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Airbnb, airport security, Al Roth, Alvin Roth, Andrei Shleifer, attribution theory, autonomous vehicles, barriers to entry, Brownian motion, centralized clearinghouse, Chuck Templeton: OpenTable, clean water, conceptual framework, constrained optimization, continuous double auction, creative destruction, deferred acceptance, Donald Trump, Edward Glaeser, experimental subject, first-price auction, framing effect, frictionless, fundamental attribution error, George Akerlof, Goldman Sachs: Vampire Squid, Gunnar Myrdal, helicopter parent, information asymmetry, Internet of things, invisible hand, Isaac Newton, iterative process, Jean Tirole, Jeff Bezos, Johann Wolfgang von Goethe, John Nash: game theory, John von Neumann, Joseph Schumpeter, Kenneth Arrow, late fees, linear programming, Lyft, market clearing, market design, market friction, medical residency, multi-sided market, mutually assured destruction, Nash equilibrium, Occupy movement, Pareto efficiency, Paul Samuelson, Peter Thiel, pets.com, pez dispenser, pre–internet, price mechanism, price stability, prisoner's dilemma, profit motive, proxy bid, RAND corporation, ride hailing / ride sharing, Robert Shiller, Robert Shiller, Ronald Coase, school choice, school vouchers, sealed-bid auction, second-price auction, second-price sealed-bid, sharing economy, Silicon Valley, spectrum auction, Steve Jobs, Tacoma Narrows Bridge, technoutopianism, telemarketer, The Market for Lemons, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, trade route, transaction costs, two-sided market, uranium enrichment, Vickrey auction, Vilfredo Pareto, winner-take-all economy
., 22 Liu, Qihong, 128–129 Lyft car service, 173 MAD (doctrine of nuclear deterrence by mutually assured destruction), 26 mail-in-bids, for auctions, 83–84 “The Market for Lemons” (Akerlof), 44–51, 64 market frictions, 169–174 market fundamentalists, 16–17 market insights, 14–15 market makers, 107–110, 118–121 markets 18th-century book, 90–91 competitive, 35, 124–126, 172–174, 180–181 design, 133, 137–142 dysfunction of, 36, 75–77, 143 economics of platform, 107–112 equilibrium, 33 fixed-price versus auctions, 96–97 food bank system, 154–160 image problem of, 152–153 labor, 48, 64–66 lemon, 44–51, 58–59, 64, 112 multisided, 108–112, 118–124 one-sided, 108–112 in POW camps, 4, 7–13, 175–177 rules for platform, 112–117 school choice in Sweden, 151–152 selfishness in, 177–179 technology and, 169–173 trade with uninformed parties, 166–169 transformation of, 13–17 two-sided, 108–112, 118–124 See also auctions; economics; platforms Marx, Karl, 20, 23 matching problems middle school dance partners, 131–132, 134, 137–140 student to school, 138–139, 141–142, 143–149 mathematics algebraic topology, 44–45 economic theory transformed by, 15, 19–27 game theory, 136 general equilibrium model, 29, 31–34, 36–37, 40, 45, 76 kidney exchange algorithm, 163–165 models, 20, 24–25, 30 in real world economics, 35–37 Samuelson connecting economics and, 28–29 Shapley-Gale algorithm, 137–140 Matsuzaka, Daisuke, 79–81, 87–89 Maxwell, James Clark, 24 McManus, Brian, 73–75 mechanism design, 133, 134 medical residency programs, 140 merchant from Prato, 105–107 middle school dance-matching, 131–132, 134, 137–140 Milgrom, Paul, 70–71, 98, 102–103 mobile market platform, 116 modeling applied theory, 45, 50, 75–76 competition, 35, 166, 172–173 congestion pricing, 86, 94 dysfunction of, 75–77 economic, 15, 24–29 mathematical, 20, 24–25, 30 reality-based economic, 35–37, 45, 49–51, 141 models auction, 82–84 eBay, 43, 46, 48 general equilibrium, 31–34, 36–37, 40, 76 lemons, 44–51, 58–59, 64, 112 Solow, 35 See also platforms; signaling model Moldovanu, Benny, 90–91 money burning costs, 70–71 money-back guarantees, 69–71 Morals & Markets: The Development of Life Insurance in the United States (Zelizer), 153 Morgenstern, Oskar, 25–27 mortality rates, of Japanese vs German POW camps, 10–13 MS-13 gang, 67 multisided markets, 108–112, 118–124 multisided platform, 14 multiunit Vickrey auction, 93 Murphy, Frank, 9 Nasar, Sylvia, 29 Nash, John, 32 National Archives’ World War II Prisoners of War Data File, 11 network externalities, 121–124 New England Program for Kidney Exchange, 164–165 New York Department of Education, 143–144, 145, 149 Nobel Prize in Economics, 34 See also Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel noncustomers, paying, 123–124 Nordstrom’s return policy, 69–70 no-risk money-back guarantees, 69–71 normal good, 180 no-trade rule, Japanese POW camps, 10–13 nuclear deterrence, 26 Omidyar, Pierre, 39–40 one-sided markets, 108–112 online retail, 41–43, 52–55 optimized efficiency, 85–86, 133 organ sales, 160–161 organizations, sick, 142–143 out-of-town bids, for auctions, 83–84 Pareto, Vilfredo, 20, 21–22 Pareto efficiency, 22 Penny Black stamp, 82–84 Percy P.
In many of its new shapes and incarnations—the innumerable e-commerce sites, the airline ticket you bought online for your next vacation, the digital magazine subscription that substitutes for the paper ones you used to read—today’s markets are governed by the same market principles that Radford documented in 1945, just a lot bigger and faster. At the same time, these principles are getting applied in ever-broader, more novel, and more sophisticated contexts. Ever wonder where the ads come from when you perform a Google search? They appear based on principles of auction design that didn’t exist in 1945. And that smart phone in your pocket? It’s both a technological and market innovation, what economists call a multisided platform. You acquire apps—sometimes paying for them but not always—created by developers on the other side of the trade. The free apps survive by delivering messages from advertisers who sit on yet another side of the phone-as-platform. And finally the phone itself is essentially another piece of the phone “ecosystem” built around the operating system—Android or iOS or Windows—that ultimately directs traffic in this many-sided set of relationships.
The count of Champagne was, in his medieval way, a pioneer in market design. And the curious story of the merchant of Prato, his delinquent customer, and the count’s response illustrates some of the principles that make a market platform tick. As economists have focused their modeling efforts ever more on real world phenomena, leading researchers have turned their attention to platforms, bringing some much-needed clarity to the rules that dictate how these multisided markets work. As a result, we now have a deeper understanding of what makes a platform work and a set of guiding principles—many of which can be traced back to twelfth-century innovations in market design—that can help us build them better. Since platforms now encompass such significant parts of our lives, it’s important to understand the trade-offs that come with participating in them. The Economics of Platforms It’s not exactly clear when and why people started referring to economic phenomena like medieval fairs, credit cards, and internet services as platform markets.
3D printing, A Declaration of the Independence of Cyberspace, AI winter, Airbnb, Albert Einstein, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, commoditize, computer age, connected car, crowdsourcing, dark matter, dematerialisation, Downton Abbey, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, game design, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, Marc Andreessen, Marshall McLuhan, means of production, megacity, Minecraft, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, recommendation engine, RFID, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, the scientific method, transport as a service, two-sided market, Uber for X, Watson beat the top human players on Jeopardy!, Whole Earth Review, zero-sum game
Unlike traditional two-sided markets—say, a farmers’ market that enables buyers and sellers—a platform ecosystem became a multisided market. A good example of this is Facebook. The firm created some rules and protocols that formed a marketplace where independent sellers (college students) produced their own profiles, which were matched up in a marketplace with their friends. The attention of the students was sold to advertisers. Game companies sold to students. Third-party apps sold to advertisers. Third-party apps sold to other third-party apps. And so on in multiple-way matches. This ecosystem of interdependent species keeps expanding, and will keep expanding as long as Facebook can manage its rules and its own growth as a firm. The wealthiest and most disruptive organizations today are almost all multisided platforms—Apple, Microsoft, Google, and Facebook. All these giants employ third-party vendors to increase the value of their platform.
A couple of maverick startups in 2016 are trying to disrupt the current attention system, but it may take a number of tries before some of the radical new modes stick. The missing piece between this fantasy and reality is the technology to track the visits, to weed out fraud, and quantify the attention that a replicating ad gets, and then to exchange this data securely in order to make a correct payment. This is a computational job for a large multisided platform such as Google or Facebook. It would require a lot of regulation because the money would attract fraudsters and creative spammers. But once the system was up and running, advertisers would release ads to virally zip around the web. You catch one and embed it in a site. It then triggers a payment if a reader clicks on it. This new regime puts the advertisers in a unique position. Ad creators no longer control where an ad will show up.
All these giants employ third-party vendors to increase the value of their platform. All employ APIs extensively that facilitate and encourage others to play with it. Uber, Alibaba, Airbnb, PayPal, Square, WeChat, Android are the newer wildly successful multiside markets, run by a firm, that enable robust ecosystems of derivative yet interdependent products and services. Ecosystems are governed by coevolution, which is a type of biological codependence, a mixture of competition and cooperation. In true ecological fashion, supporting vendors who cooperate in one dimension may also compete in others. For instance, Amazon sells both brand-new books from publishers and, via its ecosystem of used-book stores, cheaper used versions. Used-book vendors compete with one another and with the publishers.
Matchmakers: The New Economics of Multisided Platforms by David S. Evans, Richard Schmalensee
Airbnb, Alvin Roth, big-box store, business process, cashless society, Chuck Templeton: OpenTable, creative destruction, Deng Xiaoping, if you build it, they will come, information asymmetry, Internet Archive, invention of movable type, invention of the printing press, invention of the telegraph, invention of the telephone, Jean Tirole, John Markoff, Lyft, M-Pesa, market friction, market microstructure, mobile money, multi-sided market, Network effects, Productivity paradox, profit maximization, purchasing power parity, QR code, ride hailing / ride sharing, sharing economy, Silicon Valley, Snapchat, Steve Jobs, Tim Cook: Apple, transaction costs, two-sided market, Uber for X, Victor Gruen, winner-take-all economy
Matchmakers has three parts. Part I (chapters 1–3) presents an overview of the new economics of multisided platforms and shows how this business model with ancient roots has been turbocharged by modern technologies. Part II (chapters 4–10) provides a deep dive into key concepts that matchmakers must deal with in building, igniting, and operating their businesses. Finally, part III (chapters 11–13) describes how turbocharged, multisided platforms are creating new industries, destroying old ones, and forcing existing businesses to reinvent themselves to survive. A glossary at the back of the book provides definitions for the key concepts surrounding multisided platforms. To understand the perils and promise of multisided platforms we begin by describing the arduous journey, and near-death experiences, of a matchmaker we often use.
The newspaper has to weigh the costs and benefits of the balance between ads and content in designing its newspaper. The core of this book in part II uses case studies of multisided platforms to provide a deeper understanding of the concepts that we’ve presented in this chapter. We focus on six critical issues that multisided platforms must address. The opportunity for a multisided platform ordinarily arises when frictions keep market participants from dealing with each other easily and directly. Entrepreneurs can identify opportunities for starting a matchmaker by looking for significant transaction costs that keep willing buyers and sellers apart and that a well-designed matchmaker can reduce. Multisided platforms have to secure critical mass in order to ignite. They have to solve the chicken-and-egg problem of getting both sides on board, in adequate numbers, to create value.
It just needed to recruit drivers and hire lawyers to fight regulatory battles. Users still have to get access to these foundational multisided platforms. The cost of obtaining a connection and the cost of uploading and downloading data has declined dramatically, however, making it possible for 44 percent of people on earth to have access to the Internet.20 A considerable portion of the remaining 56 percent will obtain access in the next few years as cellular networks expand and as costs come down further. Global multisided platforms such as Facebook and Google, which benefit from getting more people on board, are investing in satellite and other technologies for spreading the Internet to the poorest parts of the world. Thus, multisided platforms, powered by fixed and mobile ISPs, themselves multisided platforms, can connect billions of people and millions of companies around the globe.
Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker
3D printing, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, big data - Walmart - Pop Tarts, bitcoin, blockchain, business process, buy low sell high, chief data officer, Chuck Templeton: OpenTable, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, digital map, discounted cash flows, disintermediation, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Haber-Bosch Process, High speed trading, information asymmetry, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, Khan Academy, Kickstarter, Lean Startup, Lyft, Marc Andreessen, market design, Metcalfe’s law, multi-sided market, Network effects, new economy, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Satoshi Nakamoto, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the payments system, Tim Cook: Apple, transaction costs, two-sided market, Uber and Lyft, Uber for X, winner-take-all economy, zero-sum game, Zipcar
As we saw in chapter 2, the new technology-driven communities known as platform businesses are creating a vast amount of new wealth outside the firm, and these external benefits must be designed and managed fairly. Because these value-creating networks grow faster outside the firm than inside, ruling the ecosystem wisely puts a premium on not ruling it selfishly. If navigating the rules for governance is hard for one-sided platforms like the Keurig brewing system, it’s exponentially harder when platforms are multisided. After all, multisided platforms involve numerous interests that don’t always align. This makes it difficult for platform managers to ensure that various participants create value for one another, and it makes it likely that conflicts will emerge that governance rules must resolve as fairly and efficiently as possible. This is a juggling act that even giants and geniuses often get wrong. Facebook, for example, has alienated users with its privacy policies.4 LinkedIn has angered its developers by turning off their access to APIs.5 And Twitter has expropriated technologies developed by other members of its ecosystem while permitting Twitter users to harass one another.
Understanding this forces a shift in corporate governance from a narrow focus on shareholder value to a broader view of stakeholder value. Market designer and Nobel Prize-winning economist Alvin Roth described a model of governance that uses four broad levers to address market failures.19 According to Roth, a well-designed market increases the safety of the market via transparency, quality, or insurance, thereby enabling good interactions to occur. It provides thickness, which enables participants from different sides of a multisided market to find one another more easily. It minimizes congestion, which hampers successful searches when too many people participate or low quality drives out high. And it minimizes repugnant activity—which explains why platform designers forbid porn on iTunes, human organ sales on Alibaba, and child labor on Upwork. According to Roth, good governance occurs when market managers use these levers to address market failures.
Bill Gurley, “All Revenue Is Not Created Equal: Keys to the 10X Revenue Club,” Above the Crowd, May 24, 2011, http://abovethecrowd.com/2011/05/24/all-revenue-is-not-created-equal-the-keys-to-the-10x-revenue-club/. 23. Douglas MacMillan, “The Fiercest Rivalry in Tech: Uber vs. Lyft,” Wall Street Journal, August 11, 2014; C. Newton, “This is Uber’s Playbook for Sabotaging Lyft,” The Verge, August 26, 2014, http://www.theverge .com/2014/8/26/6067663/this-is-ubers-playbook-for-sabotaging-lyft. CHAPTER 11: POLICY 1. Kevin Boudreau and Andrei Hagiu, Platform Rules: Multi-Sided Platforms as Regulators (Cheltenham, UK: Edward Elgar, 2009), 163–89. 2. Malhotra and Van Alstyne, “The Dark Side of the Sharing Economy.” 3. Felix Gillette and Sheelah Kolhatkar, “Airbnb’s Battle for New York,” Businessweek, June 19, 2014, http://www.bloomberg .com/bw/articles/2014-06-19/airbnb-in-new-york-sharing-startup-fights-for-largest-market. 4. Ron Lieber, “A Liability Risk for Airbnb Hosts,” New York Times, December 6, 2014. 5.
Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson
3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, book scanning, British Empire, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, Plutocrats, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, two-sided market, Uber and Lyft, Uber for X, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day
They have a variety of other levers to manage, including the user interface and user experience, reputation systems, marketing budgets, and core network technology. The most successful platform owners carefully curate the value that each side of the market gets from participating and aren’t too greedy. Once you understand the logic of two-sided markets, the next step is to apply it to multisided markets. Two-sided markets often become multisided markets with dozens or even thousands of distinct subgroups interacting through the platform. For instance, iTunes is a great way to get music on an iPhone. The more artists who put their music on iTunes, the more attractive it is to buy an iPhone. That’s a nice two-sided network. But the increase in iPhone sales not only makes iTunes more attractive to music artists; it also increases the value of the platform for developers Pandora, Waze, Uber, Lyft, Evernote, Clash of Clans, and every other mobile app.
Louis, 163 fees Stripe, 172–73 in two-sided networks, 215 fiat currencies, 280, 286, 305 FICO scores, 46–47 file sharing platforms, 144–45 film photography, 131 financial crisis (2008), 285, 308 financial services automated investing, 266–70 crowdlending platforms, 263 as least-trusted industry, 296 and regulation, 202 TØ.com, 290 virtualization of, 91 find-fix-verify, 260 firms economics of, 309–12 theory of, See TCE (transaction cost economics) FirstBuild, 11–14 Fitbit, 163 5G wireless technology, 96 fixed costs, 137 flat hierarchy, 325 Fleiss, Jennifer, 187 Flexe, 188 focus groups, 189–90 “food computers,” 272 food preparation recipes invented by Watson, 118 robotics in, 93–94 Forbes magazine, 303 forks, operating system, 244 Forsyth, Mark, 70 “foxes,” 60–61 fraud detection, 173 “free, perfect, instant” information goods complements, 160–63 economics of, 135–37 free goods, complements and, 159 freelance workers, 189 free market, See market “freemium” businesses, 162 Friedman, Thomas, 135 Friendster, 170 Fukoku Mutual Life, 83 Gallus, Jana, 249n garments, 186–88 Garvin, David, 62 Gazzaniga, Michael, 45n GE Appliances, 15 Gebbia, Joe, 209–10 geeky leadership, 244–45, 248–49 gene editing, 257–58 General Electric (GE), 10–15, 261 General Growth Properties, 134 General Theory of Employment, Interest, and Money, The (Keynes), 278–79 generative design, 112–13 genome sequencing, 252–55, 260–61 Georgia, Republic of, 291 Gershenfeld, Neil, 308 GFDL, 248 Gill, Vince, 12n Giuliano, Laura, 40 global financial crisis (2008), 285, 308 GNU General Public License (GPL), 243 Go (game), 1–6 Goethe, Johann Wolfgang von, 178 Go-Jek, 191 golden ratio, 118 Goldman Sachs, 134 gold standard, 280n Goodwin, Tom, 6–10, 14 Google, 331; See also Android acquiring innovation by acquiring companies, 265 Android purchased by, 166–67 Android’s share of Google revenue/profits, 204 autonomous car project, 17 DeepMind, 77–78 hiring decisions, 56–58 iPhone-specific search engine, 162 and Linux, 241 origins of, 233–34 and self-driving vehicles, 82 as stack, 295 Google AdSense, 139 Google DeepMind, 4, 77–78 Google News, 139–40 Google search data bias in, 51–52 incorporating into predictive models, 39 Graboyes, Robert, 274–75 Granade, Matthew, 270 Grant, Amy, 12n graphics processing units (GPUs), 75 Great Recession (2008), 285, 308 Greats (shoe designer), 290 Grid, The (website design startup), 118 Grokster, 144 Grossman, Sandy, 314 group drive, 20, 24 group exercise, See ClassPass Grove, William, 41 Grubhub, 186 Guagua Xiche, 191–92 gut instincts, 56 gyro sensor, 98 Haidt, Jonathan, 45 Hammer, Michael, 32, 34–35, 37, 59 hands, artificial, 272–75 Hannover Messe Industrial Trade Fair, 93–94 Hanson, Robin, 239 Hanyecz, Laszlo, 285–86 Hao Chushi, 192 “hard fork,” 304–5, 318 Harper, Caleb, 272 Hart, Oliver, 313–15 Hayek, Friedrich von, 151, 235–39, 279, 332 health care, 123–24 health coaches, 124, 334 health insurance claims, 83 Hearn, Mike, 305–6 heat exchangers, 111–13 “hedgehogs,” 60–61 Hefner, Cooper, 133 Hefner, Hugh, 133 hierarchies flat, 325 production costs vs. coordination costs in, 313–14 Hinton, Geoff, 73, 75–76 HiPPOs (highest-paid person’s opinions), 45, 63, 85 hiring decisions, 56–58 Hispanic students, 40 HIStory (Michael Jackson), 131 hive mind, 97 HMV (record store chain), 131, 134 Holberton School of Software Engineering, 289 “hold-up problem,” 316 Holmström, Bengt, 313, 315 Honor (home health care platform), 186 hotels limits to Airbnb’s effects on, 221–23 Priceline and, 223–24 revenue management’s origins, 182 “hot wallet,” 289n housing sales, 39 Howell, Emily (music composition software), 117 Howells, James, 287 Hughes, Chris, 133 human condition, 121, 122 human genome, 257–58 human judgment, See judgment, human Hyman, Jennifer, 187 hypertext, 33 IBM; See also Watson (IBM supercomputer) and Linux, 241 System/360 computer, 48 ice nugget machine, 11–14 idAb algorithm, 253, 254 incentives, ownership’s effect on, 316 incomplete contracting, 314–17 incremental revenue, 180–81 incumbents advantages in financial services, 202 inability to foresee effects of technological change, 21 limits to disruption by platforms, 221–24 platforms’ effect on, 137–48, 200–204 threats from platform prices, 220–21 Indiegogo, 13–14, 263, 272 industrial trusts, 22–23 information business processes and, 88–89 in economies, 235–37 O2O platforms’ handling of, 192–93 information asymmetries, 206–10 information goods bundling, 146–47 as “free, perfect, instant,” 135–37 and solutionism, 297–98 information transfer protocols, 138 infrared sensors, 99 InnoCentive, 259 innovation crowd and, 264–66 ownership’s effect on, 316 Instagram, 133, 264–66 institutional investors, 263 Intel, 241, 244 Internet as basis for new platforms, 129–49 economics of “free, perfect, instant” information goods, 135–37 evolution into World Wide Web, 33–34 in late 1990s, 129–31 as platform of platforms, 137–38 pricing plans, 136–37 intuition, See System 1/System 2 reasoning inventory, perishing, See perishing/perishable inventory investing, automated, 266–70 investment advising, 91 Iora Health, 124, 334 Iorio, Luana, 105 iOS, 164–67, 203 iPhone apps for, 151–53, 161–63 Blackberry vs., 168 curation of apps for, 165 demand curve for, 156 introduction of, 151–52 and multisided markets, 218 opening of platform to outside app builders, 163–64 user interface, 170 widespread adoption of, 18 iron mining, 100 Irving, Washington, 252 Isaac, Earl, 46 Isaacson, Walter, 152, 165 iteration, 173, 323; See also experimentation iTunes, 217–18 iTunes Store, 145, 165 Jackson, Michael, 131 Java, 204n Jelinek, Frederick, 84 Jeopardy! (TV show), 17 Jeppesen, Lars Bo, 259 Jobs, Steve curation of iPhone platform, 165 Dropbox acquisition offer, 162 and iPhone apps, 151–53, 157, 163 joint-stock company, 320 journalism, See newspapers Joyce, James, 178 judges, parole granted by, 39–40 judgment, human as complement to computer power, 35 in decision-making loop, 53–56 flaws in, 37–42 and justification, 45 “superforecasters” and, 60–61 System 1/System 2 reasoning, 35–46 justification, 45 Kadakia, Payal, 178, 179, 184 Kaggle, 261 Kahneman, Daniel, 35–36, 43, 44, 56, 325 Kalanick, Travis, 200 Kapor, Mitch, 142 Katz, Michael, 141n Kaushik, Avinash, 45 Kay, Alan, 61 Kazaa, 144 Kehoe, Patrick J., 21 Keirstead, Karl, 143 kernel, 240 Keynes, John Maynard, 278–79, 287, 309–10 Khosla, Vinod, 94 Kickstarter, 262 “killer app,” 157 Kim, Pauline, 40–41 Kimberley Process, 289–90 kinases, 116–17 kitchen, automated, 94 Kiva Systems, 103 Klein, Gary, 56 knowledge access to, in second machine age, 18 markets and, 332 prediction markets and, 238 knowledge differentials, See information asymmetries Kodak, 131, 132 Kohavi, Ronny, 45, 51 Kohl’s, 62–63 Koike, Makoto, 79–80 Komatsu, 99 Koum, Jan, 140 Krawisz, Daniel, 304 Kurzweil, Ray, 308 Lakhani, Karim, 252–55, 259 landline telecommunications, 134–35 land title registry, 291 language learning styles, 67–69 Lasker, Edward, 2 Lawee, David, 166 law of one price, 156 Lea, Ed, 170 leadership, geeky, 244–45, 248–49 lead users, 265 LeCun, Yann, 73, 80, 121 ledger, See blockchain Legg, Shane, 71 Lehman, Bastian, 184 Lei Jun, 203 Leimkuhler, John F., 182 “lemons,” 207 Lending Club, 263 level 5 autonomy, 82 leveraging of assets, O2O platforms for, 196–97 Levinovitz, Alan, 3 Levinson, Art, 152 libraries, 229–32 Library of Congress, 231 links, 233 Linq, 290–91 Linux, 240–45, 248, 249, 260 liquidity and network effects, 206 O2O platforms as engines of, 192–96 Livermore, Shaw, 22–23 locking in users, 217 lodging; See also Airbnb differences between Airbnb and hotels, 222–23 Priceline and, 223–24 “Logic Theorist” program, 69 Long, Tim, 204 Los Angeles, California hotel occupancy rates, 221–22 Postmates in, 185 Uber’s effect on taxi service, 201 LTE networks, 96 Luca, Michael, 209n Lyft, 186, 201, 208, 218 Ma, Jack, 7 machine age, See second machine age machine intelligence mind as counterpart to, 15 superiority to System 1 reasoning, 38–41 machine learning, 66–86; See also artificial intelligence AlphaGo and, 73 back-office work and, 82–83 early attempts, 67–74 in Obama’s 2012 presidential campaign, 48–51 O2O business data and, 194 statistical pattern recognition and, 72–74 machine(s); See also artificial intelligence; robotics; standard partnership and business process reengineering, 32–33 and creativity, 110–19 defined, 14 human connection in digitized world, 122–24 human judgment and, 34–45 new mind-machine partnership, 46–62 and uniquely human domains, 110–26 Mad Men (TV drama), 48 Madrigal, Alexis, 295–96 magazines ad revenue (late 1990s), 130 ad revenue (2013), 132–33 new content platforms’ effect on revenue, 139 MakerBot, 273 maker movement, 271–72 Makhijani, Vish, 324–25 malls, 131, 134 Malone, Tom, 311, 313 management/managers continued importance of, 320–23 and economics of the firm, 309 as portion of US workforce, 321 in post-standard partnership world, 323–26 manufacturing electricity’s effect on, 19–24 robotics in, 102 transition from molds to 3D printing, 104–7 Manyika, James, 332 Manzi, Jim, 62–63 Marchant, Jo, 66n Marcus, Gary, 5, 71 marginal costs bundling and, 147 of computer storage, 136 of digital copies, 136, 137 of perishing inventory, 180, 181 of platforms, 137 of platforms vs. products, 147, 220 and Uber’s market value, 219 marginal utility, 258–59 “Market for ‘Lemons,’ The” (Akerlof), 207 market research, 13–14, 261–63 market(s) centrally planned economies vs., 235–37 companies and, 309–11 costs inherent in, 310–11 as crowd, 235–39 information asymmetries and, 206–7 prediction markets, 237–39 production costs vs. coordination costs, 313–14 Markowitz, Henry, 268 Marshall, Matt, 62 Martin, Andrew, 40–41 Marx, Karl, 279 Masaka, Makoto, 79–80 “Mastering the Game of Go with Deep Neural Networks and Tree Search” (Nature article), 4 Maugham, Somerset, 110 Mazzella, Frédéric, 190 McCarthy, John, 67 McClatchy Company, 132 McDonald’s, 92 McElheren, Kristina, 42 McKinsey Global Institute, 332 Mechanical Turk, 260 Medallion Fund, 267 medical devices crowd-designed, 272–75 3D printing and, 106 medical diagnosis, 123–24 Meehl, Paul, 41–42, 53–54, 56, 81 MegaBLAST, 253, 254 Menger, Carl, 25 Men’s Fitness, 132 Merton, Robert K., 189 Metallica, 144 Microsoft core capabilities, 15 machine learning, 79 proprietary software, 240 as stack, 295 Windows Phone platform, 167–68 Microsoft Research, 84 Milgrom, Paul, 315n milking systems, 101 Mims, Christopher, 325 mind, human as counterpart to machine intelligence, 15 undetected biases in, 42–45 Minsky, Marvin, 73, 113 Mitchell, Alan, 11, 12 MIT Media Lab, 272 mobile telephones, 129–30, 134–35 Mocan, Naci, 40 molds, 104–5 Moley Robotics, 94 Momentum Machines, 94 Moody’s, 134 Moore, John, 315 Moore’s law, 308 and Cambrian Explosion of robotics, 97–98 defined, 35 neural networks and, 75 System 2 reasoning and, 46 and 3D printing, 107 Morozov, Evgeny, 297 Mt.
., 189 Metallica, 144 Microsoft core capabilities, 15 machine learning, 79 proprietary software, 240 as stack, 295 Windows Phone platform, 167–68 Microsoft Research, 84 Milgrom, Paul, 315n milking systems, 101 Mims, Christopher, 325 mind, human as counterpart to machine intelligence, 15 undetected biases in, 42–45 Minsky, Marvin, 73, 113 Mitchell, Alan, 11, 12 MIT Media Lab, 272 mobile telephones, 129–30, 134–35 Mocan, Naci, 40 molds, 104–5 Moley Robotics, 94 Momentum Machines, 94 Moody’s, 134 Moore, John, 315 Moore’s law, 308 and Cambrian Explosion of robotics, 97–98 defined, 35 neural networks and, 75 System 2 reasoning and, 46 and 3D printing, 107 Morozov, Evgeny, 297 Mt. Gox, 286, 289n multihoming, 168 multisided markets, 217–18 multiunit enterprises (MUEs), 62–63 musical composition, 117 music industry, See recorded music industry “Music Is Satisfied with Mr. Bertram’s Mind, The” (AI-generated prose), 121 MySpace, 170–71 Naam, Ramez, 258n Nakamoto, Satoshi, 279–85, 287, 296–97, 306, 312 Nakamoto Institute, 304 Nappez, Francis, 190 Napster, 144–45 NASA, 15 Nasdaq, 290–91 National Association of Realtors, 39 National Enquirer, 132 National Institutes of Health, 253 National Library of Australia, 274 Naturalis Historia (Pliny the Elder), 246 natural language processing, 83–84 “Nature of the Firm, The” (Coase), 309–10 Navy, US, 72 negative prices, 216 Nelson, Ted, 33 Nelson, Theodore, 229 Nesbitt, Richard, 45 Netflix, 187 Netscape Navigator, 34 network effects, 140–42 defined, 140 diffusion of platforms and, 205–6 O2O platforms and, 193 size of network and, 217 Stripe and, 174 Uber’s market value and, 219 networks, Cambrian Explosion and, 96 neural networks, 73–74, 78 neurons, 72–73 Newell, Allen, 69 Newmark, Craig, 138 New Republic, 133 news aggregators, 139–40 News Corp, 170, 171 newspapers ad revenue, 130, 132, 139 publishing articles directly on Facebook, 165 Newsweek, 133 New York City Postmates in, 185 taxi medallion prices before and after Uber, 201 UberPool in, 9 New York Times, 73, 130, 152 Ng, Andrew, 75, 96, 121, 186 Nielsen BookScan, 293, 294 99Degrees Custom, 333–34 99designs, 261 Nixon, Richard, 280n Nokia, 167–68, 203 noncredentialism, 241–42 Norman, Robert, 273–74 nugget ice, 11–14 Nuomi, 192 Nupedia, 246–48 Obama, Barack, election of 2012, 48–51 occupancy rates, 221–22 oDesk, 188 Office of Personnel Management, US, 32 oil rigs, 100 on-demand economy, future of companies in, 320 online discussion groups, 229–30 online payment services, 171–74 online reviews, 208–10 O2O (online to offline) platforms, 185–98 business-to-business, 188–90 consumer-oriented, 186–88 defined, 186 as engines of liquidity, 192–96 globalization of, 190–92 interdisciplinary insights from data compiled by, 194 for leveraging assets, 196–97 and machine learning, 194 Opal (ice maker), 13–14 Open Agriculture Initiative, 272 openness (crowd collaboration principle), 241 open platforms curation and, 165 downsides, 164 importance of, 163–65 as key to success, 169 open-source software; See also Linux Android as, 166–67 development by crowd, 240–45 operating systems, crowd-developed, 240–45 Oracle, 204 O’Reilly, Tim, 242 organizational dysfunction, 257 Oruna, 291 Osindero, Simon, 76 Osterman, Paul, 322 Ostrom, Elinor, 313 outcomes, clear (crowd collaboration principle), 243 outsiders in automated investing, 270 experts vs., 252–75 overall evaluation criterion, 51 Overstock.com, 290 Owen, Ivan, 273, 274 Owen, Jennifer, 274n ownership, contracts and, 314–15 Page, Larry, 233 PageRank, 233 Pahlka, Jennifer, 163 Painting Fool, The, 117 Papa John’s Pizza, 286 Papert, Seymour, 73 “Paperwork Mine,” 32 Paris, France, terrorist attack (2015), 55 Parker, Geoffrey, 148 parole, 39–40 Parse.ly, 10 Paulos, John Allen, 233 payments platforms, 171–74 peer reviews, 208–10 peer-to-peer lending, 263 peer-to-peer platforms, 144–45, 298 Peloton, 177n Penthouse magazine, 132 People Express, 181n, 182 Perceptron, 72–74 Perceptrons: An Introduction to Computational Geometry (Minsky and Papert), 73 perishing/perishable inventory and O2O platforms, 186 and revenue management, 181–84 risks in managing, 180–81 personal drones, 98 perspectives, differing, 258–59 persuasion, 322 per-transaction fees, 172–73 Pew Research Center, 18 p53 protein, 116–17 photography, 131 physical environments, experimentation in development of, 62–63 Pindyck, Robert, 196n Pinker, Steven, 68n piracy, of recorded music, 144–45 Plaice, Sean, 184 plastics, transition from molds to 3D printing, 104–7 Platform Revolution (Parker, Van Alstyne, and Choudary), 148 platforms; See also specific platforms business advantages of, 205–11 characteristics of successful, 168–74 competition between, 166–68 and complements, 151–68 connecting online and offline experience, 177–98; See also O2O (online to offline) platforms consumer loyalty and, 210–11 defined, 14, 137 diffusion of, 205 economics of “free, perfect, instant” information goods, 135–37 effect on incumbents, 137–48, 200–204 elasticity of demand, 216–18 future of companies based on, 319–20 importance of being open, 163–65; See also open platforms and information asymmetries, 206–10 limits to disruption of incumbents, 221–24 multisided markets, 217–18 music industry disruption, 143–48 network effect, 140–42 for nondigital goods/services, 178–85; See also O2O (online to offline) platforms and perishing inventory, 180–81 preference for lower prices by, 211–21 pricing elasticities, 212–13 product as counterpart to, 15 and product maker prices, 220–21 proliferation of, 142–48 replacement of assets with, 6–10 for revenue management, 181–84 supply/demand curves and, 153–57 and unbundling, 145–48 user experience as strategic element, 169–74 Playboy magazine, 133 Pliny the Elder, 246 Polanyi, Michael, 3 Polanyi’s Paradox and AlphaGo, 4 defined, 3 and difficulty of comparing human judgment to mathematical models, 42 and failure of symbolic machine learning, 71–72 and machine language, 82 and problems with centrally planned economies, 236 and System 1/System 2 relationship, 45 Postmates, 173, 184–85, 205 Postmates Plus Unlimited, 185 Postrel, Virginia, 90 Pratt, Gil, 94–95, 97, 103–4 prediction data-driven, 59–60 experimentation and, 61–63 statistical vs. clinical, 41 “superforecasters” and, 60–61 prediction markets, 237–39 premium brands, 210–11 presidential elections, 48–51 Priceline, 61–62, 223–24 price/pricing data-driven, 47; See also revenue management demand curves and, 154 elasticities, 212–13 loss of traditional companies’ power over, 210–11 in market economies, 237 and prediction markets, 238–39 product makers and platform prices, 220 supply curves and, 154–56 in two-sided networks, 213–16 Principia Mathematica (Whitehead and Russell), 69 print media, ad revenue and, 130, 132, 139 production costs, markets vs. companies, 313–14 productivity, 16 products as counterpart to platforms, 15 loss of profits to platform providers, 202–4 pairing free apps with, 163 platforms’ effect on, 200–225 threats from platform prices, 220–21 profitability Apple, 204 excessive use of revenue management and, 184 programming, origins of, 66–67 Project Dreamcatcher, 114 Project Xanadu, 33 proof of work, 282, 284, 286–87 prose, AI-generated, 121 Proserpio, Davide, 223 Prosper, 263 protein p53, 116–17 public service, 162–63 Pullman, David, 131 Pullum, Geoffrey, 84 quantitative investing firms (quants), 266–70 Quantopian, 267–70 Quinn, Kevin, 40–41 race cars, automated design for, 114–16 racism, 40, 51–52, 209–10 radio stations as complements to recorded music, 148 in late 1990s, 130 revenue declines (2000–2010), 135 Ramos, Ismael, 12 Raspbian, 244 rationalization, 45 Raymond, Eric, 259 real-options pricing, 196 reasoning, See System 1/System 2 reasoning rebundling, 146–47 recommendations, e-commerce, 47 recorded music industry in late 1990s, 130–31 declining sales (1999-2015), 134, 143 disruption by platforms, 143–48 Recording Industry Association of America (RIAA), 144 redlining, 46–47 Redmond, Michael, 2 reengineering, business process, 32–35 Reengineering the Corporation (Hammer and Champy), 32, 34–35, 37 regulation financial services, 202 Uber, 201–2, 208 Reichman, Shachar, 39 reinforcement learning, 77, 80 Renaissance Technologies, 266, 267 Rent the Runway, 186–88 Replicator 2 (3D printer), 273 reputational systems, 209–10 research and development (R&D), crowd-assisted, 11 Research in Motion (RIM), 168 residual rights of control, 315–18 “Resolution of the Bitcoin Experiment, The” (Hearn), 306 resource utilization rate, 196–97 restaurants, robotics in, 87–89, 93–94 retail; See also e-commerce MUEs and, 62–63 Stripe and, 171–74 retail warehouses, robotics in, 102–3 Rethinking the MBA: Business Education at a Crossroads (Datar, Garvin, and Cullen), 37 revenue, defined, 212 revenue management defined, 47 downsides of, 184–85 O2O platforms and, 193 platforms for, 181–84 platform user experience and, 211 problems with, 183–84 Rent the Runway and, 187 revenue-maximizing price, 212–13 revenue opportunities, as benefit of open platforms, 164 revenue sharing, Spotify, 147 reviews, online, 208–10 Ricardo, David, 279 ride services, See BlaBlaCar; Lyft; Uber ride-sharing, 196–97, 201 Rio Tinto, 100 Robohand, 274 robotics, 87–108 conditions for rapid expansion of, 94–98 DANCE elements, 95–98 for dull, dirty, dangerous, dear work, 99–101 future developments, 104–7 humans and, 101–4 in restaurant industry, 87–89 3D printing, 105–7 Rocky Mountain News, 132 Romney, Mitt, 48, 49 Roosevelt, Teddy, 23 Rosenblatt, Frank, 72, 73 Rovio, 159n Roy, Deb, 122 Rubin, Andy, 166 Ruger, Ted, 40–41 rule-based artificial intelligence, 69–72, 81, 84 Russell, Bertrand, 69 Sagalyn, Raphael, 293n Saloner, Garth, 141n Samsung and Android, 166 and Linux, 241, 244 sales and earnings deterioration, 203–4 San Francisco, California Airbnb in, 9 Craigslist in, 138 Eatsa in, 87 Napster case, 144 Postmates in, 185 Uber in, 201 Sanger, Larry, 246–48 Sato, Kaz, 80 Satoshi Nakamoto Institute, 304 scaling, cloud and, 195–96 Schiller, Phil, 152 Schumpeter, Joseph, 129, 264, 279, 330 Scott, Brian, 101–2 second machine age origins of, 16 phase one, 16 phase two, 17–18 secular trends, 93 security lanes, automated, 89 Sedol, Lee, 5–6 self-checkout kiosks, 90 self-driving automobiles, 17, 81–82 self-justification, 45 self-organization, 244 self-selection, 91–92 self-service, at McDonald’s, 92 self-teaching machines, 17 Seychelles Trading Company, 291 Shanghai Tower, 118 Shapiro, Carl, 141n Shaw, David, 266 Shaw, J.
Affordable Care Act / Obamacare, Airbnb, Al Roth, Black Swan, buy low sell high, Chuck Templeton: OpenTable, Credit Default Swap, cross-subsidies, crowdsourcing, disintermediation, diversified portfolio, experimental economics, George Akerlof, Goldman Sachs: Vampire Squid, income inequality, index fund, information asymmetry, Jean Tirole, Kenneth Arrow, Lean Startup, Lyft, Marc Andreessen, Mark Zuckerberg, market microstructure, Martin Wolf, McMansion, Menlo Park, Metcalfe’s law, moral hazard, multi-sided market, Network effects, patent troll, Paul Graham, Peter Thiel, pez dispenser, ride hailing / ride sharing, Robert Metcalfe, Sand Hill Road, sharing economy, Silicon Valley, social graph, supply-chain management, TaskRabbit, The Market for Lemons, too big to fail, trade route, transaction costs, two-sided market, Uber for X, ultimatum game, Y Combinator
For the second quarter of 2014, the most recent period for which data is available, Talent Solutions revenue totaled $322 million while revenue from Premium Subscriptions was $105 million, and revenue from Marketing Solutions was $106 million. See “LinkedIn Announces Second Quarter 2014 Results,” LinkedIn press release, July 31, 2014, retrieved from http://investors.linked in.com/releasedetail.cfm?ReleaseID=863494). This is no accident: the company understands that it is operating in a multisided market, where offering low-price or even free entry to one side (in this case, job seekers) will attract plenty of high-paying participants on the other side (recruiters whose livelihood depends on finding the right job seekers). 8.Joe Light, “In Zillow-Trulia Deal, Making Room for Brokers,” Wall Street Journal, July 28, 2014. For a deeper look at the business models of the real estate sites, see Brad Stone, “Why Redfin, Zillow, and Trulia Haven’t Killed Off Real-Estate Brokers,” Bloomberg Businessweek, March 7, 2013. 9.This is the figure for the most recent year available, 2013, according to the National Association of Realtors, which reports that 9 percent of houses were listed for sale by owner.
See Dina Mayzlin, Yaniv Dover, and Judith Chevalier, “Promotional Reviews: An Empirical Investigation of Online Review Manipulation,” American Economic Review 104, no. 8: 2421–55. 17.This is a central argument of a book chapter that examines enforcement activities by middlemen as seemingly varied as the Roppongi Hills shopping center in Tokyo and the Harvard Business School. See Kevin J. Boudreau and Andrei Hagiu, “Platform Rules: Multi-Sided Platforms as Regulators,” in Annabelle Gawer (ed.), Platforms, Markets and Innovation (Northampton, MA: Edward Elgar Publishing, 2009). 18.Hongbin Cai, Ginger Zhe Jin, Chong Liu, Li-an Zhou, “Seller Reputation: From Word-of-Mouth to Centralized Feedback,” International Journal of Industrial Organization 34 (May 2014): 51–65. 19.Interview with Ginger Jin, November 20, 2013. 20.W. Scott Frame, Aruna Srinivasan, and Lynn Woosley, “The Effect of Credit Scoring on Small-Business Lending,” Journal of Money, Credit and Banking 33, no. 3 (August 2001): 813–25. 21.The Prisoner’s Dilemma, dealing with situations in which both sides have reasons to distrust the other, is probably the most common game for studying trust.
Finally, note that Georg Simmel described another middleman, one without the sinister overtones of tertius gaudens—this is Simmel’s tertius iungens (the third who joins). 20.Ron Burt, “Structural Holes and Good Ideas,” American Journal of Sociology 110, no. 2 (September 2004): 349–399. 21.Victoria Barret, “Silicon Valley Cinderella,” Forbes, March 21, 2012. 22.Ronald Burt, Structural Holes: The Social Structure of Competition (Harvard University Press, 1992), 28. 23.Burt used the word “bridge” to refer to the relationship, whereas I am using it to refer to the person. 24.Fortune Editors, “The Real Way to Build a Social Network,” Fortune, January 24, 2012. 25.Interview with Ron Burt, February 10, 2014. 26.This research is described in Ron Burt, Neighbor Networks (New York: Oxford University Press, 2010). 27.Interview with LaJuan Stoxstill-Diggs, January 31, 2014. 28.See, for example, “Maureen Orth, “Killer@Craigslist,” Vanity Fair, October 2009. 29.Buyers of classified ads saved $5 billion between 2000 and 2007 as a result of Craigslist entering the market. See Robert Seamans and Feng Zhu, “Responses to Entry in Multi-Sided Markets: The Impact of Craigslist on Local Newspapers,” Management Science 60, no. 2 (February 2014): 476–493. 30.He is selling it online as an e-book. See LaJuan Stoxstill-Diggs, The Craigslist Hustle (LSD Publishing, 2009). 31.Interview with Jim Angel, February 3, 2014. 32.Anil K. Kashyap, Raghuram Rajan, and Jeremy C. Stein, “Banks as Liquidity Providers: An Explanation for the Coexistence of Lending and Deposit Taking,” The Journal of Finance 57, no. 1 (February 2002): 33–73. 33.Interview with Genevieve Thiers, January 27, 2014. 34.Libby Kane, “Entrepreneurship 101: Interview with Genevieve Thiers,” LearnVest, September 12, 2012. 35.Interview with Marc Rysman, January 31, 2014.
3D printing, Airbnb, Amazon Web Services, barriers to entry, bitcoin, blockchain, business process, Chuck Templeton: OpenTable, Clayton Christensen, collaborative economy, commoditize, crowdsourcing, cryptocurrency, data acquisition, frictionless, game design, hive mind, Internet of things, invisible hand, Kickstarter, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, means of production, multi-sided market, Network effects, new economy, Paul Graham, recommendation engine, ride hailing / ride sharing, shareholder value, sharing economy, Silicon Valley, Skype, Snapchat, social graph, social software, software as a service, software is eating the world, Spread Networks laid a new fibre optics cable between New York and Chicago, TaskRabbit, the payments system, too big to fail, transport as a service, two-sided market, Uber and Lyft, Uber for X, Wave and Pay
Accounting for currency and value capture, the following is the fully laid out platform canvas (see Figure 17f). The Platform Canvas Figure 17f PULL-FACILITATE-MATCH AND THE PLATFORM CANVAS The design of tools and services should closely align with the three roles of the platform: pull, facilitate, and match. The platform must ensure that it pulls, facilitates, and matches users on an ongoing basis and designs its tools and services to do so. MULTI-SIDED PLATFORMS WITH MULTIPLE INTERACTIONS Some platforms focus almost entirely on enabling one core interaction, but many platforms have more than one interaction. These platforms enable edge interactions around the central core interaction. The architecture of these platforms must evolve one interaction at a time. Starting with the core interaction, the platform canvas may be used to lay out the architecture of the platform.