattiness (hence its name), so users can easily search and identify the best matches for them, and downplaying the importance of price (ride-sharers can select price only within a limited range) BlaBlaCar’s ride-sharing market isn’t alone in using rich data From Internet travel site Kayak to online investment company SigFig, to digital labor platform Upwork, more and more markets that use data to help participants find better matches are gaining traction and attracting attention In this book, we connect the dots between the difficulties faced by traditional online markets; the error of the stock market’s trusted pricing mechanism; and the rise of markets rich with data We argue that a reboot of the market fueled by data will lead to a fundamental reconfiguration of our economy, one that will be arguably as momentous as the Industrial Revolution, reinventing capitalism as we know it The market is a tremendously successful social innovation It’s a mechanism to help us divvy up scarce resources efficiently That’s a simple statement—with enormous impact Markets have enabled us to feed, clothe, and house most of billion humans, and to greatly improve their life expectancy as well as life quality Market transactions have long been social interactions, making them superbly well aligned with human nature That’s why markets seem so natural to most of us and are so deeply ingrained in society’s fabric They are the building blocks of our economy To their magic, markets depend on the easy flow of data, and the ability of humans to translate this data into decisions—that’s how we transact on markets, where decision-making is decentralized This is what makes markets robust and resilient, but it requires that everyone has easy access to comprehensive information about what’s available Until recently, communicating such rich information in markets was difficult and costly So we used a workaround and condensed all of this information into a single metric: price And we conveyed that information with the help of money Price and money have proved to be an ingenious stopgap to mitigate a seemingly intractable challenge, and it worked—to a degree But as information is compressed, details and nuance get lost, leading to suboptimal transactions If we don’t fully know what is on offer or are misled by condensed information, we will choose badly For millennia, we tolerated this inadequate solution, as no better alternative was available That’s changing Soon, rich data will flow through markets comprehensively, swiftly, and at low cost We’ll combine huge volumes of such data with machine learning and cutting-edge matching algorithms to create an adaptive system that can identify the best possible transaction partner on the market It will be easy enough that we’ll this even for seemingly straightforward transactions Suppose, for instance, you are looking for a new frying pan An adaptive system, residing perhaps on your smartphone, accesses your past shopping data to gather that you bought a pan for induction cooktops last time, and also that you left a so-so review of it Parsing the review, the system understands that the pan’s coating really matters to you, and that you favor a ceramic one (it also notes your preferred material for the grip) Equipped with these preferences, it then looks at online markets for optimal matches, even factoring in the carbon footprint of the delivery (because it knows how worried you are about that) It negotiates automatically with sellers, and because you are ready to pay by direct transfer it is able to get a discount With a single tap, your transaction is complete It sounds seamless and simple—because it should be It’s far faster and less painful than having to the search yourself, but it also takes into account more variations and evaluates more offers than you would Neither does the system tire easily (as we humans when searching for something offline or online), nor is it distracted in its decision advice by price, derailed by cognitive bias, or lured by clever marketing Of course, we’ll still use money as a store of value, and price will still be valuable information; but no longer being focused on price broadens our perspective, yields better matches, a more efficient transaction, and, we believe, less trickery in the market Such decision-assistance systems based on data and machine learning will help us identify optimal matches in these data-rich markets, but we humans will retain the ultimate decision-making power and will decide how much or how little we delegate as we transact That way we can happily have our decision-assistance system hail a ride for us, but when it comes to our next job, we’ll choose ourselves from among the employment options our data-driven advisers suggest Conventional markets have been highly useful, but they simply can’t compete with their datadriven kin Data translates into too much of an improvement in transactions and efficiency Data-rich markets finally deliver what markets, in theory, should always have been very good at—enabling optimal transactions—but because of informational constraints really weren’t The benefits of this momentous change will extend to every marketplace We’ll see it in retail and travel, but also in banking and investment Data-rich markets promise to greatly reduce the kind of irrational decision-making that led to Yahoo’s crazy stock price in 2014 and to diminish bubbles and other disasters of misinformation or erroneous decision-making that afflict traditional money-based markets We have experienced the debilitating impact of such market disasters in the recent subprime mortgage crisis and in the 2001 burst of the dot-com bubble, but also in the countless calamities that have affected money-based markets over the past centuries The promise of data-rich markets is not that we’ll eradicate these market failures completely, but that we’ll be able to greatly reduce their frequency and the resulting financial devastation Data-rich markets will reshape all kinds of markets, from energy markets, where built-in inefficiencies have lined the pockets of large utilities and deprived households of billions in savings, to transportation and logistics, and from labor markets to health care Even in education, we can use markets fueled by data to better match teachers, pupils, and schools The goal is the same for all datarich markets: to go beyond “good enough” and aim for perfection, giving us not just more bang for the buck, but more satisfaction in the choices we make, and a more sustainable future for our planet THE KEY DIFFERENCE BETWEEN CONVENTIONAL MARKETS and data-rich ones is the role of information flowing through them, and how it gets translated into decisions In data-rich markets, we no longer have to condense our preferences into price and can abandon the oversimplification that was necessary because of communicative and cognitive limits This makes it possible to pair decentralized decision-making, with its valuable qualities of robustness and resilience, with muchimproved transactional efficiency To achieve data-richness, we need to reconfigure the flow and processing of data by market participants, an idea that was already suggested as far back as 1987 Massachusetts Institute of Technology (MIT) professor Thomas Malone and his colleagues foresaw “electronic markets,” but only recently have we achieved the technical progress to extend that early vision and bring it into full bloom One may assume that the advent of data-rich markets rests mainly on advances in data-processing capacity and network technology After all, far more information permeates data-rich markets compared with conventional ones, and Internet bandwidth has been increasing steadily with no end in sight Leading network technology providers such as Cisco suggest that growth rates in Internet traffic will continue to exceed 20 percent per year until at least 2021—a rate that when compounded over just a decade will add up to a staggering 500 percent upturn Processing capacity has risen dramatically, too: we now measure our personal computer’s power in thousands of billions of calculations per second, and we still have room for improvement, even if that power may no longer be doubling every two years as it has in the past These are necessary developments toward data-rich markets, but they aren’t sufficient What we need is to things not just faster but to them differently In our data-rich future, it will matter less how fast we process information than how well and how deeply we so Even if we speed up the communication of price on traditional markets to milliseconds (as we have already done with highfrequency trading), we’d still be oversimplifying Instead, we suggest that we need to put recent breakthroughs to use in three distinct areas: the standardized sharing of rich data about goods and preferences at low cost; an improved ability to identify matches along multiple dimensions; and a sophisticated yet easy-to-use way to comprehensively capture our preferences Just getting raw data isn’t enough; we need to know what it signifies, so that we don’t compare apples with oranges With recent technical breakthroughs, we can that far more easily than in the past Just think of how far we have come in the ability to search our digital photos for concepts, such as people, beaches, or pets What works for images in our photo collections can be applied to markets and can translate data into insights that inform our decision-making Identifying best matches is easy when we compare only by price; but as we look for matches along numerous dimensions, the process gets complex and messy, and humans easily get overwhelmed We need smart algorithms to help us Fortunately, here, too, substantial progress has been made in recent years Finally, knowing exactly what we want isn’t easy We may forget an important consideration or erroneously disregard it; for humans, it’s actually quite difficult to articulate our multifaceted needs in a simple, structured way That’s the third area in which recent technical advancements matter And today, adaptive systems can learn our preferences over time as they watch what we are doing and track our decisions In all three of these areas, highly evolved data analytics and advanced machine learning (or “artificial intelligence,” as it is often called) have fueled important progress When combined, we have all the key building blocks of data-rich markets Digital thought leaders and energetic online entrepreneurs are already taking note There is a gold rush just around the corner, and it will soon be in full swing It’s a rush toward data-rich markets that deliver ample efficiency dividends to their participants and offer to the providers a sizable chunk of the total transaction volume The digital innovations of the last two decades are finally beginning to alter the foundations of our economy Some companies have already set their sights on data-rich markets and put the necessary pieces in place As eBay celebrated its twentieth anniversary and pondered its future, its new CEO announced a highly ambitious, multiyear crash program and forged a number of key acquisitions The aim is to greatly improve the flow of rich information on the marketplace at all levels, to ease discovery of matches, and to assist eBay users in their transaction decisions EBay is not alone From retail behemoth Amazon and niche players, such as BlaBlaCar, to talent markets, marketplaces are reconfiguring themselves and pushing into a data-rich future Because datarich markets are so much better at helping us get what we need, we’ll use them a lot more than traditional markets, further fueling the shift from conventional markets to data-rich ones But the impact of data-rich markets is far larger, the consequences far bigger MARKETS AREN’T JUST FACILITATING TRANSACTIONS When we interact on markets, we coordinate with each other and achieve beyond our individual abilities By reconfiguring markets and making them data rich, we shape human coordination more generally If done well, market-driven coordination greased by rich data will allow us to meet vexing challenges and work toward sustainable solutions, from enhancing education to improving health care and addressing climate change Gaining the ability to better coordinate human activity is a big deal This will have repercussions for more conventional ways of coordinating our activities Among them, the most well known and best studied is the firm The stories we usually tell about firms are about vicious competition between them, whether it is General Motors versus Ford, Boeing versus Airbus, CNN versus Fox News, Nike versus Adidas, Apple versus Google, or Baidu versus Tencent We love tales about individual battles that bloodied one of the contestants and advanced the position of the other Entire libraries of business books and hundreds of business-school cases are dedicated to chronicling and analyzing these epic battles But rather than battles between firms, we now see a more general shift from firms to markets, as the market, thanks to data, gets so much better at what it does This shift doesn’t mean the end of the firm, but it represents its most formidable challenge in many decades Responding to the rise of data-rich markets isn’t going to be simple If firms could utilize the technical breakthroughs we describe, reshape the flow of information within them, and capture similar efficiency gains, it would be straightforward Alas, as we’ll explain, the technical advances that underlie and power data-richness can’t be used as easily in firms as they can in markets They are constrained by the way information flows in firms To adapt, the nature of the firm will need to be reimagined Possible responses to the challenge from data-rich markets involve finding ways to either more narrowly complement or emulate them Firms might automate decision-making of (certain) managerial decisions and introduce more marketlike features, such as decentralized information flows and transaction-matching These strategies offer medium-term advantages, and they are being adopted in a growing number of companies They are useful for ensuring the continuing existence of firms in the medium term (although they bring their own set of weaknesses), but they are unlikely in the long run to stop the slide of the firm’s relevance in organizing human activity Just as firms will continue to have some, albeit diminished, role to play in the economy, in the future we’ll also still use money, but in data-rich markets money will no longer play first violin As a result, banks and other financial intermediaries will need to refocus their business models And they are going to need to move quickly, as a new breed of data-driven financial technology companies, the so-called fintechs, are embracing data-rich markets and challenge the conventional financial services sector It is easy to see how banking will be severely affected by the decline of money, but the implications are larger, and more profound At least in part, the role of finance capital rests on the informational function it plays in the economy But as data takes over from money, capital no longer provides as strong a signal of trust and confidence as it currently does, undermining the belief that capital equates with power that underlies the concept of finance capitalism Data-richness enables us to disentangle markets and finance capital by furthering the one while depreciating the other We are about to witness both the rather immediate reconfiguration of the banking and finance sector, and the later but more profound curbing of the role of money, shifting our economy from finance to data capitalism DATA-DRIVEN MARKETS OFFER SUCH COMPELLING ADVANTAGES over traditional, money-based ones that their advent is assured But they are not without shortcomings of their own The fundamental problem is the reliance on data and machine learning and the lack of diversity of data and algorithms These make them particularly vulnerable to troubling concentration as well as systemic failure Because of this structural weakness (which we’ll explain further), data-rich markets could turn into enticing targets for ruthless companies and radical governments to not only cripple the economy but also undermine democracy To mitigate this vulnerability, we propose an innovative regulatory measure A progressive data-sharing mandate would ensure a comprehensive but differentiated access to feedback data and would maintain choice and diversity in decision assistance It’s not only the antitrust measure of the data age, but it also guards against far bigger and more sinister developments that could threaten society The rise of a market in which a substantial part of the transactional process is automated, and the decline of the firm as the dominating organizational structure to organize human activity efficiently will uproot labor markets around the world Nations will face the need to respond to this profound shift in the economy as it endangers many millions of jobs, fuels widespread worries in countless nations, and is already driving populist political movements As we’ll detail, many of the conventional policy measures at our disposal are unfortunately no longer effective A shift from finance to data capitalism will question many long-held beliefs, such as work as a standardized bundle of duties and benefits Breaking up this bundle is going to be a challenging but necessary strategy for firms looking for the right human talent, and for societies worried about mass unemployment, to bring back to employees jobs as well as meaning and purpose Central to the changes we’ll witness in labor markets is data Comprehensive and rich data flows drive the revival of the market and the decline of firms and money, prompting massive upheavals in the labor market By the same token, rich data also enables us to upgrade labor markets so that they’ll offer far more individualized and satisfying work far more easily and more frequently than before (although, as we decline in, 1–3 network effects and, 163 worth of goods traded on, Economist, 89 education sector, 6–7, 199, 214 Ek, Daniel, 122–123 Embark, 182 Emergency Economic Stabilization Act of 2008, 134 Encyclopédie, 21 energy markets, 213 enterprise resource planning (ERP), 100 ESPN, 67, 69 eToro, 152 Europe, 135, 136, 164, 196, 198 European Parliament, 187 European Union, 140 evolution, 20–21, 22 Expedia, 70 Expertmaker, 70 externalities, 73, 74 Ezrachi, Ariel, 166 Facebook, 30, 148, 178, 196 feedback effects and, 169 market concentration in, 161 network effects and, 163, 166 fair value, 172–173 feedback effects, 78–80, 104, 157–179, 210, 211 development of theory, 159–160 government control via, 175–179 market concentration and, 161–169, 171 regulatory measures proposed for, 171–175 threat posed by, 166–167 Ferguson, Niall, 45 Ferrucci, David, 115 finance capital See capital financial crisis of 2007 See Great Recession/financial crisis financial intermediaries, 12, 146–156 choice expansion in, 215–216 payment solutions and, 146–147 regulations affecting, 139–140 traditional role of, 138–139 See also banks Finkel, Eli, 83, 84 Finland, 147, 191 fintechs, 11 banks investing in, 149–156 niche markets targeted by, 147, 152 worldwide investments in, 149 firms, 87–107, 109–131 Amazon as, 88–89, 106 automation in, 109, 111–112, 113–120, 128, 130–131 centralization in (see centralization) cognitive constraints and, 102–104 communicative coordination and, 26, 28–33, 90, 102 comparison of markets and, 28, 111 competition between markets and, 30, 107 decline in influence of, 12–13, 33 delegation in, 97–101, 106, 117 efficiency as focus of, 112–113 estimated number of, 28 human-centric, 214–215 internal talent management in, 126–129 intuition and heuristics in, 104–106 key difference between markets and, 32–33, 90 “noise” reduction strategies in, 100–101 organizational innovation in, 97, 110–111, 120–131 profits of, 195–197 reporting methods in, 90–97 rise in importance of, 33 shift to markets from, 10–11, 30–32, 125–126 structure of, 29–30 superstar, 195–197 tax credits for job creation proposed, 200–202 Flores, Fernando, 175–176 flying shuttle, 111 Forbes, 209 Ford, Henry, 29–30, 114 Ford Motor Company, 29–30, 31, 33, 98, 99–100 Fortune magazine, 208 Fox News, 178 Freightliner, 182 Friedman, Milton, 190 Fukoku Mutual Life Insurance, 109, 110–111, 113–114, 117, 120, 183, 188 fully automated luxury communism, 221 fundamental attribution error, 103 Funding Circle, 152, 163 Gates, Bill, 187 Gawande, Atul, 101 General Motors (GM), 98–99, 101 Germany, 134, 135, 136 gig economy, 186 Gigerenzer, Gerd, 105 Giza pyramids, 21 Glassdoor, 88 GoDaddy, 161 gold standard, 48 “Goobles,” 51 Google, 78, 110, 148, 151, 161, 196 antitrust case against, 165 feedback effects and, 30, 163, 169 prediction markets and, 50–51 Google Glass, 138 Google Shopping, 52 government, central planning for, 175–179 grain (as currency), 47 Great Depression, 51, 136 Great Famine (Soviet Union), 177 Great Recession/financial crisis, 134–135, 136, 215 See also subprime mortgage crisis Great Wall of China, 21, 24 Grünenthal, 42 Guardian, 221 Hagel, John, 31 Harvard Business Review, 99 Harvard Business School, 96 Harvard Medical School, 101 Harvard University, 45 Hayek, Friedrich August von, 39, 46–47 health care sector, 213–214 heuristics, 104–106 Higgs boson, 22 Hollerith, Herman, 96, 99 Holvi, 147 Honda, 30, 32 Huawei, 196 human choice See choice Human Use of Human Beings, The (Wiener), 160 human-centric firms, 214–215 Hurricane Grace, 133–134 IBM, 96, 109, 127, 163, 183 India, 184 Industrial Revolution, 4, 111, 162, 188, 201 Infi (machine learning system), 79 information asymmetry, 40, 72–73 information flows centralized vs decentralized (see centralization; decentralization) complexity of processing, 43–44 cost of, 39–40, 44, 45, 81 data-rich markets and, 63, 64, 70, 72–73, 81 dyadic, 72 firms and, 90–100, 102, 106 historical improvements in, 51–52 impact of bad information on, 39 information overload and, 40–41, 70 information sharing and, 41, 46 information shortage and, 40, 52–56 markets and, 38–57, 63, 90 obstacles to, 41–42 innovation, 166, 199, 200 Instagram, 163 intercontinental nuclear missiles, 159 interest rates, 134–135, 143, 150–151, 165, 194 internal talent management, 126–129 Internet network effects and, 163 projected growth rates in traffic, 7–8 See also specific sites investment banking, 150, 154–155 investors, 143–144 capital share of, 185, 186 opportunities in data-rich markets, 144–145 problems caused by data-rich markets, 143–144 returns for, 195 “invisible hand” (Adam Smith), 27 InvisibleHand (app), 52 iPad, 54–55 iPhone, 136, 164 Italy, 134, 136 iTunes, 121–122 Japan, 30–31, 32 Jensen, Robert, 36 JetBlue, 112 Jobs, Steve, 54–55 Kabbage, 150 Kahneman, Daniel, 102 Kant, Immanuel, 223 Kawasaki, 30 Kay, John, 111 Kayak, 3, 52 Kensho, 155 Kerala fishermen, 35–37, 39 Kickstarter, 152–153, 156 kidneys, donor, 73–74 Kleiner, Eugene, 216 labor augmenting technology, 194 labor market, 13, 181–206 algorithms used in, 75 automation in, 181–188, 200–202, 205 declining participation in, 183–184 distributive measures for, 186–187, 189, 190, 193, 197–200 money unbundled from, 203–206, 218 participatory measures for, 186, 188–189, 190, 193, 200–202 self-employment in, 185–186 universal basic income and, 189–193, 205–206 labor share, 184–186, 188, 193–195, 198 labor unions, 205 Lake, Katrina, 207–209 Large Hadron Collider, 22, 25 Le Pen, Marine, 186 Lehman Brothers, 155 lending by banks, 150–151 by fintechs, 152–153 Les, Jason, 59–60 liberals, 190 libertarians, 190–191 Libratus (machine learning system), 59–62, 78 Lindblom, Charles, 23, 26 LinkedIn, 202 Linnaeus, Carolus, 22, 23 Linux, 166 Lorentzon, Martin, 122 Lufax, 152 LVMH, 75 machine learning See automation/machine learning Mainichi Shimbun, 109 Malone, Thomas, MAN, 182 market failure, reducing, markets, 35–57 Amazon as, 87–88 chaotic, unplanned nature of, 160 choice limitations in, 13–14 communicative coordination and, 26–28, 30–33 comparison of firms and, 28, 111 competition between firms and, 30, 107 concentrated, 161–169, 171, 217 data-rich (see data-rich markets) decentralization in (see decentralization) feedback effects and, 160–175 fintechs and, 153 historical improvements in, 51–52 irrational decision-making in, 42–44 key difference between firms and, 32–33, 90 limitations of, 63 network effects and, 162–166 for noneconomic activities, 49–50 physical design of, 160–161 prediction, 50–51 resilience of, 39 scale effects and, 162–166 shift from firms to, 10–11, 30–32, 125–126 success of, 4, 49–50, 222 thick, 2, 82–83, 164, 213 Martin, Walt, 181–182 Marx, Karl, 143, 162 Mason, Vicki, 42 Massachusetts Institute of Technology (MIT), 7, 142, 159, 184, 195, 220 matching, 8–9, 11, 64, 66, 71–85, 212 algorithms for (see algorithms) centralized, 74 complexity of task, 43–44 in conventional vs data-rich markets, 70–71 decentralized, 74, 127 fintechs and, 151–152 firms and, 127–129 nonmarket providers of, 75–76 variety of contexts for, 74–75 Max Planck Institute for Human Development, 105 McAfee, Andrew, 184 McDonald’s, 215 McGovern, George, 190 McNamara, Robert, 99–100 Medici, Cosimo de’ the Elder, 92, 93 Medici family, 91, 93 Mercedes-Benz, 110 Merrill, Douglas, 151 Merrill Lynch, 155 metadata, 66 Micro Ventures, 152–153 Microsoft, 165, 166, 169 Microsoft Imagine Cup, 75 Minyons club, 17–20 mobile phones iPhone, 136, 164 Kerala fishermen and, 36–37 payment business and, 147 Model T Ford, 29, 98, 162 money, 4, 45–57, 63, 64, 143–144, 212 advantages of using, 45–49 banks’ decreased use of, 136–137 data as a substitute for, 148–149 future role of, 5, 149 historical forms of currency, 47–48 importance of linked to utility, 45 informational function of, 48–49 intrinsic value not required for, 48 market efficiency improved by, 47–49 move from physical to virtual, 48 role of capital affected by demise of, 141 signaling with, 142 work unbundled from, 203–206, 218 See also capital; price monopolies, 30, 203 moon landing, 22, 159 Mosaic, 189 motorcycle manufacturing, 30–32, 33 Musk, Elon, 78, 189 My Years with General Motors (Sloan), 99 MySpace, 166 NASDAQ Composite Index, 196 National Aeronautics and Space Administration (NASA), 22 national champions, 30 National Oceanic and Atmospheric Administration, 133 negative income tax, 190 Netflix, 74, 75, 161, 196, 209 Netherlands, 191 network effects, 162–166 New York Central Railroad, 96 New York Times, 88–89, 208–209 Nixon, Richard, 190 Nobel Prize winners, 39, 74, 190 nominal tax rate, 198 Nordstrom, 211 Northwestern University, 83, 194 oligopolies, 30 Omidyar, Pierre, ontology, 67–70, 81, 84, 136 defined, 67 firms and, 128 labor market and, 204 Organization for Economic Cooperation and Development (OECD) countries, 28 organized labor, 205 Orwell, George, 179 Otto, 181–183 Paine, Thomas, 190 Parthenon Group, 207 participatory policy measures, 186, 188–189, 190, 193, 200–202 patent system, 199 payment solutions businesses, 146–147, 149 PayPal, 135–136, 146, 189 Pearson, 69 Peep Trade, 76, 152 peer-to-peer lending, 152–153 Pentland, Sandy, 142 Peruzzi family, 91 Piketty, Thomas, 186 Pinterest, 210 poker, 59–62 populism, 13, 186 post-price retailers, 209 prediction markets, 50–51 preferences complexity of processing, 43–44 fintech extraction of, 151–152 improved means of capturing, 8, 64, 71–72, 76–81 standard language for comparing, 64 See also matching price, 7, 45–57 data-rich markets’ advantages over, 70–71, 72, 136–137 deemphasis on, 3, 122, 129, 136–137, 138, 212 detailed information lacking in, 4, 52–56 future role of, information condensed by, 4, 46–47, 48–49, 63, 65 internal talent management and, 128 markets and, 36 volatility of, 36 PriceBlink, 52 PriceGrabber, 52 PriOS, 115 privacy issues, 145, 174 Procter & Gamble, 128 profits, 195–197 progressive consumption tax (PCT), 198 progressive data-sharing mandate, 12, 171, 199, 203 choice expanded by, 217 explained, 167–169 Prüfer, Jens, 167 punch-card tabulator, 96 Qin, Emperor, 24 Rack Habit, 207–208 Rawls, John, 223 regulatory measures for banks and financial institutions, 139–140 for feedback problems, 171–175 research and development, 196 resource scarcity, overcoming, 220–221 retail sector, 138, 207–212 retirement savings, 143–144, 195 returns on investments, 195 Robinhood Markets, 146 robo tax, 186–187 Rognlie, Matthew, 194 Ron, Lior, 182–183 Roth, Alvin, 74 Ryanair, 112 Saberr, 75 salary bands, 128, 129 salt (as currency), 47 Samsung, 196 Sandholm, Tuomas, 60, 62 SAP, 100 scale effects, 162–166 Scania, 182 Schottmüller, Christoph, 167 Schumpeter, Joseph, 120 scientific management, 96 Second Payment Service Directive (European Union), 140 Seedcamp, 75 self-employment, 185–186 Shapley, Lloyd, 74 Shepherd, Alistair, 75 shipping industry, 213 SigFig, 3, 151–152, 153, 156 silver standard, 48 Simon, Herbert, 104 Simon, Julian, 220 Siri, 79, 164 Six Sigma, 112 Sloan, Alfred P., 98–99, 101 Sloan School of Business, 220 Smith, Adam, 27, 143, 223 Snapchat, 166 Social Security, 192 SoFi, 150, 151 Soll, Jacob, 91 Solomon, Madi, 69–70 SOP See standard operating procedure South Korea, 196 Soviet Union, 177 Spotify, 74, 75, 121–125, 196, 215 squadification, 123–125 St Peter’s Basilica, 21 Stalin, Joseph, 177 standard operating procedure (SOP), 100–101, 106 start-ups, 141, 146, 199 increased capital available for, 142–143 network effects and, 165–166 See also fintechs Stash, 151, 215 steam engine, 111, 113 steel industry, 161 Stitch Fix, 208–212, 215 stock markets, 146 decreased investment options in, 143 share prices in, 2–3, 6, 196 Stripe and Square, 147 Stucke, Maurice, 166 subprime mortgage crisis, 6, 41, 55–56, 134, 155, 173 Suez Canal, 21 superstar firms, 195–197 Suzuki, 30 Switzerland, 136 Synco See Cybersyn Systemized Intelligence Lab, 115 Taj Mahal, 21 talent management, internal, 126–129 tax credits, 200–202, 218 taxes, 197–202 capital gains, 187 data, 199–200, 203, 218 negative income, 190 nominal rate, 198 progressive consumption, 198 robo, 186–187 wealth, 187 Taylor, Frederick Winslow, 89, 95–96 Taylorism, 89, 95–96, 112 telecommunications industry, 162–163 Tesla, 78, 110, 120, 169, 189 thalidomide, 42 thick markets, 2, 82–83, 164, 213 Thiel, Peter, 203 time firm reorganization and, 112–113 meaningful use of, 221–222 Tinder, 83, 163 µ Torrent, 122 TransferWise, 135 transparency, 172, 173, 178 Trump, Donald, 186, 203 Trunk Club, 211 T-shaped skill set, 118 Tversky, Amos, 102 Twitter, 163 Uber, 163, 182 UBI See universal basic income UniCredit bank, 136 Unilever, 75 United Kingdom, 134, 147, 164 United States banking crisis in, 134, 135 capital share of, 185 corporate taxes in, 197–198 health care sector in, 213 labor market of, 184, 185, 186, 195 market concentration in, 164 stock market investment options in, 143 subprime mortgage crisis in (see subprime mortgage crisis) universal basic income proposed in, 190, 191 universal basic income (UBI), 189–193, 205–206 University of Pennsylvania’s Wharton School, 36 Upstart, 151 Upwork, used car market, 40 venture capital (VC) firms, 141, 142–143, 216 Vocatus, 55 Volkswagen, 182 Volvo, 182 Wall Street Journal, 203 Walmart, 28, 52 Walt Disney Company, 69 Watson (machine learning system), 109, 111, 113–114, 115, 117, 163, 183 Watt, James, 111, 113 wealth tax, 187 Webvan, 112 WeChat, 147, 163 Wedgwood, Josiah, 94 welfare reducing transactions, 73 Wenger, Albert, 156, 189 Wenig, Devin, 1–2, 209 Wharton School, 36 Which?, 52 Wiener, Norbert, 159–160, 179 Wikipedia, 21–22 Windows, 166 Wired magazine, 181 WordPress, 161 work See labor market Y Combinator, 191 Yahoo, 2–3, Yamaha, 30 Yegge, Steve, 88 YouTube, 67, 68–69 ZestFinance, 151 Zetsche, Dieter, 110, 121 Zopa, 152 Zulu Trade, 152 ... ABOUT THE AUTHOR ALSO BY VIKTOR MAYER- SCHÖNBERGER PRAISE FOR REINVENTING CAPITALISM IN THE AGE OF BIG DATA NOTES INDEX –1– REINVENTING CAPITALISM IT SHOULD HAVE BEEN A VICTORY CELEBRATION BY THE. .. priorities into a single unit of information The efficiency of the market is reflected in the simplicity of prices as conveyors of information In a system where the knowledge of relevant data is... decision-making Around the beginning of the new millennium, “innovative” financial institutions began to bundle together subprime mortgages—those carrying a higher risk of default—with other mortgages into