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Platform Scale: How an Emerging Business Model Helps Startups Build Large Empires With Minimum Investment by Sangeet Paul Choudary
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, uber lyft, Wave and Pay
Lowering investment ensures higher ROI and greater participation from producers, which ensures that the platform’s core interaction is highly repeatable. 3.4 THE CREATION OF CUMULATIVE VALUE Lock-In For An Opt-In World The power of network effects cannot be disputed. As platforms gather more value through external production, they attract more consumption, which in turn attracts even more production. Network effects guarantee repeatable interactions. Both producers and consumers repeatedly participate on a platform that has strong network effects. Network effects hold the keys to the long-term retention of producers and consumers, which increases the repeatability of the core interaction. But today, the power of the network effect is fading. The network effect isn’t the one-stop solution for repeatable interactions that it once was. SUFFICIENT NETWORK EFFECT Platforms with network effects often benefit from a winner-takes-all dynamic. The winner usually aggregates all producers and consumers onto one platform because of ever-strengthening network effects.
Such use cases are best supported by the platform itself, acting as a producer through a captive base of partner producers. 4.9 DISRUPTING CRAIGSLIST Quality As A Competitive Advantage Question: Why does Craigslist refuse to get disrupted, despite poor user experience and an utter lack of innovation? Answer: Strong Network Effects THREE FACTORS GOVERNING PLATFORM ADOPTION The success of platforms depends on the following three factors: 1.Network Effects. The single most important factor for a platform is its ability to build the network effect. Without a minimum number of buyers and sellers, platforms simply are not valuable enough. With network effects, a platform continues to attract producers and consumers sustainably. 2.Curation Of Content. The platform should have a mechanism for separating signal from noise. Owing to network effects, platforms encourage abundance, and users need a mechanism to sift through the abundance and find the most relevant items. Platforms should have a mechanism to reliably and credibly signal quality to consumers. 3.Curation Of Participants.
A scaling strategy for platforms should involve: 1.Scaling of production 2.Scaling of consumption 3.Strengthening of filters through ongoing data acquisition 4.Scaling social curation 5.Scaling community culture 6.Minimizing interaction risk There are significant management challenges when scaling a network effects platform, which are often underestimated. The above framework helps platforms scale in a manner that ensures repeatability and sustainability of the core interaction. 6.2 REVERSE NETWORK EFFECTS Why Scale May Be The Biggest Threat To Platforms Platforms may lose value as they scale. Scale, in fact, may be the greatest threat to platforms, if not managed well. As noted in the previous chapter, a failure in any one of the four actions – Creation, Curation, Customization, and Consumption – may lead to a failure of the core interaction. When one or more of these actions start to fail with increasing scale, we see the onset of reverse network effects. Network effects make a platform useful as more users use it, however, beyond a certain scale, network effects may work against the platform.
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 cycle, 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, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, winner-take-all economy, zero-sum game, Zipcar
Sacks’s napkin sketch captures a classic example of network effects. It shows how the value of Uber to each of its participants grows the more people use it—which attracts still more users, thereby increasing the value of the service even more. Network effects refers to the impact that the number of users of a platform has on the value created for each user. Positive network effects refers to the ability of a large, well-managed platform community to produce significant value for each user of the platform. Negative network effects refers to the possibility that the growth in numbers of a poorly-managed platform community can reduce the value produced for each user. As we’ll see, positive network effects are the main source of value creation and competitive advantage in a platform business. However, network effects can also be negative, and in this chapter we’ll explain how and why negative network effects arise and what platform business managers can do about them.
And while platform businesses themselves are often extraordinarily profitable, the chief locus of wealth creation is now outside rather than inside the organization. Network effects are creating the giants of the twenty-first century. Google and Facebook each touch more than one-seventh of the world’s population. In the world of network effects, ecosystems of users are the new source of competitive advantage and market dominance. TAKEAWAYS FROM CHAPTER TWO Whereas giant industrial-era firms were made possible by supply economies of scale, today’s giants are made possible by demand economies of scale—expressed as network effects. Network effects are not the same as price effects, brand effects, or other familiar growth-building tools. Frictionless entry and other features of scalability maximize the value-building impact of network effects. A two-sided market (with both producers and consumers) gives rise to four kinds of network effects: same-side effects (positive and negative) and cross-side effects (positive and negative).
., despite competitive markets and the regulatory pressure created by antitrust legislation, a handful of firms dominate industries in which supply economies of scale play a large role—for example, the auto industry. As we also saw in chapter 2, network effects are the Internet-era source of market power. Thanks to positive network effects, the value created and the profit margins enjoyed by the company both increase as more users join the ecosystem.21 This is why firms with network effects can enjoy a 10x multiple in value relative to other firms that have comparable revenues but lack network effects.22 With their current product focus and business models, Houghton Mifflin Harcourt, NBC, Lexis, and Whirlpool do not have strong network effects. Amazon, Netflix, LegalZoom, and Nest do. Because positive network effects attract more users to whichever platform is larger, they are a second force that is likely to strengthen a market’s winner-take-all tendency.
Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies by Reid Hoffman, Chris Yeh
activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, autonomous vehicles, bitcoin, blockchain, Bob Noyce, business intelligence, Chuck Templeton: OpenTable:, cloud computing, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, database schema, discounted cash flows, Elon Musk, Firefox, forensic accounting, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, hydraulic fracturing, Hyperloop, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, margin call, Mark Zuckerberg, minimum viable product, move fast and break things, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, recommendation engine, ride hailing / ride sharing, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, Tesla Model S, thinkpad, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, Y Combinator, yellow journalism
So it’s no surprise that smart entrepreneurs strive to create (and smart investors want to invest in) these network effects start-ups. Several generations of start-ups have tapped these dynamics to build dominant positions, from eBay to Facebook to Airbnb. To accomplish these goals, it’s critical to develop a rigorous understanding of how network effects work. My Greylock colleague Simon Rothman is one of the world’s premier experts on network effects from building eBay’s $14 billion automotive marketplace. Simon warns, “A lot of people try to bolt on network effects by doing things like adding a profile. ‘Marketplaces have profiles,’ they reason, ‘so if I add profiles, I’ll be adding network effects.’ ” Yet the reality of building network effects is a bit more complicated. Rather than simply imitate specific features, the best blitzscalers study the different types of network effects and design them into their business models.
Rather than simply imitate specific features, the best blitzscalers study the different types of network effects and design them into their business models. Five Categories of Network Effects On his industrial organization of information technology website, the NYU professor Arun Sundararajan classifies network effects into five broad categories: Direct Network Effects: Increases in usage lead to direct increases in value. (Examples: Facebook, messaging apps like WeChat and WhatsApp) Indirect Network Effects: Increases in usage encourage consumption of complementary goods, which increases the value of the original product. (Example: Adoption of an operating system such as Microsoft Windows, iOS, or Android encourages third-party software developers to build applications, increasing the value of the platform.) Two-Sided Network Effects: Increases in usage by one set of users increases the value to a different set of complementary users, and vice versa.
Any of these different network effects can have a major impact; Microsoft’s ability to tap into multiple network effects with Windows and Office contributed greatly to its unprecedentedly durable franchise. Even today, Windows and Office remain dominant in the PC market; it’s simply that other platforms like mobile have achieved similar or greater importance. Network Effects Both Produce and Require Aggressive Growth A key element of leveraging network effects is the aggressive pursuit of network growth and adoption. Because the impact of network effects increases in a superlinear fashion, at lower levels of scale, network effects actually exert downward pressure on user adoption. Once all your friends are on Facebook, you have to be on Facebook too. But conversely, why would you join Facebook if none of your friends had joined yet? The same is true for the first user of marketplaces like eBay and Airbnb.
The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--And How We Must Adapt by Sinan Aral
Airbnb, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, Bernie Sanders, bitcoin, carbon footprint, Cass Sunstein, computer vision, coronavirus, correlation does not imply causation, COVID-19, Covid-19, crowdsourcing, cryptocurrency, death of newspapers, disintermediation, Donald Trump, Drosophila, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental subject, facts on the ground, Filter Bubble, global pandemic, hive mind, illegal immigration, income inequality, Kickstarter, knowledge worker, longitudinal study, low skilled workers, Lyft, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, meta analysis, meta-analysis, Metcalfe’s law, mobile money, move fast and break things, move fast and break things, multi-sided market, Nate Silver, natural language processing, Network effects, performance metric, phenotype, recommendation engine, Robert Bork, Robert Shiller, Robert Shiller, Second Machine Age, sentiment analysis, shareholder value, skunkworks, Snapchat, social graph, social intelligence, social software, social web, statistical model, stem cell, Stephen Hawking, Steve Jobs, Telecommunications Act of 1996, The Chicago School, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, Uber and Lyft, uber lyft, WikiLeaks, Yogi Berra
This is a vivid example of platform strategy at work. But for the Hype Machine, the most important type of network effect is also the least known: the local network effect. The term originates from the importance of location to the economic power of network effects. Local network effects are proportional to the geographic proximity of the connections in the network. For example, when a new user in Dallas joins NextDoor, the private social networking service for neighborhoods, it improves the service for other NextDoor users in Dallas, but has little effect on the quality of the service for users in San Francisco. It turns out that, in addition to geographic proximity, local network effects are also driven by social proximity. A product exhibits local network effects when users are influenced directly by only a small subset of other users in the network—those with whom they are “connected.”
The inset “Understanding Network Effects” explains how network effects can allow an inferior social media network to dominate a superior entrant and monopolize the market. UNDERSTANDING NETWORK EFFECTS Consider the following example of how network effects can empower an inferior network to dominate higher-quality competitors. Imagine that the value (V) of a social media network is its intrinsic value plus the value of its network effect: V = a + ct Here a is the intrinsic value of the network, without network effects (think of this as the social network’s features, privacy controls, data security, etc.); c is the value of network effects (the additional value that users receive as more of their friends join the network); and t indicates time and also represents the number of people who have joined the network at any moment in time (the number of users on the platform).
The value of joining Beta is still just b, the value of the service itself, because no one has yet joined Beta—it has no network effect. And although we have agreed that Beta, or b, is better than Alpha, or a, it is less valuable than Alpha plus its network effect, or a + ct. So while Beta’s value should increase linearly (as shown in the dotted line in Figure 5.1), fewer people adopt Beta because it has a smaller network effect. In this way, an inferior network (Alpha) can dominate a superior network (Beta) by having a large installed base and thus a large network effect. This is a key concept in ongoing debates about whether Facebook’s monopoly power hurts innovation. Indirect network effects are different. As more people start using a particular platform or network, third parties have more incentive to add value to that platform or network.
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, disruptive innovation, 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, uber lyft, ubercab, Victor Gruen, winner-take-all economy
As Rochet and Tirole realized in their pioneering paper, “[M]any, if not most markets with network externalities are characterized by the presence of two distinct sides whose ultimate benefit stems from interacting through a common platform.”7 The French economists pointed to the general significance of indirect network effects. A network effect is indirect when the value of a matchmaker to one group of customers depends on how many members of a different group participate. FIGURE 2-1 Mentions of “network effect(s)” and “first-mover advantage,” 1950–2008 Note: Both Ngrams are case-insensitive. The Ngram for “network effect(s)” includes both the singular and plural. The Ngram for “first-mover advantage” includes both the hyphenated and unhyphenated forms. The units for both axes are per 10 million, e.g., a value of 32 for “network effect(s)” indicates that 32 bigrams per 10 million bigrams (two-word phrases) in the Google Book database were “network effects.” The importance of indirect network effects is obvious, at least in retrospect, when a business has two distinct types of customers who desire to interact with each other.
The new economics of multisided platforms has revealed why that, and a lot of other business advice, is often wrong when it comes to most businesses that have what economists call network effects, in which adding customers attracts other customers.2 The Great Network Effects Mistake Economists started the serious study of network effects in the early 1970s. That work gained momentum in the 1980s. By the time entrepreneurs were starting dot-coms in the mid-1990s, there was an extensive academic literature. Business writers had latched onto the concept. Simple versions of network effects seeped into popular writings along with simplistic strategic advice. Unfortunately, right around the time dot-commers in Silicon Valley were being told they should build market share as quickly as possible, Rochet and Tirole in Toulouse were realizing that network effects were much more complicated, and different in practice, than many economists had thought.
A better two-sided strategy made the difference. Mastering Multifaceted Network Effects It turns out that networks effects are far more pervasive and complex than economists and business strategists thought they were before 2000. Rochet and Tirole’s deep insight that year was that businesses in a diverse range of industries faced indirect network effects. The way they built their businesses, designed their products, ran their operations, and priced their offerings were heavily influenced by these network effects. These businesses operated multisided platforms. Economists, including ourselves, then started studying how these firms and industries worked in fact. It soon became apparent that much of the received wisdom about network effects was wrong. The first-mover advantage and winner-take-all theories, for example, were shaky at best.
The Little Book That Builds Wealth: The Knockout Formula for Finding Great Investments by Pat Dorsey
Airbus A320, barriers to entry, business process, call centre, creative destruction, credit crunch, discounted cash flows, intangible asset, knowledge worker, late fees, low cost airline, low cost carrier, Network effects, pets.com, price anchoring, risk tolerance, risk/return, rolodex, shareholder value, Stewart Brand
A professional investor might buy a thousand shares of IBM on the NYSE, but wind up selling them on any one of a half dozen other exchanges that also trade Big Blue’s shares, if one of those other exchanges offers a better price. Because the pool of liquidity in IBM shares is not limited to any one exchange, none of them benefits from the network effect nearly as much as futures exchanges do. The lesson here is that for a company to benefit from the network effect, it needs to operate a closed network, and when formerly closed networks open up, the network effect can dissipate in a hurry. It’s a good question to ask whenever you’re evaluating a company that might benefit from network economics: How might that network open up to other participants? Moving on from exchanges to other industries, we also see the network effect at work in lots of other areas of the market. Money-transfer firm Western Union is just one good example, and the value of its network to users is demonstrated by the fact that even though its network is three times larger than that of its closest competitor, Western Union processes about five times as many transactions.
But of course, the very nature of the network effect means that there won’t be a very large number of businesses that benefit from it, given the propensity of networks to consolidate around the leader. Let’s put this theory to the test in a simple way by looking at the companies in the Dow Jones Industrial Average and seeing which ones benefit from the network effect. It turns out that only two companies in the Dow derive the bulk of their competitive advantage from the network effect—Amex and Microsoft. We’ve already talked about Amex’s moat, and the way the network effect helps Microsoft is fairly easy to understand as well. Lots of people use Word, Office, and Windows because, well—lots of people use Word, Office, and Windows. It’s hard to argue that Windows is the acme of PC operating systems, but its massive user base means that you pretty much have to know how to operate a Windows-based PC to survive in corporate America.
Of course, eBay’s slow response to competitive threats didn’t help matters, nor did the fact that its competitor in this case was a Chinese company, and thus gained some advantage from being a local hero of sorts. But enough about eBay—let’s look at some other examples of the network effect in action. It’s not much of a leap to go from eBay, which is really just an online exchange for all kinds of physical goods, to financial markets like the NASDAQ, the New York Stock Exchange, and the Chicago Mercantile Exchange. Financial exchanges benefit from the network effect much as eBay does, but with some crucial differences that help illuminate when network economics are at their strongest, and when they can break down. The mechanics of the network effect for a financial exchange are simple: As more buyers and sellers aggregate on an exchange, exchange participants are increasingly able to find the asset they want at the price they want.
The Content Trap: A Strategist's Guide to Digital Change by Bharat Anand
Airbnb, Benjamin Mako Hill, Bernie Sanders, Clayton Christensen, cloud computing, commoditize, correlation does not imply causation, creative destruction, crowdsourcing, death of newspapers, disruptive innovation, Donald Trump, Google Glasses, Google X / Alphabet X, information asymmetry, Internet of things, inventory management, Jean Tirole, Jeff Bezos, John Markoff, Just-in-time delivery, Khan Academy, Kickstarter, late fees, Mark Zuckerberg, market design, Minecraft, multi-sided market, Network effects, post-work, price discrimination, publish or perish, QR code, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, shareholder value, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, special economic zone, Stephen Hawking, Steve Jobs, Steven Levy, Thomas L Friedman, transaction costs, two-sided market, ubercab, WikiLeaks, winner-take-all economy, zero-sum game
Apple’s experience with personal computers, and the experience of newspapers with classifieds, speaks to a challenge many organizations face: not seeing networks when they are present. But a second challenge can be equally confounding: Once managers are exposed to the idea of network effects, they start seeing them everywhere, even when they don’t exist. Witness Groupon’s attempt to build a global business. For years the company’s founders claimed that it benefited from network effects and would enjoy a winner-take-all dynamic. The more users Groupon had, the likelier merchants were to offer deals, and vice versa— indirect network effects. And because some deals would not be honored unless enough people bought them, there were direct network effects, too. The firm’s early success—Groupon was one of the fastest-growing Internet companies ever—proved it. But the network story was a myth. Merchants could just as easily offer deals on multiple sites.
When personal computers first came out, Microsoft’s advantage exemplified two types of network effects that benefit early leaders in a market. First are “direct” user-to-user networks: For every new user, the value of a PC was greater than a Mac because the number of existing PC users was higher—allowing new PC users to communicate with more people. Second are feedback loops between users and application developers: As more users chose PCs, the value of the platform for developers increased, because they could spread the fixed cost of development over a larger user base. And more applications, in turn, attracted more users—resulting in powerful “indirect” or “cross-side” network effects. Direct network effects arise from connections between similar users. To identify them, simply ask: Does the product’s value to a buyer increase as more people buy and use it?
To identify them, simply ask: Does the product’s value to a buyer increase as more people buy and use it? Indirect network effects result from connections between different types of users or suppliers—in this case, customers and app developers. To identify them, ask: Does the value to one type of user increase as the number of suppliers or other types of users rises (see Figure 6)? Figure 6: Direct Versus Indirect Network Effects Indirect networks can be as powerful as direct ones, as shown by classified ads. And consider eBay’s advantage in the market for collectible goods: The more potential buyers for a particular product on eBay, the more likely a seller will post her item there rather than elsewhere, which in turn increases the depth and selection for users on the site. Either direct or indirect network effects alone can explain why companies win big.
Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley, Jon Kleinberg
Albert Einstein, AltaVista, clean water, conceptual framework, Daniel Kahneman / Amos Tversky, Douglas Hofstadter, Erdős number, experimental subject, first-price auction, fudge factor, George Akerlof, Gerard Salton, Gerard Salton, Gödel, Escher, Bach, incomplete markets, information asymmetry, information retrieval, John Nash: game theory, Kenneth Arrow, longitudinal study, market clearing, market microstructure, moral hazard, Nash equilibrium, Network effects, Pareto efficiency, Paul Erdős, planetary scale, prediction markets, price anchoring, price mechanism, prisoner's dilemma, random walk, recommendation engine, Richard Thaler, Ronald Coase, sealed-bid auction, search engine result page, second-price auction, second-price sealed-bid, Simon Singh, slashdot, social web, Steve Jobs, stochastic process, Ted Nelson, The Market for Lemons, The Wisdom of Crowds, trade route, transaction costs, ultimatum game, Vannevar Bush, Vickrey auction, Vilfredo Pareto, Yogi Berra, zero-sum game
Each consumer wants at most one unit of the good; each consumer has a personal intrinsic interest in obtaining the good that can vary from one consumer to another. When there are no network effects at work, we model a consumer’s willingness to pay as being determined entirely by this intrinsic interest. When there are network effects, a consumer’s willingness to pay is determined by two things: • intrinsic interest; and • the number of other people using the good — the larger the user population, the more she is willing to pay. Our study of network effects here can be viewed as an analysis of how things change once this second factor comes into play. To start understanding this issue, we first consider how a market looks when there are no network effects. Reservation Prices. With no network effects, each consumer’s interest in the good is specified by a single reservation price: the maximum amount she is willing to pay for one unit of the good.
Given this, we’d want to choose x so that we collect all the positive area between y = r(x) and y = p∗, and none of the negative area. This is achieved by choosing x to be the equilibrium x∗. Hence the equilibrium quantity x∗ is socially optimal. We now introduce network effects; we’ll see that this causes several important features of the market to change in fundamental ways. 17.2 The Economy with Network Effects In this section, we discuss a model for network effects in the market for a good. We will follow a general approach suggested by Katz, Shapiro, and Varian [231, 362]; see also the writings of Brian Arthur [25, 27] for influential early discussions of these ideas. With network effects, a potential purchaser takes into account both her own reservation price and the total number of users of the good. A simple way to model this is to say that there are now two functions at work: when a z fraction of the population is using the good, the reservation price of consumer x is equal to r(x)f (z), where r(x) as before is the intrinsic interest of consumer x in the good, and f (z) measures the benefit to each consumer from having a z fraction of the population use the good.
Another alternative is to attempt to identify fashion leaders, those whose purchase or use of the good will attract others to use it, and convince them to adopt the good. This strategy also involves network effects, but they are ones that cannot be studied at the population level. Instead we would need to identify a network of connections between potential purchasers and ask who influences whom in this network. We explore this idea in Chapter 19. Social Optimality with Network Effects. We saw in Section 17.1 that for a market with no network effects, the equilibrium is socially optimal. That is, it maximizes the total difference between the reservation prices of the consumers who purchase the good and the total cost of producing the good, over all possible allocations to people. For goods with network effects, however, the equilibria are typically not optimal. At a high level, the reason is that each consumer’s choice affects each other consumer’s payoff, and the consequences of this can be analyzed as follows.
Virtual Competition by Ariel Ezrachi, Maurice E. Stucke
Airbnb, Albert Einstein, algorithmic trading, barriers to entry, cloud computing, collaborative economy, commoditize, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, demand response, disintermediation, disruptive innovation, double helix, Downton Abbey, Erik Brynjolfsson, experimental economics, Firefox, framing effect, Google Chrome, index arbitrage, information asymmetry, interest rate derivative, Internet of things, invisible hand, Jean Tirole, John Markoff, Joseph Schumpeter, Kenneth Arrow, light touch regulation, linked data, loss aversion, Lyft, Mark Zuckerberg, market clearing, market friction, Milgram experiment, multi-sided market, natural language processing, Network effects, new economy, offshore financial centre, pattern recognition, prediction markets, price discrimination, price stability, profit maximization, profit motive, race to the bottom, rent-seeking, Richard Thaler, ride hailing / ride sharing, road to serfdom, Robert Bork, Ronald Reagan, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, supply-chain management, telemarketer, The Chicago School, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, Travis Kalanick, turn-by-turn navigation, two-sided market, Uber and Lyft, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, women in the workforce, yield management
We use the word “potential” because sometimes platforms might reduce competition, as we explore next. Network Effects and Market Power Let us now consider how network effects may give rise to market power and create bottlenecks in the online environment. Network effects and market power may influence the comparison intermediates’ incentives and practice. Powerful platforms can distort the information they present us to improve their own profitability. To explain how, we first explain the operation of network effects. Then we illustrate the way in which market power could distort competition and search results online. Network Effects To understand how online platforms can exercise market power, we briefly outline several network effects involving online multisided platforms (such as Google, Bing, price comparison websites, and Facebook).5 Traditional Network Effects. Traditional network effects are observable in social network platforms, where bigger is better.
Another reason why market power will likely be durable is network effects.14 Operating systems, we saw, are a classic example of network effects: the more people that use the platform, “the more there will be invested in 238 Final Reflections developing products compatible with that platform, which, in turn reinforces the popularity of that platform with users.”15 Thus network effects help insulate Google’s and Apple’s market power over mobile phone operating systems. As The Economist reported, “Alphabet [Google], Facebook and Amazon are not being valued by investors as if they are high risk, but as if their market shares are sustainable and their network effects and accumulation of data will eventually allow them to reap monopoly-style profits.”16 Positive feedback loops and data-driven network effects can play a significant role here.17 The new currency in our Frenemy and behavioral discrimination scenarios is data.
Traditional network effects are observable in social network platforms, where bigger is better. Direct network effects arise when a consumer’s utility from a product increases as others use the product.6 One example is Facebook. As more people use the social network, the more people with whom one can interact, the easier it is to connect with other people, and the greater one’s utility in using Facebook. The value of the network increases with its growth. As the big platforms get bigger, the entry barriers to obtaining the necessary scale to meaningfully compete also increase. Trial and Error. This network effect is linked to the scale achieved by trial and error, or learning by doing. Such an effect is relevant to machine 134 Behavioral Discrimination learning. For example, for search engines an increase in the number of searches increases the search engine’s likelihood of identifying relevant results.
Who Owns the Future? by Jaron Lanier
3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, augmented reality, automated trading system, barriers to entry, bitcoin, book scanning, Burning Man, call centre, carbon footprint, cloud computing, commoditize, computer age, crowdsourcing, David Brooks, David Graeber, delayed gratification, digital Maoism, Douglas Engelbart, en.wikipedia.org, Everything should be made as simple as possible, facts on the ground, Filter Bubble, financial deregulation, Fractional reserve banking, Francis Fukuyama: the end of history, George Akerlof, global supply chain, global village, Haight Ashbury, hive mind, if you build it, they will come, income inequality, informal economy, information asymmetry, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, moral hazard, mutually assured destruction, Network effects, new economy, Norbert Wiener, obamacare, packet switching, Panopticon Jeremy Bentham, Peter Thiel, place-making, plutocrats, Plutocrats, Ponzi scheme, post-oil, pre–internet, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart meter, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, Ted Nelson, The Market for Lemons, Thomas Malthus, too big to fail, trickle-down economics, Turing test, Vannevar Bush, WikiLeaks, zero-sum game
CHAPTER 13 Coercion on Autopilot Specialized Network Effects Rewarding and Punishing Network Effects “Network effects” are feedback cycles that can make a network become ever more influential or valuable.* A classic example is found in the rise of Facebook. It attracted people because of the people already on it, a little like the old joke about someone being famous for being famous. *Network effects were an obsession for those interested in the pre-digital phone system. They have become an even greater obsession in the age of digital networks. Metcalf’s Law is a famous claim that a network becomes as valuable as the square of the number of its nodes. That means value climbs with an insane, ever-increasing pitch as a network grows. The economist W. Brian Arthur pioneered the understanding of economic network effects. To understand how Siren Servers work, it’s useful to divide network effects into those that are “rewarding” and those that are “punishing.”
To understand how Siren Servers work, it’s useful to divide network effects into those that are “rewarding” and those that are “punishing.” Siren Servers gain dominance through rewarding network effects, but keep dominance through punishing network effects. Here’s a classic example of a rewarding network effect: A cliché in the advertising world is that in the old days you knew you were wasting half of your advertising budget, but you didn’t know which half. For instance, you’d spend tens of millions of dollars on TV and print ads, and somehow there would be a benefit, but you never knew exactly how or why. Surely many of the ads were playing when people were going to the bathroom, laying waste to your precious spend. An oft-repeated trope goes like this: Because of all of Google’s data and placement algorithms, an advertiser can now finally know which half is waste.
This will happen to only a tiny token number of people, though. It is really you, the proprietor of the Siren Server, who will benefit above all others. At first, all you’ll have is rewarding network effect. That means that people will benefit from using your server because other people are using it. A virtuous cycle causes more and more people to use your offering. That’s not enough, however, if you want to build a world-class, persistent Siren Server. In addition, you have to inject some sort of punishing network effect. Make Others Pay for Entropy Once both rewarding and punishing network effects are taking hold, another crucial task is to make sure that risk is being radiated out to other people and institutions, and not accruing to your server. Sites like Pinterest invariably demand that users click through an agreement that places all responsibility for copyright violations or anything else squarely on the user.
Platform Capitalism by Nick Srnicek
3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, collaborative economy, collective bargaining, deindustrialization, deskilling, disintermediation, future of work, gig economy, Infrastructure as a Service, Internet of things, Jean Tirole, Jeff Bezos, knowledge economy, knowledge worker, liquidity trap, low skilled workers, Lyft, Mark Zuckerberg, means of production, mittelstand, multi-sided market, natural language processing, Network effects, new economy, Oculus Rift, offshore financial centre, pattern recognition, platform as a service, quantitative easing, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, software as a service, TaskRabbit, the built environment, total factor productivity, two-sided market, Uber and Lyft, Uber for X, uber lyft, unconventional monetary instruments, unorthodox policies, Zipcar
Some argue that capitalism renews itself through the creation and adoption of new technological complexes: steam and railways, steel and heavy engineering, automobiles and petrochemicals – and now information and communications technologies.1 Are we witnessing the adoption of a new infrastructure that might revive capitalism’s moribund growth? Will competition survive in the digital era, or are we headed for a new monopoly capitalism? With network effects, a tendency towards monopolisation is built into the DNA of platforms: the more numerous the users who interact on a platform, the more valuable the entire platform becomes for each one of them. Network effects, moreover, tend to mean that early advantages become solidified as permanent positions of industry leadership. Platforms also have a unique ability to link together and consolidate multiple network effects. Uber, for instance, benefits from the network effects of more and more drivers as well as from the network effects of more and more riders.2 Leading platforms tend consciously to perpetuate themselves in other ways as well. Advantages in data collection mean that the more activities a firm has access to, the more data it can extract and the more value it can generate from those data, and therefore the more activities it can gain access to.
It also lends platforms a dynamic of ever-increasing access to more activities, and therefore to more data. Moreover, the ability to rapidly scale many platform businesses by relying on pre-existing infrastructure and cheap marginal costs means that there are few natural limits to growth. One reason for Uber’s rapid growth, for instance, is that it does not need to build new factories – it just needs to rent more servers. Combined with network effects, this means that platforms can grow very big very quickly. The importance of network effects means that platforms must deploy a range of tactics to ensure that more and more users come on board. For example – and this is the third characteristic – platforms often use cross-subsidisation: one arm of the firm reduces the price of a service or good (even providing it for free), but another arm raises prices in order to make up for these losses.
The same holds for Apple’s App Store, which enabled the production of numerous useful apps that tied users and software developers increasingly into its ecosystem. The challenge of maintaining platforms is, in part, to revise the cross-subsidisation links and the rules of the platform in order to sustain user interest. While network effects strongly support existing platform leaders, these positions are not unassailable. Platforms, in sum, are a new type of firm; they are characterised by providing the infrastructure to intermediate between different user groups, by displaying monopoly tendencies driven by network effects, by employing cross-subsidisation to draw in different user groups, and by having a designed core architecture that governs the interaction possibilities. Platform ownership, in turn, is essentially ownership of software (the 2 billion lines of code for Google, or the 20 million lines of code for Facebook)18 and hardware (servers, data centres, smartphones, etc.), built upon open-source material (e.g.
Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson
"Robert Solow", 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, British Empire, business cycle, 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, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, 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, longitudinal study, 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, Mitch Kapor, 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 Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day
For the simple reason that many of the people with whom they wanted to exchange messages already used WhatsApp, so they too had to adopt it. This is a clear example of what economists call a “network effect”: the fact that some goods, like WhatsApp, become more valuable to each user as more people use them. The economics of network effects are central to understanding business success in the digital world and were worked out in a series of papers in the 1980s,‡‡ which is, not coincidentally, when modern computer networks and digital software started becoming especially important economically. Network effects are also called demand-side economies of scale,§§ and as the WhatsApp example shows, they can be extremely compelling—so compelling that in 2014, Facebook paid $22 billion to acquire the company.
To see the importance of network effects, imagine an app, call it “WhatsWrong,” that was identical in all its functionality and user experience design to WhatsApp, except it had zero users. How much do you think Facebook, or anyone else, would pay for WhatsWrong? WhatsApp shows that network effects arise in part because of the choices made by platform creators. If the app’s developers had decided to make their creation easily interoperable with established SMS networks, users of these networks would have switched over to WhatsApp for cost reasons only, if at all. As the app grew in popularity, however, SMS users increasingly felt left out, so they became more likely to turn their backs on the old messaging technology in favor of the new one. And as more and more of them did this, the network effects grew stronger.
Third, switching costs make it attractive to invest heavily in growing the network in the early stages of adoption, to bring on more users and riders. Uber’s investors are making the bet that the (two-sided) network effects and switching costs are large enough to make it worth investing billions of dollars to encourage adoption of the platform by both riders and drivers. Their strategy is complicated by the fact that geographically distinct markets each have their own local network effects. If you’re hailing a ride in Beijing, it makes little difference if Uber has lots of drivers in New York or New Delhi. The battle isn’t one big winner-take-all contest, but hundreds of separate ones, with only weak network effects across different geographies. They’re winning some and losing others. As it works to build its platform, Uber has two huge advantages. The first is a set of deep-pocketed and patient investors, who are willing to cover Uber’s costs while it scales.
The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism by Arun Sundararajan
additive manufacturing, Airbnb, AltaVista, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, basic income, bitcoin, blockchain, Burning Man, call centre, collaborative consumption, collaborative economy, collective bargaining, commoditize, corporate social responsibility, cryptocurrency, David Graeber, distributed ledger, employer provided health coverage, Erik Brynjolfsson, Ethereum, ethereum blockchain, Frank Levy and Richard Murnane: The New Division of Labor, future of work, George Akerlof, gig economy, housing crisis, Howard Rheingold, information asymmetry, Internet of things, inventory management, invisible hand, job automation, job-hopping, Kickstarter, knowledge worker, Kula ring, Lyft, Marc Andreessen, megacity, minimum wage unemployment, moral hazard, moral panic, Network effects, new economy, Oculus Rift, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, peer-to-peer rental, profit motive, purchasing power parity, race to the bottom, recommendation engine, regulatory arbitrage, rent control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Ross Ulbricht, Second Machine Age, self-driving car, sharing economy, Silicon Valley, smart contracts, Snapchat, social software, supply-chain management, TaskRabbit, The Nature of the Firm, total factor productivity, transaction costs, transportation-network company, two-sided market, Uber and Lyft, Uber for X, uber lyft, universal basic income, Zipcar
As Burnham further clarifies about OpenBazaar in his blog post, “There is no way for a central authority to leverage network effect market power to extract rents from the participants.”5 At a time when most venture capitalists seem to be plowing money into sharing economy platforms that are able to do precisely what Burnham claimed OpenBazaar made impossible (i.e., “leverage network effect market power to extract rents from the participants”), why would a visible and successful venture capitalist like USV want to invest in a company aimed at furthering a technology that is not only, in a sense, “off the grid,” but is also open-source and committed to “zero fees.” Burnham explains further: This begs the question of how OB1 can be a for profit business that will generate a return on the investment we are announcing today. How can a business that is consciously architected to undo network effect defensibility, one that is tearing down the walls and filling in the moats that every paper on market based competition has insisted are necessary for success … succeed.
But it’s fascinating: there’s a real chance that the economic models of crowd-based capitalism may actually be able to distribute production across millions of smaller providers without having to sacrifice significantly on the gains from scale that 20th-century organizations enjoyed. In contrast, it seems unequivocally clear that demand-side economies of scale will become more prevalent as crowd-based capitalism gathers steam. A particular kind of network effect—the two-sided network effect—governs many economic aspects of platforms. As Thomas Eisenmann, Geoffrey Parker, and Marshall Van Alstyne explain in an influential Harvard Business Review article: With two-sided network effects, the platform’s value to any given user largely depends on the number of users on the network’s other side. Value grows as the platform matches demand from both sides. For example, video game developers will create games only for platforms that have a critical mass of players, because developers need a large enough customer base to recover their upfront programming costs.
In contrast, it is completely conceivable that all of Uber’s drivers in New York could collectively switch to a different platform (or start one of their own), eventually taking all of the demand with them. Now let’s contrast the network effects of Uber with those enjoyed by Airbnb. Again, this is a market in which supply has to be built out market by market, in thousands of different cities and towns. But the “network” benefits from hosts in Paris extend far beyond the Paris consumers. This is because unlike local transport, short-term accommodation is sought primarily by travelers rather than by local residents. You favor a platform that can get you accommodation anywhere in the world, rather than one that specializes in one city. Thus, on the Airbnb platform, network effects are more resilient. In a sense, the “fractal” structure of the network effects in both these examples makes their economics more complex than those of traditional two-sided markets, potentially making them either stronger or weaker.
Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US by Rana Foroohar
"side hustle", accounting loophole / creative accounting, Airbnb, AltaVista, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, Bernie Sanders, bitcoin, book scanning, Brewster Kahle, Burning Man, call centre, cashless society, cleantech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, death of newspapers, Deng Xiaoping, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Filter Bubble, future of work, game design, gig economy, global supply chain, Gordon Gekko, greed is good, income inequality, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, Kenneth Rogoff, life extension, light touch regulation, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, paypal mafia, Peter Thiel, pets.com, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, South China Sea, sovereign wealth fund, Steve Jobs, Steven Levy, subscription business, supply-chain management, TaskRabbit, Telecommunications Act of 1996, The Chicago School, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, WikiLeaks, zero-sum game
Google investor Michael Moritz in particular had become worried that the existing revenue model of licensing search technology to other companies simply wasn’t going to work, calling it “a brutal path.”28 Why try to make money deal by deal, rather than by leveraging the power of big data and advertising? Here again, the network effect was key; more data meant better search results, which meant more advertisers, which meant more traffic clicking through to more ads, which meant more data, and so on. He pushed Google to look closely at GoTo’s technology as a model, and they did. “People started reading about how much money was being brought into various other companies by search advertising, and it was kind of decided that we were leaving money on the table,” noted Ray Sidney, Google employee number five.29 He had a point. The network effect was proving to be formidable. Google had powered 3 million searches a day in August 1999—and by the summer of 2000, that number was up to 18 million.
Some have tried, but the monopoly Google and Facebook hold in their respective areas makes it very hard for innovators to gain traction.36 As Guillaume Chaslot, the former Googler who tried (unsuccessfully) to shift the nature of algorithms at YouTube to combat filter bubbles, put it to me, “There just aren’t any incentives at the big companies to change business models. You need start-ups to do this. But they don’t have scale to compete, and they can’t get the funding to grow,” since nobody will invest in competing technology because the network effects harnessed by the largest players seem too powerful to disrupt.37 How these networks and their disruptive effects work and how they are moving throughout not just consumer technology, but every industry, is the topic of the next chapter. CHAPTER 7 The Network Effect Emails are the gift that keeps on giving. Facebook and Google have tried for years to brand themselves as champions of freedom, democratizers of information, and connectors of the world. But when you look at their internal email trails, you often see a different story.
But no big carmaker has shown itself able or willing to create the platform ecosystems that big technology companies create. This is a problem, because the network effect really kicks in when a company controls 30 or 40 percent of a given market, which means that the major auto companies of the world would need to team up in order to achieve such a share. Certainly, it would be a big shift for a company to think about its most aggressive competitors as collaborators. Yet it may be the only choice they have. Developing an ecosystem and owning the software and data within it will be the key to success not just in the car business, but in many industries. Neoliberalism on Steroids As powerful as the network effect is, to understand the seemingly unstoppable growth of the platform companies like Google or Facebook, you also have to look at how much the politics of Silicon Valley changed between the era of hippie idealism represented by Steve Jobs, and the libertarian epoch of Peter Thiel and his ilk.
Reinventing Capitalism in the Age of Big Data by Viktor Mayer-Schönberger, Thomas Ramge
accounting loophole / creative accounting, Air France Flight 447, Airbnb, Alvin Roth, Atul Gawande, augmented reality, banking crisis, basic income, Bayesian statistics, bitcoin, blockchain, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, centralized clearinghouse, Checklist Manifesto, cloud computing, cognitive bias, conceptual framework, creative destruction, Daniel Kahneman / Amos Tversky, disruptive innovation, Donald Trump, double entry bookkeeping, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ford paid five dollars a day, Frederick Winslow Taylor, fundamental attribution error, George Akerlof, gig economy, Google Glasses, information asymmetry, interchangeable parts, invention of the telegraph, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, joint-stock company, Joseph Schumpeter, Kickstarter, knowledge worker, labor-force participation, land reform, lone genius, low cost airline, low cost carrier, Marc Andreessen, market bubble, market design, market fundamentalism, means of production, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, multi-sided market, natural language processing, Network effects, Norbert Wiener, offshore financial centre, Parag Khanna, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price mechanism, purchasing power parity, random walk, recommendation engine, Richard Thaler, ride hailing / ride sharing, Sam Altman, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, Snapchat, statistical model, Steve Jobs, technoutopianism, The Future of Employment, The Market for Lemons, The Nature of the Firm, transaction costs, universal basic income, William Langewiesche, Y Combinator
When the phone market consolidated around AT&T at the turn of the century, customers realized that every new subscriber who joined the network increased the system’s utility for everyone already on it because it added to the number of people they could potentially reach. It provided a strong incentive for even more people to subscribe to AT&T. It was as if the service got better as people were added to it—even though the service itself did not improve, only the opportunities to use it. Today, this network effect (economists sometimes prefer the term “network externality”) is very familiar to us. It’s what allowed the Internet to dominate the flow of digital information and what has driven the success of social media platforms from Facebook to WeChat to Twitter and Instagram. The network effect also enhances the value of market platforms—from eBay to Alibaba, to ride-hailing companies such as Uber and Didi Chuxing, and from Tinder to peer-lending pioneer Funding Circle—although the exact value a new participant adds depends not only on that person but also on the existing players in the market.
Similarly, in a market with an abundance of sellers, every new buyer will be particularly welcome. The third effect, though related to scale and network effects, occurs whenever computer systems use feedback data to learn. When we react as Google autocorrects a spelling, our response creates feedback information that improves Google’s spell checker. IBM’s Watson gets better at recognizing skin cancer the more skin cancers it “sees.” The most popular products and services improve the most because they are fed the most data. In such a context, innovation is no longer about breakthrough ideas but rather about collecting the greatest amount of feedback data. The scale effect lowers cost, the network effect expands utility, and the feedback effect improves the product. Each lead to significant benefits for market participants: they can lower the cost of production, grow the value of their services, or offer a good that continuously evolves seemingly by itself.
Moreover, thanks to the plummeting cost of information processing and storage, especially through cloud computing, the initial investment necessary for start-ups is often much lower than it was in the industrial age. In contrast, network effects remain problematic. Even with a lot of money, start-ups often have great difficulty attracting customers. The only path to success seems to be through innovation: to offer something substantially better than what the incumbent is offering. There’s a robust debate among lawyers and economists as to the extent that innovation offsets the network effect. Some point to the persistence of dominating platforms, such as Microsoft Windows for PC operating systems and Facebook for social media. Others highlight the fact that Facebook unseated the previous incumbent, MySpace, and is now being threatened by Snapchat, a clever start-up based on the innovative idea of vanishing messages.
Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann
affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, housing crisis, Ignaz Semmelweis: hand washing, illegal immigration, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, lateral thinking, loss aversion, Louis Pasteur, Lyft, mail merge, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nuclear winter, offshore financial centre, p-value, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, survivorship bias, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, Vilfredo Pareto, wikimedia commons
Having a million telephones on the phone network is much more than twice as valuable as having five hundred thousand. And knowing that everyone is connected is extremely valuable, which explains why Facebook has such a strong network effect. Critical mass occurs when there are enough nodes present to make a network useful. Amazingly, the fax machine was invented in the 1840s, but people didn’t regularly use it until the 1970s, when there were enough fax machines to reach critical mass. The modern equivalent is internet messaging services: they need to reach critical mass within a community to be useful. Once they pass this tipping point, they can rapidly make their way into the mainstream. Network effects have value beyond communication, however. Many modern systems gain network effects by simply being able to process more data. For example, speech recognition improves when more voices are added.
Other systems gain advantages by being able to provide more liquidity or selection based on the volume or breadth of participants. Think of how more goods are available on Etsy and eBay when more people are participating on those sites. Network effects apply to person-to-person connections within a community as well. Being part of the right alumni network can help you find the right job or get you answers quickly to esoteric questions. Any time you have nodes in a system participating in some kind of exchange, such as for information or currency, you have the potential for network effects. Once an idea or technology reaches critical mass, whether through network effects or otherwise, it has gained a lot of inertia, and often has a lot of momentum as well. In the fax example, after a hundred years of struggling for adoption, once fax technology passed the critical mass point, it became embedded in society for the long term.
In the fax example, after a hundred years of struggling for adoption, once fax technology passed the critical mass point, it became embedded in society for the long term. The lesson here is, when you know that the concept of critical mass applies to your endeavor, you want to pay special attention to it. Just as we suggested questions to ask about tipping points, there are similar questions you can ask about critical mass and network effects: What is the critical mass point for this idea or technology? What needs to happen for it to reach critical mass? Are there network effects or other catalysts that can make reaching critical mass happen sooner? Can I reorganize the system so that critical mass can be reached in a sub-community sooner? It’s important to note that these critical mass models apply in both positive and negative scenarios. Harmful ideas and technologies can also reach critical mass and spread quickly through societies.
Quality Investing: Owning the Best Companies for the Long Term by Torkell T. Eide, Lawrence A. Cunningham, Patrick Hargreaves
air freight, Albert Einstein, backtesting, barriers to entry, buy and hold, cashless society, cloud computing, commoditize, Credit Default Swap, discounted cash flows, discovery of penicillin, endowment effect, global pandemic, haute couture, hindsight bias, low cost airline, mass affluent, Network effects, oil shale / tar sands, pattern recognition, shareholder value, smart grid, sovereign wealth fund, supply-chain management
Leading in crop protection, with 20% of the global market and with a strong position in seed technology, Syngenta’s unique portfolio of assets and strong culture of innovation position it well to maintain its attractive and stable long-term returns on capital. Network effects Network effects arise when a system’s value increases as more people use it. In most cases, network effects represent a tangible benefit to customers, as with social media sites. An auction site is a classic example of a business benefiting from network effects. More sellers offering products attract more buyers, which entices more sellers and so on in a compounding circle. Other examples are classified ad forums and stock exchanges. Internet search is another, though with a twist: the generation of data that enables the refinement of search algorithms keeps drawing in more users who in turn leave more data for endless harvesting and refinement. Ironically, when network effects are too strong, they may backfire. An extremely efficient network can produce monopoly power and government intervention risk rises.
An extremely efficient network can produce monopoly power and government intervention risk rises. As much as network effects are to a consumer’s benefit, a monopoly isn’t. Other stakeholders and users can also turn against a company that is perceived to be too dominant. In the case of online housing portals in the UK, an alliance of real estate agents has coordinated to form a rival, onthemarket.com, to compete against dominant players Rightmove and Zoopla; although the impact of this disruptive new entrant is currently unclear. Another area of concern is the high pace of innovation in many areas where network effects are particularly prevalent. While it is easy to spot the benefit of network effects, networks face potential disruption that can be sudden and devastating. In social media, Facebook unilaterally killed several network businesses, including MySpace and MSN Chat.
The ice cream vendor might be able to increase revenues through price hikes, or by improved product mix, but the advantage is not scalable – there is no guarantee of being the sole licensee on any new beaches. The competitive advantages we seek can create economic moats in the same way as the ice cream seller’s monopoly, but have the added benefit of being replicable. Competitive advantage is a broad topic and is integral to all the patterns we examine in the chapter that follows. Here, we highlight three aspects of the subject to set the stage: technology, network effects, and distribution advantages. Technology The most important facet of competitive advantage derived through technology is sustainability. A product offering superior benefits for customers will have a competitive advantage and should yield above-average economic returns, but having just one product in this category is usually insufficient to sustain a competitive advantage. Rest assured, a superior product will quickly be copied.
The System: Who Owns the Internet, and How It Owns Us by James Ball
Bill Duvall, bitcoin, blockchain, Chelsea Manning, cryptocurrency, don't be evil, Donald Trump, Douglas Engelbart, Edward Snowden, en.wikipedia.org, Firefox, Frank Gehry, Internet of things, invention of movable type, Jeff Bezos, jimmy wales, Julian Assange, Kickstarter, Leonard Kleinrock, Marc Andreessen, Mark Zuckerberg, Menlo Park, Minecraft, Mother of all demos, move fast and break things, move fast and break things, Network effects, Oculus Rift, packet switching, patent troll, Peter Thiel, pre–internet, ransomware, RFC: Request For Comment, risk tolerance, Ronald Reagan, Rubik’s Cube, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Crocker, Stuxnet, The Chicago School, undersea cable, uranium enrichment, WikiLeaks, yield management, zero day
Unless such networks had means to inter-operate – and why would they, if they’re rivals? – that could easily be much worse for users. In practice, though, network effects go much further than just social networks, and gather power for whoever controls the networks. A pre-internet idea of a network effect can be found in, for example, railways: add an extra stop to an existing railway line, and it helps existing customers, who now have an additional place they can visit, as well as the ones living by the new stop. ‘A network effect is, as you add nodes, which could be railway stops or customers, you create more value for everybody in the system,’ Wenger says. ‘I believe that network effects are one of the defining aspects of the digital age.’ These effects are everywhere, he says, because data is ‘super-modular’: on any given type of data, there are diminishing marginal returns.
The key effect that makes for a ‘super-modular’ function is that knowing a little bit more about each separate thing has much more value – knowing your rough income, your rough location, your gender and your rough age, all together is worth far more than knowing just one or even two of those. Wenger is essentially saying that in an era of AI, algorithms and machine learning, joined-up data points across a lot of areas have strong network effects, generating much more information and value than used to be the case. This means network effects go far, far beyond social media. ‘The reason I’m saying all of this is because I think when people think of network effects, they often think about it too literally, like in the Twitter-type way. Like I connect with you, the Facebook-type way. Even the internet, or railroad network. But there is a much more profound way in which network effects are deeply baked into the fabric of any production function that’s based on data.’ The result of these data oligopolies, Wenger explains, is that a large majority of us – in the developed world at least – carry around supercomputers capable of speaking to any other supercomputer on the planet, and then reduce them into dumb terminals for big tech.
In other words, he hopes the potential for blockchains to allow databases to be widely distributed, impossible to alter and publicly verifiable, could lead to a change in the power structure of the internet, which hands control to the companies sitting on the most data. This matters because control over databases is even more important than it first appears, he explains, partly because of one of the most discussed phenomena of the online world: network effects. These are most typically discussed, obviously enough, in the context of social networks, as it’s here that their benefits are most immediately apparent. Let’s imagine two social networks which have both been operating in one country for around a year or so. One has about 100,000 users, while the other has close to a million. If you’re a new user looking to sign up to a network, and you try both out, you’re far likelier to find people you already know on the second network – and so also much more likely to get an invitation to join that one too.
The Flat White Economy by Douglas McWilliams
"Robert Solow", access to a mobile phone, banking crisis, Big bang: deregulation of the City of London, bonus culture, Boris Johnson, Chuck Templeton: OpenTable:, cleantech, cloud computing, computer age, correlation coefficient, Edward Glaeser, en.wikipedia.org, Erik Brynjolfsson, eurozone crisis, George Gilder, hiring and firing, income inequality, informal economy, Kickstarter, knowledge economy, loadsamoney, low skilled workers, mass immigration, Metcalfe’s law, Network effects, new economy, offshore financial centre, Pareto efficiency, Peter Thiel, Productivity paradox, Robert Metcalfe, Silicon Valley, smart cities, special economic zone, Steve Jobs, working-age population, zero-sum game
In my experience, supereconomies of scale have tended to mean that large changes tend to be delayed while incremental improvements tend to be accelerated to keep ahead of competition. The other economic factor that affected the timing with which the digital economy bore fruit is its sensitivity to network effects. Network effects are a phenomenon common to communications systems. Essentially, the value of a network to an individual participant in that network increases with the number of participants in it. So a single telephone is of no use on its own – it only gains some value when there is a second phone. And as the value of each phone increases, the more phones tend to become available to contact on the network, until there are so many other connections that the value of an additional connection is negligible. Network effects were first developed as a concept by the President of Bell Telephones in making his case for a monopoly in 1908, but the ideas were developed and refined in the 1980s and 1990s.
Robert Metcalfe, one of the co-inventors of the Ethernet, was the progenitor of Metcalfe’s Law – that the value of a communications network varied with the square of the number of connections in the network. This idea was vigorously promoted by the economic guru George Gilder2 during the 1990s. Where there are network effects, investment typically doesn’t take place until there is a critical mass of potential users. Network effects tend to cause investment to be held back in a similar fashion to supereconomies of scale, although in the case of the latter, the tendency to delay investment is moderated by the possibility of gaining first-mover advantage. It is the combination of supereconomies of scale and network effects that meant that the economic exploitation of the digital technologies took place on a different, tardier timetable than that which had been predicted by those who only understood the technological issues.3 Online retail and marketing Internet usage in most Western economies really took off during the first decade of the 21st century.
‘London Key Facts and Statistics’ produced by the Association of London Councils claims that London is one of the world’s most diverse cities ethnically,29 quoting ONS neighbourhood statistics based on the 2011 Census. It also claims that “London has the largest number of community languages spoken in Europe. Over 300 languages are spoken in London schools”. Not only did London have a long-established skill base in advertising and marketing, but it had a massive supply of diverse and creative young people, many of them looking for work. Once the impediments of those supereconomies of scale and the network effects described earlier had been overcome, and once online retail and marketing began to take off, London was in prime position towards the end of the 21st century’s first decade. It all percolated together and hey presto, we had the Flat White Economy. CHAPTER 3 What is the Flat White Economy and where is it based? The epicentre of the Flat White Economy is the London postal district known as EC1V.
Twitter and Tear Gas: The Power and Fragility of Networked Protest by Zeynep Tufekci
4chan, active measures, Affordable Care Act / Obamacare, AltaVista, anti-communist, Bernie Sanders, British Empire, citizen journalism, collective bargaining, conceptual framework, crowdsourcing, Donald Trump, Edward Snowden, feminist movement, Ferguson, Missouri, Filter Bubble, Howard Rheingold, income inequality, index card, interchangeable parts, invention of movable type, invention of writing, loose coupling, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, Mikhail Gorbachev, moral hazard, moral panic, Naomi Klein, Network effects, new economy, obamacare, Occupy movement, offshore financial centre, pre–internet, race to the bottom, RAND corporation, ride hailing / ride sharing, Rosa Parks, sharing economy, Silicon Valley, Skype, Snapchat, The Structural Transformation of the Public Sphere, Thorstein Veblen, We are the 99%, WikiLeaks
It is true that network effects did not provide absolute protection early in the race to commercialize the internet: MySpace was beaten out by Facebook, for example, and Yahoo and Altavista by Google—they had gotten started earlier, but had not yet established in as dominant a position. Network effects doesn’t protect companies from initial missteps, especially in the early years before they pulled way ahead of everyone else, and such dominance does not occur independent of the quality of the company’s product. Google’s new method of ranking web pages was clearly superior to the earlier competitors. Network effects may not mean that the very first companies to enter a new and rapidly growing market and achieve sizable growth will necessarily be the ones to emerge as dominant once the market has matured and growth has slowed. But at that point, whichever companies are dominant will be very difficult for competitors to unseat. Network effects are certainly apparent in the dynamics we see currently in the use of, for example, Facebook, Google, and eBay.
This is one reason some nations, like China, have never allowed Facebook to become established, and likely will not do so unless Facebook succumbs to draconian measures of control, censorship, and turning over of user information to the government.31 Additionally, these internet platforms harness the power of network effects—the more people who use them, the more useful they are to more people. With so many people already on Facebook, there are huge incentives for new people to get on Facebook even if they dislike some of its policies or features. Network effects also create a twist for activists who find themselves compelled to use whatever the dominant platform may be, even if they are uncomfortable with it. A perfect social media platform without users is worthless for activism. One that is taking off on a society-wide scale is hard to stop, block, or ban.
The dominance of a few platforms online is not a historical coincidence; rather, it is the product of two important structural dynamics: network effects11 and the dominance of the ad-financing model for online platforms. The term “network effects” (or “network externalities”) is a shorthand for the principle that the more people who use a platform, the more useful that platform is to each user.12 Such effects are especially strong for online social networking platforms since the main point is to access other users and the content they have posted. Think of a telephone that could talk only to telephones made by the same company: what good is a wonderful telephone if you cannot call anyone with it? You would want to get the one most of your friends used even if you liked another company’s model better. When network effects operate, potential alternatives are less useful simply because fewer people use them. Thus a platform that achieves early success can become dominant as more and more people flock to it.
The Starfish and the Spider: The Unstoppable Power of Leaderless Organizations by Ori Brafman, Rod A. Beckstrom
Atahualpa, barriers to entry, Burning Man, creative destruction, disintermediation, experimental economics, Firefox, Francisco Pizarro, jimmy wales, Kibera, Lao Tzu, Network effects, peer-to-peer, pez dispenser, shareholder value, Silicon Valley, Skype, The Wisdom of Crowds, union organizing
How hard is it to start an online classified ad site? Not very. Size matters. The small rule. RULE 2: The Network Effect The network effect is the increase in the overall value of the network with the addition of each new member. Each additional telephone or fax machine makes all the other phones or fax machines in the world more worthwhile. Historically, creating the network effect could be tough. The fax network had to be built one expensive fax machine at a time. Starfish organizations, however, are particularly well positioned to take advantage of the network effect. For some of the most successful starfish organizations, like Skype and craigslist, it costs absolutely nothing to add a new customer. While it used to cost millions or billions to create a significant network effect, for many starfish organizations the cost has gone down to zero.
Buyers were reluctant to switch to a new auction site where sellers didn't have a proven track record; they preferred to stay at eBay. Likewise, sellers with established positive ratings on eBay had a huge incentive to stay on the site rather than go elsewhere and start anew. For one thing, they were able to fetch premium prices based on their established reputations. They also had an incentive to stay where the buyers were. In addition, eBay benefited from what's called the "network effect." Say there's only one telephone in the world. It's not going to be worth much, right? After all, who are you going to call? But when there are two telephones, their value goes up dramatically. Each additional telephone adds value to the overall phone system. Likewise, eBay's network becomes more valuable with each new user rating. One user rating doesn't do anyone much good. But millions of ratings on millions of users have immense value.
While it used to cost millions or billions to create a significant network effect, for many starfish organizations the cost has gone down to zero. Often without spending a dime, starfish organizations create communities where each new member adds value to the larger THE NEW WORLD network. With every new eMule user, there's more music to be shared. Every new site on the World Wide Web makes the whole network richer with information. Companies like eBay have used the network effect not only to survive but to thrive: buyers and sellers have stayed loyal to the site because of the value of network. RULE 3: The Power of Chaos As you read this, parents worldwide are beseeching their kids to clean their rooms. "How can you get anything done in this mess?" they ask. Similarly, the conventional thinking is that to run an organization you'd better be organized and structured. But in the decentralized world, messy kids can rejoice. It pays to be chaotic.
The Gig Economy: A Critical Introduction by Jamie Woodcock, Mark Graham
Airbnb, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, British Empire, business process, business process outsourcing, call centre, collective bargaining, commoditize, corporate social responsibility, crowdsourcing, David Graeber, deindustrialization, disintermediation, en.wikipedia.org, full employment, future of work, gender pay gap, gig economy, global value chain, informal economy, information asymmetry, inventory management, Jaron Lanier, Jeff Bezos, job automation, knowledge economy, Lyft, mass immigration, means of production, Network effects, new economy, Panopticon Jeremy Bentham, planetary scale, precariat, rent-seeking, RFID, ride hailing / ride sharing, Ronald Reagan, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, TaskRabbit, The Future of Employment, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, women in the workforce, working poor, young professional
Thus, the platform is like a shell, with just the ‘bare extractive minimum – control over the platform that enables a monopoly rent to be gained’ (Srnicek, 2017: 76). The organization of the platform means that they are particularly reliant on network effects. The more workers and users on the platform, the greater the benefits of participating (Srnicek, 2017: 45). Conversely, if there is significant competition between platforms in any particular sector, those network effects are diminished. For instance, multiple taxi apps in a city will fragment both the driver and customer base, increasing waiting times and reducing the ease of access. Many platforms have been able to achieve these network effects relatively easily because of their relatively rapid expansion. In the case of Uber, there is no need for the platform itself to buy new cars. Instead, expansion is limited by server capacity, effective advertising and available workers.
It provides ‘tools to bring together the supply of, and demand for, labour’ (Graham and Woodcock, 2018: 242), including the app, digital infrastructure and algorithms for managing the work. As Nick Srnicek (2017: 48) has argued: Platforms, in sum, are a new type of firm; they are characterized by providing the infrastructure to intermediate between different user groups, by displaying monopoly tendencies driven by network effects, by employing cross-subsidization to draw in different user groups, and by having designed a core architecture that governs the interaction possibilities. Platforms have become central to our social activities. They bring together users, capture and monetize data, as well as needing to scale to be effective. Indeed, they are now starting to mediate just about every imaginable economic activity, and they tend to do so through gig economy models.
The designation ‘platform’ comes from its more traditional usage as a raised surface on which people can stand. In this context, the platform is a digital environment upon which other software can be run. In organizational terms, Nick Srnicek (2017: 48) argues that: Platforms, in sum, are a new type of firm; they are characterized by providing the infrastructure to intermediate between different user groups, by displaying monopoly tendencies driven by network effects, by employing cross-subsidization to draw in different user groups, and by having a designed core architecture that governs the interaction possibilities. For the gig economy, as we have written elsewhere, ‘the common feature of all digital labour platforms is that they offer tools to bring together the supply of, and demand for, labour’ (Graham and Woodcock, 2018: 242). Similarly, Niels van Doorn (2017: 901) describes these organizations as ‘platform labour intermediaries that, despite their self-presentation as tech companies, operate as new players in a dynamic temporary staffing industry.’
Zero to One: Notes on Startups, or How to Build the Future by Peter Thiel, Blake Masters
Airbnb, Albert Einstein, Andrew Wiles, Andy Kessler, Berlin Wall, cleantech, cloud computing, crony capitalism, discounted cash flows, diversified portfolio, don't be evil, Elon Musk, eurozone crisis, income inequality, Jeff Bezos, Lean Startup, life extension, lone genius, Long Term Capital Management, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, Nate Silver, Network effects, new economy, paypal mafia, Peter Thiel, pets.com, profit motive, Ralph Waldo Emerson, Ray Kurzweil, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Singularitarianism, software is eating the world, Steve Jobs, strong AI, Ted Kaczynski, Tesla Model S, uber lyft, Vilfredo Pareto, working poor
“Microsoft Windows XP Tablet PC Edition” products first shipped in 2002, and Nokia released its own “Internet Tablet” in 2005, but they were a pain to use. Then Apple released the iPad. Design improvements are hard to measure, but it seems clear that Apple improved on anything that had come before by at least an order of magnitude: tablets went from unusable to useful. 2. Network Effects Network effects make a product more useful as more people use it. For example, if all your friends are on Facebook, it makes sense for you to join Facebook, too. Unilaterally choosing a different social network would only make you an eccentric. Network effects can be powerful, but you’ll never reap them unless your product is valuable to its very first users when the network is necessarily small. For example, in 1960 a quixotic company called Xanadu set out to build a two-way communication network between all computers—a sort of early, synchronous version of the World Wide Web.
If you focus on near-term growth above all else, you miss the most important question you should be asking: will this business still be around a decade from now? Numbers alone won’t tell you the answer; instead you must think critically about the qualitative characteristics of your business. CHARACTERISTICS OF MONOPOLY What does a company with large cash flows far into the future look like? Every monopoly is unique, but they usually share some combination of the following characteristics: proprietary technology, network effects, economies of scale, and branding. This isn’t a list of boxes to check as you build your business—there’s no shortcut to monopoly. However, analyzing your business according to these characteristics can help you think about how to make it durable. 1. Proprietary Technology Proprietary technology is the most substantive advantage a company can have because it makes your product difficult or impossible to replicate.
For example, in 1960 a quixotic company called Xanadu set out to build a two-way communication network between all computers—a sort of early, synchronous version of the World Wide Web. After more than three decades of futile effort, Xanadu folded just as the web was becoming commonplace. Their technology probably would have worked at scale, but it could have worked only at scale: it required every computer to join the network at the same time, and that was never going to happen. Paradoxically, then, network effects businesses must start with especially small markets. Facebook started with just Harvard students—Mark Zuckerberg’s first product was designed to get all his classmates signed up, not to attract all people of Earth. This is why successful network businesses rarely get started by MBA types: the initial markets are so small that they often don’t even appear to be business opportunities at all. 3.
Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy by Jonathan Taplin
1960s counterculture, affirmative action, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, American Legislative Exchange Council, Apple's 1984 Super Bowl advert, back-to-the-land, barriers to entry, basic income, battle of ideas, big data - Walmart - Pop Tarts, bitcoin, Brewster Kahle, Buckminster Fuller, Burning Man, Clayton Christensen, commoditize, creative destruction, crony capitalism, crowdsourcing, data is the new oil, David Brooks, David Graeber, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Dynabook, Edward Snowden, Elon Musk, equal pay for equal work, Erik Brynjolfsson, future of journalism, future of work, George Akerlof, George Gilder, Google bus, Hacker Ethic, Howard Rheingold, income inequality, informal economy, information asymmetry, information retrieval, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, Kickstarter, labor-force participation, life extension, Marc Andreessen, Mark Zuckerberg, Menlo Park, Metcalfe’s law, Mother of all demos, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, Norbert Wiener, offshore financial centre, packet switching, Paul Graham, paypal mafia, Peter Thiel, plutocrats, Plutocrats, pre–internet, Ray Kurzweil, recommendation engine, rent-seeking, revision control, Robert Bork, Robert Gordon, Robert Metcalfe, Ronald Reagan, Ross Ulbricht, Sam Altman, Sand Hill Road, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, smart grid, Snapchat, software is eating the world, Steve Jobs, Stewart Brand, technoutopianism, The Chicago School, The Market for Lemons, The Rise and Fall of American Growth, Tim Cook: Apple, trade route, transfer pricing, Travis Kalanick, trickle-down economics, Tyler Cowen: Great Stagnation, universal basic income, unpaid internship, We wanted flying cars, instead we got 140 characters, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator
Thiel says, “It’s always a red flag when entrepreneurs talk about getting 1% of a $100 billion market.” He wanted to invest in monopolies, not competitive businesses. 2. Build businesses that have “network effects.” Thiel’s first two major investments, PayPal and Facebook, both benefit from having millions of users who want to connect with each other. When PayPal was just a payment system for Palm Pilot, it was a failure. As soon as it became the standard payment system for eBay, it got the network-effect wind at its back. 3. Economies of scale are critical. Google is pretty much unassailable in search-engine advertising because it has huge economies of scale. This leads to the conclusion that there will be very few winners in each sector of tech. The combination of scale and network effects makes it very hard to dislodge the winners, especially if you are in a business like tech, which is so lightly regulated. 4.
The brand promise also helps you defend yourself against government intrusion. Google’s original “Don’t be evil” brand promise gave them a patina of social entrepreneurship that helps protect them from accusations of monopoly power tactics. As John Seely Brown has pointed out, the end of the decentralized Web that Engelbart and the PARC visionaries had imagined occurs at this point, when “we moved from products to platforms, which let the network effect play out in a hub and spoke model.” From this point on the economies of scale enjoyed by a platform whose users are measured in the billions becomes the ultimate metric for success. Thiel understood this, and from PayPal, the original founding group began to spread through Silicon Valley after eBay’s $1 billion acquisition of the company. Even companies not funded by Thiel adopted his unique view of capitalism.
North Dakota that “the lack of a physical presence in a state is sufficient grounds to exempt a corporation from having to pay sales and use taxes to a state.” For Bezos, who studied the growth of the Internet while at Shaw, a light went on. He began to imagine an online retailer that could totally disrupt the local bookstore business. His principles were very similar to Thiel’s four guidelines. First he would build a proprietary online platform (even going so far as to patent “one-click” ordering). Then he would harness the network effect, using user recommendations to build individual taste profiles of every customer. He would also create economies of scale by buying in bulk from publishers and negotiating the lowest prices, which individual bookstores could not match. In this effort he could use the wholesaler Ingram in order to list those titles he was not buying from publishers, thereby dramatically expanding the number of books he could offer on Amazon and still avoid charging sales tax.
Value of Everything: An Antidote to Chaos The by Mariana Mazzucato
"Robert Solow", activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, bank run, banks create money, Basel III, Berlin Wall, Big bang: deregulation of the City of London, bonus culture, Bretton Woods, business cycle, butterfly effect, buy and hold, Buy land – they’re not making it any more, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, cleantech, Corn Laws, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, David Ricardo: comparative advantage, debt deflation, European colonialism, fear of failure, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, financial repression, full employment, G4S, George Akerlof, Google Hangouts, Growth in a Time of Debt, high net worth, Hyman Minsky, income inequality, index fund, informal economy, interest rate derivative, Internet of things, invisible hand, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour market flexibility, laissez-faire capitalism, light touch regulation, liquidity trap, London Interbank Offered Rate, margin call, Mark Zuckerberg, market bubble, means of production, money market fund, negative equity, Network effects, new economy, Northern Rock, obamacare, offshore financial centre, Pareto efficiency, patent troll, Paul Samuelson, peer-to-peer lending, Peter Thiel, profit maximization, quantitative easing, quantitative trading / quantitative ﬁnance, QWERTY keyboard, rent control, rent-seeking, Sand Hill Road, shareholder value, sharing economy, short selling, Silicon Valley, Simon Kuznets, smart meter, Social Responsibility of Business Is to Increase Its Profits, software patent, stem cell, Steve Jobs, The Great Moderation, The Spirit Level, The Wealth of Nations by Adam Smith, Thomas Malthus, Tobin tax, too big to fail, trade route, transaction costs, two-sided market, very high income, Vilfredo Pareto, wealth creators, Works Progress Administration, zero-sum game
Evgeny Morozov, ‘Where Uber and Amazon rule: welcome to the world of the platform', the Guardian, 7 June 2015: http://www.theguardian.com/technology/2015/jun/07/facebook-uber-amazon-platform-economy 68. https://www.bloomberg.com/news/articles/2017-02-28/in-video-uber-ceo-argues-with-driver-over-falling-fares 69. http://fortune.com/2016/10/20/uber-app-riders/ 70. A useful distinction can be made between direct and indirect network effects. When a higher number of participants increases the benefit to each individual member - as in the case of Facebook - the effect is direct. Where, instead, a higher number of members (for example, buyers) increases the convenience of using the platform, not for the members but for another group (for example, the sellers), we talk of indirect network effects. 71. Source: Statista database (www.statista.com), and http://uk.businessinsider.com/facebook-and-google-winners-of-digital-advertising-2016-6?r=US&IR=T 72. Morozov, ‘Where Uber and Amazon rule'. 73. See note 70 for the distinction between direct and indirect network effects. 74. Mazzucato, The Entrepreneurial State. 75. Foley, ‘Rethinking financial capitalism and the “information” economy'. 76.
But the result of this sharing economy is that Uber Black drivers are paid less, ‘standards' rise (with pressure for drivers to offer ‘pool' services to customers) and competition from Uber's other services intensifies.68 While drivers are increasingly complaining, Uber's market reach is higher than ever and growing every day: as of October 2016 it had 40 million monthly riders worldwide.69 In 2016 it had 160,000 drivers in the US, with millions more spread across 500 cities globally - all working as ‘independent contractors', so that Uber does not have to provide them with the kind of healthcare and other benefits which they would receive as full-time employees. Uber, like Google, Facebook and Amazon, seems to have no limit to its size. The network effects that pervade online markets add an important peculiarity: once a firm establishes leadership in a market its dominance increases and becomes self-perpetuating almost automatically. If everyone is on Facebook, no one wants to join a different social network. As most people search on Google, the gap between Google and its competitors grows wider because it can elaborate on more data. And as its market share rises, so does its capacity to attract users, which in turn increases its market dominance.70 Contrary to the pious pronouncements of Internet pioneers, network effects are increasingly centralizing the Internet, thereby placing an enormous concentration of market power in the hands of a few firms.
Financialization of the Real Economy The Buy-back Blowback Maximizing Shareholder Value The Retreat of ‘Patient' Capital Short-Termism and Unproductive Investment Financialization and Inequality From Maximizing Shareholder Value to Stakeholder Value 7. Extracting Value through the Innovation Economy Stories about Value Creation Where Does Innovation Come From? Financing Innovation Patented Value Extraction Unproductive Entrepreneurship Pricing Pharmaceuticals Network Effects and First-mover Advantages Creating and Extracting Digital Value Sharing Risks and Rewards 8. Undervaluing the Public Sector The Myths of Austerity Government Value in the History of Economic Thought Keynes and Counter-cyclical Government Government in the National Accounts Public Choice Theory: Rationalizing Privatization and Outsourcing Regaining Confidence and Setting Missions Public and Private Just Deserts From Public Goods to Public Value 9.
Zucked: Waking Up to the Facebook Catastrophe by Roger McNamee
4chan, Albert Einstein, algorithmic trading, AltaVista, Amazon Web Services, barriers to entry, Bernie Sanders, Boycotts of Israel, Cass Sunstein, cloud computing, computer age, cross-subsidies, data is the new oil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Electric Kool-Aid Acid Test, Elon Musk, Filter Bubble, game design, income inequality, Internet of things, Jaron Lanier, Jeff Bezos, John Markoff, laissez-faire capitalism, Lean Startup, light touch regulation, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, Menlo Park, Metcalfe’s law, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Network effects, paypal mafia, Peter Thiel, pets.com, post-work, profit maximization, profit motive, race to the bottom, recommendation engine, Robert Mercer, Ronald Reagan, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software is eating the world, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, The Chicago School, Tim Cook: Apple, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, WikiLeaks, Yom Kippur War
They rode the wave of wired broadband adoption and then 4G mobile to achieve global scale in what seemed like the blink of an eye. Their products enjoyed network effects, which occur when the value of a product increases as you add users to the network. Network effects were supposed to benefit users. In the cases of Facebook and Google, that was true for a time, but eventually the value increase shifted decisively to the benefit of owners of the network, creating insurmountable barriers to entry. Facebook and Google, as well as Amazon, quickly amassed economic power on a scale not seen since the days of Standard Oil one hundred years earlier. In an essay on Medium, the venture capitalist James Currier pointed out that the key to success in the internet platform business is network effects and Facebook enjoyed more of them than any other company in history. He said, “To date, we’ve actually identified that Facebook has built no less than six of the thirteen known network effects to create defensibility and value, like a castle with six concentric layers of walls.
Increasing integration with Instagram and changes to WhatsApp’s business model signal that Facebook has not changed course despite widespread pressure to do so. Relative to privacy, Google is also a serious offender. Both Google and Facebook subvert the intent of privacy efforts like Europe’s GDPR with option-dialogue boxes designed to prevent users from taking advantage of their new rights. Facebook remains a threat to innovation. The company enjoys all the privileges of a monopoly. It has network effects on top of network effects, protective moats outside of protective moats, with scale advantages that make life miserable for Snapchat, to say nothing of every startup that wants to innovate in social. Regulation can influence Facebook’s behavior, but the problems are features of Facebook’s business model and probably beyond the reach of all but the most onerous regulation. Google and Amazon also enjoy monopoly power, though with different symptoms than Facebook.
He said, “To date, we’ve actually identified that Facebook has built no less than six of the thirteen known network effects to create defensibility and value, like a castle with six concentric layers of walls. Facebook’s walls grow higher all the time, and on top of them Facebook has fortified itself with all three of the other known defensibilities in the internet age: brand, scale, and embedding.” By 2004, the United States was more than a generation into an era dominated by a hands-off, laissez-faire approach to regulation, a time period long enough that hardly anyone in Silicon Valley knew there had once been a different way of doing things. This is one reason why few people in tech today are calling for regulation of Facebook, Google, and Amazon, antitrust or otherwise. One other factor made the environment of 2004 different from earlier times in Silicon Valley: angel investors.
The Truth Machine: The Blockchain and the Future of Everything by Paul Vigna, Michael J. Casey
3D printing, additive manufacturing, Airbnb, altcoin, Amazon Web Services, barriers to entry, basic income, Berlin Wall, Bernie Madoff, bitcoin, blockchain, blood diamonds, Blythe Masters, business process, buy and hold, carbon footprint, cashless society, cloud computing, computer age, computerized trading, conceptual framework, Credit Default Swap, crowdsourcing, cryptocurrency, cyber-physical system, dematerialisation, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, failed state, fault tolerance, fiat currency, financial innovation, financial intermediation, global supply chain, Hernando de Soto, hive mind, informal economy, intangible asset, Internet of things, Joi Ito, Kickstarter, linked data, litecoin, longitudinal study, Lyft, M-Pesa, Marc Andreessen, market clearing, mobile money, money: store of value / unit of account / medium of exchange, Network effects, off grid, pets.com, prediction markets, pre–internet, price mechanism, profit maximization, profit motive, ransomware, rent-seeking, RFID, ride hailing / ride sharing, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, smart contracts, smart meter, Snapchat, social web, software is eating the world, supply-chain management, Ted Nelson, the market place, too big to fail, trade route, transaction costs, Travis Kalanick, Turing complete, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, universal basic income, web of trust, zero-sum game
The idea is that if Brave is successful, the BATs’ price will rise, which will in turn encourage more and more people to join the community and abide by its good-behavior-inducing rules. It aims for a network effect, one that feeds a virtuous circle of better-aligned incentives and rewards within the online content market. Network effects like these are a critical source of market power for many companies in the digital economy. Amazon, Alibaba, Uber, and other digital behemoths all depend on them—on how widely an idea is adopted and reinforced in a positive feedback loop. The more people use Uber, the more drivers are drawn to the system, and the easier it is to find a ride, which attracts even more people to the service, and so forth. Issuers of tokens are arguing that they will incentivize these kinds of network effects and positive feedback loops. So far that’s an unproven contention. Success will likely hinge on the liquidity of each respective token, on how frequently it is traded back and forth.
It turned out that simply giving networks of computers a way to share data directly wasn’t enough to prevent large corporate entities from taking control of the information economy. Silicon Valley’s anti-establishment coders hadn’t reckoned with the challenge of trust and how society traditionally turns to centralized institutions to deal with that. That failure was clear in the subsequent Internet 2.0 phase, which unlocked the power of social networks but also allowed first-mover companies to turn network effects into entrenched monopoly power. These included social media giants like Facebook and Twitter and e-marketplace success stories of the “sharing economy” such as Uber and Airbnb. Blockchain technologies, as well as other ideas contained in this Internet 3.0 phase, aim to do away with these intermediaries altogether, letting people forge their own bonds of trust to build social networks and business arrangements on their own terms.
But, inevitably, the added transaction costs translated into barriers to entry that helped the largest incumbents ward off competitors, limiting innovation and denying billions of financially excluded people the opportunity to fully exploit the Internet’s many possibilities for advancement. It’s how we’ve ended up with Internet monopolies. Those with first-mover advantages have not only enjoyed the benefits of network effects; they’ve been indirectly protected by the hefty transaction costs that competitors face in trying to grow to the same scale. In a very tangible way, then, the high cost of trust management has fed the economic conditions that allow the likes of Amazon, Netflix, Google, and Facebook to keep squashing competitors. Just as important, it has also meant that these monolithic players have become all-powerful stewards of our ever-growing pools of vital, sensitive data.
Ten Arguments for Deleting Your Social Media Accounts Right Now by Jaron Lanier
4chan, basic income, cloud computing, corporate governance, Donald Trump, en.wikipedia.org, Filter Bubble, gig economy, Internet of things, Jaron Lanier, life extension, Mark Zuckerberg, market bubble, Milgram experiment, move fast and break things, move fast and break things, Network effects, ransomware, Ray Kurzweil, recommendation engine, Silicon Valley, Snapchat, Stanford prison experiment, stem cell, Steve Jobs, Ted Nelson, theory of mind, WikiLeaks, zero-sum game
It’s hard to quit a particular social network and go to a different one, because everyone you know is already on the first one. It’s effectively impossible for everyone in a society to back up all their data, move simultaneously, and restore their memories at the same time. Effects of this kind are called network effects or lock-ins. They’re hard to avoid on digital networks. Originally, many of us who worked on scaling the internet16 hoped that the thing that would bring people together—that would gain network effect and lock-in—would be the internet itself. But there was a libertarian wind blowing, so we left out many key functions. The internet in itself didn’t include a mechanism for personal identity, for instance. Each computer has its own code number, but people aren’t represented at all. Similarly, the internet in itself doesn’t give you any place to store even a small amount of persistent information, any way to make or receive payments, or any way to find other people you might have something in common with.
Information warfare units sway elections, hate groups recruit, and nihilists get amazing bang for the buck when they try to bring society down. The unplanned nature of the transformation from advertising to direct behavior modification caused an explosive amplification of negativity in human affairs. We’ll return to the higher potency of negative emotions in behavior modification many times as we explore the personal, political, economic, social, and cultural effects of social media. ADDICTION, MEET NETWORK EFFECT Addiction is a big part of the reason why so many of us accept being spied on and manipulated by our information technology, but it’s not the only reason. Digital networks genuinely deliver value to us. They allow for great efficiencies and convenience. That’s why so many of us worked so hard to make them possible. Once you can use a pocket device to order rides and food and find out where to meet your friends right away, it’s hard to go back.
Similarly, the internet in itself doesn’t give you any place to store even a small amount of persistent information, any way to make or receive payments, or any way to find other people you might have something in common with. Everyone knew that these functions and many others would be needed. We figured it would be wiser to let entrepreneurs fill in the blanks than to leave that task to government. What we didn’t consider was that fundamental digital needs like the ones I just listed would lead to new kinds of massive monopolies because of network effects and lock-in. We foolishly laid the foundations for global monopolies. We did their hardest work for them. More precisely, since you’re the product, not the customer of social media, the proper word is “monopsonies.”17 Our early libertarian idealism resulted in gargantuan, global data monopsonies. One of the main reasons to delete your social media accounts is that there isn’t a real choice to move to different social media accounts.
Capitalism Without Capital: The Rise of the Intangible Economy by Jonathan Haskel, Stian Westlake
"Robert Solow", 23andMe, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, Andrei Shleifer, bank run, banking crisis, Bernie Sanders, business climate, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive bias, computer age, corporate governance, corporate raider, correlation does not imply causation, creative destruction, dark matter, Diane Coyle, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Glaeser, Elon Musk, endogenous growth, Erik Brynjolfsson, everywhere but in the productivity statistics, Fellow of the Royal Society, financial innovation, full employment, fundamental attribution error, future of work, Gini coefficient, Hernando de Soto, hiring and firing, income inequality, index card, indoor plumbing, intangible asset, Internet of things, Jane Jacobs, Jaron Lanier, job automation, Kenneth Arrow, Kickstarter, knowledge economy, knowledge worker, laissez-faire capitalism, liquidity trap, low skilled workers, Marc Andreessen, Mother of all demos, Network effects, new economy, open economy, patent troll, paypal mafia, Peter Thiel, pets.com, place-making, post-industrial society, Productivity paradox, quantitative hedge fund, rent-seeking, revision control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Sand Hill Road, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, software patent, sovereign wealth fund, spinning jenny, Steve Jobs, survivorship bias, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, total factor productivity, Tyler Cowen: Great Stagnation, urban planning, Vanguard fund, walkable city, X Prize, zero-sum game
While rivalry might then be the economic primitive behind scalability, we shall use scalability for mnemonic convenience.2 Scalability becomes supercharged with “network effects.” A network effect exists when assets become more valuable the more of them exist. Network effects can be found among both tangible and intangible assets. So, for example, telephones or fax machines are much more valuable when almost everyone has them. Indeed, the current digital tech revolution has drawn people’s attention to the potential network effects of physical assets, mobile phones and networked computers being prime examples. But if we look closer, it’s really the intangible investments of the current wave of digital technologies where the big network effects are. The network of Uber drivers and AirBnB hosts and Instagram users (all organizational development investments) or the power of HTML and the innumerable standards the Web is based on (variously, investments in software, design, and organizational development) are intangibles, not tangibles.
His view is that commercial success is built on four characteristics: building a proprietary technology; exploiting network effects; benefiting from economies of scale; and branding. These recommendations are firmly in line with the strategy for an intangible-rich business, based on the four S’s that we discussed in chapter 4. So, for example, he rightly points out that Twitter can easily scale up: a prime example of economies of scale in action. By contrast, he uses a yoga studio as an example of a business that cannot scale up and so is destined to stay small. As we have seen, Les Mills International had to adopt a very different business model from traditional gym businesses in order to grow to the size it did. The emphasis on network effects is an insight of Thiel’s that suggests that governments might become more important to company success in the future.
Starbucks has been able to leverage an effective brand, operating processes, and supply chains to allow it to spread across the world. Google, Microsoft, and Facebook need relatively few tangible assets compared to the manufacturing giants of yesteryear. They can scale their intangible-asset bundle or software and reputation and so get very big. This type of scalability is, of course, enhanced by network effects.3 Second, with the prospects of such large markets, more and more firms will be encouraged to try their luck in these markets. They face a hard choice, for although the prospective market might be large, encouraging them to have a go, competition might be very tough, thereby discouraging them. The net result of this was described by the economist John Sutton in the early 1990s: in markets where scalable investments (like R&D or branding) are important, you’d expect to see “industry concentration”—a relatively small number of dominant large companies.
Startup Communities: Building an Entrepreneurial Ecosystem in Your City by Brad Feld
barriers to entry, cleantech, cloud computing, corporate social responsibility, G4S, Grace Hopper, job satisfaction, Kickstarter, Lean Startup, minimum viable product, Network effects, paypal mafia, Peter Thiel, place-making, pre–internet, Richard Florida, Ruby on Rails, Silicon Valley, Silicon Valley startup, smart cities, software as a service, Steve Jobs, text mining, Y Combinator, zero-sum game, Zipcar
As more and more startups in an area can share the costs of specialized inputs, the average cost per startup drops for the specialized inputs. This provides direct economic benefit to companies located within a startup community. Another economic concept, network effects, explains why geographic concentration yields further advantage. Network effects operate in systems where the addition of a member to a network enhances value for existing users. The Internet, Facebook, and Twitter are examples in which network effects operate powerfully. These services may have some value to you if there are just 100 other users. However, these networks are immensely more useful if there are 100 million other users that you can connect with. Startup communities similarly feature strong network effects. For example, an area with 10 great programmers provides a valuable pool of labor talent for a startup. However, an additional 1,000 amazing programmers in the same area is vastly more valuable to startups, especially if programmers share best practices with other programmers, inspire one another, or start new companies.
However, an additional 1,000 amazing programmers in the same area is vastly more valuable to startups, especially if programmers share best practices with other programmers, inspire one another, or start new companies. External economies of scale lower certain costs; meanwhile, network effects make co-location more valuable. The second explanation of startup communities, horizontal networks, comes from sociology. In her PhD work at MIT, AnnaLee Saxenian (currently Dean of the UC Berkeley School of Information) noticed that external economies do not fully explain the development and adaptation of startup communities. In particular, in her seminal book Regional Advantage: Culture and Competition in Silicon Valley and Route 128 (1994) Saxenian noted that two hotbeds for high-tech activity—Silicon Valley and Boston’s Route 128—looked very similar in the mid-1980s.
Yet just a decade later, Silicon Valley gained a dominant advantage over Route 128. External economies alone did not provide an answer. Saxenian set out to resolve the puzzle of why Silicon Valley far outpaced Route 128 from the mid-1980s to mid-1990s. Saxenian persuasively argues that a culture of openness and information exchange fueled Silicon Valley’s ascent over Route 128. This argument is tied to network effects, which are better leveraged by a community with a culture of information sharing across companies and industries. Saxenian observed that the porous boundaries between Silicon Valley companies, such as Sun Microsystems and HP, stood in stark contrast to the closed-loop and autarkic companies of Route 128, such as DEC and Apollo. More broadly, Silicon Valley culture embraced a horizontal exchange of information across and between companies.
The Middleman Economy: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit by Marina Krakovsky
Affordable Care Act / Obamacare, Airbnb, Al Roth, Ben Horowitz, 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, Joan Didion, 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, uber lyft, ultimatum game, Y Combinator
Because buyers usually want to be where sellers are, and sellers usually want to be where buyers are, a two-sided network with an abundance of participants on one side tends to attract more comers on the other side. In the language of economics—and increasingly of tech investors—two-sided markets usually create “indirect network effects.”36 As one side grows, the network becomes more attractive to the other side, and as the other side grows, it attracts more users who want to connect with that side. This positive feedback loop promotes rapid growth once you reach critical mass. That’s why tech investors love network effects. But this same growth curve means two-sided markets are extremely hard to get off the ground, as Thiers found out firsthand: how do you get a sitter to sign up for a service that doesn’t have a single parent, and likewise, how do you get a parent to sign up for a service that has no sitters?
In fact, without a good system in place to keep diners accountable, OpenTable would have a hard time getting restaurants to become customers in the first place. So Templeton and his team had a strong incentive to create rules to minimize no-shows. Rules for proper behavior are a crucial element of any middleman business that hopes to attract enough partners on both sides. In writing about SitterCity in the Bridge chapter, I pointed out that two-sided markets benefit from network effects, whereby buyers want to be where sellers are and sellers want to be where buyers are. But network effects aren’t just about quantity (the more the merrier)—usually, buyers care about the quality of sellers, too, and sellers care something about the quality of buyers.10 If you go on a dating site or a singles event, for example, you don’t just care about how many potential partners there will be, but also whether they’re the sort of people you want to meet.
Brian Arthur, “Competing Technologies, Increasing Returns, and Lock-In By Historical Events,” The Economic Journal 99, no. 394 (March 1989). A less academic account of these ideas by the same author is W. Brian Arthur, “Increasing Returns and the New World of Business,” Harvard Business Review 74, no. 4 (July/August 1996): 100–109. 36.These are called “indirect” because they refer to what is happening on the other side; direct network effects occur if users care how many other users are on the same side as they are. In addition, not all two-sided markets produce positive indirect network effects, and growth on one side can actually be a turnoff to the other side. For example, advertisers want to be where many readers in their target audience are, but readers aren’t nearly as eager to see ads, so a media company can often attract a larger audience if it doesn’t show ads. Whether the media company makes more money that way, though, is another story: the company might be better off taking ads, investing the ad revenue into higher quality, which can attract more readers. 37.For one discussion of this challenge, see David S.
Zero to Sold: How to Start, Run, and Sell a Bootstrapped Business by Arvid Kahl
"side hustle", business process, centre right, Chuck Templeton: OpenTable:, continuous integration, coronavirus, COVID-19, Covid-19, crowdsourcing, domain-specific language, financial independence, Google Chrome, if you build it, they will come, information asymmetry, information retrieval, inventory management, Jeff Bezos, job automation, Kubernetes, minimum viable product, Network effects, performance metric, post-work, premature optimization, risk tolerance, Ruby on Rails, sentiment analysis, Silicon Valley, software as a service, source of truth, statistical model, subscription business, supply-chain management, trickle-down economics, web application
Empty pockets: Customers would admit needing the savings from the referral system. We don’t like admitting we can't afford something. If sharing a link could make other people think we’re cheap, we’d instead not share it. Conversely, a referral system will work best for these reasons: Network effects: Customers stand to gain something from another user joining the service. Reputation gain: Customers can show their peers that they are experts in their industry. Way above the bottom line: Customers can show that they are doing well using your service. For FeedbackPanda, we had a powerful network effect built into the product: our customers could share their feedback templates with each other. That meant that every new customer might bring fresh content that the existing customers could use immediately. Inviting a new customer to the website was a way to do less work eventually for our customers, so they had no problem sharing the service.
People are extremely sensitive to interactions where they are at risk of being exploited. Even a harmless remark can cause them to be at high alert, and it will be impossible to make a case for whatever you're selling if your prospect is skeptical of your honesty and reliability. Let people find your service; don't force it on them. Leave traces by providing valuable content; don't throw yourself at your niche audience with discounts and savings. Tribal Network Effects The network effects in tribal communities are powerful, as people share a large number of commonalities. When you reach out with a question, you can be sure that you will receive an avalanche of responses. They may be very different, as a tribe is not homogenous. But they will all be aimed at accomplishing a shared desire, a common goal. Setting that goal is the job of a leader. Transforming the shared interests of your tribe's members into a common goal that your product can help accomplish is why marketing works so well in tribes.
We documented our internal processes so we could easily outsource or take over each other's activities. We built the business as if we were to eventually sell it, even though that was never our goal. The only goal we set was to help teachers do their jobs better and pay our bills. We had noticed that teachers loved to share, so we added a collaboration system where they could help each other out by sharing their templates. All of a sudden, we had a product that developed a strong network effect overnight. And that feature made the business grow beyond our wildest expectations. Every day, new teachers would sign up, and since we provided a service that solved their problems well, we had incredibly high retention and conversion rates. For many of our customers, teaching from home was a side hustle. Using our product enabled many of them to turn this into a full-time source of income.
The Facebook era: tapping online social networks to build better products, reach new audiences, and sell more stuff by Clara Shih
business process, call centre, Clayton Christensen, cloud computing, commoditize, conceptual framework, corporate governance, crowdsourcing, glass ceiling, jimmy wales, Mark Zuckerberg, Metcalfe’s law, Network effects, pets.com, pre–internet, rolodex, semantic web, sentiment analysis, Silicon Valley, Silicon Valley startup, social graph, social web, software as a service, Tony Hsieh, web application
Traditionally, popular delivery vehicles include e-mail, instant message, SMS, and phone. The ease of sending and viewing messages and the pervasiveness of the delivery technology can make or break a viral campaign. For example, you would not get very far disseminating a message by fax in a country where most people don’t have fax machines—it wouldn’t matter that you had a sticky idea. Delivery is where Facebook excels. Virality depends on network effects, and social networking sites amplify network effects. Online social networking offers an ideal communication environment for sending and receiving viral messages. There are four reasons, explained in the following list—widespread adoption, broadcast format, connections across networks, and longer message life: • Widespread adoption. Like phone, e-mail, and SMS, social networking sites are widely adopted around the world.
Only time, of course, will tell, but important trends are already taking shape. There are fewer numbers of new social networking sites emerging. The online social graph is becoming better integrated with other emerging technologies, such as video and mobile. First, there appears to be a consolidation of social networking services. This is thanks in part to standardization initiatives, such as OpenSocial, but also owes largely to the network effects governing online social sites. Network effects mean large sites get larger and small sites get smaller much more quickly. Therefore we should expect that over the next several years, clear winners and losers will emerge. Second, social networking services are becoming more technologically sophisticated. As compelling and revolutionary as the early days of the social networking revolution have been, the interaction possibilities on sites like Facebook and LinkedIn have been limited at best, largely around asynchronous, low-fidelity communication.
Second printing April 2009 From the Library of Kerri Ross To my parents James and Sophia Shih, my brother Vic, and to finding the American Dream From the Library of Kerri Ross vi Th e Fa ce b o o k E ra Contents Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ix Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xii About the Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xiv Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 Why You’re Reading This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 Welcome to the Facebook Era How to Use This Book Part I: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 A Brief History of Social Media 1 The Fourth Revolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11 Mainframe Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 The PC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14 The World Wide Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 The Online Social Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 Empowering the End User 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22 The Evolution of Digital Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25 Storage and Creation Media Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 The Future: Social Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29 Why Facebook Is Different . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .34 Social Network Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36 What the Social Graph Means for Digital Media . . . . . . . . . . . . . . . . . . . . . . . . . . .42 3 Social Capital from Networking Online . . . . . . . . . . . . . . . . . . . . . . . . . . .43 Establishing a New Category of Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44 Online Interactions Supplement Offline Networking The Flattening Effect . . . . . . . . . . . . . . . . . . . .50 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52 Creating New Value from Network Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52 Blurring the Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57 From the Library of Kerri Ross Co n te n t s Part II: vii Transforming the Way We Do Business 4 Social Sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61 Transforming the Sales Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62 The Need for Multiple Network Structures CRM—The First Social Network?
Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest
23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Ben Horowitz, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen: Great Stagnation, uber lyft, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game
Examples of companies that have successfully exposed their data this way are the Ford Company, Uber, IBM Watson, Twitter and Facebook. We can’t emphasize the following strongly enough: the world that is emerging is very different from the one we’ve known. Power is becoming easier to acquire but harder to keep. Thanks to strong viral and social network effects that allow startups to scale rapidly, it is now easier than ever before to start new companies and disrupt industries. But when it comes to social networks, the reverse is also true. Facebook, for example, is an incumbent, and its network effects and lock-in make it hard to usurp—underscoring the great advantage a platform has over a product or service. In her book, The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business, Rita Gunther McGrath illustrates that we can only obtain what she calls Transient Competitive Advantages via platforms and purpose, community, and culture.
Key attributes of Engagement include: Ranking transparency Self-efficacy (sense of control, agency and impact) Peer pressure (social comparison) Eliciting positive rather than negative emotions to drive long-term behavioral change Instant feedback (short feedback cycles) Clear, authentic rules, goals and rewards (only reward outputs, not inputs) Virtual currencies or points Properly implemented, Engagement creates network effects and positive feedback loops with extraordinary reach. The biggest impact of engagement techniques is on customers and the entire external ecosystem. However, these techniques can also be used internally with employees to boost collaboration, innovation and loyalty. For the Millennial generation, gaming is a way of life. Today, more than seven hundred million people around the world play online games—159 million in the U.S. alone—and most play for more than an hour each day.
Indeed, what might seem like the least serious tool in a company’s user and employee engagement program often proves to be one of its most powerful in terms of finding and training the individuals it needs to reach the next level. Although a comparatively minor issue as far as traditional enterprises are concerned, engagement proves to be critical for ExOs. It is a key element for scaling the organization into the community and crowd and for creating external network effects. No matter how promising its product or premise, unless an ExO is able to optimize the engagement of its community and crowd, it will wither and fade. Why Important? Dependencies or Prerequisites • Increases loyalty • Amplifies ideation • Converts crowd to community • Leverages marketing • Enables play and learning • Provides digital feedback loop with users • MTP • Clear, fair and consistent rules without conflicts of interest Passion and Purpose.
The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines by William Davidow, Michael Malone
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Francis Fukuyama: the end of history, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Snapchat, speech recognition, Stuxnet, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, zero day, zero-sum game, Zipcar
Rockefeller’s time. A phenomenon called “network effects” has been powering monopolies in the virtual world. The term refers to the fact that, as networks add nodes, their power increases. These effects have been with us for a very long time—since long before the Internet. Coauthor William Davidow was first introduced to network effects when he was eight years old and his mother told him stories about her own childhood in Reading, Pennsylvania. Three telephone companies served the city. If you were on one network, you could not talk with someone who subscribed to another. Ultimately, one network got ahead in the number of subscribers, and pretty soon, if you wanted to talk with most of your friends, you had to be on that network. In short, network effects occur because each new user added increases the value of the network to other users.
In the future, a more powerful Amazon might be able to push that price all the way down to $2.99 and take half. At that point, authors, most of whom are already struggling, might have to supplement their income by flipping burgers. One of the most concerning aspects of platforms is that they, too, are linked together by network effects. If you control a mobile platform, it gives you the leverage to control a payments platform that uses it. In the case of Amazon, control of a retail platform sets the company up to control a delivery platform. Smaller businesses that might have been successful if those network effects were weaker get sucked up in the vortex. In many cases, the entrepreneurs of smaller companies feel that if they do not let themselves be acquired, the giant suitor will develop its own competing application and drive them out of business. That is what happened to Snapchat.
See also economic policy and metrics microprocessor, 55–56, 172 middle class, decline of, 59, 67, 95–97, 105, 163, 164 Minsky, Marvin, 46 MMORPGs. See massively multi-player online role-playing games money lending, peer-to-peer, 80–81, 83 monopolies: BAADD, 88–93 commercial trend of, 71 independent companies acquired by, 91–92 Industrial Revolution and rise of, 160 laws and regulations on, 93, 160 network effects of virtual, 89–92 platform, 90–92 Moore’s Law, 47, 56, 57 Morhaime, Mike, 141–142 Morris, Robert Tappan, 172 multitasking, 155–157 Murray, Charles, 110–111 music industry, 72–73, 87 narcissism, 146–147, 185 Netflix, 50, 65, 98–99, 105 network effects, 89–92 neural networks, 45–47 newspaper industry, 28, 62–64, 87–88, 169 Noyce, Robert, 58, 66 number systems, emergence of, 24, 25 Ogburn, William, 38, 181 Ohlone tribe, 19–20 operant conditioning, 136–138 optimism, 193–195 Orwell, George, 7, 115 Otto, Nikolaus, 53 Packard, Vance, 135, 142 Pariser, Eli, 125 Parker, Donn, 75 payment systems, 10, 76–77, 80–82, 83, 171–172, 186 PayPal, 76, 171 pharmaceutical industry, 57–58 phase change, defining, x, 8–9.
The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar
Airbnb, airport security, Albert Einstein, altcoin, Amazon Web Services, bitcoin, Black Swan, blockchain, business process, centralized clearinghouse, Clayton Christensen, cloud computing, cryptocurrency, disintermediation, distributed ledger, Edward Snowden, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, fixed income, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer, peer-to-peer lending, prediction markets, pull request, QR code, ride hailing / ride sharing, Satoshi Nakamoto, sharing economy, smart contracts, social web, software as a service, too big to fail, Turing complete, web application
It is neither communism nor a version of cyberpunk fiction. Decentralization boosts capitalism by creating new layers of work production and value creation. It is granted that a blockchain will move value. But go further and start imagining multiple blockchains interacting with one another, all of them trading value with one another, and you will be led to a composite of network effects, potentially more significant than the previous generation of network effects. It will be the equivalent of a huge overlay of decentralized services that are open and accessible to anyone. Maybe the blockchain will lead us to the not-so utopian view of Nobel Prize winner, economist, and philosopher, Friedrich Hayek. He believed that the path to a functioning economy—or society—was decentralization, and asserted that a decentralized economy complements the dispersed nature of information spread throughout society.1 WHAT HAPPENED TO THE DECENTRALIZED INTERNET?
Furthermore, many industries are decentralized already, to an extent that many people outside of these industries do not appreciate, but they are decentralized in an inefficient way—a way that requires each company to maintain its own infrastructure around managing users, transactions, and data, and to reconcile with the systems of other companies every time it needs to interact. Consolidation around a single market leader would, in fact, make these industries more efficient. But neither the competitors of the likely leader nor antitrust regulators are willing to accept that outcome, leading to a stalemate. Until now. With the advent of decentralized databases that can technologically replicate the network effect gains of a single monopoly, everyone can join and align for their benefit, without actually creating a monopoly with all the negative consequences that it brings. This is the story that arguably drives the interest in consortium chains in finance, blockchain applications in the supply chain industry, and blockchain-based identity systems. They all use decentralized databases to replicate the gains of everyone being on one platform without the costs of having to agree on who gets to control that platform and then put up with them if they choose to try to abuse their monopoly position.
The blockchain has ten characteristics, and they all need to be understood in a holistic manner. NOTES 1. Bitcoin: A Peer-to-Peer Electronic Cash System, https://bitcoin.org/en/bitcoin-paper. 2. Bitcoin “maximalism” refers to the opinion that solely supports Bitcoin at the expense of all other blockchain or cryptocurrency related projects, because maximalists believe we only a need a single blockchain, and single currency in order to achieve desired network effects benefits. 3. The Untapped Potential of Corporate Narratives. http://edgeperspectives.typepad.com/edge_perspectives/2013/10/the-untapped-potential-of-corporate-narratives.html. 4. Myerson, Roger B. (1991). Game Theory: Analysis of Conflict, Harvard University Press. 5. Leslie Lamport, Robert Shostak, and Marshall Pease, The Byzantine Generals Problem. http://research.microsoft.com/en-us/um/people/lamport/pubs/byz.pdf. 6.
Humans as a Service: The Promise and Perils of Work in the Gig Economy by Jeremias Prassl
3D printing, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Andrei Shleifer, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, disruptive innovation, Donald Trump, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, hiring and firing, income inequality, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, low skilled workers, Lyft, Mahatma Gandhi, Mark Zuckerberg, market friction, means of production, moral hazard, Network effects, new economy, obamacare, pattern recognition, platform as a service, Productivity paradox, race to the bottom, regulatory arbitrage, remote working, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Rosa Parks, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, transaction costs, transportation-network company, Travis Kalanick, two tier labour market, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, working-age population
Indeed, from egregious undercutting tactics based on promotional give- aways to turning a blind eye to exploitative labour practices thanks to the * * * The Economics of the Gig Economy 23 cheap funding of aggressive lobbying campaigns aimed at changing legal frameworks or the reckless flooding of the market with huge amounts of spare capacity, none of it would be possible without access to cheap financing.42 If Kaminska is correct, we are still left with one final question: assuming that the rapid growth of gig-economy platforms has really been fuelled by little more than a combination of regulatory arbitrage and cheap venture capital, why are savvy investors competing to invest? What is behind their willing- ness to burn unprecedented amounts of cash? Network Effects and Monopoly Power It is common for start-up businesses to lose money early on, of course—not least by subsidizing products so as to gain market share. This is particularly important in industries in which investors hope to harness so-called net- work effects—that is, where all users of a particular service gain if additional consumers adopt it.43 Think about the growth of a ride-sharing platform in a new city, for example.
A large available pool of drivers, in turn, will make it easier and cheaper for consumers to find their next ride, further increasing the incen- tives for new drivers to join—and so on. It is unsurprising that gig-economy platforms will often try to kick-start this process by investing significant amounts of cash in subsidies for drivers as well as passengers. Hubert Horan, however, is sceptical that this is the entire story. Cash burn, he suggests, is not merely about harnessing network effects, but rather a step in platforms’ quest for monopoly power. Focusing once more on Uber as the most pointed example, he explains the links: [M]ost critically, the staggering $13 billion in cash its investors provided is consistent with the magnitude of funding required to subsidize the many years of predatory competition required to drive out more efficient incumbents. Uber’s investors did not put $13 billion into the company because they thought they could vanquish those incumbents under ‘level playing field’ mar- ket conditions; those billions were designed to replace ‘level playing field’ competition with a hopeless battle between small scale incumbents with no access to capital struggling to cover their bare bone costs and a behemoth company funded by Silicon Valley billionaires willing to subsidize years of multi-billion dollar losses.
Given Uber’s growth to date, investor expectations that monopoly rents justifies the current level of subsidies and financial risks appears quite plausible.44 * * * 24 Work on Demand This account stands in stark contrast with the idea that the rise of gig- economy platforms will lead to increased competition, with lower prices and higher quality as the result: in the expensive pursuit of network effects, some platforms’ goal may well be to smother competition, rather than to encourage it. Individual operators will always vary in the extent to which their busi- ness model combines the factors thus set out. Whichever way we approach the question, however, the underlying economics don’t appear to stack up—a quandary to which we return in the final chapter. Faster matching, digital work intermediation, and assorted rating algorithms have the potential to create much economic benefit.
Joel on Software by Joel Spolsky
AltaVista, barriers to entry, c2.com, commoditize, George Gilder, index card, Jeff Bezos, knowledge worker, Metcalfe's law, Mitch Kapor, Network effects, new economy, PageRank, Paul Graham, profit motive, Robert X Cringely, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, slashdot, Steve Ballmer, Steve Jobs, the scientific method, thinkpad, VA Linux, web application
(I'll talk about how to do that in the next chapters.) Another question about displacing competitors has to do with network effects and lock-in: Ben & Jerry's Amazon No network effect, weak customer lock-in Strong network effect, strong customer lock-in A network effect is a situation where the more customers you have, the more customers you will get. It's based on Metcalfe's Law:1 The value of a network is equal to the number of users squared. A good example is eBay. If you want to sell your old Patek Philippe watch, you're going to get a better price on eBay, because there are more buyers there. If you want to buy a Patek Philippe watch, you're going to look on eBay, because there are more sellers there. Another extremely strong network effect is created by proprietary chat systems like ICQ or AOL Instant Messenger. If you want to chat with people, you have to go where they are, and ICQ and AOL have the most people by far.
If you want to chat with people, you have to go where they are, and ICQ and AOL have the most people by far. Chances are, your friends are using one of those services, not one of the smaller ones like MSN Instant Messenger. With all of Microsoft's muscle, money, and marketing skill, they are just not going to be able to break into auctions or instant messaging, because the network effects there are so strong. __________ 1. See www.mgt.smsu.edu/mgt487/mgtissue/newstrat/metcalfe.htm. Lock-in is the characteristic of the business that makes people not want to switch. Nobody wants to switch their Internet provider, even if the service isn't very good, because of the hassle of changing your email address and notifying everyone. People don't want to switch word processors if their old files can't be read by the new word processor.
But when the three months are up, if you don't want to continue with the service, you have no choice but to contact every single bill provider and ask them to change the billing address back to your house. The sheer chore of doing this is likely to prevent you from switching away from PayMyBills.com—better just to let them keep sucking $8.95 out of your bank account every month. Gotcha! If you are going into a business that has natural network effects and lock-in, and there are no established competitors, then you better use the Amazon model, or somebody else will, and you simply won't be able to get a toehold. Quick case study. In 1998, AOL was spending massively to grow at a rate of a million customers every five weeks. AOL has nice features, like chat rooms and instant messaging, that provide stealth lock-in. Once you've found a group of friends you like to chat with, you are simply not going to switch Internet providers.
Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success by Sean Ellis, Morgan Brown
Airbnb, Amazon Web Services, barriers to entry, Ben Horowitz, bounce rate, business intelligence, business process, correlation does not imply causation, crowdsourcing, DevOps, disruptive innovation, Elon Musk, game design, Google Glasses, Internet of things, inventory management, iterative process, Jeff Bezos, Khan Academy, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, market design, minimum viable product, Network effects, Paul Graham, Peter Thiel, Ponzi scheme, recommendation engine, ride hailing / ride sharing, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, software as a service, Steve Jobs, subscription business, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, working poor, Y Combinator, young professional
For example, if you refer your next-door neighbor to the app, that will have no impact on your own user experience. But many companies have some degree of network effect potential that they can and should tap, even if it’s not obvious on the surface. For example, with Dropbox, the more files I have stored, the more likely I will invite people to join Dropbox to collaborate on them, while the more people I know who use Dropbox, the easier file sharing is going to be. That’s why doing the legwork to learn about how your customers use your product and where potential loops can be created and optimized is essential for tapping into viral growth driven through network effects. Eventbrite is a company that has created a powerful viral loop by tapping its network effect potential. The company is a hub for event promotion that makes money from taking a cut of ticket sales made through the site, and quite cleverly built in a social-driven loop by encouraging ticket buyers to share with their friends that they’re going to an event.
User experience experts call tricks to get users to take an action they normally would not take dark patterns, and while some of these dark patterns may work in the short term, the backlash from users is a long-term drag on growth. The negative press and bad feelings these kinds of tricks stir up can even be enough to torpedo the best products—we’ve seen it happen. Here are a number of best practices for experimenting with creating loops that will help you avoid such pitfalls. CONSIDER THE POTENTIAL TO TAP NETWORK EFFECTS The best loops are ones in which users are motivated to help sign up more users because doing so will improve their own experience of the product, such as with Facebook or LinkedIn. Network effect products have a great natural advantage with viral growth for this reason; they get better the more people are using them, so people are inclined to urge others to come on board. Social networks and messaging apps are obvious examples, as are big marketplaces that connect buyers directly with sellers, like eBay and Etsy, because more people using the site quite simply means: more potential customers for me as a seller and selection for me as a buyer.
When the team noticed auction owners promoting the PayPal service as an easy way for winners to pay, they created AutoLink, a tool that automatically added the PayPal logo and a link to sign up to all of their active auction listings. This tool tripled the number of auctions using PayPal on eBay and ignited its viral growth on the platform.4 LinkedIn, which had struggled to gain traction in its first year, saw their growth begin to skyrocket in late 2003, when the engineering team worked out an ingenious way for members to painlessly upload and invite their email contacts stored in their Outlook address book, kicking network effects growth into high gear.5 And in each of these cases, growth was achieved not with traditional advertising, but rather with a dash of programming smarts and on a shoestring budget. Approaches like these to building, growing, and retaining a customer base that relied not on traditional marketing plans, a pricey launch, and a big ad spend, but rather on harnessing software development to build marketing into products themselves, were proving both extraordinarily powerful and incredibly cost effective.
Success and Luck: Good Fortune and the Myth of Meritocracy by Robert H. Frank
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, attribution theory, availability heuristic, Branko Milanovic, Capital in the Twenty-First Century by Thomas Piketty, carried interest, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, en.wikipedia.org, endowment effect, experimental subject, framing effect, full employment, hindsight bias, If something cannot go on forever, it will stop - Herbert Stein's Law, income inequality, invisible hand, labor-force participation, lake wobegon effect, loss aversion, minimum wage unemployment, Network effects, Paul Samuelson, Report Card for America’s Infrastructure, Richard Thaler, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Rory Sutherland, selection bias, side project, sovereign wealth fund, Steve Jobs, The Wealth of Nations by Adam Smith, Tim Cook: Apple, ultimatum game, Vincenzo Peruggia: Mona Lisa, winner-take-all economy
The upshot is that if an economic opportunity arises anywhere in the networked world, ambitious entrepreneurs are quickly able to discover and exploit it. Modern communications technology has also reinforced powerful network effects that have increased the rewards to top performers. Those effects helped explain the growing dominance of the Windows PC platform during the late 1980s. Once Microsoft’s Windows graphical user interface reached parity with earlier rival Apple’s Macintosh, the numerical superiority of Windows users became a decisive advantage. Software developers concentrated their efforts on the Windows platform because more users meant more sales. And the greater availability of software titles, in turn, lured still more users to Windows, creating a positive feedback loop in the form of a network effect that drove Apple to the brink of bankruptcy. Network effects sometimes permit one firm’s ephemeral advantage to defeat a rival’s otherwise superior offering, as apparently happened in the battle between Betamax and VHS several decades ago.
Another popular use of VCRs at the time was for people to send home videos to their children’s grandparents. But that worked only if both households used the same format, so here was an additional positive feedback loop that reinforced the reasons for choosing VHS. In the meantime, Sony had managed to extend Betamax recording times. But by then the downward spiral was well underway, and Betamax was doomed. Network effects merit special emphasis because they are perhaps the most important source of randomness in high-stakes winner-take-all contests. One reason for reading a book or seeing a film is to enjoy the experience of discussing it with others. Opportunities for such exchanges are of course more numerous when you read best-selling titles or watch popular films. But of the thousands of entries released in any given year, only a relative handful find their way onto the most widely circulated best-seller lists.
(This was a period during which overall sales nearly doubled, so sales of these slow-moving titles were growing substantially in absolute terms.) The market shares of top offerings have also been growing in the publishing and film industries, according to Elberse. In some cases, they’ve been gaining ground because social media have amplified their attractiveness. Here again, we see the influence of network effects. Simple arithmetic ensures that Face-book exchanges are far more likely to be stimulated by posts on best-selling titles. Another factor is that new technology has done little to relieve an important market constraint—the scarcity of people’s time and energy. No one could possibly examine each of the million-plus offerings in Apple’s app store. And as the Swarth-more psychologist Barry Schwartz argued in his 2004 book, The Paradox of Choice, most people find it unpleasant to sift through a plethora of options.5 Many people sidestep that problem by focusing on only the most popular entries in each category.
Open Standards and the Digital Age: History, Ideology, and Networks (Cambridge Studies in the Emergence of Global Enterprise) by Andrew L. Russell
American ideology, animal electricity, barriers to entry, borderless world, Chelsea Manning, computer age, creative destruction, disruptive innovation, Donald Davies, Edward Snowden, Frederick Winslow Taylor, Hacker Ethic, Howard Rheingold, Hush-A-Phone, interchangeable parts, invisible hand, John Markoff, Joseph Schumpeter, Leonard Kleinrock, means of production, Menlo Park, Network effects, new economy, Norbert Wiener, open economy, packet switching, pre–internet, RAND corporation, RFC: Request For Comment, Richard Stallman, Ronald Coase, Ronald Reagan, Silicon Valley, Steve Crocker, Steven Levy, Stewart Brand, technoutopianism, Ted Nelson, The Nature of the Firm, Thomas L Friedman, Thorstein Veblen, transaction costs, web of trust
Interfaces between various components of computer hardware provide many familiar examples of compatibility standards, such as Universal Serial Bus and Ethernet ports. Of these three categories, my primary focus in here is the third category: the creation of compatibility standards for communication networks. Scholars who have studied the interconnection of various components of communication networks often use three concepts – infrastructure, platforms, and network effects – to describe the important role of standards that enable compatibility and interoperability. The utility of these concepts lies in their connotations of stability, support, and external benefits that standards can generate. They emphasize the potential for a heap of standardized components to combine into a cohesive and flexible network that can, in turn, sustain more complex social and economic activity.43 The second fundamental question posed above – who makes standards?
Russell, “Industrial Legislatures: The American System of Standardization,” in International Standardization as a Strategic Tool (Geneva: International Electrotechnical Commission, 2006). 43 National Telecommunications and Information Administration, The National Information Infrastructure: An Agenda for Action (1993), http://www.ibiblio.org/nii (accessed January 4, 2012); Michael L. Katz and Carl Shapiro, “Systems Competition and Network Effects,” The Journal of Economic Perspectives 8 (1994): 93–115; Richard R. John, “Recasting the Information Infrastructure for the Industrial Age,” in Alfred D. Chandler, Jr. and James Cortada, eds., A Nation Transformed by Information: How Information Has Shaped the United States from Colonial Times to the Present (New York: Oxford University Press, 2000); Philip J. Weiser, “Law and Information Platforms,” Journal of Telecommunications and High Technology Law 1 (2002): 1–35; Steven W.
These committees were important in their own era because they maintained fluidity in the dynamic industrial economy of the late nineteenth century: by defining standards, they facilitated the existence of multiple sources of supply and thus provided a means for small- and medium-sized industrial firms to avoid the specter of monopoly. As subsequent chapters in this book will show, these committees were also important in later eras because they created institutional and ideological precedents that computer and telecommunications engineers used in the latter decades of the twentieth century as they, too, sought to generate network effects and a competitive market structure. The history of industrial standards in the late nineteenth century, and my specific claim that the production of these standards fits within a conceptual middle ground between markets and hierarchies, might strike some readers as something of a detour from the gradual evolution of communication technologies of the digital age. After all, many standards for American telephone and telegraph networks in the nineteenth century were established within the corporate hierarchies of AT&T and Western Union, and the self-conscious movement for open systems and open standards did not begin until the 1970s.
The Long Good Buy: Analysing Cycles in Markets by Peter Oppenheimer
"Robert Solow", asset allocation, banking crisis, banks create money, barriers to entry, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, business cycle, buy and hold, Cass Sunstein, central bank independence, collective bargaining, computer age, credit crunch, debt deflation, decarbonisation, diversification, dividend-yielding stocks, equity premium, Fall of the Berlin Wall, financial innovation, fixed income, Flash crash, forward guidance, Francis Fukuyama: the end of history, George Akerlof, housing crisis, index fund, invention of the printing press, Isaac Newton, James Watt: steam engine, joint-stock company, Joseph Schumpeter, Kickstarter, liberal capitalism, light touch regulation, liquidity trap, Live Aid, market bubble, Mikhail Gorbachev, mortgage debt, negative equity, Network effects, new economy, Nikolai Kondratiev, Nixon shock, oil shock, open economy, price stability, private sector deleveraging, Productivity paradox, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, Simon Kuznets, South Sea Bubble, special economic zone, stocks for the long run, technology bubble, The Great Moderation, too big to fail, total factor productivity, trade route, tulip mania, yield curve
In this context, it makes sense that the digital revolution has not yet boosted productivity.11 New technologies often have huge potential for productivity growth but can be difficult to adopt efficiently until there is a reorganisation in the manufacturing process and, in many cases, there exists a global standard in the technology. At the same time, the requirement to build out the full network effects can slow the initial penetration and therefore the productivity boost. The use of the steam engine, and coal for smelting, was also subject to these network effects. Coal transport was eventually a major boost to growth and productivity but could not be fully adopted until transport networks were in place. Equally, the large fixed costs of investment could be recouped only when enough new users had switched to the new power source. At the same time, the use of steam power required the building of factories and then the building of canals to facilitate the transportation of raw materials and finished products.
More recently, in a testimony before the US Congress on 26 February 1997, then-chairman of the Federal Reserve Alan Greenspan noted that ‘regrettably, history is strewn with visions of such “new eras” that, in the end, have proven to be a mirage’. A recent study by data scientists found that, in a sample of 51 major innovations introduced between 1825 and 2000, bubbles in equity prices were evident in 73% of the cases. They also found that the magnitude of these bubbles increases with the radicalness of innovations, with their potential to generate indirect network effects and with their public visibility at the time of commercialisation.12 Although it is not obvious that innovation was a trigger in the case of the tulip mania, it could be argued that it was important in the financial bubbles of the South Sea Company in Great Britain and the Mississippi Company in France in 1720. Although these bubbles involved frenzied speculation and price rises in the shares of the companies involved, and may appear no more rational than the tulip mania a century earlier, more recent interpretations have suggested that innovations and new technologies did play a part in their development.
The printing press, similar to the internet today, acted as a springboard to generate many other important technologies, which, in turn, spurred new businesses, while at the same time disrupting traditional industries and forcing many to change and evolve. The Railway Revolution and Connected Infrastructure Other parallels with the current wave of innovations can be found in the Industrial Revolution, when technology was again at the heart of growth. Many of these technologies developed from each other and even relied on each other, just as smartphones today rely on the internet, and vice versa. The network effect of innovation proved pivotal both following the invention of the printing press and during the railway revolution. During the Industrial Revolution, much of the opportunity was spurred by the extraordinary success and growth of railways. In 1830, England had 98 miles of railway track; by 1840 this had grown to about 1,500 miles, and by 1849 about 6,000 miles of track linked all of its major cities.3 Cheap money and a new (revolutionary) technology attracted a surge in investment, which, in turn, had knock-on effects for the growth in the number of factories, urbanisation and the emergence of new retail markets, all of which was not an obvious consequence at the time.
Choose Yourself! by James Altucher
Airbnb, Albert Einstein, Bernie Madoff, bitcoin, cashless society, cognitive bias, dark matter, Elon Musk, estate planning, Mark Zuckerberg, money market fund, Network effects, new economy, PageRank, passive income, pattern recognition, payday loans, Peter Thiel, Ponzi scheme, Rodney Brooks, rolodex, Saturday Night Live, sharing economy, short selling, side project, Silicon Valley, Skype, software as a service, Steve Jobs, superconnector, Uber for X, Vanguard fund, Y2K, Zipcar
Or a gym where the trainer likes his training program, enjoys training people, and doesn’t want to franchise it. Franchising an idea makes something scalable. Having one physical location makes it not scalable. * * * Network Effect The more people who use a service, the more valuable it is. It’s not a business, but e-mail has the network effect going for it. The more people who signed up for e-mail, the more valuable it was that you also had to sign up for e-mail in order to communicate with everyone. Facebook and PayPal, as you may now have guessed, had the network effect. Domino’s, believe it or not, had the network effect. Where would you rather order from—the same place all your friends’ trust, or some random place that nobody has ever used before? And these three attributes are intimately connected to Peter Thiel’s fourth attribute of a successful business
Why You Have to Quit Your Job This Year Be an Idea Machine How to Sell Anything How to Convince Anyone of Anything in Sixty Seconds Once the Ideas Get Rolling…: Ten Things You Need to Know about Leading Sharing Ideas: Being a Great Public Speaker Discussing Ideas: How to Negotiate with Anyone Using Ideas to Connect People: Building a Permission Network Developing Habits for Abundance Getting Rid of Your Excuses Why To-Do Lists Don’t Work * * * Part 2: Making Money in the Twenty-First Century Trends: The Power to See Things Differently Than Everyone Else Trend #1: Biotech Trend #2: Healthcare Trend #3: Observation Trend #4: The Temp Workforce Trend #5: Robotics Trend #6: Chemistry Trend #7: Financial Technology Lessons I Learned from Building a Business A Cheat-Sheet for Starting and Building a Business Why I Want My Kids to Know Who the Mysterious S. J. Scott Is Compound Interest and Compound Abundance But How Do You Value a Company? The Zero To One Approach Monopoly Scalability Network Effect Brand Demographics The Three D’s Model The Three P’s Model * * * Part 3: Keep and Grow the Money You Make The Myths We’ve All Been Told How to Avoid The Great Financial Scam of the Twenty-First Century Stop Paying Your Debts The Ultimate Guide To Investing The Ten Most Important Rules You Need to Know about Investing What Do I Do with My Own Money? What Happened To Me When I Day Traded – Lessons Learned from Day Trading Street Smarts Are Vital: Mental Models Worth Learning Four Things I Do That Can Change Your Life in the Next Ten Minutes How to Get an MBA from Eminem Avoid Regrets How to Run a “Choose Yourself” Meetup * * * About the Author Other Books by James Altucher Connect with James Copyright © 2015 by James Altucher All rights reserved No part of this publication may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form or by any means (electronically, mechanical, photocopying, recording or otherwise), without the prior written permission of both the copyright owner and the publisher of this book.
I had the good fortune to discuss the contents of Peter’s book in depth when I had him on my podcast. He highlights the four qualities of a good business—but it’s important to note that he’s not valuing a business. His goal is to make a great business—not just a great business but also a world-changing business. And there’s almost no way to value a world-changing business. It can be priceless. Thiel’s four attributes for a good business are monopoly, scalability, network effect, and brand. * * * Monopoly This one is a funny one because technically the word monopoly stirs up negative feelings of antitrust legislation, the government stepping in, anti-competition, AT&T in the ’80s, Microsoft in the ’90s, price manipulation, and even anti-capitalism. The genius of Peter’s perspective, however, is that it’s the exact opposite. Capitalism, he says, is about profits.
The Participation Revolution: How to Ride the Waves of Change in a Terrifyingly Turbulent World by Neil Gibb
Airbnb, Albert Einstein, blockchain, Buckminster Fuller, call centre, carbon footprint, Clayton Christensen, collapse of Lehman Brothers, corporate social responsibility, creative destruction, crowdsourcing, disruptive innovation, Donald Trump, gig economy, iterative process, job automation, Joseph Schumpeter, Khan Academy, Kibera, Kodak vs Instagram, Mark Zuckerberg, Menlo Park, Minecraft, Network effects, new economy, performance metric, ride hailing / ride sharing, shareholder value, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Jobs, the scientific method, Thomas Kuhn: the structure of scientific revolutions, trade route, urban renewal
Arctic Monkeys are not four men from Sheffield who play music. They are a huge community of fans, who all share their love and passion for the music with one another. Because every fan is free to contribute and take part, information and ideas spread out fan-to-fan rather than from some central hub. This creates a self-generating and ever-expanding network, in which every node is active. This network effect gives it the ability to grow and spread with a dynamic similar to that of a biological virus. What makes the viral nature of networks of fans so virulent is their fidelity. Fans don’t just randomly pass stuff on. They are discerning, thoughtful and caring. They trust those they receive from and direct to other like-minded people. This creates a high likelihood that recipients will not only share what they receive, but share it with vigour and passion – passing it on like a gift.
Within just a few months, it had hundreds of thousands of users. It was an impressive surge, but a combination of easy access, no cost, and novelty meant that many apps initially spread rapidly, only to fall out of favour when the next shiny new thing emerged. What kicked Instagram beyond fad, though, was when its developers added the ability to tag images in early 2011. This was when the retroviral network effect kicked in. Biological retroviruses are malignant in nature – HIV being the highest-profile example. They negatively affect their recipients, often severely compromising them. But social retroviruses spread because they are altruistic – they positively change the lives of their hosts. Hashtags meant that users could connect and build relationships based around shared interests. Instagram started to weave its way into the social fabric of its users’ lives as communities emerged and relationships were formed that didn’t exist anywhere else.
Mirror neurons explain why the combination of GoPro’s “point-of-view” camera and easy-to-use software was so successful – GoPro didn’t just allow those involved in extreme pursuits to participate more fully, it allowed the brains of those viewing it to have the experience of participating too. In an industry sector dominated by electrical giants, and with digital video cameras, seemingly in every shape, size, and price bracket, already available, the retroviral network effect kicked in. GoPro videos started to clock up millions of views on YouTube, and as they did, surfers and mountain bikers started to rethink what they might do for those who now had the experience of travelling with them rather than watching from a distance – it became about sharing the rush, not the spectacle. They also explain why Instagram’s low-res strategy and Notch Persson’s rudimentary graphical approach were so successful.
Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll, Benjamin Yoskovitz
Airbnb, Amazon Mechanical Turk, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, barriers to entry, Bay Area Rapid Transit, Ben Horowitz, bounce rate, business intelligence, call centre, cloud computing, cognitive bias, commoditize, constrained optimization, en.wikipedia.org, Firefox, Frederick Winslow Taylor, frictionless, frictionless market, game design, Google X / Alphabet X, Infrastructure as a Service, Internet of things, inventory management, Kickstarter, lateral thinking, Lean Startup, lifelogging, longitudinal study, Marshall McLuhan, minimum viable product, Network effects, pattern recognition, Paul Graham, performance metric, place-making, platform as a service, recommendation engine, ride hailing / ride sharing, rolodex, sentiment analysis, skunkworks, Skype, social graph, social software, software as a service, Steve Jobs, subscription business, telemarketer, transaction costs, two-sided market, Uber for X, web application, Y Combinator
Businesses that can grow revenues while incremental costs stay still or decline have the potential to grow massively overnight. There are inherent network effects in the model. The phone system is the classic example of a business with a network effect: the more people who use it, the more useful it becomes. Network-effect businesses are wonderful, but they often have a two-edged sword: it’s great when you have 10 million users, but you may be deluding yourself about how easily users will adopt the product or service, and it’s hard to test the basic value with a small market at first. You need a plan for getting to the point where the network effects kick in and become obvious. You have several ways to monetize. It’s unlikely that any one payment model will work, but if you can find several ways to make money from a business—one obvious one, and several incidental ones—then you can diversify your revenue streams and iterate more easily, improving your chances of success.
Two-sided marketplaces—twice the metrics, twice the fun Wrinkles: Chicken and Egg, Fraud, Keeping the Transaction, and Auctions In the early days of the Web, pundits predicted an open, utopian world of frictionless markets that were transparent and efficient. But as Internet giants like Google, Amazon, and Facebook have shown, parts of the Web are dystopian. Two-sided marketplaces are subject to strong network effects—the more inventory they have to offer, the more useful they become. A marketplace with no inventory, on the other hand, is useless. Successful two-sided marketplaces find a way to artificially populate either the buyer or the seller side early on. As a particular niche matures, this network effect means there will be a few dominant players—as is the case with Airbnb, VRBO, and a few others in the rental property space. Fraud and trust are the other big issues for such marketplaces. You don’t want to assume responsibility for the delivery of goods or services within your marketplace, but you need to ensure that there are reliable reputation systems.
This is common for two reasons: First, the bar for success in a consumer application is always going up. A few years ago, hundreds of thousands of users was considered big. Today, 1 million users is the benchmark, but it’s quickly going to 10 million. That’s a lot of users. Certain categories of product, such as social networks and e-commerce, are ossifying, with a few gigantic players competing and leaving little room for upstarts. Second, many consumer applications rely on network effects. The more users, the more value created for everyone. Nobody wants to use the telephone when they’re the only one with a telephone. Location-based applications typically require lots of scale, as do most marketplaces and user-generated content businesses, so that there are enough transactions and discussions to make things interesting. Without a critical mass of users, Facebook is an empty shell.
The Paypal Wars: Battles With Ebay, the Media, the Mafia, and the Rest of Planet Earth by Eric M. Jackson
bank run, business process, call centre, creative destruction, disintermediation, Elon Musk, index fund, Internet Archive, iterative process, Joseph Schumpeter, market design, Menlo Park, Metcalfe’s law, money market fund, moral hazard, Network effects, new economy, offshore financial centre, Peter Thiel, Robert Metcalfe, Sand Hill Road, shareholder value, Silicon Valley, Silicon Valley startup, telemarketer, The Chicago School, the new new thing, Turing test
They simply could not afford to pay the new fees given that most of their buyers lurked on eBay. If nothing else, Yahoo’s collapse provided some insight into the relative strength of PayPal and eBay’s network effects. PayPal’s payments network was strong enough to survive the forced upgrade process, although our competitor did make up some ground by undercutting our pricing. EBay’s older auctions network, though, was far more powerful. It allowed Whitman to raise prices and still grow following the introduction of a minor fee system from Yahoo. PayPal may have enjoyed a modest network effect, but by banishing Yahoo auctions eBay had gone one step further and established itself as a person-to-person marketplace monopolist. Whenever Luke Nosek turned up in the product department after a long stretch in seclusion, he tended to have either a sagacious idea with him or, just as likely, a wacky scheme.
X.com, just like dotBank, launched its payments service with many features designed solely to spur user growth. PayPal had a race on its hands, and it became clear there would be no prize for second place. Peter Thiel often claimed that growth was the most critical objective for a business like Confinity. Our CEO maintained that creating a successful payments service could only happen if we achieved something called a network effect. An interactive, inter-connected system, he explained, could exist only if it conferred value on the people who voluntarily chose to join it. The more people participating in it, the more beneficial the network would become since all the members could interact together. Hence a large, established network is very valuable to enter and very costly to leave; in essence it locks in its members and prevents would-be competitors from getting off the ground.
So, for PayPal to grow rapidly and expand all over the Internet, the quickest way to do that is to first grow on eBay!” Luke went on to explain that since dotBank and X.com were now competing with Confinity, Peter and the management team believed that we needed to find the fastest way possible to scale up PayPal’s customer base. If we could increase our number of accounts to reach critical mass before our competitors, the resulting network effect would freeze out any opponents. Potential users would not waste time signing up for multiple accounts with different payment services if PayPal were ubiquitous. But if we failed and another service outpaced us, then there probably would be nothing we could do to catch up. Although stopping short of defining how many users constituted critical mass, the management team did agree that customer acquisition had to take on a higher importance than profitability in the short run.
How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs by Tim Draper
3D printing, Airbnb, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Berlin Wall, bitcoin, blockchain, Buckminster Fuller, business climate, carried interest, connected car, crowdsourcing, cryptocurrency, Deng Xiaoping, discounted cash flows, disintermediation, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, family office, fiat currency, frictionless, frictionless market, high net worth, hiring and firing, Jeff Bezos, Kickstarter, low earth orbit, Lyft, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, Metcalfe's law, Metcalfe’s law, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Nelson Mandela, Network effects, peer-to-peer, Peter Thiel, pez dispenser, Ralph Waldo Emerson, risk tolerance, Robert Metcalfe, Ronald Reagan, Rosa Parks, Sand Hill Road, school choice, school vouchers, self-driving car, sharing economy, short selling, Silicon Valley, Skype, smart contracts, Snapchat, sovereign wealth fund, stealth mode startup, stem cell, Steve Jobs, Tesla Model S, Uber for X, uber lyft, universal basic income, women in the workforce, Y Combinator, zero-sum game
Every time you write a tweet, you are spreading the use of Twitter. Kik and Tango are two services that allow people to get free video calls, but any call that is made on those platforms adds a new customer to Kik or Tango. If you provide these companies with your contacts, their user base spreads, but you can more easily reach your friends. These viral marketing approaches induce “network effects.” One of the greatest network effects is described through Bitcoin. Bitcoin is nothing if no one uses it or values it (as is true with any currency), but as more people adopt Bitcoin as a way of transmitting funds, the more people recognize its value and its value rises. Metcalfe’s Law states that the value of a communications network is proportional to the square of the number of connected users of the system (n^2).
What I learned is that there are advantages to going first. Since we had so many firsts in the venture capital business, our competitors often had to play catch up. In many geographies, we were the only one the entrepreneurs would think to go to, so we had no competition there. If you are first in your industry, you can also define that industry, leading to better long-term positioning and network effects that give you an edge. Be a leader. Decision Making I learned a lot about group decision making. As we grew the team at Draper Fisher Jurvetson (DFJ), we found that it became harder and harder to make decisions to invest in unusual and innovative companies because what one partner saw, other partners may not envision. Without support for the outliers, the investments might end up being too “safe.”
I remember when I first used the Internet, the only things I could do were buy diamonds and try to break into NORAD. There were very few uses. It took many years for the Internet to become mainstream, but when it did, it transformed industries. HTTP was the first real working protocol, so people standardized on it even though there were more elegant solutions, just as people today are frustrated with the limitations of Bitcoin but have made it a standard because of all the network effects around the early winner. The US was wise to leave the Internet unregulated and free because all the Internet entrepreneurs created startups in the US, and the economy around the Internet blossomed. Keeping its regulatory hands light should help innovators stay in the US. At this writing, the Commodities Futures Trade Board (CFTB) and the Securities and Exchange Commission (SEC) are both taking a wait-and-see attitude as they approach the burgeoning virtual market.
The Googlization of Everything: by Siva Vaidhyanathan
1960s counterculture, activist fund / activist shareholder / activist investor, AltaVista, barriers to entry, Berlin Wall, borderless world, Burning Man, Cass Sunstein, choice architecture, cloud computing, computer age, corporate social responsibility, correlation does not imply causation, creative destruction, data acquisition, death of newspapers, don't be evil, Firefox, Francis Fukuyama: the end of history, full text search, global pandemic, global village, Google Earth, Howard Rheingold, informal economy, information retrieval, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge worker, libertarian paternalism, market fundamentalism, Marshall McLuhan, means of production, Mikhail Gorbachev, moral panic, Naomi Klein, Network effects, new economy, Nicholas Carr, PageRank, Panopticon Jeremy Bentham, pirate software, Ray Kurzweil, Richard Thaler, Ronald Reagan, side project, Silicon Valley, Silicon Valley ideology, single-payer health, Skype, Social Responsibility of Business Is to Increase Its Profits, social web, Steven Levy, Stewart Brand, technoutopianism, The Nature of the Firm, The Structural Transformation of the Public Sphere, Thorstein Veblen, urban decay, web application, zero-sum game
Wagner’s argument about user behavior could be valid if boycotting or migrating from Google did not incur signiﬁcant downgrades in service by losing the advantages of integration with other Google services. Google’s argument also ignores the “network effect” in communication markets: a service increases in value as more people use it.15 A telephone that is connected to only one other person has very limited value compared with one connected to 250 million people. YouTube is more valuable as a video platform because it attracts more contributors and viewers than any other comparable service. The more users it attracts, the more value each user derives from using it, and thus the more users it continues to attract. Network effects tend toward standardization and thus potential monopoly. The network effect for most of Google’s services is not the same exponential effect we saw with the proliferation of the telephone or fax machine.
If only one person in the world used Gmail, it would still be valuable to her, because it can work well with every other standard e-mail interface. But if only a few people used Google for Web searching, 20 R END E R UNTO CAESA R Google would not have the data it needs to improve the search experience. Google is better because it’s bigger, and it’s bigger because it’s better. This is an arithmetic, rather than geometric, network effect, but it matters nonetheless. Opting out or switching away from Google services degrades one’s ability to use the Web. It may seem as if I’m arguing that Google is a monopoly and needs to be treated as such, broken up using the antimonopoly legislation and regulations developed over the late nineteenth and early twentieth centuries. But because Google is sui generis, business competition and regulation demand fresh thinking.
Although Google’s contextual advertising and instant auctions often serve the interests of small ﬁrms, its freedom to set such rates at any level it desires allows it to crowd out some of the small ﬁrms that have grown to depend on Google for their most valuable advertising outlets—including small ﬁrms that are Google’s potential competitors. That’s mean, but it’s not illegal. If Google’s adver- REN D E R UNTO CA ESA R 29 tising dominance and revenues are a legal problem at all, it’s because of a touchy issue called cross-subsidization. Google can use its prominence in people’s lives—the network effect— and its surplus revenues to support its other ventures—its online document business, for example, which is likely to lose trivial money for the company. This process is not yet a direct threat to Microsoft, which can withstand a few thousand customers sneaking off to the “cloud” instead of using Word on their own laptops. But it poses a serious threat to small, creative companies that offer Web-based word processors, such as Zoho, Thinkfree, Writely, and Ajaxwrite.
The Myth of Capitalism: Monopolies and the Death of Competition by Jonathan Tepper
Affordable Care Act / Obamacare, air freight, Airbnb, airline deregulation, bank run, barriers to entry, Berlin Wall, Bernie Sanders, big-box store, Bob Noyce, business cycle, Capital in the Twenty-First Century by Thomas Piketty, citizen journalism, Clayton Christensen, collapse of Lehman Brothers, collective bargaining, computer age, corporate raider, creative destruction, Credit Default Swap, crony capitalism, diversification, don't be evil, Donald Trump, Double Irish / Dutch Sandwich, Edward Snowden, Elon Musk, en.wikipedia.org, eurozone crisis, Fall of the Berlin Wall, family office, financial innovation, full employment, German hyperinflation, gig economy, Gini coefficient, Goldman Sachs: Vampire Squid, Google bus, Google Chrome, Gordon Gekko, income inequality, index fund, Innovator's Dilemma, intangible asset, invisible hand, Jeff Bezos, John Nash: game theory, John von Neumann, Joseph Schumpeter, Kenneth Rogoff, late capitalism, London Interbank Offered Rate, low skilled workers, Mark Zuckerberg, Martin Wolf, means of production, merger arbitrage, Metcalfe's law, multi-sided market, mutually assured destruction, Nash equilibrium, Network effects, new economy, Northern Rock, offshore financial centre, passive investing, patent troll, Peter Thiel, plutocrats, Plutocrats, prediction markets, prisoner's dilemma, race to the bottom, rent-seeking, road to serfdom, Robert Bork, Ronald Reagan, Sam Peltzman, secular stagnation, shareholder value, Silicon Valley, Skype, Snapchat, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, The Chicago School, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, undersea cable, Vanguard fund, very high income, wikimedia commons, William Shockley: the traitorous eight, zero-sum game
Yet in order to generate returns, in the words of one manager, they have to look for “corporate killer whales that can feast on baby seals.” Libraries of books at business schools are devoted to explaining different kinds of moats. Investors search for companies that achieve such scale that they become the “Low-Cost Producer.” Investors try to find firms with “High Switching Costs” that lock clients into a relationship. They try to find businesses with “Network Effects” where you win by being the only system people can use to call or pay each other, for example. They also look for industries with “Intangible Assets” such as patents that keep your competitors out by law. In the medical industry, in particular, patents allow companies to charge astronomic prices because, by law, no other companies can compete with them while they hold a patent. Company CEOs and investors are all behaving in a perfectly rational way when they buy competitors and find ways to monopolize their industries.
For example, the latest Boeing 787 costs over $200 million and has parts from 45 separate companies.37 For other industries, the scale of research and development is now so great that no startup could ever compete. For example, given the complexity of microchips few companies can spend what Intel does. Intel's latest chip will have the equivalent of over 100 billion synapses.38 And finally, for some businesses network effects create winner-takes-all outcomes that favor vast size. In social networks, everyone wants to be on the network with the highest number of users, which is why Facebook has over two billion users. Yet not all industries fall into these categories. When it comes to productivity, small is often good. Sometimes scale does not necessarily help. For some industries, throwing more people at a problem is not an answer.
The more people search on Google, the better the company gets at understanding what users are searching for and the better searching becomes. The more people search, the more likely advertisers will flock to Google, and the more revenue that is generated. The more advertisers there are, the more efficient ad auctions become. Most of the tech monopolies are known as “platform” companies with strong network effects: Google, Facebook, Amazon, Uber. What these companies have in common is that they all connect members of one group, like vacationers looking for rooms to rent, with another group, like landlords with spare rooms. Traditional manufacturing businesses, for instance, buy raw materials, make products, and sell those to customers. Platform companies on the other hand take different groups of customers that they help bring together.
Radical Markets: Uprooting Capitalism and Democracy for a Just Society by Eric Posner, E. Weyl
3D printing, activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, anti-communist, augmented reality, basic income, Berlin Wall, Bernie Sanders, Branko Milanovic, business process, buy and hold, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collective bargaining, commoditize, Corn Laws, corporate governance, crowdsourcing, cryptocurrency, Donald Trump, Elon Musk, endowment effect, Erik Brynjolfsson, Ethereum, feminist movement, financial deregulation, Francis Fukuyama: the end of history, full employment, George Akerlof, global supply chain, guest worker program, hydraulic fracturing, Hyperloop, illegal immigration, immigration reform, income inequality, income per capita, index fund, informal economy, information asymmetry, invisible hand, Jane Jacobs, Jaron Lanier, Jean Tirole, Joseph Schumpeter, Kenneth Arrow, labor-force participation, laissez-faire capitalism, Landlord’s Game, liberal capitalism, low skilled workers, Lyft, market bubble, market design, market friction, market fundamentalism, mass immigration, negative equity, Network effects, obamacare, offshore financial centre, open borders, Pareto efficiency, passive investing, patent troll, Paul Samuelson, performance metric, plutocrats, Plutocrats, pre–internet, random walk, randomized controlled trial, Ray Kurzweil, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, risk tolerance, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Rory Sutherland, Second Machine Age, second-price auction, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, special economic zone, spectrum auction, speech recognition, statistical model, stem cell, telepresence, Thales and the olive presses, Thales of Miletus, The Death and Life of Great American Cities, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thorstein Veblen, trade route, transaction costs, trickle-down economics, Uber and Lyft, uber lyft, universal basic income, urban planning, Vanguard fund, women in the workforce, Zipcar
It especially appealed to a Silicon Valley mentality that grew from the counterculture of the 1960s.8 During the 1990s, venture capital poured in to commercialize the booming Internet before online services had established how they would monetize their offerings. Internet companies relentlessly pursued users under the banner “usage, revenues later” (a “backronym” for “url”). While partly driven by the dot-com stock market bubble, this strategy was also influenced by the dominant position Microsoft had established by offering its operating system at relatively low cost and in a form compatible with many hardware platforms. The “network effects” created by this strategy were widely viewed as placing Microsoft in a position to reap enormous rewards.9 This encouraged many venture capitalists to fund services that rapidly enlarged their user base even if their business model was unclear. As the bursting of the tech bubble cooled this euphoria, emerging tech giants like Google had to find a way to make money from their user base. Google’s Sergey Brin and Larry Page initially considered user fees and paid subscriptions, while insisting they would never turn to advertising.
In short, the siren servers have occupied the central piece of real estate in a “digital commons” that has room for only a few players, and their interests are now opposed to paying technoserfs who are at present voluntarily tilling this land. Beyond the market structure and the nature of AI technology, the nature of social media makes these sites particularly resistant to competition. Most users want to be part of a social network that includes all of their friends. These network effects can make it difficult for competitors to enter the market unless they have enough financial backing to subsidize users for years—and the social norms around money not changing hands makes even that strategy challenging to pull off. Many social scientists have also argued that siren servers use techniques similar to those employed by casinos to make their content addictive.35 Together these properties raise the power of siren servers to lock users into patterns that may not serve their long-term interests.
Without some way to keep track of users, which would necessarily impose further burdens on the users themselves, paying for data could easily be exploited. The last three factors we highlight are mostly reasons that treating data as labor might also be socially undesirable. We believe these factors would be outweighed by the benefits in the medium term. However, when these factors are combined with siren servers’ monopsony power, network effects, and interests in manipulating user psychology, it is unsurprising that siren servers have not yet undertaken this ambitious transition. On the other hand, it is possible that siren servers that are poorer in data, such as Amazon, Apple, and Microsoft, could have both the scale to make competition possible and the incentive to break up this unproductive monopsony. By creating an alternative ideology to the prevailing focus on “free” stuff online, they could help break the dominant business model of their rivals and open up a chance to compete.
The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses by Eric Ries
3D printing, barriers to entry, call centre, Clayton Christensen, clean water, cloud computing, commoditize, Computer Numeric Control, continuous integration, corporate governance, disruptive innovation, experimental subject, Frederick Winslow Taylor, Lean Startup, Marc Andreessen, Mark Zuckerberg, Metcalfe’s law, minimum viable product, Mitch Kapor, Network effects, payday loans, Peter Thiel, pets.com, Ponzi scheme, pull request, risk tolerance, selection bias, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, stealth mode startup, Steve Jobs, the scientific method, Toyota Production System, transaction costs
By contrast, a marketplace company that matches buyers and sellers such as eBay will have a different growth model. Its success depends primarily on the network effects that make it the premier destination for both buyers and sellers to transact business. Sellers want the marketplace with the highest number of potential customers. Buyers want the marketplace with the most competition among sellers, which leads to the greatest availability of products and the lowest prices. (In economics, this sometimes is called supply-side increasing returns and demand-side increasing returns.) For this kind of startup, the important thing to measure is that the network effects are working, as evidenced by the high retention rate of new buyers and sellers. If people stick with the product with very little attrition, the marketplace will grow no matter how the company acquires new customers.
In 2004, that market had hundreds of millions of consumers actively participating worldwide. However, the majority of the customers who were using IM products were not paying for the privilege. Instead, large media and portal companies such as AOL, Microsoft, and Yahoo! operated their IM networks as a loss leader for other services while making modest amounts of money through advertising. IM is an example of a market that involves strong network effects. Like most communication networks, IM is thought to follow Metcalfe’s law: the value of a network as a whole is proportional to the square of the number of participants. In other words, the more people in the network, the more valuable the network. This makes intuitive sense: the value to each participant is driven primarily by how many other people he or she can communicate with. Imagine a world in which you own the only telephone; it would have no value.
The top three networks controlled more than 80 percent of the overall usage and were in the process of consolidating their gains in market share at the expense of a number of smaller players.2 The common wisdom was that it was more or less impossible to bring a new IM network to market without spending an extraordinary amount of money on marketing. The reason for that wisdom is simple. Because of the power of network effects, IM products have high switching costs. To switch from one network to another, customers would have to convince their friends and colleagues to switch with them. This extra work for customers creates a barrier to entry in the IM market: with all consumers locked in to an incumbent’s product, there are no customers left with whom to establish a beachhead. At IMVU we settled on a strategy of building a product that would combine the large mass appeal of traditional IM with the high revenue per customer of three-dimensional (3D) video games and virtual worlds.
Competition Demystified by Bruce C. Greenwald
additive manufacturing, airline deregulation, AltaVista, asset allocation, barriers to entry, business cycle, creative destruction, cross-subsidies, deindustrialization, discounted cash flows, diversified portfolio, Everything should be made as simple as possible, fault tolerance, intangible asset, John Nash: game theory, Nash equilibrium, Network effects, new economy, oil shock, packet switching, pets.com, price discrimination, price stability, selective serotonin reuptake inhibitor (SSRI), shareholder value, Silicon Valley, six sigma, Steve Jobs, transaction costs, yield management, zero-sum game
When the applications involved are critical to the company’s operations—order entry, inventory, invoicing and shipping, patient records, or bank transactions—few want to abandon a functioning system, even for one that promises vast increases in productivity, if it holds the threat of terminating the business through systemic failure, the ultimate “killer app.” These costs are reinforced by network effects. If your computer system must work compatibly with others, then it is difficult to change to an alternative when others do not, even if the alternative is in some ways superior. The move will be costly, to ensure continued compatibility, and perhaps disastrous if the new system cannot be meshed with the existing one. Software is not the only product or service that imposes substantial switching costs on customers and thus gives the incumbent a leg up on potential competitors.
The company does have captive customers, partially because much of the software they own is not compatible with other operating systems, making change expensive and time-consuming. Its economies of scale are vast, since writing standard programs is almost entirely a fixed-cost business. With its enormous customer base, Microsoft has been able to throw years of program writing into any project it thinks important and still end up spending less per unit sold than its competitors. Finally, there is the network effect, the fact that the value of the product to the user depends on how many other people also use it. A competitor to Microsoft in both the operating system and applications software businesses is at a huge disadvantage, no matter the quality of its offerings. TABLE 4.1 Microsoft’s returns on investment, 2002 ($ billion) Cash at end of year $ 38.6 Debt $ 0 Equity $ 52.2 Capital—cash $ 13.6 Net income $ 7.8 Earnings on cash $ 1.2 Earnings on software $ 6.6 Total return on capital 15.0% Return on capital invested in software 48.8% Apple has been competing against Microsoft since IBM introduced the PC in 1981.
Microsoft has had virtually no competition in the desktop PC market. Intel has had Advanced Micro Devices as a distant second. Its research and development spending has always been larger on an absolute basis, while smaller as a percentage of sales, than AMD’s. In 1988 through 1990, for example, Intel spent twice as many dollars on R&D as AMD, but only two-thirds as much per dollar of sales (twelve cents versus eighteen cents). Network effects enhance both customer captivity and economies of scale. For programmers, computer designers, and typists in large firms, costs are reduced when users have to learn and interact only with the single software system that others are also using. The many PC manufacturers, each perpetually introducing new models, also benefit in cost, speed, and mutual compatibility by having a single chip standard on which to work.
Hit Makers: The Science of Popularity in an Age of Distraction by Derek Thompson
Airbnb, Albert Einstein, Alexey Pajitnov wrote Tetris, always be closing, augmented reality, Clayton Christensen, Donald Trump, Downton Abbey, full employment, game design, Gordon Gekko, hindsight bias, indoor plumbing, industrial cluster, information trail, invention of the printing press, invention of the telegraph, Jeff Bezos, John Snow's cholera map, Kodak vs Instagram, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, Minecraft, Nate Silver, Network effects, Nicholas Carr, out of africa, randomized controlled trial, recommendation engine, Robert Gordon, Ronald Reagan, Silicon Valley, Skype, Snapchat, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, subscription business, telemarketer, the medium is the message, The Rise and Fall of American Growth, Uber and Lyft, Uber for X, uber lyft, Vilfredo Pareto, Vincenzo Peruggia: Mona Lisa, women in the workforce
Privately, they talk about schedules. Publicly, they deploy strategic emotions. Privately, they tend to share small troubles. Publicly, they want to be interesting. Privately, they want to be understood. The science of network effects says that, as a network grows, it becomes exponentially more valuable to each user. But if larger networks reward preening messages, this might turn off audiences seeking the intimate authenticity that tends to come from less crowded conversations—one to five, or even one to one. For those who want to avoid a deluge of self-congratulation, there is something like an “anti–network effect” in which large social networks become cloyingly self-congratulatory. One common criticism of Facebook and Instagram is that users make their lives look so fantastic that our own appears bleak by comparison.
Consider a news ecosystem quite opposite from ESPN and cable news: Reddit, a community news site created entirely by anonymous users. On Reddit, each piece of content is two parts: a headline and an article link. Users can promote links with “upvotes” or register their dissatisfaction with “downvotes.” Several years ago, a team of computer science researchers at Stanford University submitted and resubmitted thousands of images to Reddit with different headlines and controlled for network effects to see if the Reddit community had a clear preference for certain titles. They wanted to understand a question that I think about all the time: What makes a great headline? In my time at The Atlantic, I’ve developed, refined, discarded, and revived countless pet theories about what makes a perfect headline. When I was younger, I used to say a great headline should be “definitive or delightful.”
Subscriptions insulate a business like HBO, Netflix, or The New Yorker from the need to maximize attention or use on a per-unit basis. That gives companies a dependable revenue stream and offers creators a bit of slack. There is a third dimension to giving people what they want beyond stated preferences (what I say I want) and revealed preferences (what I do). Those are latent preferences: what I don’t even know I want. Facebook observes several network effects that are too complicated to ask someone in a survey. For example, users never ask to see notifications of other people making new friends on Facebook. But “friending stories”—notifications that two people have become friends—have a contagious effect. When people see friendships forming, they’re more likely to add friends themselves, which creates more connections, which means more content, which makes News Feed a better experience for people.
The Captured Economy: How the Powerful Enrich Themselves, Slow Down Growth, and Increase Inequality by Brink Lindsey
"Robert Solow", Airbnb, Asian financial crisis, bank run, barriers to entry, Bernie Sanders, Build a better mousetrap, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Cass Sunstein, collective bargaining, creative destruction, Credit Default Swap, crony capitalism, Daniel Kahneman / Amos Tversky, David Brooks, diversified portfolio, Donald Trump, Edward Glaeser, endogenous growth, experimental economics, experimental subject, facts on the ground, financial innovation, financial intermediation, financial repression, hiring and firing, Home mortgage interest deduction, housing crisis, income inequality, informal economy, information asymmetry, intangible asset, inventory management, invisible hand, Jones Act, Joseph Schumpeter, Kenneth Rogoff, Kevin Kelly, knowledge worker, labor-force participation, Long Term Capital Management, low skilled workers, Lyft, Mark Zuckerberg, market fundamentalism, mass immigration, mass incarceration, medical malpractice, Menlo Park, moral hazard, mortgage debt, Network effects, patent troll, plutocrats, Plutocrats, principal–agent problem, regulatory arbitrage, rent control, rent-seeking, ride hailing / ride sharing, Robert Metcalfe, Ronald Reagan, Silicon Valley, Silicon Valley ideology, smart cities, software patent, too big to fail, total factor productivity, trade liberalization, transaction costs, tulip mania, Uber and Lyft, uber lyft, Washington Consensus, white picket fence, winner-take-all economy, women in the workforce
Drug makers (through patent protection) and healthcare professionals (through occupational licensing) have exaggerated their gains from this rising demand by using the political process to constrict supply. In the rapidly growing information technology sector, the presence of strong network effects in information technology guarantees that some industries will feature “winner take all” markets with high levels of concentration. Lobbyists for strong copyright and patent protection for software have further amplified this dynamic by fortifying the winners’ market power with additional barriers to entry. Meanwhile, network effects have also led to geographic concentration, as highly skilled knowledge workers are increasingly congregating together in “human capital hubs.” As a result, a few big coastal cities have come to account for an outsized share of the nation’s productive capacity, as well as its opportunities for upward mobility.
The slowdown in the growth of workers’ average years of schooling completed means that the relative supply of skilled workers lags behind relative demand. Mass immigration expands the ranks of low-skill workers even as demand for them has flagged. People increasingly marry within their social class, reducing the marital pathway to social mobility. The factors contributing to outsized gains at the very top are similarly diverse. They include the rise of “winner-take-all” markets produced by information technology’s network effects as well as globalization’s expansion of relevant market size; a huge run-up in stock prices; continuing growth in the size of big corporations (which has helped to fuel rising CEO pay); and a big drop in the top income tax rate (which has facilitated the use of high compensation as a strategy for attracting top managers, professionals, and executives). The changing nature of rent-creating policies has lent further momentum to this robust underlying trend toward greater inequality.
In all these industries, the upfront fixed costs of producing the first copy of a product are high while the variable costs of producing additional copies are low. The larger the sales volume, the more sales there are over which fixed costs can be spread and for which the unit costs of production will be lower. For pharmaceuticals, these scale economies are amplified by the high upfront costs of securing FDA approval; for software, they are ramped up by network effects (software used by many people can be much more valuable than software used by only a few). These factors push in the direction of high levels of inter-firm inequality within these industries; in other words, high levels of concentration in which a few firms account for the vast bulk of sales and profits. Pushing in the other direction, however, is the vulnerability of companies in these industries to relatively easy imitation.
What's Yours Is Mine: Against the Sharing Economy by Tom Slee
4chan, Airbnb, Amazon Mechanical Turk, asset-backed security, barriers to entry, Berlin Wall, big-box store, bitcoin, blockchain, citizen journalism, collaborative consumption, congestion charging, Credit Default Swap, crowdsourcing, data acquisition, David Brooks, don't be evil, gig economy, Hacker Ethic, income inequality, informal economy, invisible hand, Jacob Appelbaum, Jane Jacobs, Jeff Bezos, Khan Academy, Kibera, Kickstarter, license plate recognition, Lyft, Marc Andreessen, Mark Zuckerberg, move fast and break things, move fast and break things, natural language processing, Netflix Prize, Network effects, new economy, Occupy movement, openstreetmap, Paul Graham, peer-to-peer, peer-to-peer lending, Peter Thiel, pre–internet, principal–agent problem, profit motive, race to the bottom, Ray Kurzweil, recommendation engine, rent control, ride hailing / ride sharing, sharing economy, Silicon Valley, Snapchat, software is eating the world, South of Market, San Francisco, TaskRabbit, The Nature of the Firm, Thomas L Friedman, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ultimatum game, urban planning, WikiLeaks, winner-take-all economy, Y Combinator, Zipcar
For several years Apple’s iPod and Google’s YouTube had no serious competitors. Many of the sources of increasing returns for digital marketplaces are lumped under the term “network effects”: each new user of a service makes that service more valuable. Social media companies obviously benefit from network effects: you join the social media platform where the people you want to meet hang out. But there are other, less obvious forms of increasing returns: Google learns from every search carried out on its platform and so has a continuing advantage over its rivals (such as they are); advertisers want to be on the search engine that most people frequent, so the leading search engine will attract the most advertising money to feed its further growth. Other network effects are familiar from the brick-and-mortar world: a successful corporate brand can communicate familiarity and dependability and so grow faster.
Or, as is the case with Amazon, a growing business provides money that can be re-invested in building more efficient infrastructure, driving the next cycle of competitive advantage. Not that network effects continue without limit. New technologies do come along to challenge existing ones such as music streaming companies challenging Apple’s iTunes Store. Also, cultural instincts don’t just bind us all together on Facebook, they drive us apart too. What teen wants to be on the same social networking site as their parents? So Snapchat and Instagram become the social networks for the next generation, and new entrants (Yoho, Whisper, WhatsApp, Kik) try to create new identities that will appeal to a new demographic. Faced with generational changes in tastes, the best thing that this generation’s company can do is sometimes to buy its challengers, as Facebook has done by buying Instagram and WhatsApp. Network effects are not unique to the Internet, but the Internet provides an environment in which they can become particularly powerful.
Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism by Robin Chase
Airbnb, Amazon Web Services, Andy Kessler, banking crisis, barriers to entry, basic income, Benevolent Dictator For Life (BDFL), bitcoin, blockchain, Burning Man, business climate, call centre, car-free, cloud computing, collaborative consumption, collaborative economy, collective bargaining, commoditize, congestion charging, creative destruction, crowdsourcing, cryptocurrency, decarbonisation, different worldview, do-ocracy, don't be evil, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, Ferguson, Missouri, Firefox, frictionless, Gini coefficient, hive mind, income inequality, index fund, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Lean Startup, Lyft, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, openstreetmap, optical character recognition, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, transaction costs, Turing test, turn-by-turn navigation, Uber and Lyft, uber lyft, Zipcar
Meanwhile, every driver has a cell phone in his or her pocket that could make electronic payments. But some platforms for participation deliver infrastructure and significant network effects. In other words, the value of the service goes up the more people participate. Zipcar, for example, is useful if there is just one car parked near your house, but when people in another city make it possible for you to rent cars there too, there is added value to you, albeit a marginal one. For BlaBlaCar, on the other hand, all the value is derived from network effects: Ridesharing only works when lots of people are participating. These network effects are powerful and can make it very hard for a new entrant to enter the market and go up against an established company with a very large network. The pinnacle in a pure capitalist economy is creating so many barriers to entry that no one else can really compete.
What I failed to appreciate back then was the much larger movement made possible by the Internet. Zipcar was a trailblazer. When you can connect and share assets, people, and ideas, everything changes, not just how you rent a car. Google, eBay, Facebook, OKCupid, YouTube, Waze, Airbnb, WhatsApp, Duolingo—all are part of this transformation of capitalism. Web 2.0, the sharing economy, crowdsourcing, collaborative production, collaborative consumption, and network effects are simply terms we’ve created along the way in an effort to capture what is going on. Attributing all this to “the Internet” misses the building blocks and therefore the ability to replicate this type of activity in a more controlled way. There is one structure that underlies all these—excess capacity + a platform for participation + diverse peers—and it is fundamentally changing the way we work, build businesses, and shape economies.
Then, the profits of all the failed companies, along with the difference in efficiency between the manual and the automated business, accrue to the center. And, it learns. If managed correctly, it becomes a recursive learning machine that just gets more effective with every measured mistake. This is what I’ve called miracle #2, exponential learning. As the platform starts to really perform, the tendency is for larger platforms to eat smaller ones because of network effects: We all want to use the short-term rental service with the broadest possible range of rooms advertised, or be on the social network that has most of our friends on it. The winners keep winning and tend to wind up with monopolistic power. She continues: It soon grows beyond replacement. Just look at how even after [Microsoft] management wasted billions of dollars on Bing, it is still failing against [Google], while [Google] never even missed a quarter.
No Filter: The Inside Story of Instagram by Sarah Frier
Airbnb, Amazon Web Services, blockchain, Clayton Christensen, cloud computing, cryptocurrency, Donald Trump, Elon Musk, Frank Gehry, Jeff Bezos, Marc Andreessen, Mark Zuckerberg, Menlo Park, Minecraft, move fast and break things, move fast and break things, Network effects, new economy, Oculus Rift, Peter Thiel, ride hailing / ride sharing, side project, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, TaskRabbit, Tony Hsieh, Travis Kalanick, ubercab, Zipcar
Facebook was a master at strategically massaging the truth to reduce government scrutiny, presenting itself as a scrappy upstart when it wasn’t. But the company’s paranoia was real. Any fast-growing social media product was a threat to Facebook’s network effect and the time users spent there. It was Facebook’s job to not let anyone else catch up; Zuckerberg had instilled this value in his employees by ending all staff meetings with an unambiguous rallying cry: “Domination!” There were signs Instagram was achieving a winner-take-all effect. Its growth was accelerating. At the time of the acquisition, the company had 30 million users. By the middle of the summer, it had more than 50 million. The Office of Fair Trading’s report says nothing about network effects, indicating that Facebook didn’t fully explain its logic behind the deal. They took an opposite read on Instagram’s growth. “Whilst this indicates the strength of Instagram’s product, it also indicates that barriers to expansion are relatively low and that the attractiveness of apps can be ‘faddish,’ ” the report said.
He worked with Guy Oseary, Madonna’s manager, to sort through all the opportunities, and ended up giving money to dozens of companies—not just in social media—including Uber, Airbnb, Spotify, and Instagram competitor Path. “Whenever there was a new type of experience for consumers, there would be like three companies doing the same thing,” he remembers. There were several versions of Instagram, Pinterest, and Uber. “Who would get traction first? And then the network effect would take all.” To know if Instagram was a fad or a lasting network, Kutcher and Oseary looked at data that showed users were spending more and more time there, building a habit. “It’s a competition for attention,” Kutcher explained. “Everybody learned that from Facebook and Twitter.” Oseary and Kutcher struggled to get a meeting. But eventually, they made it to the South Park office, with its brown carpet and 1980s glass-block windows that barely let any light inside.
The only names on the list that were truly similar to Instagram, complete with filters and social features, were Path, which had fewer than 3 million users, and Hipstamatic, which had peaked at 4 million users and was about to lay off half a dozen of its employees. PicPlz, the app that Systrom and Krieger were so determined to beat after Andreessen Horowitz’s investment in 2010, had shut down in July 2012 and wasn’t even mentioned. The regulators were shortsightedly looking at the current marketplace and ignoring what Facebook and Instagram had the potential to be in a few years or even months. The real value of Facebook and Instagram was in their network effects—the momentum they gained as more people joined. Even if someone enjoyed using an Instagram competitor like Path more, if their friends weren’t on it, they wouldn’t stay. (Path shut down in 2018 after selling to a South Korean company, Daum Kakao, three years before.) Zuckerberg understood that the hardest part of creating a business would be creating a new habit for users and a group they all wanted to spend time with.
The People vs Tech: How the Internet Is Killing Democracy (And How We Save It) by Jamie Bartlett
Ada Lovelace, Airbnb, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, Dominic Cummings, Donald Trump, Edward Snowden, Elon Musk, Filter Bubble, future of work, gig economy, global village, Google bus, hive mind, Howard Rheingold, information retrieval, Internet of things, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta analysis, meta-analysis, mittelstand, move fast and break things, move fast and break things, Network effects, Nicholas Carr, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, prediction markets, QR code, ransomware, Ray Kurzweil, recommendation engine, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ross Ulbricht, Sam Altman, Satoshi Nakamoto, Second Machine Age, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Jobs, Steven Levy, strong AI, TaskRabbit, technological singularity, technoutopianism, Ted Kaczynski, the medium is the message, the scientific method, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, too big to fail, ultimatum game, universal basic income, WikiLeaks, World Values Survey, Y Combinator
If you join Facebook, your friends will be more likely to join too, which in turn makes their friends more likely to join. When everything is connected, such network effects can spread further, and far more quickly. It is such a powerful force that the biggest problem faced by Facebook today is that it’s running out of humans to connect. The same thing happens with online markets. When I was young, I bought my music at a nearby record shop, where my choices were constrained by geography and limited information. As a result I bought some niche albums. In small, local markets, there can be lots of best taxi services, best bookstores or best record shops. But, with digital markets, you only need one. Why would I use the quite good local taxi firm when I can use brilliant Uber? Humans are bad at understanding the power of these network effects, because we tend to think in linear form, whereas networks can grow exponentially.
However, at a deep level, these two grand systems – technology and democracy – are locked in a bitter conflict. They are products of completely different eras and run according to different rules and principles. The machinery of democracy was built during a time of nation-states, hierarchies, deference and industrialised economies. The fundamental features of digital tech are at odds with this model: non-geographical, decentralised, data-driven, subject to network effects and exponential growth. Put simply: democracy wasn’t designed for this. That’s not really anyone’s fault, not even Mark Zuckerberg’s. I’m hardly alone in thinking this, by the way. Many early digital pioneers saw how what they called ‘cyberspace’ was mismatched with the physical world, too. John Perry Barlow’s oft-quoted 1996 Declaration of the Independence of Cyberspace sums up this tension rather well: ‘Governments derive their just powers from the consent of the governed.
Humans are bad at understanding the power of these network effects, because we tend to think in linear form, whereas networks can grow exponentially. And so we are stunned every time a new billion-dollar mega-corporation appears almost overnight. Network effects are so powerful because they are self-reinforcing. The more users Uber has, the more drivers (and data) it attracts, and the better service it can offer, which means it gets more users, which means it continues to grow. Google goes through this iterative nano-improvement millions of times each day and is starting to look like what economists call a natural monopoly, because it can provide a better service than two competitive firms could.2* Digital companies can also scale up any advantage at breakneck speed because the cost to them of expanding is often very low. It costs hardly anything for Airbnb to add a new unit, whereas for a hotel company constructing a new building is slow, expensive and risky.
Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind
3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population
When Facebook OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 320 FUTURE POLITICS acquired WhatsApp in 2014, for a whopping $19 billion,WhatsApp had just fifty-five employees, meaning a price of $345 million for every employee.27 This book is not about Amazon, Alphabet, Facebook, Microsoft, or Apple. I do not know if these firms will dominate the economy of the digital lifeworld as they do today’s. But there are structural reasons why digital technology is likely to facilitate the concentration of more and more wealth in the hands of fewer and fewer people and corporations. Probably the most important, besides automation, is the network effect. The economy is increasingly interconnected in a web of overlapping networks, which have a number of important characteristics. Firstly, they are generally united by standards: shared rules or practices that set the rules of cooperation among members. Secondly, the more people adopt a given standard (by joining the network and abiding by its rules) the more valuable that standard becomes.28 Metcalfe’s Law holds that the value of a network increases exponentially with the number of nodes attached: doubling the number of nodes means quadrupling the value, and so on.
If you can get out ahead of the competition, every additional user/member will contribute to the acceleration of growth—and before long it’ll be too late for others to catch up. An upstart rival to Facebook may offer superior functionality but it’ll be worthless as a social network if it doesn’t reach a critical mass of members. The big tech firms mentioned above have all benefitted from the network effect. As soon as Microsoft Windows became the standard operating system for personal computers, it was always going to take decades for other firms to establish a rival standard. OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS The Wealth Cyclone 321 Facebook sits at the apex of the social network it provides (often called a ‘platform’), setting the standards in the code that constitutes the platform.
Take the example of financial trading, which now largely takes place online.The rise of automated and high-frequency trading has caused an explosion in financial activity—mostly to the disadvantage of human traders.29 As Jaron Lanier explains:30 ‘if you have a more effective computer than anyone else in an open network [then] Your superior calculation ability allows you to choose the least risky option for yourself, leaving riskier options for everyone else.’ Lanier’s point may remind you of the discussion of bots and democracy in chapter thirteen. If deliberation takes place over an open network and one group brings a horde of powerful AI bots to argue their case, then they’ll end up dominating the discussion. It’s like bringing a gun to a knife-fight. The network effect, and the ability to dominate a network using powerful digital technology, partly explain why tech and finance have grown more than any other sector since the 1980s, rising from about 10 per cent to 40 per cent of market capitalization.31 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 322 FUTURE POLITICS Whether you own the platform or dominate the network, the point is the same: in an increasingly networked economy, those with the most productive digital technologies will do better and better.
Makers by Chris Anderson
3D printing, Airbnb, Any sufficiently advanced technology is indistinguishable from magic, Apple II, autonomous vehicles, barriers to entry, Buckminster Fuller, Build a better mousetrap, business process, commoditize, Computer Numeric Control, crowdsourcing, dark matter, David Ricardo: comparative advantage, death of newspapers, dematerialisation, Elon Musk, factory automation, Firefox, future of work, global supply chain, global village, IKEA effect, industrial robot, interchangeable parts, Internet of things, inventory management, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, Lean Startup, manufacturing employment, Mark Zuckerberg, means of production, Menlo Park, Network effects, private space industry, profit maximization, QR code, race to the bottom, Richard Feynman, Ronald Coase, Rubik’s Cube, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, South of Market, San Francisco, spinning jenny, Startup school, stem cell, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supply-chain management, The Nature of the Firm, The Wealth of Nations by Adam Smith, transaction costs, trickle-down economics, Whole Earth Catalog, X Prize, Y Combinator
By simply bringing the Web’s culture and collaboration to the process of making, they’re combining to build something on a scale we’ve never seen from DIY before. What the Web taught us was the power of “network effects”: when you connect people and ideas, they grow. It’s a virtual circle—more people combined create more value, which in turn attracts even more people, and so on. That’s what has driven the ascent of Facebook, Twitter, and practically every other successful company online today. What Makers are doing is taking the DIY movement online—“making in public”—which introduces network effects on a massive scale. In short, the Maker Movement shares three characteristics, all of which, I’d argue, are transformative: 1. People using digital desktop tools to create designs for new products and prototype them (“digital DIY”). 2.
Growth continues at about 75 to 100 percent per year, which is common for open-source hardware companies like ours. We’ve been profitable from the first year (it’s actually not that hard in the hardware business—just charge more than your costs!), but try to reinvest as much of the profits as possible into building new factory lines. Because we’re online, we’re global from the start and tend to grow more quickly than traditional manufacturing companies because of the network effects of online word of mouth. But because we’re making hardware, which costs money and takes time to make, we don’t show the hockey-stick exponential growth curve of the hottest Web companies. So, as a business, we’re a hybrid: the simple business model and cash-flow advantages of traditional manufacturing, with the marketing and reach advantages of a Web company. We’re still a small business, but the difference between our kind of small business and the dry cleaners and corner shops that make up the majority of micro-enterprise in the country is that we’re Web-centric and global.
Think of the tens of thousands of apps that support and reinforce Android, an open (mostly) mobile operating system. Or the hundreds of plug-ins and utilities designed to work with WordPress, the open-source blogging platform. In each case, openness built a constituency for the product’s continued success. The fact that others could copy it didn’t matter, because all that goodwill had created a network effect that was far harder to copy than simply code. But what if someone wants to rip us off anyway? Well, it depends on what you mean by “rip us off.” If someone else decides to use our files, make no significant modifications or improvements, and just manufacture them and compete with us, they’ll have do so much more cheaply than we can to get traction in the marketplace. If they can do so, at the same or better quality, then that’s great: the consumer wins and we can stop making that product and focus on those that add more value (we don’t want to be in the commodity manufacturing business).
The Rise of Carry: The Dangerous Consequences of Volatility Suppression and the New Financial Order of Decaying Growth and Recurring Crisis by Tim Lee, Jamie Lee, Kevin Coldiron
active measures, Asian financial crisis, asset-backed security, backtesting, bank run, Bernie Madoff, Bretton Woods, business cycle, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, debt deflation, distributed ledger, diversification, financial intermediation, Flash crash, global reserve currency, implied volatility, income inequality, inflation targeting, labor-force participation, Long Term Capital Management, Lyft, margin call, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, negative equity, Network effects, Ponzi scheme, purchasing power parity, quantitative easing, random walk, rent-seeking, reserve currency, rising living standards, risk/return, sharing economy, short selling, sovereign wealth fund, Uber and Lyft, uber lyft, yield curve
That is what cumulative advantage means. Another example is that of network effects and lock-in in business, economics, and technology: the competition between VHS and Betamax, or between Facebook and Myspace and Friendster, or between Uber and Lyft. Betamax is often considered to have been a better technology, but it lost. Today, a direct competitor to Facebook could never succeed, no matter how much better its technology or design or business plan—because the appeal of a social network is the users who are already there, and those users are on Facebook. If Facebook is ever dethroned (and in time it will be), it will not be by another social network of the same kind, but by something entirely new that makes social networks of that kind irrelevant—which is to say, it will be disrupted. Network effects, and therefore cumulative advantage, are especially prominent in communications technologies and technological standards.
Network effects, and therefore cumulative advantage, are especially prominent in communications technologies and technological standards. Microsoft Office may be possible (just about) to dethrone—but only ever by a competitor that also could manipulate the .xls file format, which dom- 186 THE RISE OF CARRY inates global business. Financial instruments, too, can be viewed as communications standards that have network effects. As mentioned in Chapter 10, while the centrality of the S&P 500 to global markets today is quite reasonable, it was not predestined and may not be eternal. So, ultimately, even today’s convergence of the financial carry trade onto the S&P 500 and the VIX is an example of cumulative advantage at work. In business generally, regular old-fashioned economies of scale are an attenuated form of the cumulative advantage effect.
Plausibly, cumulative advantage could be related to the persistence of social class, structural racism, and wealth inequality. Another example, the murkiest but likely the most significant in our daily lives, is the natural formation of social hierarchies in animals: a big dog will Carry Is Synonymous with Power 187 slink away fearfully from a small dog who outranks it, and the fact that it could easily take the small dog in a fight does not even occur to it. As with celebrity, or network effects, social hierarchies in animals are not imaginary but are perpetuated by genuine social realities. Namely, the fact that the hierarchy is stable at all implies that all its members will act together to support it. The big dog slinks away not because it fears the small dog but because it fears the wrath of the whole pack (although surely it is unconscious of this). A solipsist might point out that such examples of cumulative advantage exist only in our heads.
Data for the Public Good by Alex Howard
23andMe, Atul Gawande, Cass Sunstein, cloud computing, crowdsourcing, Hernando de Soto, Internet of things, Kickstarter, lifelogging, Network effects, openstreetmap, Silicon Valley, slashdot, social intelligence, social software, social web, web application
“Data by itself isn’t useful. You don’t go and download data and slather data on yourself and get healed,” he said. “Data is useful when it’s integrated with other stuff that does useful jobs for doctors, patients and consumers.” What Lies Ahead There are four trends that warrant special attention as we look to the future of data for public good: civic network effects, hybridized data models, personal data ownership and smart disclosure. Civic Network Effects Community is a key ingredient in successful open government data initiatives. It’s not enough to simply release data and hope that venture capitalists and developers magically become aware of the opportunity to put it to work. Marketing open government data is what repeatedly brought federal Chief Technology Officer Aneesh Chopra and Park out to Silicon Valley, New York City and other business and tech hubs.
Why It's Still Kicking Off Everywhere: The New Global Revolutions by Paul Mason
anti-globalists, back-to-the-land, balance sheet recession, bank run, banking crisis, Berlin Wall, business cycle, capital controls, centre right, citizen journalism, collapse of Lehman Brothers, collective bargaining, creative destruction, credit crunch, Credit Default Swap, currency manipulation / currency intervention, currency peg, do-ocracy, eurozone crisis, Fall of the Berlin Wall, floating exchange rates, Francis Fukuyama: the end of history, full employment, ghettoisation, illegal immigration, informal economy, land tenure, low skilled workers, mass immigration, means of production, megacity, Mohammed Bouazizi, Naomi Klein, Network effects, New Journalism, Occupy movement, price stability, quantitative easing, race to the bottom, rising living standards, short selling, Slavoj Žižek, Stewart Brand, strikebreaker, union organizing, We are the 99%, Whole Earth Catalog, WikiLeaks, Winter of Discontent, women in the workforce, working poor, working-age population, young professional
The network’s basic law was explained by Bell Telephone boss Theodore Vail as early as 1908: the more people who use the network, the more useful it becomes to each user. This is known as the ‘network effect’: what it describes is the creation, out of two people’s interaction, of a ‘third thing’ which comes for free. Because network theory originated in the boardroom, this ‘third thing’ has tended to be identified in terms of economic value. But, in recent years, it has become clear it can provide much more than that. There’s another difference: when it was first theorized by Vail’s technologists, the ‘network effect’ seemed like a by-product, a happy accident. Today we are conscious users and promoters of the network effect. Everyone who uses information technology understands that they are—whether at work, on Facebook, on eBay or in a multiplayer game—a ‘node’ on a network: not a foot-soldier, not a bystander, not a leader, but a multitasking version of all three.
Vail’s customers probably had no idea that, by buying and using telephones, they were enhancing the technology’s value for others and creating spin-off effects for Bell’s other businesses (what are now termed ‘network externalities’). Nowadays, many of us have a very clear understanding of all this. The result is that, in the past ten years, the ‘network effect’ has blasted its way out of corporate economics and into sociology. The most obvious impact has been on the media and ideology. Long before people started using Twitter to foment social unrest, mainstream journalists noticed—to their dismay—that the size of one’s public persona or pay cheque carried no guarantee of popularity online. People’s status rises and falls with the reliability and truthfulness of what they contribute. This is a classic network effect—but it is not measurable as profit and loss. If you look at the full suite of information tools that were employed to spread the revolutions of 2009–11, it goes like this: Facebook is used to form groups, covert and overt—in order to establish those strong but flexible connections.
I. 46 Len-len 193–96, 209 Liberal Democrats 43–44, 46 liberalizers 31 Libya 25, 31, 119; National Transitional Council 178 Life and Fate (Grossman) 129 Lilico, Andrew 121 link-shorteners 75 Linux 139–40 @littlemisswilde 41–42, 44, 45, 135–36, 138 living conditions, urban slums 196–99 London: anti-capitalist demonstrations 33; arrests 61–62; Day X, 24 November 2010 41–42, 46–48; the Dubstep Rebellion 48–52; Fortnum & Mason 60–61; HM Revenue and Customs building 51; Hyde Park 60; Millbank riot 42–44; Millbank Tower 43; Museum Tavern 1; National Gallery teach-in 53, 53–54; Oxford Circus 60; Palladium Theatre 51; Parliament Square 49, 51, 52–53; Piccadilly Circus 58; police–student confrontation 50–51; Regent Street 58; Ritz Hotel 60; Tate Modern 53; trade-union demonstration, March 2011 57–61; Trafalgar Square 47; Victoria Street 50; Victorinox 59 London School of Oriental and African Studies, occupation of 44–46 López, Fernando 166–67, 170 Lopez, Gina 200–2 Lopez Inc. 200–2 Loubere, Leo 174 Loukanikos (riot dog) 94, 96 L’Ouverture, Toussaint 149 LulzSec 151 McIntyre, Jody 51 McPherson, James 182 Madison, Wisconsin revolt 184–87 Madrid 33 Mahalla uprising, 2008 10, 71 Maher, Ahmed 83 Mahfouz, Asmaa, @AsmaaMahfouz 11, 177 Mahmoud (Zamalek Sporting Club ultra) 16–17 Makati, Manila 204–6 malnutrition 9 Mandelson, Peter 17, 26, 114 Manila 33; Estero de Paco 200–2; Estero de San Miguel 196–99; Makati 204–6; waterways 200–2 manipulated consciousness 29–30 Manufacturing Consent (Chomsky and Herman) 28–29 Mao Tse Tung 46 Marxism 141–45 Marx, Karl 46, 141–45, 174, 187, 188–89, 190, 192 Masai with a mobile, the 133–34 Masoud, Tarek 27 Masry Shebin El-Kom textile factory 22–23 mass culture 29–30 Matrix, The (film) 29 Meadows, Alfie 51 media, the 28–29 @mehri912 34 Meltdown (Mason) 31–32 memes 75, 150–52, 152 Merkel, Angela 96, 98, 99, 112 Michas, Takis 103 Middle East: balance of power 178; Facebook usage 135; failure of specialist to understand 25–27 Milburn, Alan 114 Miliband, Ed 58, 60, 188 Millbank riot 42–44 Millennium Challenge 2002 82–83 Miller, Henry 128 misery 209 mobile telephony 75–76, 133–34 modernism 28 mortgage-backed securities 106–8 Moses, Jonathan 48 Mousavi, Mir-Hossein 33–34 movement without a name 66 Mubarak, Alaa 17–18 Mubarak, Gamal 8, 10, 17–18, 26 Mubarak, Hosni 9, 10, 14, 15, 18–19, 19–20, 26, 31 Murdoch, Rupert 31, 106, 148–49 Muslim Brotherhood 21, 177 NAFTA 166–67 Napoleon III 172, 191 Nasser, Gamal Abdel 19 National Gallery teach-in 53, 53–54 nationalism 124 Native Americans 162, 163 Negri, Toni 42 Netanyahu, Binyamin 180 network animals 147 networked individualism 130, 130–33, 141 networked protests 81–82, 85 networked revolution, the 79–85; erosion of power relations 80–81; informal hierarchies 83; networked protests 81–82; network relationships 81; swarm tactics 82–83 network effect, the 2, 74–75, 77; erosion of power relations 80–81; strength 83; usefulness 84 network relationships 81 Nevins, Allan 182 New Journalism 3 News Corporation 148—49 News of the World 49; phone hacking scandal 61, 148–49 New Unrest, social roots of 65–66, 85; demographics of revolt 66–73; information tools 75–76; the networked revolution 79–85; organizational format 77–78; technology and 74–79; the urban poor 70–72 New York Times 170 1984 (Orwell) 30, 129 Nomadic Hive Manifesto, The 53–54 @norashalaby 13 North Africa: demographics of revolt 66; students and the urban poor 71 Obama, Barack 72, 116–18, 120, 122, 162, 167, 170, 180, 183, 187 OccupiedLondon blog 88–89 Occupy Wall Street movement, the 139, 144, 187, 210 Office for National Statistics 115 Ogden-Nussbaum, Anna, @eponymousthing 184 Oklahoma 153, 153–56 Oldouz84 36, 37 Olives, Monchet 202–4 online popularity 75 On the Jewish Question (Marx) 143 Open Source software 139–40 Operation Cast Lead 33 organizational format, changing forms of 77–78 Organisation of Labour, The (Blanc) 187 organized labour 71–72, 143 Ortiz, Roseangel 161 Orwell, George 30, 129, 208, 210 Owen, Robert 142 Palafox, Felino 204–5 Palamiotou, Anna 97 Palestine 25, 121, 179, 180 Palin, Sarah 181, 182 PAME (Greek trade union) 90 Papaconstantinou, George 91, 97 Papandreou, George 88, 96 Papayiannidis, Antonis 103 Paris 39; 1968 riots 46; revolution of 1848 171, 172 Paris Commune, the 1, 72–73, 84, 132 PASOK 89, 91, 98, 99 Paulson, Hank 110 Petrache, Ruben 203–4 Philippines: Calauan, Laguna Province 202–4; Estero de Paco, Manila 200–2; Estero de San Miguel, Manila 196–99, 205–6, 206–9; Gapan City 193–96; Makati, Manila 204–6; New People’s Army 203 Philippines Housing Development Corporation 198 philosophy 29 phone hacking scandal 61, 148–49 Picasso, Pablo 127, 128, 132 Pimco 170 Poland 172 police car protester (USA) 4 Policy Exchange think tank 55 political mainstream, youth disengagement from 89–90 popular culture 65, 176 Porter, Brett 154, 155, 156 Port Huron Statement, the 129–30, 145 Portugal 92, 112, 188 postmodernism 28 poverty 121–22, 210, 211 Powell, Walter 77 power, refusal to engage with 3 power relations, erosion of 80–81 Procter & Gamble 23 propaganda of the deed 62 property 48 property bubble collapse 106–8 protectionism 124 protest, changing forms of 54–57 pro-Western dictators, support for 31 Prussia 191 Puente 165 Putnam, Robert 134 Quantitative Easing II 120–23 radicalization 33, 37, 47–48 radical journalists 149 Ramírez, Leticia 165 Real Estate Tax Authority Workers (Egypt) 19 Really Free School, the 1–2 @rebeldog_ath 96 reciprocity 77 Reed Elsevier 146 Reider, Dimi 179 Research and Destroy group 38–39 revolt, demographics of 66, 66–73 revolutionary wave 65 revolution, definition 79–80 revolutions: 1848 171–73, 173–75, 191, 192; 1917 173; 1968 173; 1989 173 Reynalds, Jeremy 159–60, 162–63 rice crops 195 Riches, Jessica, @littlemisswilde 41–42, 44, 45, 135–36, 138 Rimbaud, Arthur 132 River Warriors 201 Roads to Freedom (Sartre) 129 Road to Wigan Pier, The (Orwell) 208 Romer, Christina 117 Roosevelt, Franklin D. 169–70 Rove, Karl 30–31, 32 Rowan, Rory 54 Said, Edward 26–27 Said, Khaled 11, 148 @Sandmonkey 13 Sandra (Joy Junction resident) 160 Santa Cruz, University of California 37–39 Sarkozy, Nicolas 91–92, 98 @sarrahsworld 11–12, 14, 135 Sartre, Jean-Paul 129 Saudi Arabia 121 savings, and investment 107 Savio, Mario 4 SB1070 (USA) 164, 165–66, 166–67 self-esteem, and consumption 80–81 self-interest 111 self-reliance 68 self, the, social networks impact on 136–38 Sennett, Richard 68, 80–81, 131 Sentimental Education (Flaubert) 171 el-Shaar, Mahmoud 22 Shafiq, Mohammed 20–22 Shalit, Gilad 179 shared community 84 Sharp, Gene 83 Sharpton, Al 184 Shirky, Clay 138, 139, 140, 146 Sinclair, Cameron 199, 208 Sioras, Dr Ilias 90–91 Situationist movement 46–47 Situationist Taliban 1 slum-dwellers 68; numbers 198 social capital 134 social democracy 145 social housing 199 Socialist International 19–20 social justice 177, 191, 192, 209, 210 social media 7, 74–75, 77; collective mental arena 137; lack of control 37; power of 34–35; role of 56; and the spread of ideas 151 social micro-history 173 social networks 77, 82; impact of 147; impact on activism 138–41; and the self 136–38 social-republicanism 187 solidaristic slum, the 207 Solidarity 42 ‘Solidarity Forever’ (song) 42 Soviet Union 28 Spain 66, 104, 105, 188 Spanish Civil War 209–10 species-being 143 @spitzenprodukte (art activist) 1 spontaneous horizontalists 44–46 spontaneous replication 55 Starbucks Kids 79 Steinbeck, John 153, 155, 159, 163, 164, 169 Stephenson, Paul 52 Stiglitz, Joseph 118 Strategy Guide (Sharp) 83 Strauss-Kahn, Dominique 188 strongman threat, the 177–78 student occupations 37–39, 44–46, 53, 53–54 students: economic attack on 38; expectations 67–68; population 70 Sudan 25 Suez Canal Port Authority 19 Supreme Council of the Armed Forces (SCAF) (Egypt) 18, 20 surveillance 148 swarm tactics 82–83 swine flu epidemic 9 Switzerland 123 syndicalism 175–76 synthesis, lack of 57 Syria 25 tactics 54–57 Tahrir Square, Cairo 6, 69, 89, 139; chants 191, 211; Day of Rage, 28 May 15–17; demonstration, 25 January 10–14; numbers 13; Twitter feeds 13; volunteer medics 20–22 Taine, Hippolyte 73 Tantawi, General 19 Tarnac Nine, the 189 Tea Party, the 117–18, 124–25, 180–81 tear gas 93–94, 100–1 technology 65, 66, 74–79, 85, 133–36, 138–39; and the 1848 revolutions 173–74 Tehran, Twitter Revolution 34–37 teleology 131, 152 Tent City jail, Arizona 164–67 Territorial Support Group 50 Thatcher, Margaret 106 @3arabawy 10, 22, 71 Third Way, the 31 Time magazine 36 Tim (human rights activist) 1–2 Tim (Joy Junction resident) 160 Tocqueville, Alexis de 192 totalitarianism 147–48 toxic debt 110–11 trade wars 122, 124–25 transnational culture 69 Transparency International 119 Trichet, Jean-Claude 112 Truman Show, The (film) 29 trust 57 Tunisia: Army 178; economic growth 119; inflation 121; organized workforce 72; revolution 10, 11, 25–26; unemployment 119 Turkle, Sherry 136 Twitpic 75 Twitter and tweets 3, 74, 137–38; #wiunion 184, 185; @Ghonim 13; @mehri912 34; @norashalaby 13; @rebeldog_ath 96; @Sandmonkey 13; Egyptian revolution 13, 14; importance of 135–36; Iranian revolution and 33–37; Madison, Wisconsin revolt 184; news dissemination 75; real-time organization 75; reciprocity 77; user numbers 135; virtual meetings 45 Twitter Revolution, Iran 33–37, 78, 178 Ukraine 177–78 UK Uncut 54–57, 58, 61 ultra-social relations 138 unemployment: America 159–63; Egypt 119; Spain 105; Tunisia 119; youth 66, 105, 119–20 UN-Habitat 199 Unison 57 United Nations, The Challenge of Slums 198–99 United States of America: agriculture 154–56; Albuquerque 159, 159–63; Arizona 164–67, 183; armed struggle 181–83; Bakersfield, California 168–70; budget cuts 156, 161, 167, 170; California 168–70; campus revolts, 1964 4; Canadian River 159; cattle prices 156; collapse of bipartisan politics 116–19; culture wars 179, 180–84; current-account deficit 107; debt 118; deportations 166; devaluation 123; Dodd–Frank Act 167; the Dust Bowl 154–55; economic decline 183–84; economic growth 170; Federal budget 156, 161; fiscal management 183; fiscal stimulus 117–18; fruit pickers 169; hamburger trade 156; healthcare bill 180, 183; homeless children 160; homelessness 159–63; Indiana 116–17, 125; Interstate 40 157, 170; job market 161; Joy Junction, Albuquerque 159–63; Madison, Wisconsin revolt 184–87; minimum wage workers 158; the Mogollon Rim 163; motels 157–58, 162–63; the New Deal 169–70; Oklahoma 153, 153–56; Phoenix, Arizona 164–67; police car protester 4; political breakdown, 1850s 182–83; property bubble 106–8; Quantitative Easing II 120–23; radical blogosphere 184; the religious right 118; repossessions 168; Route 66 157–59; San Joaquin valley 169; SB1070 164, 165–66, 166–67; State Department 178; states’ rights 183; student occupation movement 37–39; the Tea Party 117–18, 124–25, 180–81, 186; Tent City jail, Arizona 164–67; Tucson, Arizona 182; undocumented migrants 164–67; unemployment 159–63; wages 108; war spending 162; welfare benefits 162, 170 Unite Union 55 university fees 44, 47, 50, 54 urban poor 70–72 urban slums 191; Calauan, Laguna Province 202–4; clearance policies 198–99; education levels 207; Estero de Paco, Manila 200–2; Estero de San Miguel, Manila 196–99, 205–6, 206–9; Gapan City, Philippines 193–96; improvement policies 199, 205–6; internet access 207; labour force 208; living conditions 196–99; Moqattam, Cairo 6–10; population numbers 198 Vail, Theodore 74 Vanderboegh, Mike 181 Van Riper, Lieutenant General Paul 82 Venizelos, Evangelos 97–98 Vietnam War 129 virtual meetings 45 virtual societies 134 Vodafone 54–55 Vradis, Antonis 87–89 wages 108, 112 Walker, Scott 184 Walorski, Jackie 116–17 Walt, Stephen M. 26 war, threat of 178 Warwick University, Economics Conference 67–68 Washington Times 35 Wasim (Masry Shebin El-Kom delegate) 23 water supplies 194 wave creation 78 wealth, monopolization of 108 We Are Social 148 Weeks, Lin, @weeks89 184 Wellman, Barry 130 Wertheim, Margaret 136 White House, the 92 ‘Why the Tunisian revolution won’t spread’ (Walt) 26 WikiLeaks 140 Wikipedia 46, 140 wikis 140–41 #wiunion 184, 185 Wobblies 176 Women’s liberation 132 Woods, Alan 33 Woollard, Edward 43 working class 68, 71–72, 79–80, 145; culture 72; revolutions, 1848 172–73 World of Yesterday, The (Zweig) 128 World Trade Organization 122 Yemen 25, 119, 121 youth 68; alienation 62; British 41–42, 44, 53–54; culture 70; disconnected 190; disengagement from political mainstream 89–90; radicalization 33, 37, 47–48; unemployment 66, 119–20 YouTube 75; Egyptian revolution on 11, 14, 15; Iranian revolution on 34, 35 Zamalek Sporting Club, ultras 16–17 Zapatistas 1 Zekry, Musa 5–6, 7, 23–24 Zola, Emil 191 Zweig, Stefan 128, 132–33, 152, 176 Copyright This revised and updated second edition first published by Verso 2013 First published by Verso 2012 © Paul Mason 2012, 2013 All rights reserved The moral rights of the author have been asserted Verso UK: 6 Meard Street, London W1F 0EG US: 20 Jay Street, Suite 1010, Brooklyn, NY 11201 www.versobooks.com Verso is the imprint of New Left Books ISBN: 978-1-781-68245-6 (e-book) British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress Typeset in Fournier by MJ Gavan, Truro, Cornwall Printed by ScandBook AB, Sweden
Remix: Making Art and Commerce Thrive in the Hybrid Economy by Lawrence Lessig
Amazon Web Services, Andrew Keen, Benjamin Mako Hill, Berlin Wall, Bernie Sanders, Brewster Kahle, Cass Sunstein, collaborative editing, commoditize, disintermediation, don't be evil, Erik Brynjolfsson, Internet Archive, invisible hand, Jeff Bezos, jimmy wales, Joi Ito, Kevin Kelly, Larry Wall, late fees, Mark Shuttleworth, Netflix Prize, Network effects, new economy, optical character recognition, PageRank, peer-to-peer, recommendation engine, revision control, Richard Stallman, Ronald Coase, Saturday Night Live, SETI@home, sharing economy, Silicon Valley, Skype, slashdot, Steve Jobs, The Nature of the Firm, thinkpad, transaction costs, VA Linux, yellow journalism
This is a consequence of network effects: the more who join, the more valuable the resource is for everyone. There are many contexts in which this network effect is true. Think, for example, about the English language. Every time someone in China struggles to learn English or a school in India continues to push English as a primary language, all of us English speakers benefit. But 80706 i-xxiv 001-328 r4nk.indd 153 8/12/08 1:55:26 AM REMI X 154 in neither of these cases—with AOL or English—are people joining the movement because it is a movement. People join because it gives them something they want. In each case, there is a resource that is shared among everyone within the community—information about the market, computer resources to make VOIP work better, the network effect from a popular network.
There’s a trust-building exercise there that doesn’t traditionally happen, because companies are inherently private because historically, competition was the first order of concern of companies. Therefore privacy [or secrecy], for the purpose of giving you a leg up on your competitors, has always been a kind of a central building block of corporate behavior. And, in a world where openness and network effects are likely to decide the winner, you now have to break down that perception. You want to build a company whose first value is not privacy but, instead, disclosure. Some companies go even further. Brewster Kahle describes the decision of the search company Alexa. [W]e wanted to build a new-generation search engine, which is sort of what Alexa and the Internet Archive strove to do in ’96. It turns out that we were wrong, that the world didn’t need a fundamentally different kind of search engine, because the search engines were going along okay.
And then that repository became the CDDB.” 80706 i-xxiv 001-328 r4nk.indd 237 8/12/08 1:55:57 AM 238 REMI X All this typing was done voluntarily. People wanted their machines to know what the tracks were; they were happy to share with others the information they typed into their machines. And Kan and Scherf built the tools to aggregate the results of this voluntary work “with the best intentions in the world. They were not trying to make any money off of it. They really wanted to make just a social network and get all the network effects.” They built a commons for others to add to; volunteers demonstrated the “grace of the commons” through the contributions they made. But, Marglin explained to me, as more and more people began to rely upon this database to identify their CDs, Kan and Scherf began to realize “very quickly that they had a beast on their hands because: one, in order to be any good, the software would have to do a lot more reconciling than they first expected . . . and two, the amount of server space that they would need in order to take in, not only all the lookups, but . . . all the submissions, was just going to overwhelm them.”21 So the founders of CDDB started looking for a way to make sure their creation would survive.
The Great Reversal: How America Gave Up on Free Markets by Thomas Philippon
airline deregulation, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, barriers to entry, bitcoin, blockchain, business cycle, business process, buy and hold, Carmen Reinhart, carried interest, central bank independence, commoditize, crack epidemic, cross-subsidies, disruptive innovation, Donald Trump, Erik Brynjolfsson, eurozone crisis, financial deregulation, financial innovation, financial intermediation, gig economy, income inequality, income per capita, index fund, intangible asset, inventory management, Jean Tirole, Jeff Bezos, Kenneth Rogoff, labor-force participation, law of one price, liquidity trap, low cost airline, manufacturing employment, Mark Zuckerberg, market bubble, minimum wage unemployment, money market fund, moral hazard, natural language processing, Network effects, new economy, offshore financial centre, Pareto efficiency, patent troll, Paul Samuelson, price discrimination, profit maximization, purchasing power parity, QWERTY keyboard, rent-seeking, ride hailing / ride sharing, risk-adjusted returns, Robert Bork, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, Snapchat, spinning jenny, statistical model, Steve Jobs, supply-chain management, Telecommunications Act of 1996, The Chicago School, the payments system, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, transaction costs, Travis Kalanick, Vilfredo Pareto, zero-sum game
In fact, they argue that this is how they became leaders in the first place. The attractive feature of a theory of intangible assets is that it can explain concentration both through increasing productivity (superstar firms) and through decreasing domestic competition since intangible assets can create barriers to entry. To test this idea, we will look carefully at intangible investments across firms and industries in Chapter 5. Network effects and increasing differences in the productivity of information technology could also increase the efficient scale of operation of the top firms, leading to higher concentration. Persistence of Market Shares Economists who specialize in industrial organization and antitrust rightfully complain about the use of industry-level HHIs to measure concentration. Indeed, we have pointed out the limitations of HHI in Chapter 2 and discussed cases where they can lead to misleading conclusions about the state of an industry.
But the details vary, and this is what makes these industries interesting. Finance will teach us that efficiency and complexity are not the same thing and that deregulation is easier said than done. Health care will teach us how oligopolies can spread from one side of an industry to another. Finally, the internet giants are particularly relevant because they are often presented as examples of efficient concentration driven by “network” effects. While there is some truth to this argument, it is widely overrated. The data will also teach us that the stars of today may be no match for the stars of yesterday. This sounds like fun, at least to me … but then again, I am an economist. CHAPTER 11 Why Are Bankers Paid So Much? I would rather see Finance less proud and Industry more content. WINSTON CHURCHILL, 1925 FINANCE IS THE one industry that (almost) everyone loves to hate.
They want either to protect a privilege they already have or to convince policy makers to bestow one that they don’t have yet. The other two reasons are related to costs they hope to avoid. Companies lobby to convince policy makers to lift an existing burden or to prevent them from imposing a new one. We’re going to start with an issue that is relevant to all corporations: taxes. Then we are going to focus on one particular benefit that tech companies currently enjoy: massive concentration and network effects. We’ll also consider one burden they are anxious to avoid: new privacy protections for their users. Do the GAFAMs Pay Their Taxes? No, the GAFAMs don’t really pay their fair share of taxes, but to be honest, neither do the other global firms. All top companies have been paying fewer taxes over time. Figure 14.2 shows the taxes paid by large firms relative to their operating income. Effective corporate tax rates have decreased over time.
The Facebook Effect by David Kirkpatrick
Andy Kessler, Burning Man, delayed gratification, demand response, don't be evil, global village, happiness index / gross national happiness, Howard Rheingold, Jeff Bezos, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Network effects, Peter Thiel, rolodex, Sand Hill Road, sharing economy, Silicon Valley, Silicon Valley startup, Skype, social graph, social software, social web, Startup school, Steve Ballmer, Steve Jobs, Stewart Brand, the payments system, The Wealth of Nations by Adam Smith, Whole Earth Review, winner-take-all economy, Y Combinator
He started saying that the goal of Thefacebook was “to help people understand the world around them.” They loved to talk about how Thefacebook showed what economists call “network effects.” And it did, just as have many of the great communications and software innovations of the last hundred years. A product or service is said to have a network effect when its value grows greater to all users each time one new user joins. Since every incremental user thus in effect strengthens the service, growth tends to lead to more growth, in a virtuous cycle. That was surely the case with Thefacebook, just as it was with instant messaging, AOL, the Internet itself, and even the telephone. Businesses or technologies with network effects tend to grow steadily and to have a durable market presence. While they wanted working at Thefacebook to be seen as cool—that helped in recruiting—the product was another matter.
It’s been a joke around the Facebook offices for years that the company seeks “total domination.” But the reason it’s funny is that it evokes a surprising truth. Zuckerberg realized a long time ago that most users are not going to take the time to create multiple profiles for themselves on multiple social networks. He also knew from his endless bull sessions at Harvard and in Palo Alto about “network effects” that once consolidation begins on a communications platform it can accelerate and become a winner-take-all market. People will join and use the communications tool that the largest number of other people already use. He therefore made it a goal to create a tool not for the United States but for the world. The objective was to overwhelm all other social networks wherever they are—to win their users and become the de facto standard.
The software service called TweetDeck enables this already, among others. Just two days after the Stream API announcement, I had dinner in New York with Sean Parker, who spent a good portion of our time together that night denouncing it. “It’s the greatest strategic gamble the company has ever made and will ever make,” he said in his intense rapid cadence. “Opening the stream to the world has the possibility of breaking the company’s network effect. As a closed network the switching costs are incredibly high and everybody’s forced to play in Facebook’s sandbox. But when you open the stream to the world you open the possibility of better Facebook clients that can process all the same data that Facebook itself can.” These words were still ringing in my ears a week later when I sat alone with Zuckerberg for a long interview in a corner conference room near his desk in Palo Alto.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee
"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, global village, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K
Networks and Standards: The Value of Scale Thirdly, the increased importance of networks (like the Internet or credit card networks) and interoperable products (like computer components) can also create winner-take-all markets. Just as low marginal costs create economies of scale on the production side, networks can create ‘demand side economies of scale’ that economists sometimes call network effects. We see them at work when users prefer products or services that other people are flocking to. If your friends keep in touch via Facebook, that makes Facebook more attractive to you, too. If you then join Facebook, the site becomes more valuable to your friends as well. Sometimes network effects are indirect. You can make a phone call equally well to someone using an iPhone or an Android phone. But the total number of users on a given platform influences app developers: the bigger network of users will tend to attract more developers, or encourage app developers to invest more in a given platform.
Its servers use the map, these updates, and a set of sophisticated algorithms to generate driving directions. If Andy wants to drive to Erik’s at 8:45 a.m. on a Tuesday, Waze is not going to put him on the highway. It’s going to keep him on surface streets where traffic is comparatively light at that hour. That Waze gets more useful to all of its members as it gets more members is a classic example of what economists call a network effect—a situation where the value of a resource for each of its users increases with each additional user. And the number of Wazers, as they’re called, is increasing quickly. In July of 2012 the company reported that it had doubled its user base to twenty million people in the previous six months.3 This community had collectively driven more than 3.2 billion miles and had typed in many thousands of updates about accidents, sudden traffic jams, police speed traps, road closings, new freeway exits and entrances, cheap gas, and other items of interest to their fellow drivers.
The more apps available for a given phone, the greater its appeal to users. Thus, your benefits from buying one or the other will be affected by the number of other users who buy the same product. When Apple’s app ecosystem is strong, buyers will want to buy into that platform, attracting even more developers. But the opposite dynamic can unravel a dominant standard, as it almost did for the Apple Macintosh platform in the mid-1990s. Like low marginal costs, network effects can create both winner-take-all markets and high turbulence.18 The Social Acceptability of Superstars In addition to the technical changes that have increased digitization, telecommunication, networks, and other factors that create superstar products and companies, there are more aspects at work in boosting superstar compensation for individuals. In some cases, cultural barriers to very large pay packages have fallen.
The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions...and Created Plenty of Controversy by Leigh Gallagher
Airbnb, Amazon Web Services, barriers to entry, Ben Horowitz, Bernie Sanders, cloud computing, crowdsourcing, don't be evil, Donald Trump, East Village, Elon Musk, housing crisis, iterative process, Jeff Bezos, Jony Ive, Justin.tv, Lyft, Marc Andreessen, Mark Zuckerberg, medical residency, Menlo Park, Network effects, Paul Buchheit, Paul Graham, performance metric, Peter Thiel, RFID, Sam Altman, Sand Hill Road, Saturday Night Live, sharing economy, side project, Silicon Valley, Silicon Valley startup, South of Market, San Francisco, Startup school, Steve Jobs, TaskRabbit, the payments system, Tony Hsieh, Travis Kalanick, uber lyft, Y Combinator, yield management
The 3 percent host-booking fee basically covers payment processing only; if anything, Airbnb subsidizes hosts with not just the fee but also its free-professional-photography policy and many other forms of coddling, from mailing out free mugs to featuring stories about some of the hosts on its website to flying certain hosts to its occasional launch events and annual conventions. Airbnb’s business is fundamentally about leveraging a network effect: the more people who list on Airbnb, the more inherently attractive the platform becomes to anyone who wants to travel, because there are more choices; and the more people who travel, the more appealing it becomes for people to list on it, because there are more customers. In Airbnb’s case, because its product is travel and the very act of using it involves moving from point A to point B, it is a global network effect enabled by fast and cheap cross-pollination: when a traveler from France uses Airbnb in New York, he or she is more likely to go back to France and consider hosting, or to talk up the company to his or her friends, sparking awareness and ultimately leading to more listing activity in that market.
But when they tried to open the site, they realized it had crashed—and they hadn’t brought their slide deck. “It was mostly us staring at each other for an hour,” Chesky later said. Maples did not invest. The founders had another problem leading up to the DNC, which was supply: no one wanted to list his or her home if no one else was going to book it; and with few homes listed, no one would use the site. They weren’t going to be able to get off the ground, let alone trigger any kind of “network effect,” where the more people use something, the more valuable it becomes—leading even more people to use it. Their preliminary outreach showed them people either didn’t want to rent their homes or thought they were being asked to participate in some kind of weird social experiment. Chesky may not always have known what angels or slide decks were, but he and his cofounders always had a very good instinct for using the media, and, much like that first October weekend, they knew that success or failure lay in their ability to drum up news coverage.
These two points are often continents away from each other, yet new markets are seeded quickly, cheaply, and organically, without staffers or teams ever having to set foot in them. This is a big difference between Airbnb and, say, Uber, which has to physically launch each new market with a heavy investment of fresh marketing, employees, and other resources. The vast majority of Airbnb’s growth, both travelers and listings, has come through these travel patterns and this global network effect. You can look at Airbnb’s size and scale in a number of ways. The easiest is those 140 million “guest arrivals” since its inception. Its 3 million active listings—80 percent of which are outside North America—makes Airbnb the largest provider of accommodations in the world, bigger than any hotel chain. (With its acquisition of Starwood, Marriott International has the largest inventory of any hotel company, with 1.1 million rooms.)
The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing by Michael J. Mauboussin
Amazon Mechanical Turk, Atul Gawande, Benoit Mandelbrot, Black Swan, Checklist Manifesto, Clayton Christensen, cognitive bias, commoditize, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, disruptive innovation, Emanuel Derman, fundamental attribution error, Gini coefficient, hindsight bias, hiring and firing, income inequality, Innovator's Dilemma, Long Term Capital Management, loss aversion, Menlo Park, mental accounting, moral hazard, Network effects, prisoner's dilemma, random walk, Richard Thaler, risk-adjusted returns, shareholder value, Simon Singh, six sigma, Steven Pinker, transaction costs, winner-take-all economy, zero-sum game, Zipf's Law
The rich get richer, and the poor get poorer.16 The effect of increasing returns is especially pronounced when it is accompanied by high up-front costs followed by low incremental costs and by network effects, where the value of a product or service increases as more people use it. Microsoft's strength in personal computer operating systems neatly demonstrates high up-front costs, network effects, and increasing returns. The first version of Microsoft Windows had a high up-front cost. It took substantial time and money to develop. But the cost of creating additional copies was extremely low—the price of a floppy disk, which was how software was distributed at the time. Network effects were strong for operating systems and related computer programs, because the de facto standard ensured that people could exchange files. As the standard grew in popularity, computer programmers were encouraged to develop new software for Windows, which made it even more popular.
As simple as this example appears, it can work with assumptions that are only modestly more realistic as a model for the adoption of innovations (the iPad), for fads (South Beach Diet), for fashions (yoga clothes), and for the spread of disease (the flu). Once an innovation reaches a certain level of popularity, its success is virtually assured. By the same token, great innovations can fail because the domino effect doesn't kick in.15 In economics, the lopsided outcomes are frequently the result of increasing returns and network effects. Much of conventional economic theory is based on diminishing returns. If the demand for a commodity exceeds supply, prices will rise, and whoever makes that product will earn more money. Those high profits will attract competitors, who will increase production and effectively push prices back down. This is called negative feedback, a mechanism that promotes stability. The strong get weaker and the weak get stronger.
As the standard grew in popularity, computer programmers were encouraged to develop new software for Windows, which made it even more popular. People wanted a product that was widely used because that made it more valuable to them. That resulted in what's known as demand-side economies of scale, where more value for the consumer leads to more demand for the software and, ultimately, more profit for the producer. Add together the economics of software and network effects, and the result is increasing returns. Profitability soars for the winner, creating a level of success that is all out of proportion to the differences between the various products when competition began. The point of reviewing these mechanisms is to show that they are sensitive to initial conditions, frequently undergo critical transitions, and can lead to skewed results. Skill doesn't carry the day, luck does.
Ours to Hack and to Own: The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet by Trebor Scholz, Nathan Schneider
1960s counterculture, activist fund / activist shareholder / activist investor, Airbnb, Amazon Mechanical Turk, barriers to entry, basic income, bitcoin, blockchain, Build a better mousetrap, Burning Man, capital controls, citizen journalism, collaborative economy, collaborative editing, collective bargaining, commoditize, conceptual framework, crowdsourcing, cryptocurrency, Debian, deskilling, disintermediation, distributed ledger, Ethereum, ethereum blockchain, future of work, gig economy, Google bus, hiring and firing, income inequality, information asymmetry, Internet of things, Jacob Appelbaum, Jeff Bezos, job automation, Julian Assange, Kickstarter, lake wobegon effect, low skilled workers, Lyft, Mark Zuckerberg, means of production, minimum viable product, moral hazard, Network effects, new economy, offshore financial centre, openstreetmap, peer-to-peer, post-work, profit maximization, race to the bottom, ride hailing / ride sharing, SETI@home, shareholder value, sharing economy, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, Snapchat, TaskRabbit, technoutopianism, transaction costs, Travis Kalanick, Uber for X, uber lyft, union organizing, universal basic income, Whole Earth Catalog, WikiLeaks, women in the workforce, Zipcar
Emilia-Romagna, an area in Italy that encouraged employee ownership, consumer cooperatives, and agricultural co-ops, has lower unemployment than other regions in Italy. The flagship of cooperatives, Mondragon, is a network of co-ops that employed 74,061 people in 2013. But in the United States, despite its dominance in areas like orange juice production, the cooperative model has been faced with many challenges, including competition with multinational corporate giants, public awareness, self-exploitation, and the network effect. So, it is essential for platform co-ops to study the communities they’d like to serve and get their value proposition right. In opposition to the black-box systems of the Snowden-era Internet, these platforms need to distinguish themselves by making their data flows transparent. They need to show where the data about customers and workers are stored, to whom they are sold, and for what purpose.
To fund such work, publishers use legal and technical restrictions to make access exclusive to those who pay. Funding is necessary, but restrictions have terrible side-effects, including blocking sharing, discouraging derivative work, and excluding people—ultimately, limiting the work’s value. Snowdrift.coop is developing a platform to fund creative projects without artificial restrictions such as those listed above. Our matching pledge creates a network effect: each patron’s monthly donation to their favorite projects is based on others joining them, such as a pledge of $1 for every 1,000 patrons. This flexible approach minimizes risk and maximizes collective impact. As a multi-stakeholder co-op, we propose three member classes: the worker class made of employees of the platform itself; the project class made up of those funding their creative work; the general class made up of users who only donate.
But if one thinks about it, today’s sharing-economy platforms do exhibit some characteristics in common with Sunkist, and a worker-owned equivalent to Lyft and Uber seems quite feasible. Point-to-point urban transportation is a fairly uniform service in an industry with a limited amount of competition. Once the technology associated with “e-hail” and logistics is commoditized, which it will be, the economic fundamentals for the emergence of a platform cooperative would appear to be in place. More important, the network effects associated with ridesharing are geographically concentrated. Thus, unlike platforms such as eBay and Facebook, the barriers to entry posed by an incumbent platform may not be onerous. True, passengers gravitate toward the platforms with more drivers, and vice versa. However, these effects are localized. Most potential passengers in New York care little about the scale of a platform in Los Angeles or Minneapolis.
Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman
23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, commoditize, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, drone strike, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, lifelogging, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta analysis, meta-analysis, Minecraft, move fast and break things, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social intelligence, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar
It made sense to erect the scaffolding of a traditional, profile-based social network around that. Google+ offered a familiar shape and central input for the kind of scattered but all-encompassing data collection that Google had been doing for years. Both aesthetically and in terms of privacy features (its “Circles,” which allowed users to tailor their updates to narrow audiences, was soon copied by Facebook), Google+ seemed a worthy social network on its own. But Facebook had network effects on its side—who would join Google+ if it seemed like an also-ran where nobody wanted to hang out? Google then had a challenge: how to get the hundreds of millions of people using its other products, like Search, Gmail, Docs, and YouTube, to embrace Google+. And it was a problem they desperately needed to solve, since Facebook’s huge, active membership and its use of Like buttons and other tracking tools meant that it had a glut of information that advertisers wanted.
As the artist Fatima Al Qadiri has said, “There’s no such thing at the most recent update. It immediately becomes obsolete.” Why, then, do we do it? If it’s so easy to become cynical about social media, to see amid the occasionally illuminating exchanges or the harvesting of interesting links (which themselves come in bunches, in great indigestible numbers of browser tabs) that we are part of an unconquerable system, why go on? One answer is that it’s a by-product of the network effect: the more people who are part of a network, the more one’s experience can seem impoverished by being left out. Everyone else is doing it. A billion people on Facebook, hundreds of millions scattered between these other networks—who wants to be on the outside? Who wants to miss a birthday, a friend’s big news, a chance to sign up for Spotify, or the latest bit of juicy social intelligence? And once you’ve joined, the updates begin to flow, the small endorphin boosts of likes and repins becoming the meager rewards for all that work.
Twitter is work, Facebook is work. Words are being written, content produced and shared, ads sold against it. A welter of data, some of it structured by us, is produced, and this has value. Yes, this work is often voluntary. You put in what you want, and if you don’t like that Facebook is profiting off of your relationships and communication with friends and your very identity, then you can quit. But the flip side of network effects—of a network rising in value and utility as more people join it—is that there can be a real social cost to opting out. And professional cost, too. Engagement in social media and other digital products has become required for many white-collar jobs, representing another way in which the work/life divide is broken down. The digital work of producing for Tumblr or Pinterest, then, becomes part of the work for producing for one’s day job.
The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski
Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, connected car, crowdsourcing, data acquisition, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, lifelogging, Metcalfe’s law, Network effects, Paul Graham, Ray Kurzweil, RFID, Robert Metcalfe, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, web application, Y Combinator, yield management
The last wire to disappear is the power cable — driven by advances in wireless power, energy harvesting, and power management. Finally, there is another important factor, which is the network effect. This tends to show exponential growth once you have a critical mass of intelligent items and connected items that are talking to one another. The law that affects this development is Metcalfe’s Law,32 which states that the value of a network is proportional to the square of the number of connected users of the system. This law is applicable not only to telecommunications networks and social networks, but also to connected things, like M2M nodes. Assuming such a network effect will take place, one can assume it will create huge value for the ecosystem. What this means for investors is that we are about to see an explosion of connected things and an explosion of data being generated.
This market is still highly fragmented and allows for high margins for system integrators, making many business cases less attractive than they could be. This market always reminds us of the mobile phone industry in the early days and the fragmentation issues that there were for a number of years. If a couple of players are able to design application platforms that allow for easy and cost-effective development and deployment of applications, these platforms would be able to benefit from massive network effects, like the ones we have seen around mobile platforms starting in 2006. Also, data analysis and workflow optimization will be an interesting growth market going forward. Because these markets are still immature at this point, companies that are able to become central market players will have a very attractive position. The last segment to be mentioned is end-to-end solutions providers. Being able to take the complexity out of solution design and become a comprehensive one-stop shop will be an attractive proposition for technology companies and system integrators.
The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah
accounting loophole / creative accounting, Ada Lovelace, Airbnb, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, Ben Bernanke: helicopter money, bitcoin, blockchain, Bretton Woods, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, David Graeber, deskilling, Diane Coyle, discrete time, disruptive innovation, distributed ledger, diversification, double entry bookkeeping, Ethereum, ethereum blockchain, fiat currency, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, large denomination, liquidity trap, London Whale, low skilled workers, M-Pesa, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, MITM: man-in-the-middle, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative ﬁnance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, Satoshi Nakamoto, Satyajit Das, savings glut, seigniorage, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Von Neumann architecture, Washington Consensus
The company is responsible for moving millions of dollars in investment and does so with a team of only 22 personnel. Almost every operation including fundraising, recruiting, engineering, systems operations, product development, customer service, marketing, inbound deal processing, and deal closing is done in code. Of the 22 personnel, 16 are coders or have coding experience. (Slayton, 2014). As digitization leads to massive increases in efficiency and network effects, markets are getting more Schumpeterian in the sense that those market players who are capable of digitizing business models are capable of overtaking existing incumbents and gaining larger portions of market share. In The Wealth of Nations, Adam Smith famously said that the division of labor is limited by the extent of the market. Thus, as digitization overcomes the physical barriers to access markets, we notice that it is creating room for specializations that address granular subdivisions in existing and niche markets.
Furthermore, there are interoperability issues between banks and clients: banks require a complete view of a company’s transaction flow to provide value services at key points of the value chain. However, as a large number of the processes are still manual in nature, there are a spate of platforms that provide individual solutions but with low interoperability. This lack of interoperability reduces transparency, creates a higher risk of fraud and higher fees, and does not allow for the development of network effects that can transform the industry. In order to respond to these limitations, an increasing number of institutions have opted for the use of open account transactions, where the exporter supplies the goods and the importer pays for them on reception or based on pre-agreed payment conditions. In recent times, open trade accounting has become increasingly popular and currently makes up 90% of global trade (Euro Banking Association, 2016).
This event has to be proved by the electronic reconciliation of data produced by the Swift TSU (trade services utility). 5 essDOCS is a UK-based trade services company that provides paperless trade documentation services, such as Electronic Bills of Lading (eB/Ls), Electronic Barge Nominations & Documents, Bank Payment Obligations plus (BPO+), eDocs, eDocumentary Collections, Electronic Bunker Receipts, etc. 6 SWIFT's Trade for Corporates, the MT798, offers corporates the use of established interbank industry standards in trade finance through structured messages. 4 99 Chapter 3 ■ Innovating Capitalism In spite of these changes, the complexity of regulation from border to border has meant that the interoperability problem still remains an issue and large-scale network effects remain elusive. Banks have adapted by providing online portals which allows their clients to replace paper documentation flows with digital data flows and provide added value services such as invoice matching, forecasting, and balance sheet and cash flow analysis. But even these changes have been insufficient in addressing the problem. The existence of this prevailing setback has resulted in new entrants trying to find solutions, which in turn has increased competition.
How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time by George Berkowski
Airbnb, Amazon Web Services, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, disruptive innovation, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Marc Andreessen, Mark Zuckerberg, minimum viable product, MITM: man-in-the-middle, move fast and break things, move fast and break things, Network effects, Oculus Rift, Paul Graham, QR code, Ruby on Rails, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software as a service, software is eating the world, Steve Jobs, Steven Levy, Travis Kalanick, ubercab, Y Combinator
Chapter 31 Killer Product Expansion When the best mobile-app companies scale, their products do not stand still. These companies constantly seek a new way to delight users more, become even more sticky or launch inventive ways to make it easier for users to spend more money. At scale, the product challenge is slightly different. It’s not just about launching ‘simple’ features. Real product innovation involves taking advantage of the scale you already have, of leveraging network effects to become even more powerful. Network effects are massive: passengers use Uber because it has more cars than the competitors; drivers work for Uber because it has more passengers than the other apps. It’s a system that feeds off itself, one that reinforces itself. Merchants use the Square register app because it’s simple, beautiful and gives them the ability to process credit cards. But now customers use the Square Wallet app to pay in a store because increasing numbers of merchants use the register app that enables Square Wallet payments.
According to Harvard University professors Diana Tamir and Jason Mitchell, sharing information about ourselves is intrinsically rewarding and gives us a few squirts of dopamine every time we do it.14 When coming up with your big idea or big problem to solve, think about whether it is inherently social, or whether it could be made social, thus rendering it a lot more disruptive and a lot more powerful. People love to share rich content – such as photos, news and magazine articles – and this builds very strong network effects. A great example is Instagram. One of the main ways it drove growth from the very first day was by allowing users to simultaneously share their photos on Facebook and Twitter from the moment the photo was taken using the Instagram app. This massively increased the reach of the new app to big social networks – with very compelling photo content. This promotion of the Instagram app on Twitter and Facebook led people to its website to download the app.
But it did have a basic user registration function, allowed you to add friends, and also allowed you to send messages to those friends that disappeared after 10 seconds. The app was very much an MVP, a minimum viable product. They invited a bunch of college and high school students to use it, and they invited their friends. Their user-acquisition metric was self-sustaining (because of the inherent network effect of users inviting their friends). Their user activation (effectively creating an account) converted at close to 100 per cent because it was a super-simple registration (just a user name and password). Their retention started growing as users began messaging one another and sending snaps (or photos), and their referral metric was off the charts because it was baked into the app (the app works better when you invite more friends to it).
Digital Disconnect: How Capitalism Is Turning the Internet Against Democracy by Robert W. McChesney
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, Albert Einstein, American Legislative Exchange Council, American Society of Civil Engineers: Report Card, Automated Insights, barriers to entry, Berlin Wall, business cycle, Cass Sunstein, citizen journalism, cloud computing, collaborative consumption, collective bargaining, creative destruction, crony capitalism, David Brooks, death of newspapers, declining real wages, Double Irish / Dutch Sandwich, Erik Brynjolfsson, failed state, Filter Bubble, full employment, future of journalism, George Gilder, Gini coefficient, Google Earth, income inequality, informal economy, intangible asset, invention of agriculture, invisible hand, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Julian Assange, Kickstarter, Mark Zuckerberg, Marshall McLuhan, means of production, Metcalfe’s law, mutually assured destruction, national security letter, Nelson Mandela, Network effects, new economy, New Journalism, Nicholas Carr, Occupy movement, offshore financial centre, patent troll, Peter Thiel, plutocrats, Plutocrats, post scarcity, price mechanism, profit maximization, profit motive, QWERTY keyboard, Ralph Nader, Richard Stallman, road to serfdom, Robert Metcalfe, Saturday Night Live, sentiment analysis, Silicon Valley, single-payer health, Skype, spectrum auction, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, Stewart Brand, Telecommunications Act of 1996, the medium is the message, The Spirit Level, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, transfer pricing, Upton Sinclair, WikiLeaks, winner-take-all economy, yellow journalism
First and most important, the Internet exhibits what economists term network effects, meaning that just about everyone gains by sharing use of a single service or resource. Information networks, in particular, generate demand-side economies of scale, related to the capture of customers, as opposed to supply-side economies of scale (prevalent in traditional oligopolistic industry) related to reduction in costs as scale goes up.11 The largest firm in an industry increases its attractiveness to consumers by an order of magnitude as it gets a greater market share, and makes it almost impossible for competitors with declining shares to remain attractive or competitive. Wired’s Anderson puts the matter succinctly: “Monopolies are actually even more likely in highly networked markets like the online world. The dark side of network effects is that rich nodes get richer.
The dark side of network effects is that rich nodes get richer. Metcalfe’s law, which states that the value of a network increases in proportion to the square of connections, creates winner-take-all markets, where the gap between the number one and number two players is typically large and growing.”12 Bob Metcalfe, inventor of the Ethernet protocol that wires computers together, regarded the network effect as so prevalent that he formulated the law that goes by his name: the usefulness of a network increases at an accelerating rate as you add each new person to it.13 Google search is an example; the quality of its algorithm improves with more users, leaving other search engines with a less effective and attractive product. Consider Facebook, which during 2012 exceeded one billion users worldwide. “Those who sign up (and it’s free) have access to a wider circle.
The needs of advertisers drive the entire process.84 The key to commercial success is producing an immense amount of material inexpensively; the leading content farms can generate thousands of pieces of text and video on a daily basis.85 Pulse has emerged as one of the leading commercial news apps, with 13 million smartphone users who get it for free. Pulse aggregates other firms’ news and makes its money working with advertisers and merchants. It is moving into “branded-content advertising,” by which ads get slotted next to appropriate stories for individualized users. The outstanding question is whether Pulse will generate a workable business model and then can establish a monopoly position due to its scale and network effects, like Twitter. By 2012 it moved aggressively to provide local news—with the ability to place advertising in real time that addresses one’s exact location—and become a global operation; the service is already available in eight languages. Pulse does not generate original news, and its founders concede that they don’t know much about journalism.86 Nor do any of the other mobile aggregators generate any original journalism,87 but some of their revenues will probably end up in the hands of other news media and may eventually contribute to paying actual journalists.
Hustle and Gig: Struggling and Surviving in the Sharing Economy by Alexandrea J. Ravenelle
"side hustle", active transport: walking or cycling, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, barriers to entry, basic income, Broken windows theory, call centre, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Clayton Christensen, clean water, collaborative consumption, collective bargaining, creative destruction, crowdsourcing, disruptive innovation, Downton Abbey, East Village, Erik Brynjolfsson, full employment, future of work, gig economy, Howard Zinn, income inequality, informal economy, job automation, low skilled workers, Lyft, minimum wage unemployment, Mitch Kapor, Network effects, new economy, New Urbanism, obamacare, Panopticon Jeremy Bentham, passive income, peer-to-peer, peer-to-peer model, performance metric, precariat, rent control, ride hailing / ride sharing, Ronald Reagan, sharing economy, Silicon Valley, strikebreaker, TaskRabbit, telemarketer, the payments system, Tim Cook: Apple, transaction costs, Travis Kalanick, Triangle Shirtwaist Factory, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, Upton Sinclair, urban planning, very high income, white flight, working poor, Zipcar
That doesn’t seem like an issue unless you’re invested in a fax machine company or are wedded to your fax, for whatever reason. When other people stop using faxes, your fax machine stops being useful. McAfee and Brynjolfsson note that “economics of network effects are central to understanding business success in the digital world,” and they use the example of WhatsApp to illustrate network effects. They explain that as WhatsApp became more popular, users of regular text messages (SMS) felt left out and increasingly turned to the app: “As more and more of them did this, the network effects grew stronger. Computer pioneer Mitch Kapor observed that ‘architecture is politics.’ With platforms, it’s also economics.”29 But the idea that, for platforms, architecture can be economics might not be a good thing. As these platforms grow in size and become the “go-to spot” for everything from furniture assembly to taxis to hotel rooms, we run the risk of creating monopolies.
Or the proliferation of outsourcing—of hiring others to do everything from walking dogs to cleaning homes to grocery shopping and chauffeuring—may further increase the “commodification of intimate life” and lead to additional pressure to make enough to pay for market services.28 Hiring workers off of platforms risks creating platform monopolies. As noted by Andrew McAfee and Erik Brynjolfsson, when more and more people use a platform or tool, a “network effect” arises, which is economist speak for the idea that certain goods become more valuable as more and more people use them. The most frequently given example is that of a fax machine. If only one person has a fax machine, it’s not very useful. But as more and more people get fax machines, the tools become increasingly useful. One tool gives you access to many people. Eventually the tools are so prevalent than even spammers use them for sending scam offers.
See also criminal activity legislative issues, 51–53, 52fig. 8 Lepore, Jill, 208 Levin, Sam, 123 Levin, Stephen, 51 licensing requirements, 2, 222–23n64 LinkedIn, 30, 114, 170 low capital-barriers, 42–43, 43tab. 1 Lowell, Massachusetts, 66–67, 70 low skill-barriers, 42–43, 43tab. 1, 160 low-skill work, 41 Ludlow Massacre, 69 Lyft: bathroom use, 88; comparison to, 32–33, 75, 185; competition with, 78; criminal activity and, 143–47; employee monitoring, 204; general liability insurance, 110; high capital-barrier, 43tab. 1, 167; lawsuits by workers against, 38; low skill-barrier, 43tab. 1; LyftLine, 105; Lyft worker, 2–3; payment rate changes, 75; Peers.com and, 72; safety issues, 101–4, 113; as sharing economy company, 26; start-up expenses, 2; Uber comparison, 33; usage by race, 194; worker-client sexual interactions, 133 Lyman, Stanford, 124 MacArthur Foundation, 62, 224n1 makerspaces, 9, 27, 31 Makespace, 190 Managed by Q, 190, 207 manual labor, 41 marketing: Airbnb, 160; image and, 30; by Kitchensurfing, 57; Kitchensurfing, 160; Kitchensurfing and, 163–64; as peer-to-peer connection, 21; of self, 181 marketplace model: Kitchensurfing as, 57, 58; Upwork (oDesk/Elance-oDesk), 204 Mayo, Elton, 178 McAfee, Andrew, 17, 162, 186 McDonald’s, 88, 106, 185, 208 McKinsey Global Institute, 194 Meat Inspection Act of 1906, 93 Mechanical Turk, 73 Meelen, Toon, 28fig. 2 #MeToo movement, 23 Migrant and Seasonal Agricultural Worker Protection Act, 196 millennials: defined, 219n14; Great Recession and, 10; sharing economy and, 23–24; technology and, 10; unemployment rate, 10 Minieri, Alexandra, 127 minimum wage, 38, 71, 225n23 mining industry, 68–69 Mohrer, Josh, 184 muckrakers, 93 multiple jobs: overview, 3, 8, 15–16; effects of, 14; statistics on, 176 Munchery, 110, 190 Muscarella, Chris, 58 Muslims, discrimination against, 170 MyClean, 109, 172, 189–90, 191, 192, 207 National Guard, 69 National Labor Relations Act of 1935. See Wagner Act of 1935 National Labor Relations Board, 70, 89 Neighborgoods, 26 Neighborrow, 26 network effect, 17 Newcomer, Eric, 73 New Deal, 92, 227n8 Newmark, Craig, 26 New York Communities for Change and Real Affordability for All, 41 New York Times, 39–40, 69 Nickel and Dimed: On (Not) Getting By in America (Ehrenreich), 24, 91 Nnaemeka, C.Z., 231n4 Norén, Laura, 228n30 Novogratz, Michael, 76 Occupational Safety and Health Administration: overview, 22; driver homicide victim rate, 101; report on job injuries, 36 oDesk, 204 Omidyar, Pierre, 26 on-call services, 55 on-call taxi service, 26.
Bitcoin for the Befuddled by Conrad Barski
Airbnb, AltaVista, altcoin, bitcoin, blockchain, buttonwood tree, cryptocurrency, Debian, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, Isaac Newton, MITM: man-in-the-middle, money: store of value / unit of account / medium of exchange, Network effects, node package manager, p-value, peer-to-peer, price discovery process, QR code, Satoshi Nakamoto, self-driving car, SETI@home, software as a service, the payments system, Yogi Berra
Even so, this conservative approach heightens the danger that an upstart currency could emerge, à la Google, and eat Bitcoin’s lunch. Of course, we have no way of knowing what fantastic features a new currency would need such that it could supplant Bitcoin. However, three main reasons exist to believe that Bitcoin may be able survive the onslaught of newcomers: network effects, the nature of cryptocurrency volatility, and the recent development of cryptocurrency-pegging technology. The network effect is the simple concept that people want to use a currency only if other people will accept it as payment. The more users a currency has, the more useful it is. This creates a natural barrier for the adoption of new currencies (and certainly has hindered the adoption of Bitcoin relative to traditional currencies in its first few years).
., the swings, in relative terms, have become less violent). If Bitcoin volatility continues to decrease, this trend may give Bitcoin a significant advantage over future cryptocurrencies: Because Bitcoin is guaranteed to be the oldest cryptocurrency, new currencies might be unable to catch up in this “volatility race,” and Bitcoin will always remain less volatile than upstarts. If Bitcoin maintains advantages in terms of network effects and volatility, it may make sense for new cryptocurrencies to use pegging to link themselves to the Bitcoin network instead of trying to replace the Bitcoin network entirely. Recently, two well-known cryptocurrency developers and entrepreneurs, Adam Back and Austin Hill, have suggested that the value of new cryptocurrencies could be directly linked one-to-one with the value of a bitcoin by using cryptography to allow coins to “ jump” between block-chains using clever algorithms.
Working in Public: The Making and Maintenance of Open Source Software by Nadia Eghbal
Amazon Web Services, barriers to entry, Benevolent Dictator For Life (BDFL), bitcoin, Clayton Christensen, cloud computing, commoditize, continuous integration, crowdsourcing, cryptocurrency, David Heinemeier Hansson, death of newspapers, Debian, disruptive innovation, en.wikipedia.org, Ethereum, Firefox, Guido van Rossum, Hacker Ethic, Induced demand, informal economy, Jane Jacobs, Jean Tirole, Kevin Kelly, Kickstarter, Kubernetes, Mark Zuckerberg, Menlo Park, Network effects, node package manager, Norbert Wiener, pirate software, pull request, RFC: Request For Comment, Richard Stallman, Ronald Coase, Ruby on Rails, side project, Silicon Valley, Snapchat, social graph, software as a service, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, The Death and Life of Great American Cities, The Nature of the Firm, transaction costs, two-sided market, urban planning, web application, wikimedia commons, Zimmermann PGP
We’re moving toward a future where rewards are heavily influenced by the quality of one’s audience more than its size. This affords creators an enormous degree of freedom and helps perpetuate the renaissance of ideas that is already well underway. We don’t have all the answers yet, but I’m hoping this book helps point us toward the right questions. * Another way of describing this pattern is through negative cross-side network effects. Cross-side network effects describe how value is created between two groups of users: in this case, creators and their users. Ideally, the network creates positive cross-side effects, where the presence of more creators and users is mutually beneficial. But too many users can also create a negative cross-side effect, which reduces value to the creator. † Even open source projects have a version of this, such as private groups for maintainers to discuss sensitive topics (like security issues) or new features that they’re not yet ready to announce
Two of the biggest DDoS attacks in history were against GitHub itself: first in 2015, then again in 2018.209 The high fixed costs of physical infrastructure also explain why we see supply-side economies of scale as an adaptive strategy. If software consumption were truly zero marginal cost, it would be just as easy for anyone else to maintain their own version of GitHub as it is for GitHub itself. But it’s far more efficient for a single platform to manage the code, security, infrastructure, support, and whatever else comes with maintaining a software product. Developers use GitHub over GitLab not just for the network effects but also for the former’s security and reliability. The same goes for why someone would use a Google product, like Gmail or Google Docs, over that of a startup. It costs money and manpower to do these things well. Finally, there are marginal costs associated with the developer tools used to maintain software. For example, developers might use error-monitoring software like Sentry, configuration management tools like Puppet or Chef, or incident-response tools like PagerDuty, all of which are priced according to usage.
Micropayments make the transaction about content, rather than about creators, but because there is so much freely available, highly substitutable content they create decision fatigue for consumers.362 Subscription models can operate like a freemium model, but they get even more interesting as a two-sided market. In a freemium model, a creator gives away some of their content for free, but restricts other content to those with paid subscriptions. The free content helps creators grow their reputation via public network effects. In a two-sided market, paying subscribers subsidize all of the content for nonpaying readers, under the assumption that creators aren’t actually selling content but a sense of membership and identity. Instead of charging, say, all 100,000 readers ten cents to read an article, creators can instead give away the article for free, but charge 1,000 extra-dedicated subscribers ten dollars per year.
Money, Real Quick: The Story of M-PESA by Tonny K. Omwansa, Nicholas P. Sullivan, The Guardian
BRICs, business process, business process outsourcing, call centre, cashless society, cloud computing, creative destruction, crowdsourcing, delayed gratification, dematerialisation, disruptive innovation, financial exclusion, financial innovation, financial intermediation, income per capita, Kibera, Kickstarter, M-Pesa, microcredit, mobile money, Network effects, new economy, reserve currency, Silicon Valley, software as a service, transaction costs
In retrospect, analysts can tick off a litany of reasons for its success: Vodafone’s determination to reframe the business opportunity based on customer feedback during the pilot; natural urban-rural remittance patterns; the high cost of existing money******ebook converter DEMO Watermarks******* transfer options; Safaricom’s dominant market share and its all-out marketing blitz; the enterprising nature of Kenyans in building out an agent network and adopting new applications for M-PESA; a Central Bank that watched carefully but did not stifle innovation; and supportive senior management at Safaricom. M-PESA was brilliantly deployed, and sped from pilot to rollout to scale. M-PESA was a pure mobile play, not hindered by a complex relationship with a bank, the Achilles heel of many other mobile money implementations. Mobile money, like mobile telephony itself, is dependent on achieving a network effect. For the mobile operator, maintaining a system that does not scale is prohibitively expensive—which is why banks have not served the rural poor for centuries! Mobile operators depend on millions of small transactions. Without that, mobile money is merely an annoying diversion from the core business of providing voice and text (SMS) capability. Scaling requires investment, primarily to build a technology platform and to acquire customers— Safaricom invested an estimated $30 million over three years to launch MPESA—but CEOs are often hesitant to invest until the business case has been made.
“A large investment in marketing sends a signal to potential users of commitment: This service is here to stay, and so you can count on more and more people joining the network in the future,” notes the GSMA Development Fund’s Mobile Money for the Unbanked, in its 2011 Annual Report. “Second, making a big splash in a shorter time period makes more sense than investing the same amount of money into a longer, lower intensity campaign. This is an axiom in marketing that is even more important when network effects are at play because the goal is to bring lots of customers onto the platform in a short period of time, minimizing the period during which the small number of registered users makes joining seems relatively unattractive to everyone else.” “If you do a pilot with 100 people and it is working, there is no need to expand the pilot to 1,000 people,” says consultant Ignacio Mas. “The next step is a fast, national rollout that scales quickly.
Because of that dispersion and also for cultural reasons (Tanzanians are more nationalistic, Kenyans more family- and triballyoriented), the natural urban-rural remittance patterns that drove M-PESA in Kenya are less prevalent in Tanzania. Vodacom Tanzania’s market share of roughly 40% is far below Safaricom’s 80% in Kenya, and it has half as many subscribers (10 million versus 20 million). Since Vodacom launched, three competitors have joined the fray—Bharti Airtel, Tigo (Millicom) and Zantel (Etisalat). Combined, these factors make it more difficult to develop a strong network effect. Nonetheless, Vodacom has garnered 7 million subscribers in three years, although these are automatic signups with a SIM purchase, and the numbers of active users is said to be less than 2 million. “I wish it was as easy as putting up a billboard in every dusty village in Tanzania,” says Jacques Voogt, Head of Vodafone M-PESA in Tanzania. “But it’s a lot harder—it’s about guys walking around the villages with a T-shirt saying, ‘Ask me about M-PESA!’
The Internet Is Not the Answer by Andrew Keen
"Robert Solow", 3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, augmented reality, Bay Area Rapid Transit, Berlin Wall, bitcoin, Black Swan, Bob Geldof, Burning Man, Cass Sunstein, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, David Brooks, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, full employment, future of work, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Metcalfe’s law, move fast and break things, move fast and break things, Nate Silver, Nelson Mandela, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Norman Mailer, Occupy movement, packet switching, PageRank, Panopticon Jeremy Bentham, Paul Graham, peer-to-peer, peer-to-peer rental, Peter Thiel, plutocrats, Plutocrats, Potemkin village, precariat, pre–internet, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Robert Metcalfe, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, TaskRabbit, Ted Nelson, telemarketer, The Future of Employment, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, Whole Earth Catalog, WikiLeaks, winner-take-all economy, working poor, Y Combinator
Writing in the New York Times to explain “why Bitcoin matters,” Marc Andreessen—who now is the managing partner of Andreessen Horowitz, a $4 billion Silicon Valley venture fund with $50 million invested in Bitcoin-based startups like the virtual wallet Coinbase—argues that this new digital money represents “a classic network effect, a positive feedback loop.” As with the Web, Andreessen says, the more people who use the new currency, “the more valuable Bitcoin is for the people who use it.”107 “A mysterious new technology emerges, seemingly out of nowhere, but actually the result of two decades of intense research and development by nearly anonymous researchers,” writes Andreessen, predicting the historical significance of this networked currency. “What technology am I talking about? Personal computers in 1975, the Internet in 1993, and—I believe—Bitcoin in 2014.”108 What Silicon Valley euphemistically calls the “sharing economy” is a preview of this distributed capitalism system powered by the network effect of positive feedback loops. Investors like Andreessen see the Internet—a supposedly hyperefficient, “frictionless” platform for buyers and sellers—as an upgrade to the structural inefficiencies of the top-down twentieth-century economy.
This was followed by Friendster in 2002 and then, in 2003, by the Los Angeles–based MySpace, a social network with a music and Hollywood focus that, at its 2008 peak, when it was acquired by News Corporation for $580 million, had 75.9 million members.86 But Facebook, which until September 2006 was exclusively made up of high school and university students, offered a less cluttered and more intuitive interface than MySpace. So, having opened its doors to the world outside of schools and universities, the so-called Mark Zuckerberg Production quickly became the Internet’s largest social network, amassing 100 million members by August 2008. And then the network effect, that positive feedback loop that makes the Internet such a classic winner-take-all market, kicked in. By February 2010, the Facebook community had grown to 400 million members, who spent 8 billion minutes each day on a network already operating in 75 different languages.87 Facebook had become the world’s second most popular Internet site after Google, a position that it’s maintained ever since.
The Chapman University geographer Joel Kotkin has broken down what he calls this “new feudalism” into different classes, including “oligarch” billionaires like Thiel and Uber’s Travis Kalanick, the “clerisy” of media commentators like Kevin Kelly, the “new serfs” of the working poor and the unemployed, and the “yeomanry” of the old “private sector middle class,” the professionals and skilled workers in towns like Rochester who are victims of the new winner-take-all networked economy.81 The respected MIT economists Erik Brynjolfsson and Andrew McAfee, who are cautiously optimistic about what they call “the brilliant technologies” of “the Second Machine Age,” acknowledge that our networked society is creating a world of “stars and superstars” in a “winner-take-all” economy. It’s the network effect, Brynjolfsson and McAfee admit, reflecting the arguments of Frank and Cook—a consequence, they say, of the “vast improvements in telecommunications” and the “digitalization of more and more information, goods and services.” The Nobel Prize–winning Princeton economist Paul Krugman also sees a “much darker picture” of “the effects of technology on labor.” Throughout the second half of the twentieth century, Krugman says, workers competed against other workers for resources.
New Dark Age: Technology and the End of the Future by James Bridle
AI winter, Airbnb, Alfred Russel Wallace, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, British Empire, Brownian motion, Buckminster Fuller, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, drone strike, Edward Snowden, fear of failure, Flash crash, Google Earth, Haber-Bosch Process, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, John von Neumann, Julian Assange, Kickstarter, late capitalism, lone genius, mandelbrot fractal, meta analysis, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, WikiLeaks
When he was exposed, Stapel blamed his actions on a fear of failure and the pressure on academics to publish frequently and prominently in order to maintain their positions. Hwang and Stapel, while outliers, might embody one of the reasons articles in the most prominent journals are more likely to be retracted: they’re written by the scientists making the biggest claims, under the most professional and societal pressure. But such frauds are also being revealed by a series of connected, network effects: the increasing openness of scientific practice, the application of technology to the analysis of scientific publications, and the increasing willingness of other scientists – particularly junior ones – to challenge results. As more and more scientific papers become available to wider and wider communities through open access programmes and online distribution, more and more of them come under increased scrutiny.
The relentless progress of automation – from supermarket checkouts to trading algorithms, factory robots to self-driving cars – increasingly threatens human employment across the board. There is no safety net for those whose skills are rendered obsolete by machines; and even those who programme the machines are not immune. As the capabilities of machines increase, more and more professions are under attack, with artificial intelligence augmenting the process. The internet itself helps shape this path to inequality, as network effects and the global availability of services produces a winner-takes-all marketplace, from social networks and search engines to grocery stores and taxi companies. The complaint of the Right against communism – that we’d all have to buy our goods from a single state supplier – has been supplanted by the necessity of buying everything from Amazon. And one of the keys to this augmented inequality is the opacity of technological systems themselves.
Or perhaps the flash crash in reality looks exactly like everything we are experiencing right now: rising economic inequality, the breakdown of the nation-state and the militarisation of borders, totalising global surveillance and the curtailment of individual freedoms, the triumph of transnational corporations and neurocognitive capitalism, the rise of far-right groups and nativist ideologies, and the utter degradation of the natural environment. None of these are the direct result of novel technologies, but all of them are the product of a general inability to perceive the wider, networked effects of individual and corporate actions accelerated by opaque, technologically augmented complexity. Acceleration itself is one of the bywords of the age. In the last couple of decades, a variety of theorists have put forward versions of accelerationist thought, advocating that technological processes perceived to be damaging society should not be opposed, but should be sped up – either to be commandeered and repurposed for socially beneficial ends, or simply to destroy the current order.
Simple Rules: How to Thrive in a Complex World by Donald Sull, Kathleen M. Eisenhardt
Affordable Care Act / Obamacare, Airbnb, asset allocation, Atul Gawande, barriers to entry, Basel III, Berlin Wall, carbon footprint, Checklist Manifesto, complexity theory, Craig Reynolds: boids flock, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, drone strike, en.wikipedia.org, European colonialism, Exxon Valdez, facts on the ground, Fall of the Berlin Wall, haute cuisine, invention of the printing press, Isaac Newton, Kickstarter, late fees, Lean Startup, Louis Pasteur, Lyft, Moneyball by Michael Lewis explains big data, Nate Silver, Network effects, obamacare, Paul Graham, performance metric, price anchoring, RAND corporation, risk/return, Saturday Night Live, sharing economy, Silicon Valley, Startup school, statistical model, Steve Jobs, TaskRabbit, The Signal and the Noise by Nate Silver, transportation-network company, two-sided market, Wall-E, web application, Y Combinator, Zipcar
To grow, shared-economy companies have to keep both sides of the market—sellers and buyers—happy. And growing matters, because these companies face what are known as network effects—in other words, the positively reinforcing cycle in which more buyers attract more sellers and vice versa. Network effects can create exploding growth for the first company or two in a market sector, but they can also make success impossible for later and slower ones. For Airbnb to succeed, the company needed lots of good hosts to attract guests, and lots of good guests to attract hosts. The challenge was to get this chicken-and-egg cycle of network effects going. Joe and Brian brought in Nate Blecharczyk (Joe’s former roommate) as the company’s technical founder. Because of the success they’d had renting out their apartment space during the San Francisco design conference, they made it a rule to focus their young business on cities hosting conferences and festivals, with the rationale that these events attract lots of attendees on tight budgets.
., [>], [>], [>] Montsma, Sandrine, [>] Moss, Brandon, [>] “myth of requisite complexity,” [>]–[>], [>]–[>] Nader, Eduardo, [>] Nansen, Fridtjof, [>] Napoleon, [>] National Academy of Sciences, [>] National Oceanic and Atmospheric Agency. See NOAA whale-watching rules development natural selection and simple rules, [>]–[>] negotiating agreements, [>]–[>], [>]–[>] Netflix breaking rules/House of Cards, [>]–[>] data and, [>], [>] development/rules, [>]–[>], [>] human resource policy and, [>] network effects, [>]–[>] New York Times, [>] Newton, Sir Isaac, [>]–[>], [>] NOAA whale-watching rules development economics/whales decline, [>]–[>] negotiation approach and, [>]–[>], [>] n southern resident killer whales and, [>]–[>] whale-protecting rule, [>], [>]–[>] n Norgay, Tenzing, [>] Oakland Athletics/rule breaking, [>]–[>] Obama, Barack, [>]–[>] Okhuysen, Gerardo, [>]–[>] 1/N principle/Talmudic advice, [>]–[>], [>] n Oslejs, Janis Primekss/concrete and, [>]–[>], [>], [>]–[>] simple rules program, [>]–[>] overfitting, [>]–[>], [>]–[>] n Papinian, [>], [>] n Pasteur, Louis, [>] Paul III (pope), [>] PayPal, [>] Perlow, Leslie, [>], [>] personal issues/simple rules bottlenecks and, [>]–[>] charisma example, [>]–[>] crafting simple rules/research, [>]–[>] depression management example, [>]–[>] dieting example, [>], [>], [>], [>] measuring impact and, [>] moving needles and, [>]–[>], [>] n negotiating rules, [>]–[>] online dating example, [>]–[>] Pixar, [>]–[>], [>] n platitudes vs. rules, [>], [>], [>], [>] poker players/rules examples, [>]–[>] Pollan, Michael, [>], [>], [>] Primekss concrete, [>]–[>], [>], [>]–[>] printing press invention/significance, [>] prioritizing rules description, [>], [>] overview/examples, [>]–[>], [>]–[>] prison and bail decisions, [>], [>] problem types, [>]–[>] process rules description, [>] See also coordination rules; how-to rules; timing rules property rights, [>] rail system (Brazil), [>]–[>] rail system development (Tokyo), [>]–[>] Reynolds, Craig, [>]–[>] Rockefeller Foundation, [>] Rogers, Kenny, [>] Rome (ancient), [>]–[>] Rothschild, Nathan Mayer, [>]–[>] rule breaking California’s climate/gardening (Emily), [>]–[>] Netflix/House of Cards, [>]–[>] Oakland Athletics, [>]–[>] Scott vs.
The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway
activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, cloud computing, commoditize, cuban missile crisis, David Brooks, disintermediation, don't be evil, Donald Trump, Elon Musk, follow your passion, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, longitudinal study, Lyft, Mark Zuckerberg, meta analysis, meta-analysis, Network effects, new economy, obamacare, Oculus Rift, offshore financial centre, passive income, Peter Thiel, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, risk tolerance, Robert Mercer, Robert Shiller, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, Whole Earth Catalog, winner-take-all economy, working poor, young professional
So, what’s a ridiculously successful firm to do? Malcolm Gladwell, the Jesus of business books, highlights the parable of David and Goliath to make the key point: don’t fight on other people’s terms. In other words, once you’ve made the jump to light speed as a tech firm, you need to immunize yourself from the same conquering weapons your army levied on the befuddled prey. There are several obvious examples: network effects (everyone is on Facebook because . . . everyone’s on Facebook); IP protection (every firm in tech over $10 billion is suing, and being sued by, every other $10 billion tech firm), and developing an industry standard—monopoly—ecosystem (typing this on Word because I have no choice). However, I’d argue that digging deeper moats is the real key to long-term success. The iPhone will not be the best phone for long.
They use the peanut-butter-and-chocolate combination of receptors (users) and intelligence (algorithms that track usage to improve the offering). This is tantamount to a car that becomes more valuable with mileage. We now have a Benjamin Button class of products that age in reverse. Wearing your Nikes makes them less valuable. But posting to Facebook that you are wearing Nikes makes the network more valuable. This is referred to as “network effects” or “agility.” Not only do users make the network more powerful (everyone being on Facebook), but also when you turn on Waze, the service gets better for everyone, as it can geolocate you and calibrate traffic patterns. Where should you work or invest? Simple: Benjamin Buttons. Look back at the graph. In the upper right quadrant are the winners, including the three platforms: Amazon, Google, and Facebook.
This is tantamount to getting a $10 parking ticket for not feeding a meter that costs $100 every fifteen minutes. The smart choice: break the law. Chapter 7 Business and the Body IN THEIR BESTSELLING BOOKS, Ben Horowitz, Peter Thiel, Eric Schmidt, Salim Ismaiel, and others argue that extraordinary business success requires scaling at low cost, achieved by leveraging cloud computing, virtualization, and network effects to achieve a 10x productivity improvement over the competition.1 But that explanation ignores a deeper dimension that has nothing to do with technology. From the perspective of evolutionary psychology, all successful businesses appeal to one of three areas of the body—the brain, the heart, or the genitals. Each is tasked with a different aspect of survival. For anyone leading a company, knowing which realm you play in—that is, which organ you inspire—dictates business strategy and outcomes.
The Twittering Machine by Richard Seymour
4chan, anti-communist, augmented reality, Bernie Sanders, Cal Newport, Cass Sunstein, Chelsea Manning, citizen journalism, colonial rule, correlation does not imply causation, credit crunch, crowdsourcing, don't be evil, Donald Trump, Elon Musk, Erik Brynjolfsson, Filter Bubble, Google Chrome, Google Earth, hive mind, informal economy, Internet of things, invention of movable type, invention of writing, Jaron Lanier, Jony Ive, Kevin Kelly, knowledge economy, late capitalism, liberal capitalism, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, Mohammed Bouazizi, moral panic, move fast and break things, move fast and break things, Network effects, new economy, packet switching, patent troll, Philip Mirowski, post scarcity, post-industrial society, RAND corporation, Rat Park, rent-seeking, replication crisis, sentiment analysis, Shoshana Zuboff, Silicon Valley, Silicon Valley ideology, smart cities, Snapchat, Steve Jobs, Stewart Brand, Stuxnet, TaskRabbit, technoutopianism, the scientific method, Tim Cook: Apple, undersea cable, upwardly mobile, white flight, Whole Earth Catalog, WikiLeaks
They have created a machine for us to write to. The bait is that we are interacting with other people: our friends, professional colleagues, celebrities, politicians, royals, terrorists, porn actors – anyone we like. We are not interacting with them, however, but with the machine. We write to it, and it passes on the message for us, after keeping a record of the data. The machine benefits from the ‘network effect’: the more people write to it, the more benefits it can offer, until it becomes a disadvantage not to be part of it. Part of what? The world’s first ever public, live, collective, open-ended writing project. A virtual laboratory. An addiction machine, which deploys crude techniques of manipulation redolent of the ‘Skinner Box’ created by behaviourist B. F. Skinner to control the behaviour of pigeons and rats with rewards and punishments.8 We are ‘users’, much as cocaine addicts are ‘users’.
But one of Twitter’s early founders, Noah Glass, put his finger on another dimension: people would use social networking to make them feel less alone.7 Whatever was happening to them – an earthquake, redundancy, divorce, a frightening news item or just boredom – there would always be someone to talk to. Where society was missing, the network would substitute. These pleasures are redoubled by the ‘network effect’. The more people use it, the more valuable it is to each user. Zuckerberg understood, early on, that this was how he would build his site. As he told the university newspaper The Harvard Crimson, ‘The nature of the site is that each user’s experience improves if they can get their friends to join it.’8 Other colleges quickly signed up. And it took just a year for it to attract the attention of advertisers, thanks to the brute scale and objectivity of its data.
Seppukoo.com allowed users to create ‘last words’, which would be automatically sent to their friends, and created a memorial page in their name before permanently deleting their account. Suicidemachine.org deleted all friends and information, replaced the user’s profile picture with a noose icon and added the user to a group called ‘Social Media Suiciders’. Since the platforms benefit from the ‘network effect’ – the more people are connected, the more valuable it is – this would have been a catastrophic reversal. Both websites were subject to cease and desist letters from Facebook’s lawyers, and were duly forced to stop offering the service to Facebook’s users. Social industry platform protocols are carefully designed to discourage disconnection, since it is a threat to their very existence. Facebook’s own options for permanent deletion are carefully hidden, appearing nowhere in any menu or settings option.71 Users must instead fill in a form reached through the Facebook Help Center, and wait through a ‘reconsideration period’.
The Green New Deal: Why the Fossil Fuel Civilization Will Collapse by 2028, and the Bold Economic Plan to Save Life on Earth by Jeremy Rifkin
1919 Motor Transport Corps convoy, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, American Society of Civil Engineers: Report Card, autonomous vehicles, Bernie Sanders, blockchain, borderless world, business cycle, business process, carbon footprint, collective bargaining, corporate governance, corporate social responsibility, creative destruction, decarbonisation, en.wikipedia.org, energy transition, failed state, ghettoisation, hydrogen economy, information asymmetry, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, Joseph Schumpeter, means of production, megacity, Network effects, new economy, off grid, oil shale / tar sands, peak oil, planetary scale, renewable energy credits, Ronald Reagan, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, smart grid, sovereign wealth fund, Steven Levy, the built environment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, trade route, union organizing, urban planning, women in the workforce, zero-sum game
However, it’s also fair to say that nearly half of the population of the world (46 percent), living on less than $5.50 per day, the dividing line that defines poverty, is at best only marginally better off than their ancestors, and perhaps no better off.27 Meanwhile, the wealthiest human beings have triumphed. Currently, the accumulated wealth of the eight richest individuals in the world equals the total wealth of half of the human beings living on the planet—3.5 billion people.28 Conversely, the Third Industrial Revolution infrastructure is engineered to be distributed, open, and transparent, to achieve network effects, and it scales laterally, allowing billions of people to engage directly with each other both virtually and physically at very low fixed costs and near-zero marginal cost in localities and regions that stretch around the world. All they need is a smartphone and an internet connection to give them instant access to Big Data and a global network of millions of other businesses and their websites.
Even if it were, any such effort would fail if localities, regions, and countries along the Belt and Road were to exercise caution at the get-go and ensure that the build-out of the infrastructure and its subsequent ownership and management within their jurisdictions were under their various governments’ strict control. Then, too, we need to remember that the very nature of the Third Industrial Revolution digital infrastructure favors distributed rather than centralized control, and, to achieve network effects, it works best if the networks are open and transparent rather than closed and proprietary, and scale laterally rather than vertically to optimize aggregate efficiencies and circularity. The engineered platforms favor flexibility and redundancy, the two key elements in establishing regional resilience in a climate change world. Were the intention of any nation-state or renegade group to surveil, control, cripple, or take down the networks, cheap, simple technology components built into the system at the end user’s door will allow families, neighborhoods, communities, businesses, and local and regional governments to go off-grid at a moment’s notice and decentralize and reaggregate their operations.
The Second Industrial Revolution infrastructure gave rise to global markets and international organizations like the United Nations, the World Bank, the OECD, and the World Trade Organization to comanage governance alongside nation-states. As described early on, the Third Industrial Revolution infrastructure comes with a different design and engineering construction. The platform is weighted toward being distributed in operation rather than centralized, and the system itself is optimized if it remains open and transparent to create the network effect rather than being closed off in intellectual property. Last, the distributed open and transparent nature of the system is most efficient and productive if its operations are laterally scaled rather than vertically integrated. Giant internet companies, early on, seized hold of many of the platforms in vertically scaled global monopolies, but that is not likely to last, because they ultimately cannot compete with the millions of high-tech small- and medium-sized enterprises blockchained across competencies and operating in cooperatives overseen by commons governance.
Exceptional People: How Migration Shaped Our World and Will Define Our Future by Ian Goldin, Geoffrey Cameron, Meera Balarajan
Admiral Zheng, agricultural Revolution, barriers to entry, Berlin Wall, Branko Milanovic, British Empire, conceptual framework, creative destruction, demographic transition, Deng Xiaoping, endogenous growth, failed state, Fall of the Berlin Wall, Gini coefficient, global pandemic, global supply chain, guest worker program, illegal immigration, income inequality, income per capita, Intergovernmental Panel on Climate Change (IPCC), job automation, Joseph Schumpeter, knowledge economy, labor-force participation, labour mobility, Lao Tzu, life extension, longitudinal study, low skilled workers, low-wage service sector, Malacca Straits, mass immigration, microcredit, Nelson Mandela, Network effects, new economy, New Urbanism, old age dependency ratio, open borders, out of africa, price mechanism, purchasing power parity, Richard Florida, selection bias, Silicon Valley, Silicon Valley startup, Skype, spice trade, trade route, transaction costs, transatlantic slave trade, women in the workforce, working-age population
This migration stream eventually becomes self-sustaining, and as migrants maintain contact with family and friends at home, they create a “network effect” that increases the rate of migration even further. Networks relay information and resources that lower the risks for others to migrate, and a migration channel is opened up between source and destination locations through these networks. The continued movement of people through this channel strengthens the network and expands the number of people and locations it connects together. Over time and as migrant communities grow, networks can diminish in their significance as the connection between settled migrants and new arrivals from “home” weakens. Networks may eventually be used to discourage further migration if competition for migrant jobs in the destination country becomes more severe (see figure 4.2).41 Figure 4.2. Network effects of migration to a particular country.
Annual emigration rates, 1860-1913 (absolute deviations from trend) Figure 3.1. Immigration to the United States for selected years, 1900-1933 (thousands) Figure 3.2. Total number of Koreans in Japan, 1900-1944 (millions) Figure 3.3. European Immigrants into Argentina and Brazil after World War II (thousands) Figure 4.1. The relationship between socio-economic development and migration Figure 4.2. Network effects of migration to a particular country Figure 4.3. Age structure in Republic of Korea in 1960, 2000, and 2040 (projected) Figure 5.1. Patterns of global migration Figure 5.2. Low-skill migrants in Gulf Cooperation Council Countries Figure 5.3. The European Union, 2009 Figure 5.4. Propensity to migrate from A8 countries to the UK, 2005 Figure 5.5. Estimated number of world refugees (millions), 1960-2008 Figure 5.6.
.), Immigration Reconsidered: History, Sociology, and Politics. Oxford, UK: Oxford University Press, pp. 79–95. 38. Portes and Borocz, 1989: 612. 39. This general narrative of the migration process is drawn from Massey et al., 1993. 40. Castles, 1989: 106; George J. Borjas and Stephen G. Bronars. 1991. “Immigration and the Family,” Journal of Labour Economics 9(2): 123–148. 41. Gil S. Epstein. 2008. “Herd and Network Effects in Migration Decision-Making,” Journal of Ethnic and Migration Studies 34: 567–583. 42. Manolo I. Abella. 2004. “The Role of Recruiters in Labour Migration,” in Douglas S. Massey and J. Edward Taylor (eds.), International Migration: Prospects and Policies in a Global Market. Oxford, UK: Oxford University Press. 43. Ibid.: 201. 44. Ibid.: 201. 45. Stephen Castles. 2007. “Comparing the Experience of Five Major Emigration Countries,” Working Paper 7, International Migration Institute, James Martin 21st Century School, University of Oxford. 46.
What's Next?: Unconventional Wisdom on the Future of the World Economy by David Hale, Lyric Hughes Hale
affirmative action, Asian financial crisis, asset-backed security, bank run, banking crisis, Basel III, Berlin Wall, Black Swan, Bretton Woods, business cycle, capital controls, Cass Sunstein, central bank independence, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate social responsibility, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, debt deflation, declining real wages, deindustrialization, diversification, energy security, Erik Brynjolfsson, Fall of the Berlin Wall, financial innovation, floating exchange rates, full employment, Gini coefficient, global reserve currency, global village, high net worth, Home mortgage interest deduction, housing crisis, index fund, inflation targeting, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Just-in-time delivery, Kenneth Rogoff, Long Term Capital Management, Mahatma Gandhi, Martin Wolf, Mexican peso crisis / tequila crisis, Mikhail Gorbachev, money market fund, money: store of value / unit of account / medium of exchange, mortgage tax deduction, Network effects, new economy, Nicholas Carr, oil shale / tar sands, oil shock, open economy, passive investing, payday loans, peak oil, Ponzi scheme, post-oil, price stability, private sector deleveraging, purchasing power parity, quantitative easing, race to the bottom, regulatory arbitrage, rent-seeking, reserve currency, Richard Thaler, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, sovereign wealth fund, special drawing rights, technology bubble, The Great Moderation, Thomas Kuhn: the structure of scientific revolutions, Tobin tax, too big to fail, total factor productivity, trade liberalization, Washington Consensus, Westphalian system, WikiLeaks, women in the workforce, yield curve
He reviews the process by which the dollar displaced the British pound as the dominant global currency during the early decades of the twentieth century. He then analyzes the prerequisites to be a reserve currency in the modern era. They are that the currency be widely available outside its home economy, that it be fully convertible, that it be supported by a large economy, and that it have a developed financial system. When these factors converge, they generate network effects in which the greater the number of people that are using the currency, the more beneficial it becomes for the users, and the more dominant it becomes. He thinks that the euro is not fully competitive with the dollar because there is no market for European government debt. Instead, investors have to choose between the debts of individual nation-states, of which the largest debtor is Italy. The yen suffers from the low interest rates in Japan and growing investor concern about the credit quality of Japanese government debt.
In short, US dollar security markets are highly convenient for individual, corporate, and official or government users around the world. These attributes largely explain the role of the US currency as the primary vehicle for holding foreign exchange reserves, as the most widely traded currency in international private trade and capital transactions, and as the leading currency in global foreign exchange transactions. 9. Network effects. As explained earlier, the US dollar did not start out as the world’s reserve currency. Much as English did not intentionally become the world’s most widely spoken language, the dollar did not become the world’s leading reserve currency by deliberate policy. The supremacy of the dollar is, like the supremacy of the English language, the result of gradual usage and experience. Like a common language, the US dollar enjoys what economists call “network externalities”—the greater the number of people who transact using dollars, the more beneficial this is to users, and the more dominant it becomes.
Like a common language, the US dollar enjoys what economists call “network externalities”—the greater the number of people who transact using dollars, the more beneficial this is to users, and the more dominant it becomes. Consequently, the US dollar deposit, loan, and funding markets outside the United States are far larger than those of any other currency traded outside its home borders; this effectively underwrites the continued financing of trade and capital transactions in dollars around the world. To undermine these network effects and simultaneously create a truly viable alternative reserve currency would therefore require both a dramatic shock to the dollar and the ready availability of a realistic alternative. However, a major erosion of any of the nine conditions listed above could undermine confidence in the US dollar, threatening its role as a currency for international transactions and as a reserve currency. Feasible Alternatives to the US Dollar Having established the nine characteristics required of an international reserve currency, we may now ask what alternatives there might be to the US dollar, either now or in the medium-term future.
Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist by Kate Raworth
"Robert Solow", 3D printing, Asian financial crisis, bank run, basic income, battle of ideas, Berlin Wall, bitcoin, blockchain, Branko Milanovic, Bretton Woods, Buckminster Fuller, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, clean water, cognitive bias, collapse of Lehman Brothers, complexity theory, creative destruction, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, dematerialisation, disruptive innovation, Douglas Engelbart, Douglas Engelbart, en.wikipedia.org, energy transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, Eugene Fama: efficient market hypothesis, experimental economics, Exxon Valdez, Fall of the Berlin Wall, financial deregulation, Financial Instability Hypothesis, full employment, global supply chain, global village, Henri Poincaré, hiring and firing, Howard Zinn, Hyman Minsky, income inequality, Intergovernmental Panel on Climate Change (IPCC), invention of writing, invisible hand, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kickstarter, land reform, land value tax, Landlord’s Game, loss aversion, low skilled workers, M-Pesa, Mahatma Gandhi, market fundamentalism, Martin Wolf, means of production, megacity, mobile money, Mont Pelerin Society, Myron Scholes, neoliberal agenda, Network effects, Occupy movement, off grid, offshore financial centre, oil shale / tar sands, out of africa, Paul Samuelson, peer-to-peer, planetary scale, price mechanism, quantitative easing, randomized controlled trial, Richard Thaler, Ronald Reagan, Second Machine Age, secular stagnation, shareholder value, sharing economy, Silicon Valley, Simon Kuznets, smart cities, smart meter, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, statistical model, Steve Ballmer, The Chicago School, The Great Moderation, the map is not the territory, the market place, The Spirit Level, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, too big to fail, Torches of Freedom, trickle-down economics, ultimatum game, universal basic income, Upton Sinclair, Vilfredo Pareto, wikimedia commons
No need for fines or rewards to encourage compliance: the little green footprints artfully amplified an existing social norm.60 Network effects also influence social behaviour, as illustrated by the power of a prominent example. In October 2011, Brazil’s former president Lula da Silva went public with news of his throat cancer, saying he believed it was due to smoking cigarettes. Over the following four weeks, there was a national surge in Google searches for information about quitting smoking – far greater than similar searches made on World No Tobacco Day or even on New Year’s Day, when resolutions to quit are common. Likewise, when the UK reality TV star Jade Goody went public with her diagnosis of cervical cancer in 2009, there was a 43% increase in women making appointments to be tested.61 Those cases acted as warnings, but network effects can inspire too. Thanks to the courageous stance of the Pakistani educational activist Malala Yousafzai, millions of girls worldwide have been inspired by ‘the Malala effect’ to demand and cherish their right to an education.
Trickle-down economics may be a chimera, but trickle-down behaviourism is very real.’34 What is the implication for economic policy aiming to influence how we behave? Economists have traditionally sought to change people’s behaviour by changing the relative price of things, be it through a tax on sugar or a discount on solar panels. But such price signals often fail to achieve their expected results, Ormerod points out, because they can be drowned out by far stronger network effects, thanks to social norms and expectations of what others in the network are doing.35 At the same time, it may be possible to harness such interdependence for behavioural change, as we will see. From calculating to approximating Homo sapiens clearly can’t match the infallibility of rational economic man. That much has been agreed upon since the 1950s when Herbert Simon broke rank with his fellow economists and started to study how people actually behaved, finding their rationality to be severely ‘bounded’.
Thanks to the courageous stance of the Pakistani educational activist Malala Yousafzai, millions of girls worldwide have been inspired by ‘the Malala effect’ to demand and cherish their right to an education. Such effects work on a local scale too. Researchers in West Bengal, India found that when women started being appointed to lead village councils for the first time, local teenage girls began to have higher aspirations for their education and themselves, as did their parents. No prices, no payments, just pride.62 Nudges and network effects often work because they tap into underlying norms and values – such as duty, respect and care – and those values can be activated directly. That’s what researchers in the US discovered when they set out to explore how to prompt pro-environmental behaviour. They set up signs at a petrol station inviting passing motorists to have a free tyre check, offering either financial, safety or environmental reasons for doing so.
The Economics of Enough: How to Run the Economy as if the Future Matters by Diane Coyle
"Robert Solow", accounting loophole / creative accounting, affirmative action, bank run, banking crisis, Berlin Wall, bonus culture, Branko Milanovic, BRICs, business cycle, call centre, Cass Sunstein, central bank independence, collapse of Lehman Brothers, conceptual framework, corporate governance, correlation does not imply causation, Credit Default Swap, deindustrialization, demographic transition, Diane Coyle, different worldview, disintermediation, Edward Glaeser, endogenous growth, Eugene Fama: efficient market hypothesis, experimental economics, Fall of the Berlin Wall, Financial Instability Hypothesis, Francis Fukuyama: the end of history, George Akerlof, Gini coefficient, global supply chain, Gordon Gekko, greed is good, happiness index / gross national happiness, hedonic treadmill, Hyman Minsky, If something cannot go on forever, it will stop - Herbert Stein's Law, illegal immigration, income inequality, income per capita, industrial cluster, information asymmetry, intangible asset, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Jane Jacobs, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, light touch regulation, low skilled workers, market bubble, market design, market fundamentalism, megacity, Network effects, new economy, night-watchman state, Northern Rock, oil shock, Pareto efficiency, principal–agent problem, profit motive, purchasing power parity, railway mania, rising living standards, Ronald Reagan, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, South Sea Bubble, Steven Pinker, The Design of Experiments, The Fortune at the Bottom of the Pyramid, The Market for Lemons, The Myth of the Rational Market, The Spirit Level, transaction costs, transfer pricing, tulip mania, ultimatum game, University of East Anglia, web application, web of trust, winner-take-all economy, World Values Survey, zero-sum game
They form what economists refer to as a “general purpose technology” because they affect the organization of the economy in a wide-ranging way, like steam or electricity or rail in the past.20 One widespread effect has been to increase the scope of economies of scale, as there are many industries in which it is now possible to reach a much larger number of consumers at very little additional cost, thanks to the possibilities of online marketing and distribution. Network effects have amplified this dynamic. So in many industries, the structure has evolved into a small number of very large firms competing across all products and services, and often globally, and a large number of small businesses supplying particular niches. Software is a clear example: it costs Microsoft almost nothing to supply one additional copy of its software, as almost all the cost is upfront development.
Some aspects of this are matters for the companies and their shareholders: if they are not as productive as their competitors, there is no wider social issue. Other aspects are matters of public concern, however. In particular, the drive to increase in size in order to take advantage of economies of scale is an issue. There has been a sharp divide among experts on competition policy between those who think that it’s good for consumers that Microsoft is so big, because this means lower prices and the benefits of network effects, and those who think it’s bad for consumers because it hasn’t been possible for new or better operating systems and browsers to serve more consumers.26 There are merits in both sets of arguments. As someone who was a UK competition regulator for eight years in the 2000s, I’ve come to the conclusion that too many companies are simply too big and too similar. Innovation is being stifled. More important, there is a concentration of power in the hands of some big companies.
., 127–28 Calculus of Consent, The: Logical Foundations of Constitutional Democracy (Buchanan and Tullock), 242 call centers, 131, 133, 161 Cameron, David, 288 capitalism: China and, 234; communism and, 96, 182–83, 209–13, 218, 226, 230, 239–40; community and, 27, 51, 65, 117–18, 137, 141, 152–54; cultural effects of, 25–29, 230–38; current crisis of, 6–9; democracy and, 230–38; Engels on, 14; fairness and, 134, 137, 149; growth and, 268, 275, 290, 293, 297; happiness and, 25–29, 33, 45, 53–54; historical perspective on, 3, 6, 14; institutions and, 240; market failure and, 226–30; Marx on, 14; measurement and, 182; mercantile economy and, 27–28; nutrition and, 10; profit–oriented, 18; Protestant work ethic and, 13–14; protests against, 211–13; rethinking meaning of, 9; social effects of, 25–26; values and, 209–13, 218, 226, 230–32, 235–36; well-being and, 137–39 carbon prices, 70–71 celebrities, 33 charitable giving, 33, 141 Checkpoint Charlie, 147 China, 161, 262, 280; capitalism and, 234; carbon emissions and, 63; changed demographic structure of, 90; convergence and, 122; declining population in, 98; energy use in, 63, 65; global manufacturing and, 149; inequality and, 125–26; Mao and, 10; middle class of, 125–26; as next major power, 94; one–child policy and, 95–96; population growth and, 95–96; purchasing power parity (PPP) and, 306n19; rise in wealth of, 81, 122–23, 125, 212; savings and, 87, 94, 100, 108; wage penalties and, 133; World Bank influence and, 163 cities, 308n29; face-to-face contact and, 165–68; size and, 165–66; structural changes in, 165–70; urban clustering and, 166 City of London, 147, 221 Clemens, Michael, 81 climate change, 5–7, 17, 24, 90, 238; carbon prices and, 70–71; Copenhagen summit and, 62, 64–65, 68, 162, 292; domestic dissent and, 66–71; future and, 75–83; geological history and, 69; global warming and, 57, 64, 66, 68; greenhouse gases and, 23, 29, 35, 59, 61–63, 68, 70–71, 83; Himalayan glaciers and, 66–67; incandescent light bulbs and, 59–60; InterAcademy Council and, 66–67; Intergovernmental Panel on Climate Change (IPCC) and, 59, 66–69, 82, 297; Kyoto Protocol and, 62–64; lack of consensus on, 66–71; Montreal Protocol and, 59; policy dilemma of, 58–62; policy recommendations for, 267, 280, 297; politics and, 62–65; social welfare and, 71–75; technology and, 59–60, 198 Coachella Value Music Festival, 197 Cobb, John, 36 Coca Cola, 150 coherence, 49 Cold War, 93, 112, 147, 209, 213, 239, 252 Collier, Paul, 77–78, 80, 82 Commerzbank, 87 Commission on the Measurement of Economic Performance and Social Progress, 37–38 communism: Berlin Wall and, 182, 226, 239; capitalism and, 96, 182–83, 209–13, 218, 226, 230, 239–40; Cold War and, 93, 112, 147, 209, 213, 239, 252; fall of, 209–13, 226, 239–40, 252; Iron Curtain and, 183, 239, 252; Leipzig marches and, 239; one-child policy and, 95–96; Velvet Revolution and, 239 community: civic engagement and, 140–41; globalization and, 148–49; intangible assets and, 149–52, 157, 161 (see also trust); public service and, 295; Putnam on, 140–41, 152–54 commuting, 45–47 Company of Strangers, The (Seabright), 148–49, 213–14 comprehensive wealth, 81–82, 202–3, 208, 271–73 consumerism, 22, 34, 45, 138 consumption: conspicuous, 11, 22, 45, 236; consumerism and, 22, 34, 45, 138; cutting, 61; downgrading status of, 11; downshifting and, 11, 55; Easterlin Paradox and, 39–44; global per capita, 72; of goods and services, 7, 10, 24, 35–36, 40, 82, 99, 161, 188, 191, 198, 214, 218, 228–29, 282; green lifestyle and, 55, 61, 76, 289, 293; growth and, 280, 295; happiness and, 22, 29, 40, 45; hedonic treadmill and, 40; increasing affluence and, 12; institutions and, 254, 263; Kyoto Protocol and, 63–64; measurement and, 181–82, 198; missing markets and, 229; natural resources and, 8–12, 58, 60, 79–82, 102, 112, 181–82; nature and, 58–61, 71–76, 79, 82; posterity and, 86, 104–5, 112–13; reduction of, 105; Slow Movement and, 27; trends in, 138; trilemma of, 13–14, 230–36, 275; values and, 229, 236 convergence, 5, 122 Copenhagen summit, 62, 64–65, 68, 162, 292 Crackberry, 205 Crafts, Nicholas, 156–57 credit cards, 2, 21, 136, 138, 283 Csikszentmilhalyi, Mihaly, 45–49 Cultural Contradictions of Capitalism, The (Bell), 230, 235–36 Czechoslovakia, 239 Daly, Herman, 36 Damon, William, 48 Dasgupta, Partha, 61, 73, 77–78, 80, 82 David, Paul, 156 Dawkins, Richard, 118 debit cards, 2 decentralization, 7, 159, 218, 246, 255, 275, 291 defense budgets, 93 democracy, 2, 8, 16, 312n19; capitalism and, 230–38; culture and, 230–38; fairness and, 141; growth and, 268–69, 285–89, 296–97; institutions and, 242–43, 251–52, 262; nature and, 61, 66, 68; posterity and, 106; trust and, 175; values and, 230–35 Denmark, 125 Dickens, Charles, 131 Diener, Ed, 48, 49 Discourse on the Origin and Basis of Inequality among Men (Rousseau), 114 distribution, 29, 306n22; Asian influence and, 123; bifurcation of social norms and, 231–32; consumerism and, 22, 34, 45, 138; Easterlin Paradox and, 39–44; fairness and, 115–16, 123–27, 134, 136; food and, 10, 34; of goods and services, 7, 10, 24, 35–36, 40, 82, 99, 161, 188, 191, 198, 214, 218, 228–29, 282; income, 34, 116, 123–27, 134, 278; inequality and, 123 (see also inequality); institutions and, 253; measurement and, 181, 191–99, 202; paradox of prosperity and, 174; policy recommendations for, 276, 278; posterity and, 87, 94; trust and, 151, 171; unequal countries and, 124–30; values and, 226 Dorling, Danny, 224, 307n58, 308n34 Douglas, Michael, 221 downshifting, 11, 55 downsizing, 175, 246, 255 drugs, 44, 46, 137–38, 168–69, 191, 302n47 Easterlin, Richard, 39 Easterlin Paradox, 39–44 eBay, 198 Economics of Ecosystems and Biodiversity project, The (TEEB), 78–79 economies of scale, 253–58 Economy of Enough, 233; building blocks for, 12–17; first ten steps for, 294–98; growth and, 182; happiness and, 24; institutions and, 250–51, 258, 261–63; living standards and, 13, 65, 78–79, 106, 113, 136, 139, 151, 162, 190, 194, 267; Manifesto of, 18, 267–98; measurement and, 182, 186–88, 201–7; nature and, 59, 84; Ostrom on, 250–51; posterity and, 17, 85–113; values and, 217, 233–34, 238; Western consumers and, 22 (see also consumption) Edinburgh University, 221 efficiency, 2, 7; evidence–based policy and, 233–34; fairness and, 126; Fama hypothesis and, 221–22; happiness and, 9, 29–30, 61; institutions and, 245–46, 254–55, 261; limits to, 13; nature and, 61–62, 69, 82; network effects and, 253, 258; productivity and, 13 (see also productivity); trilemma of, 13–14, 230–36, 275; trust and, 158–59; values and, 210, 215–16, 221–35 Ehrlich, Paul, 70 e-mail, 252, 291 “End of History, The” (Fukuyama), 239 Engels, Friedrich, 14 Enlightenment, 7 Enron, 145 environmentalists. See nature European Union, 42, 59, 62, 162–63, 177, 219 Evolution of Cooperation, The (Axelrod), 118–19 “Evolution of Reciprocal Altruism, The” (Trivers), 118 externalities, 15, 70, 80, 211, 228–29, 249, 254 Facebook, 289 face-to-face contact, 7, 147, 165–68 fairness: altruism and, 118–22; antiglobalization and, 115; bankers and, 115, 133, 139, 143–44; behavioral econoics and, 116–17, 121; bonuses and, 87–88, 115, 139, 143–44, 193, 221, 223, 277–78, 295; capitalism and, 134, 137, 149; consequences for growth, 135–36; criticism of poor and, 142; democracy and, 141; emotion and, 118–19, 137; game theory and, 116–18, 121–22; government and, 121, 123, 131, 136; gratitude and, 118; growth and, 114–16, 121, 125, 127, 133–37; happiness and, 53; health issues and, 137–43; high salaries and, 130, 143–44, 193, 223, 277–78, 286, 296; inequality and, 115–16, 122–43; innate sense of, 114–19; innovation and, 121, 134; morals and, 116–20, 127, 131, 142, 144, 221; philosophy and, 114–15, 123; politics and, 114–16, 125–31, 135–36, 140–44; productivity and, 131, 135; Putnam on, 140–41; self-interest and, 114–22; social corrosiveness of, 139–44; social justice and, 31, 43, 53, 65, 123, 164, 224, 237, 286; statistics and, 115, 138; superstar effect and, 134; sustainability and, 115; technology and, 116, 131–34, 137; tit-for-tat response and, 118–19; trilemma of, 13–14, 230–36, 275; trust and, 139–44, 150, 157, 162, 172, 175–76; ultimatum game and, 116–17; unequal countries and, 124–30; wage penalties and, 133; well-being and, 137–43; World Values Survey and, 139 Fama, Eugene, 221–22 faxes, 252 Federal Reserve, 145 Ferguson, Niall, 100–101 financial crises: actions by governments and, 104–12; bubbles and, 3 (see also bubbles); capitalism and, 6–9 (see also capitalism); contracts and, 149–50; crashes and, 3, 28, 161, 244, 283; current, 54, 85, 90–91, 145; debt legacy of, 90–92; demographic implosion and, 95–100; goodwill and, 150; government debt and, 100–104; Great Depression and, 3, 28, 35, 61, 82, 150, 208, 281; growth debt and, 85–86; historical perspective on, 3–4; institutional blindness to, 87–88; intangible assets and, 149–50; intrusive regulatory practices and, 244; pension burden of, 92–95; as political crisis, 8–9; statistics of, 145; stimulus packages and, 91, 100–103, 111; structural change and, 25; total cost of current, 90–91; trust and, 88–89 (see also trust); weightless activities and, 150; welfare burden of, 92–95 Financial Times, 257 Fitzgerald, F.
The Mesh: Why the Future of Business Is Sharing by Lisa Gansky
Airbnb, Amazon Mechanical Turk, Amazon Web Services, banking crisis, barriers to entry, carbon footprint, Chuck Templeton: OpenTable:, cloud computing, credit crunch, crowdsourcing, diversification, Firefox, fixed income, Google Earth, industrial cluster, Internet of things, Joi Ito, Kickstarter, late fees, Network effects, new economy, peer-to-peer lending, recommendation engine, RFID, Richard Florida, Richard Thaler, ride hailing / ride sharing, sharing economy, Silicon Valley, smart grid, social web, software as a service, TaskRabbit, the built environment, walkable city, yield management, young professional, Zipcar
We knew that people enjoyed sharing photos of events, and shaped our offer accordingly. At the time, people would share photo albums with on average five or six of their friends and family. Someone would go to a party, take a bunch of pictures, and share them. Friends and family members would see how fabulous they looked in a photo, and then buy the print, often signing up for an account of their own. We would pay to acquire one customer and get five for free. The “network effect,” as it’s known, was a new phenomenon then, but has grown dramatically in the years since. Newer photo services, such as Olapic, allow shots from anyone who attends an event, such as a wedding, to be uploaded in a single place and shared on social networking sites. panning for gold. or meet my friend, the filter. In social networks, certain people act as “discoverers.” I am one in particular domains.
See Social networking starting Mesh company Sweet Spot trends influencing growth of trust building Millennial generation Mobile networks digital translation to physical and flash branding as foundation of the Mesh share-based business operation users, increase in Modular design Mohsenin, Kamran Movie rentals online, Mesh companies Mozilla Firefox Music-based businesses, Mesh companies Natural ecosystem, relationship to Mesh ecosystem Netflix annual sales as information business Mesh strategy perfection recommendation engine recommendations Network effect Niche markets for maintaining/servicing products Mesh companies opening, reason for sharing as North Portland Tool Library (NPTL) Ofoto Olapic Ombudsman Open Architecture Network Open Design Open innovation service provider Open networks advantages of Architecture for Humanity communal IP concept and marketing products openness versus proprietary approach and product improvement software development OpenTable O’Reilly, Tim Ostrom, Elinor Own-to-Mesh model car-sharing services profits, generation from retirees as customers Partnerships characteristics of corporations and Mesh companies income generation from in Mesh ecosystem unexpected value of Patagonia recycled textiles of Walmart partnership Paul, Sunil Payne, Steven Peer-to-peer lending.
Croix Falls Cinema SAP Schneeveis, Bob School of Everything (SoE) Seely Brown, John Self-storage listings, Mesh companies SellaBand Seventymm Share-based business during holidays mobile networks as foundation and niche markets shareability, compatible services See also The Mesh Shea, Lucy Sinclair, Cameron Sivers, Derek Skinnipopcorn SmartyPig smava Social lending. See Loans/social lending Social networking importance to the Mesh Mesh companies negative events, broadcasting on on Netflix network effect privacy, importance of and product improvement for word-of-mouth advertising See also Facebook Software getting for startup Mesh open development Software as a service (SaaS) Sourcemap SpareFoot Spride Share Standardization of products Starting a Mesh company capital needs customers, identifying define/redefine/scale stages first mover advantage marketing primary questions serendipity as factor shareable assets, identifying software, ASP model Stohr, Kate Supply chain forward and reverse integration reverse supply chain Sustainable design Swishing Taxi Magic TCHO Technology, Mesh-friendly Mesh companies TED Prize thinkspace thredUP adaptability of case study Tool sharing, Mesh companies Toyota, broken trust Transaction fees Transparency and adaptability “age of radical transparency,” and trust building Transportation efficiency communities developed for See also Bike sharing; Car sharing Travel-related services, Mesh companies Trials Tripkick Trust building basics of broken trust, examples of and customer complaints customer misbehavior, dealing with by delighting customers discoverers as trust agents lost trust, rebuilding maintaining trust negative events, impact on and privacy practices proprietary versus open control reviews, keeping perspective social networks, role of starting slow, necessity of and transparency trials/samples for “virtuous circle of trust,” Tryvertising Twitter Tylenol tragedy Upcycling Mesh companies Vacation Rentals by Owner (VRBO) Virgin Corporation, Mesh strategy Volkswagen, idea solicitation on Web Walmart customer data, use of greening of integration with suppliers Mesh possibilities for waste, created by Waste disposal and climate change as resource underutilization Waste management government initiatives for Mesh companies in Mesh ecosystem in natural ecosystem raw materials, sharing recycling and reuse services reverse value chain yield management See also Recycling and reuse services Water Legacy Wattzon.com Westmill Wind Farm Co-operative WhipCar Wilcox, Ronald Wilhelm, Eric Williamson, Oliver Wine cooperatives, Mesh companies Word of mouth, power of Work-space sharing, Mesh companies World of Goods Yelp Yield management Zipcar customer experience with Mesh model for partners Zopa Zynga Table of Contents Title Page Copyright Page Dedication Introduction Chapter 1 - Getting to Know the Mesh Chapter 2 - The Mesh Advantage Chapter 3 - Mesh Design Chapter 4 - In with the Mesh Chapter 5 - In Mesh We Trust Chapter 6 - The Mesh as Ecosystem Chapter 7 - Open to the Mesh Chapter 8 - Mesh Inc.
No Ordinary Disruption: The Four Global Forces Breaking All the Trends by Richard Dobbs, James Manyika
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, additive manufacturing, Airbnb, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, autonomous vehicles, Bakken shale, barriers to entry, business cycle, business intelligence, Carmen Reinhart, central bank independence, cloud computing, corporate governance, creative destruction, crowdsourcing, demographic dividend, deskilling, disintermediation, disruptive innovation, distributed generation, Erik Brynjolfsson, financial innovation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Gini coefficient, global supply chain, global village, hydraulic fracturing, illegal immigration, income inequality, index fund, industrial robot, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, inventory management, job automation, Just-in-time delivery, Kenneth Rogoff, Kickstarter, knowledge worker, labor-force participation, low skilled workers, Lyft, M-Pesa, mass immigration, megacity, mobile money, Mohammed Bouazizi, Network effects, new economy, New Urbanism, oil shale / tar sands, oil shock, old age dependency ratio, openstreetmap, peer-to-peer lending, pension reform, private sector deleveraging, purchasing power parity, quantitative easing, recommendation engine, Report Card for America’s Infrastructure, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, sovereign wealth fund, spinning jenny, stem cell, Steve Jobs, supply-chain management, TaskRabbit, The Great Moderation, trade route, transaction costs, Travis Kalanick, uber lyft, urban sprawl, Watson beat the top human players on Jeopardy!, working-age population, Zipcar
Indeed, the same forces that lifted one billion people out of extreme poverty between 1990 and 2010 will help propel nearly two billion more people into the global consuming class in the next two decades.21 This improvement in the economic status of so many people would save even more lives than the eradication of smallpox, one of the greatest medical achievements of the twentieth century. The rapid spread of technology will empower individuals and consumers in unprecedented numbers. Increasingly, companies will find that technology drives the marginal cost of delivering a new product, servicing a new customer, or completing a transaction toward zero. And as more people connect to the global communications and commercial systems, the force of network effects will make those systems more valuable—and create more value for those who can tap into them. As a result, the new world will be richer, more urbanized, more skilled, and healthier than the one it replaces. Its population will have access to powerful innovations that could address long-standing challenges, create new products and services for a growing consuming class, and present opportunities for a global entrepreneurial class.
But economic historians tell us that for hundreds of years, people living in cities have enjoyed living standards one and a half to three times better than those of their country cousins. There are many reasons that cities are such powerful engines of growth. Dense population centers generate productivity gains through economies of scale, specialization of labor, knowledge spillovers, and trade. These gains in productivity are reinforced through network effects. Recent research suggests that urban density drives superlinear productivity gains because it affords opportunities for greater social and economic interaction. People and skills attract businesses, which in turn lure migrants from rural areas looking for employment. Companies attract other companies that may want to do business or to share services such as roads, ports, and universities that offer a quick route into the talent pool.
Tap Urban Talent and Innovation Pools Cities are increasingly attracting talented, highly educated young people, with larger cities able to attract and retain talent better than smaller cities can. McKinsey research indicates that three-quarters of Europe’s GDP gap with the United States can be explained by the fact that more Americans live in big cities—even American middle-sized cities tend to be larger than large European ones. This matters because larger cities tend to have greater network effects and higher wage premiums compared to rural areas. More densely populated cities are more attractive to innovators and entrepreneurs, who tend to congregate in places where they have greater access to networks of peers, mentors, financial institutions, partners, and potential customers. Cities exhibit superlinear scaling characteristics; with every doubling of a city’s population, each inhabitant becomes, on average, 15 percent wealthier, more productive, and more innovative.28 Companies that seek to tap large cities for talent often worry about the cost of doing business in urban locations.
The Future of Technology by Tom Standage
air freight, barriers to entry, business process, business process outsourcing, call centre, Clayton Christensen, computer vision, connected car, corporate governance, creative destruction, disintermediation, disruptive innovation, distributed generation, double helix, experimental economics, full employment, hydrogen economy, industrial robot, informal economy, information asymmetry, interchangeable parts, job satisfaction, labour market flexibility, Marc Andreessen, market design, Menlo Park, millennium bug, moral hazard, natural language processing, Network effects, new economy, Nicholas Carr, optical character recognition, railway mania, rent-seeking, RFID, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, six sigma, Skype, smart grid, software as a service, spectrum auction, speech recognition, stem cell, Steve Ballmer, technology bubble, telemarketer, transcontinental railway, Y2K
In some ways, although it firms are the epitome of mass production, when it comes to standards they are still stuck in the craftsmen era, which explains in large part why they have been so amazingly profitable. Network effects make it even more attractive to control a technology, argue Carl Shapiro and Hal Varian, two economics professors, in Information Rules, still the best read on the network economy (Harvard Business School Press, 1998). If the value of a technology depends not just on its quality but also on the number of users, positive feedback can help one firm to dominate the market. For example, the more people are already connected to a data network using a particular transmission standard, the more people will see the point of hooking up to it. These network effects also explain why the it industry in the 1980s already started to move away from completely proprietary technology, the hallmark of the mainframe era.
Software is a service at heart, albeit an automated one, but it is sold much like a manufactured good. Customers have to pay large sums of money up front, bear much of the risk that a program may not work as promised and cannot readily switch vendors. it firms, for their part, have to spend a lot of resources on marketing and distribution, rather than concentrating on developing software that works well and is easy to use. Network effects and Wall Street make matters worse. In many markets it is a great advantage to be first, so vendors are tempted to release programs even if they are still riddled with bugs. And because equity analysts rightly consider software firms a risky investment, such firms must grow quickly to justify their relatively high share prices, pushing them to sell more programs than customers need. All this explains several peculiarities of the software business.
Collectively dubbed “ws splat” in geeky circles, these are now being adopted by the rest of the industry. This has raised hopes for a huge increase in their use in the next few 90 MAKE IT SIMPLE years (see Chart 3.2). Ronald 3.2 2.1 Towards ubiquity Schmelzer and Jason Web services, % of firms adopting Bloomberg at ZapThink, a 100 consultancy, think that web United 80 services are “nearing their tipStates EU ping point”, because they ben60 Asia efit from “the network effect: Pacific the adoption rate of the net40 work increases in proportion 20 to its utility”. In other words, FORECAST as with telephones or e-mail, 0 2002 03 04 05 06 07 08 09 10 a network with only a few Source: IDC people on it is not very useful; but as more people join it, it becomes exponentially more useful and thereby attracts even more members, and so on. Taking the idea of web services to its logical extreme, it is reasonable to ask why firms should continue to amass their own piles of Lego blocks, most of which will only duplicate the Lego blocks of business partners.
Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne
Affordable Care Act / Obamacare, AI winter, airport security, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Boeing 737 MAX, business process, call centre, Celtic Tiger, chief data officer, cloud computing, computer vision, corporate social responsibility, Donald Trump, Edward Snowden, en.wikipedia.org, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, school vouchers, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, The Rise and Fall of American Growth, Tim Cook: Apple, WikiLeaks, women in the workforce
According to Kai-Fu, “AI naturally gravitates toward monopolies . . . once a company has jumped out to an early lead, this kind of ongoing repeating cycle can turn that lead into an insurmountable barrier to entry for other firms.”3 The concept is a common one in information technology markets. It’s referred to as “network effects.” It has long been true in the development of applications for an operating system, for example. Once an operating system is in a leadership position, everyone wants to develop apps for it. While a new operating system might emerge with superior features, it’s difficult to persuade app developers to consider it. We benefited from this phenomenon in the 1990s with Windows and then hit the barrier on the other side twenty years later, competing against the iPhone and Android with our Windows Phone. Any new social media platform that wants to take on Facebook encounters the same problem today. It’s part of what defeated Google Plus. According to Kai-Fu, AI will benefit from a similar network effect on steroids, with AI leading to increased concentration of power in nearly every sector of the economy.
And this is creating an increasing possibility that American officials will seek to block the export of a growing number of vital technology products, not just to China but to a growing set of other countries. The risk here is that US officials will fail to appreciate that technology success almost always requires success on a global scale. The economics of information technology turn on spreading R&D and infrastructure costs over the largest number of users possible. This is what drives down prices and creates the network effects needed to turn new applications into market leaders. As LinkedIn cofounder (and Microsoft board member) Reid Hoffman has shown, the ability to “blitzscale” quickly to global leadership is fundamental to technology success.20 But it’s impossible to pursue global leadership if products can’t leave the United States. All this makes a new generation of potential US export controls even more challenging than in the past.
., 152, 157 Kirkpatrick, David, 192 Kissinger, Henry, 250, 259 Kistler-Ritso, Olga, 90–92, 317n2 Klobuchar, Amy, 176 Klynge, Casper, 109, 112, 123, 127, 128, 130 Kollar-Kotelly, Colleen, 335n7 Koontz, Elbert, 152–55, 167 Kubrick, Stanley, 328n12 Kushner, Jared, 173, 280 L Lagarde, Christine, 97 landmines, 127, 320n21 language translation, 197, 236, 239–40, 261 Law Enforcement and National Security (LENS), 24–26 Lay-Flurrie, Jenny, 334–35n1 Lazowska, Ed, 178, 325n11 LEADS Act (Law Enforcement Access to Data Stored Abroad), 314n9 League of Nations, 129 Lee, Kai-Fu, 269–70, 272, 273, 276 legal work, impact of technology on, 236, 237 Leibowitz, Jon, 29 LENS (Law Enforcement and National Security), 24–26 Leopard, HMS, 313n5 libraries, ancient, xiii, 309n1 Liddell, Chris, 173 Lincoln, Abraham, 10 LinkedIn, 100, 103, 126, 181, 325n18 Linux, 277 Long, Ronald, 43 LTE, 158, 162 M Macron, Emmanuel, 81, 123–24, 127 Mactaggart, Alastair, 144–49 Madison, James, 7 Maersk, 70–71 Mahabharata, The, 205 malware, 63, 68 see also cyberattacks Mamer, Louisan, 164–66 Manhattan Project, 171 Map to Prosperity, 157 Marino, Tom, 314n9 Markle Foundation, 325n18 Martin, “Smokey Joe,” 231 Martinon, David, 123 Mattis, James, 67 May, Theresa, 132, 238–39 Mayer, Marissa, 18 McCaskill, Claire, 83 McFaul, Michael, 117 McGuinness, Paddy, 56 McKinsey Global Institute, 241 Mercedes-Benz, 240, 326n31 Merck, 70 Meri, Lennart, 91 Merkel, Angela, 239 Mexico, 124 Microsoft: AccountGuard program of, 84, 85 AI ethics issues and, 199–201, 205, 218, 222, 223, 229–30, 294 AI for Earth team of, 288 antitrust cases against, xx, 12, 29, 96, 143, 148, 175–77, 291, 310n6, 335n7 Azure, 126, 140 Bing, 100, 104, 126, 140 board of directors of, 335n7 Brazil and, 48–49, 53 China and, 65, 250–52, 254–55, 259–61 Christchurch Call to Action and, 125–27 cloud commitments of, 30, 33, 292 Code.org and, 179 Cyber Defense Operations Center of, 111 Cybersecurity Tech Accord and, 119–21 data centers of, xiv–xix, 5, 14–15, 29–30, 34, 42–46, 48–56 Digital Crimes Unit (DCU) of, 78–81, 85, 111, 112, 316n2 ElectionGuard system of, 87 engineering structure at, 142 facial-recognition technology of, 213–15, 222–24, 226–27, 229–30 and FBI’s request for customer data, 31 Friday meetings of, 62 General Data Protection Regulation and, 140–43, 146–47, 294 Giant Company Software and, xviii GitHub, 100, 277 Google and, 12 government sued by, 12–13, 15, 16, 18–19, 33, 35–37, 83 housing initiative of, 186–90, 327n40 Immigration and Customs Enforcement and, 214–15 Ireland and, 42–45, 49–56 Law Enforcement and National Security (LENS) team of, 24–26 LinkedIn, 100, 103, 126, 181, 325n18 Muslim travel ban and, 173 NSA and, 1–4, 8, 13–14 Office, 84, 140, 253, 254 OneDrive, 126 open-source code and, 277–78 Patch Tuesdays of, 74 Philanthropies, 178–80 privacy legislation advocated by, 132, 146–48, 321n3 Research (MSR), 170–71, 194–95, 197, 237, 275, 328n12 Research Asia (MSRA), 255 Rural Airband Initiative of, 160–62, 166–67 Russia’s message to, 86 school voucher program of, 177 security feature development in, 111 Senior Leadership Team (SLT), 15, 62, 141, 221, 274, 307 Strontium and, 78–81, 84–85 Tay, 255–56 TechFest, 170–71 Technology Education and Literacy in Schools (TEALS) program of, 178–79 TechSpark program of, 233, 331n8 Threat Intelligence Center (MSTIC) of, 63, 78–79, 84 Windows, xx, 12, 29, 63–65, 203, 212, 253, 270 Word, 50, 264 Xbox, 72, 100, 126, 140, 160 military weapons, 117–18, 127, 202–6, 264, 329n29 artificial intelligence in, 202–6, 215, 216 nuclear, 116–17, 210 minimum viable product, 225–26, 296 Minority Report, 211–12 missiles, 66–67 MLATs (mutual legal assistance treaties), 47–49, 52 Mobility Fund, 158, 323n17 Moglen, Eben, 314n8 Mook, Robby, 279 Morrow, Frank, 125 MSTIC (Microsoft Threat Intelligence Center), 63, 78–79, 84 Munich Security Conference, 96–97, 208 Muslims, 288, 335n2 Christchurch mosque shootings, 99–100, 102, 125–26 travel ban on, 173 Myerson, Terry, 65 Myhrvold, Nathan, 194–95 Mylett, Steve, 187 N Nadella, Satya, 28–29, 62, 65, 66, 73, 115, 126–27, 141–43, 172–74, 186–88, 199, 200, 204–5, 218, 219, 221, 239–40, 252, 274, 276, 277, 289, 292 National Australia Bank, 213 National Federation for the Blind, 334n1 National Geographic Society, 161 National Health Service, 62 National Human Genome Research Institute, 213 National Institute of Standards and Technology, 221–22 nationalism, 112, 300–301 National Press Club, 29 national security: cybersecurity and, 110–11 individual freedoms vs., 9–10 National Security Council, 26 NATO, 82, 124, 204 Cooperative Cyber Defense Centre of Excellence, 92, 320n19 Nazi Germany, 39, 41, 61, 90, 129 negotiations, 175 Netflix, 16, 335n7 network effects, 270 neural networks, 196–97 New Deal, 164 NewsGuard, 104–5 New York, N.Y., 245 fire horses in, 231–32, 245, 247 New York Times, 63, 65–67, 99, 118, 219–20 New York University, 333n16 New Zealand, 75, 124, 125–27, 130 Christchurch mosque shootings in, 99–100, 102, 125–26 NGOs (nongovernmental organizations), 127, 128, 208, 302, 303 Nimitz, USS, 203 9/11 terrorist attacks, 8–9, 71, 72 1984 (Orwell), 227 Nisbett, Richard, 258, 261–62 North Korea, 63, 64, 67–69, 71–74 missile launch of, 67 Noski, Chuck, 335n7 NotPetya, 69–72 NSA (National Security Agency), 3, 8–9, 13, 15, 73 Google and, 2, 4, 13 Microsoft and, 1–4, 8, 13–14 PRISM program of, 1–4, 8, 9, 310–11n4 Snowden and, 4–5, 8, 9, 13–14, 17–19, 25, 41 Verizon and, 2–3 WannaCry and, 63–69, 71–74 and White House meeting with tech leaders, 16–19 nuclear power, 143–44 nuclear weapons, 116–17, 210 O Obama, Barack, 15–16, 26, 53, 83, 131, 174, 179–80, 278, 279, 284 meeting with tech leaders called by, 16–19 Office, 84, 140, 253, 254 Office of Personnel, US (OPM), 251, 263 O’Mara, Margaret, 297, 335n9 OneDrive, 126 Open Data Initiative, 285 Oracle, 120 Orwell, George, 227 O’Sullivan, Kate, 119–20 Otis, James, Jr., 6–7, 311nn14–15 Ottawa Convention, 320n21 Oxford University, 95 P Paglia, Vincenzo, 208–9 Pai, Ajit, 153–54 Pakistan, 21–22 Palais des Nations, 129 Paltalk, 2 Panke, Helmut, 335n7 paralegals, 236 Paris, terrorist attacks in, 26–28 Paris Call for Trust and Security in Cyberspace, 123–25, 127, 128, 300, 301 Paris Peace Forum, 123 Parscale, Brad, 280, 281 Partnership on AI, 200–201 Partovi, Hadi, 179 PAWS (Protection Assistant for Wildlife Security), 288 PBS NewsHour, 85–86 Pearl, Daniel, 21–22 Pearl Harbor attack, 10 Pelosi, Nancy, 57 Penn, Mark, 312–13n12 Pettet, Zellmer, 242–44 Petya, 69 Pew Research Center, 155–56, 323n9 phishing, 79, 83 Pickard, Vincent, 101 Pincus, Mark, 17–18 poachers, 288 Posner, Michael, 333n16 post office, 7, 192 Prague Spring, 40–41 presidential election of 2016, 81, 82, 139, 144, 157, 172, 189, 278–82, 331n8 Preska, Loretta, 314n10 Priebus, Reince, 279–80, 282 Princeton University, 13, 174, 218, 288, 314n10, 335n2 printing press, xiii, 209 PRISM (Planning Tool for Resource Integration, Synchronization, and Management), 1–4, 8, 9, 310–11n4 Pritzker, Penny, 136, 137, 250 privacy, 5–6, 21, 22, 30, 39–59, 131–49, 193, 229, 289, 300, 301 artificial intelligence and, 171, 199–200, 207 California Consumer Privacy Act, 147–48 data sharing and, 282–84 differential privacy, 282–83 Facebook and, 135, 144 facial recognition and, see facial recognition Fourth Amendment and, 7–8, 14, 15, 26 General Data Protection Regulation and, 131–32, 139–43, 146–49 legislation on, 132, 146–48, 321n3 Privacy Shield and, 137–38, 300 public attitudes about, 143 public safety and, 21–37, 222 reasonable expectation of, 7–8, 34 right to, 330n24 Safe Harbor and, 133–34 search warrants and, see search warrants social media and, 145 Wilkes and, 5–6, 23 see also surveillance Privacy Shield, 137–38, 300 Private AI, 171 Progressive movement, 245 ProPublica, 197–98 Proposition 13, 146 Purdy, Abraham, 232 Q Quincy, Wash., xiv–xv, 5, 34, 42 R racial minorities, 184–85 radio, 95, 100–102, 106, 159 Radio Free Europe, 107 radiologists, 236–37 railroads, 110, 299–300 Railroads and American Law (Ely), 110 ransomware, 68 WannaCry, 63–69, 71–74, 122, 294, 300, 301 Rashid, Rick, 237, 238 Reagan, Nancy, 116 Reagan, Ronald, 23, 116, 146 Red Cross, 113, 118, 127, 320n16 Reddit, 99 Redmond, Wash., 187 Reform Government Surveillance, 16–17 regulation, 102, 143, 144, 192, 206–7, 219, 224, 266, 295–98, 300, 301, 303 of artificial intelligence, 192, 296 China and, 258 of facial recognition, 221–22, 224, 225, 228, 296 of governments, 301–2 of railroads, 299 of social media, 98, 100, 102–4, 144 Republic, Wash., 151–52, 155, 167 Republican National Committee (RNC), 279–82 Republicans, 82, 106, 172, 278–80 International Republican Institute, 84 Republic Brewing Company, 167 restaurants, fast-food, 235, 241 Ries, Eric, 225 Riley v.
From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry by Martin Campbell-Kelly
Apple II, Apple's 1984 Super Bowl advert, barriers to entry, Bill Gates: Altair 8800, business process, card file, computer age, computer vision, continuous integration, deskilling, Donald Knuth, Grace Hopper, information asymmetry, inventory management, John Markoff, John von Neumann, linear programming, longitudinal study, Menlo Park, Mitch Kapor, Network effects, popular electronics, RAND corporation, Robert X Cringely, Ronald Reagan, Silicon Valley, software patent, Steve Jobs, Steve Wozniak, Steven Levy, Thomas Kuhn: the structure of scientific revolutions
Backus and his team refined the system in response to feedback from users and released a new version in 1959. FORTRAN II contained 50,000 lines of code and took 50 programmeryears to develop. FORTRAN rapidly became the industry standard for scientific and engineering applications programming, and other manufacturers were forced to adopt it in order to compete with IBM. There was no conspiracy: FORTRAN was simply the first efficient and reliable programming language. What economists later called “network effects” made FORTRAN a standard language with little or no help from IBM. Users made software investments of tens of millions of lines of FORTRAN code, and when selecting a new computer—whether from IBM or another manufacturer—they needed a compatible FORTRAN system to protect their software investment, and also to share programs with others. (Though FORTRAN was the de facto standard for scientific programming, the data processing language COBOL later became an officially sanctioned standard.)
Profits were fed back into the development of software (then given away free) and product improvement, which had the effect of making System/360 more attractive to customers; the result was more sales and profits to be invested in further software development and product improvement, and so on. That virtuous circle gave IBM its 75 percent share of the mainframe market. By the time IBM unbundled its software, System/360 was already a standard platform, and independent software vendors produced software for the IBM platform in order to maximize their sales. These “network effects” further enhanced the desirability of the IBM mainframe. It should be noted that the success of System/360 was largely independent of its original technical merits. It was the mere fact that it was a standard platform that made it desirable. IBM’s market dominance could be explained only partially in terms of the economics of increasing returns. IBM was also subject to decreasing returns. For example, selling costs tended to rise at the margin, and IBM’s marketing resources and manufacturing facilities were constraints.
Bill Gates is not so much a wizard of technology as a wizard Not Only Microsoft 243 of precognition, of discerning the shape of the next game.”18 It is clear from The Road Ahead that what Gates calls “positive feedback” is an intuitive and informal equivalent to the increasing-returns model of the academic economists.19 We also know, from a memorandum dated June 1985 and subsequently published in a history of Apple Computer, that Gates was aware of the importance of network effects in establishing an operating-system standard.20 But until Microsoft opens its archives to independent scholars, we have only some tantalizing hints of the company’s strategic thinking and the extent to which Gates was responsible for it. In the meantime, another firm, Autodesk, has made its early strategic thinking publicly available in a book titled The Autodesk File.21 From 1987 to the present, Autodesk—manufacturer of the leading CAD package— has consistently been among the top ten players in the personal computer software industry.
Rethinking Capitalism: Economics and Policy for Sustainable and Inclusive Growth by Michael Jacobs, Mariana Mazzucato
balance sheet recession, banking crisis, basic income, Bernie Sanders, Bretton Woods, business climate, business cycle, Carmen Reinhart, central bank independence, collaborative economy, complexity theory, conceptual framework, corporate governance, corporate social responsibility, creative destruction, credit crunch, Credit Default Swap, crony capitalism, David Ricardo: comparative advantage, decarbonisation, deindustrialization, dematerialisation, Detroit bankruptcy, double entry bookkeeping, Elon Musk, endogenous growth, energy security, eurozone crisis, factory automation, facts on the ground, fiat currency, Financial Instability Hypothesis, financial intermediation, forward guidance, full employment, G4S, Gini coefficient, Growth in a Time of Debt, Hyman Minsky, income inequality, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Internet of things, investor state dispute settlement, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, knowledge economy, labour market flexibility, low skilled workers, Martin Wolf, mass incarceration, Mont Pelerin Society, neoliberal agenda, Network effects, new economy, non-tariff barriers, paradox of thrift, Paul Samuelson, price stability, private sector deleveraging, quantitative easing, QWERTY keyboard, railway mania, rent-seeking, road to serfdom, savings glut, Second Machine Age, secular stagnation, shareholder value, sharing economy, Silicon Valley, Steve Jobs, the built environment, The Great Moderation, The Spirit Level, Thorstein Veblen, too big to fail, total factor productivity, transaction costs, trickle-down economics, universal basic income, very high income
Markets are ‘embedded’ in these wider institutional structures and social, legal and cultural conditions.38 In the modern world, as Polanyi pointed out, the concept of a ‘free’ market is a construct of economic theory, not an empirical observation.39 Indeed, he observed that the national capitalist market was effectively forced into existence through public policy—there was nothing ‘natural’ or universal about it.40 The orthodox notion of competition between firms is equally misleading. Many of the most important markets in modern capitalism are oligopolistic in form, characterised by economies of scale and ‘network effects’ that lead to concentration and benefit incumbents. But even where there is greater competition, capitalist businesses are not all the same, forced to behave in similar ways by the external forces of ‘the market’. On the contrary, as Lazonick shows, what we actually observe is persistent heterogeneity, both in businesses’ internal characteristics and in their reactions to different market circumstances.
Without strong policy, innovation activity tends to be focused towards the incumbent, dominant technologies, where returns on incremental improvements are easily observed and understood. However, the alignment of expectations on the likely shape of future energy networks and innovations can lead to a ‘tipping point’ where the nature and direction of mainstream innovation activity can switch quickly. This can become self-reinforcing through new network effects. So long as one network technology is dominant, products and services linked to the use of that network will receive the bulk of innovation activity and there will be less effort committed to developing an alternative; but if a new technology network becomes dominant then innovation activity can shift quickly. The recent rapid development of energy storage technologies in the wake of the growth of renewables—storage being a principal means to cope with the intermittency of solar and wind power—provides a powerful example.
Musk realised that fossil-driven networks are hard to dislodge given the vast existing network of petrol stations, and vested interests of car dealerships, that make driving a combustion-engine car so much the norm.22 Rather than trying to win this battle alone, Tesla decided to expand the new market by stimulating the innovative resources of all car companies. Since the industrial revolution, firms have frequently exploited this path-dependence in technology adoption and network effects in order to diffuse their innovations and create new markets.23 Toyota followed suit. A striking finding of recent research is that the potential spillovers from low-carbon innovation to other sectors—one of the factors which helps to drive overall growth—may be higher than for other technologies.24 Aghion et al. provide empirical evidence both for geographical knowledge spillovers (where a firm’s choice of innovation path is influenced by the practice of the countries where its researchers are located) and for path-dependence (where firms tend to direct innovation towards what they are already good at).25 Using data on 1 million patents and 3 million citations, Dechezleprêtre et al. suggest that spillovers from low-carbon innovation in the energy production and transportation sectors are more than 40 per cent greater than in conventional technologies.26 At the same time Acemoglu et al. provide a powerful theoretical case to suggest that once systems of clean innovation have been started up, they may be more productive than conventional alternatives based on existing technologies.27 The lesson for policy-makers and economists here is an important one.
Better, Stronger, Faster: The Myth of American Decline . . . And the Rise of a New Economy by Daniel Gross
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, Affordable Care Act / Obamacare, Airbnb, American Society of Civil Engineers: Report Card, asset-backed security, Bakken shale, banking crisis, BRICs, British Empire, business cycle, business process, business process outsourcing, call centre, Carmen Reinhart, clean water, collapse of Lehman Brothers, collateralized debt obligation, commoditize, creative destruction, credit crunch, currency manipulation / currency intervention, demand response, Donald Trump, Frederick Winslow Taylor, high net worth, housing crisis, hydraulic fracturing, If something cannot go on forever, it will stop - Herbert Stein's Law, illegal immigration, index fund, intangible asset, intermodal, inventory management, Kenneth Rogoff, labor-force participation, LNG terminal, low skilled workers, Mark Zuckerberg, Martin Wolf, Maui Hawaii, McMansion, money market fund, mortgage debt, Network effects, new economy, obamacare, oil shale / tar sands, oil shock, peak oil, plutocrats, Plutocrats, price stability, quantitative easing, race to the bottom, reserve currency, reshoring, Richard Florida, rising living standards, risk tolerance, risk/return, Silicon Valley, Silicon Valley startup, six sigma, Skype, sovereign wealth fund, Steve Jobs, superstar cities, the High Line, transit-oriented development, Wall-E, Yogi Berra, zero-sum game, Zipcar
The United States has demonstrated a unique ability to develop such working models. When you have a large installed user base, a product or service rolled out on it can gain scale more quickly, and its value can grow exponentially. The development of the U.S. economy over time highlights the power of network effects—the notion that the value of a network to its owner, and to each user, rises as more people join. When a country has the world’s largest economy, with a large population spread over a large landmass and possessed of excellent connections to the global economy, network effects can work wonders. As a launching pad for products and services with global scale, the United States has certain advantages: the world’s largest domestic market; the paths blazed overseas for decades by brands like Coca-Cola, Disney, and McDonald’s; and the use of English as a commercial, financial, advertising, and consuming lingua franca.
Again, one can take these distressing metrics as further evidence of relative decline, or one can look at it this way: the United States manages to do an incredibly large volume of e-commerce with only half its population fully wired in, and with many Americans accessing the Internet more slowly than people in Gyor and Szeged. As hyper-connected and wired as the U.S. economy already is, there is a great deal of room for improvement.5 Recall the power of network effects. From the telegraph to the railroad, infrastructure has always served to rope people, especially in rural areas, into the wider, larger system, enriching both the lives of the individuals and the companies that ply their trade on the new medium. It’s no different with the Internet. Faster connections mean more people are digital, doing transactions, watching content, able to take advantage of free things and become more efficient consumers themselves.
Take the High Line. The elevated freight rail line on the West Side of Manhattan was unused and in disrepair until a group of arty visionaries had the idea of turning it into a park. It has become a platform for billions of dollars of investment in stores and restaurants, condominiums, offices, and hotels. Not every place is New York, but many regions and many cities could benefit from the sort of network effects that New York enjoys. Many of the biggest metropolitan areas in the United States are remarkably unnetworked. Knitting physical space more tightly together in networks would help boost real estate values and encourage investment at a time when it is much needed. This process has already been happening, even as infrastructure enthusiasts wring their hands at the lack of investment. Contrary to the common view, significant investments and advancements have been made in recent years.
The End of Traffic and the Future of Transport: Second Edition by David Levinson, Kevin Krizek
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, American Society of Civil Engineers: Report Card, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, big-box store, Chris Urmson, collaborative consumption, commoditize, crowdsourcing, DARPA: Urban Challenge, dematerialisation, Elon Musk, en.wikipedia.org, Google Hangouts, Induced demand, intermodal, invention of the printing press, jitney, John Markoff, labor-force participation, lifelogging, Lyft, means of production, megacity, Menlo Park, Network effects, Occam's razor, oil shock, place-making, post-work, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, technological singularity, Tesla Model S, the built environment, Thomas Kuhn: the structure of scientific revolutions, transaction costs, transportation-network company, Uber and Lyft, Uber for X, uber lyft, urban renewal, women in the workforce, working-age population, Yom Kippur War, zero-sum game, Zipcar
., one rents hotel rooms and cars rather than buying condos and vehicles when on travel; it is now common to rent music, videos, and books). Previously cars, taxis, and hotel rooms were rented from companies which could achieve large economies of scale. Now it is possible to rent couches and cars and rides from individuals with excess capacity. The degree to which economies of scale trump the network effects of distributed suppliers awaits to be seen. We roll out four dimensions of sharing—cars, rides, bikes, information—that have emerging implications for transport. ￼ Sharing Cars While living with his family in Italy, Kevin was carless; his family therefore used the town's Hertz, renting a car once per month. David did the same thing while a graduate student living in a relatively high-density Berkeley, using the neighborhood Avis once a semester.
Streets designs will need to accommodate pick-up and drop-off as a major feature, so curb space will need to be re-arranged so people know where to meet their car (and vice versa), so they don't get into the wrong white Prius. While we lose the need for parking, we might think of channelizing roads more like airports or multi-way boulevards than the monolithic pavement they are today. Street design is discussed more in a later chapter. Discussion Sharing—be it cars, bikes, boats or information—has strong network effects driven by convenience (a characteristic the time-starved seem particularly mindful of). But, macro versus micro transit discussions in the following chapter bring up matters of economies of scale versus economies of scope. There's a role for both. For example, one is more likely to use carsharing if more neighbors use it, since that makes it more likely there will be a car in front of one's house, workplace, or wherever, when it is desired.
Middle-aged US systems born in the 1970s like San Francisco's BART, DC Metro, and Atlanta's MARTA face similar issues associated with aging infrastructure, brittle labor relations, and fixed networks unable to meet changing demand patterns. The challenges facing older systems differ from those that were first launched after 1980 in the era of Light Rail. Expanding and extending is immensely easier (sclerotic New York excepted), and given network effects, more promising than starting from scratch. New and emerging systems (at least in the US) are sold on their ability to yield under-appreciated environmental and societal benefits. Where there are large volumes of people already moving in key corridors, the leap to fixed route service is inevitable. But elsewhere, the assumption is precarious because MaaS transport and micro-transit will suitably serve these markets.
Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig
3D printing, Airbnb, algorithmic trading, Amazon Web Services, anti-work, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, correlation does not imply causation, creative destruction, data is the new oil, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, gig economy, global supply chain, income inequality, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Steve Jobs, strong AI, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor
They have a monopolistic tendency that emerges not through any sort of artificial means, whether collusion or mergers. Instead, the very nature of a platform business tends towards monopolisation. This is for a few different reasons. One is network effects: the more people who use a platform, the more valuable that platform becomes for everybody else. Again, Facebook is a good example. You may despise Mark Zuckerberg, hate the surveillance practices of Facebook and dislike the way they have handled fake news, but if you are going to join a social media network it will be Facebook, simply because that is where all your friends and family already are. That is the power of network effects, and once they reach a crucial tipping point, they grow and grow and grow. The other aspect that leads to a monopoly is the ability to extract and control data. By situating themselves between all these different groups, platforms position themselves in a space where they can collect a lot of data.
., 55 Méda, Dominique, 183 Medical diagnosis (automation of ), 128, 129 Menger, Pierre-Michel, 4 Mental labour, 3 Meritocracy, 28 Middle-income jobs, 90, 93, 94 Migration, 40, 47 Minimum wage, 67, 69 Mining, 26, 38, 197 Mokyr, J., 59 Monopolies, 6, 136, 138–140 Morals/morality, 48, 77, 159, 160, 162, 164, 166, 167 Moravec’s paradox, 131 Murnane, Richard, 126 N Nagel, Thomas, 100, 102 National Living wage, 184 Needs vs. Wants, 3, 30, 88 Neoclassical economics, 4, 55, 60, 62, 73 Netherlands, the/Holland, 6, 68, 151, 163, 177, 181–183 Network effects, 138 Networks, 45, 48, 138, 196 Neumann, John von, 99 New Zealand, 179 Nübler, Irmgard, 6, 194, 196 Index O Obama, Barack, 164, 165, 171 Obligation, 38, 53, 73–79 Occupations, 16, 40, 41, 46, 47, 58, 70, 83, 84, 86, 87, 90, 92, 106, 178, 184, 190–192, 194 OECD, 66–68, 178 O’Neil, Cathy, 6 Ontology of work, 65 Organisations dynamics of, 164 Osborne, Michael, 90 Oswald, A, 60 209 Pre-modern/pre-industrial work, 3, 11, 47, 48 Productivity, 7, 10, 79, 86, 87, 176, 178–180, 183–185, 190–192, 199 Professional work, 1, 39 Profits (different profit models), 14–18, 30, 48, 75, 79, 93, 134, 135, 138, 152, 191 Protestant work ethic, 28 Public services, 94, 167 Puritan (view of work), 28, 75, 166 R P Painting Fool, The, 115, 116, 120 Parenting, 75, 76 Patocka, Jan, 9, 21 Pattern recognition, 129 Peasant labour, 41 Perez, Carlota, 192 Philosophy of work, 30 Physical labour, 3 Piasna, Agnieszka, 181, 183 Piece-work, 30 Platform economy/platform capitalism, 6, 140 Polanyi, Karl, 192, 193 Polanyi, Michael, 127 Policy (argument against), 7, 21, 67, 68, 95, 157–173, 180, 181, 183–185, 189–200 Population, 2, 12, 15–17, 19, 28, 30, 89, 90, 117, 147, 158, 172, 198 Postmates, 136 Post-work society, 59 Poverty, 15, 47, 59, 67, 177 Redistribution, 79, 169, 199 Redundancy, 10, 12, 15–17, 19, 78, 179 Religion/religious ritual, 12, 28, 194 Remittances, 40 Responsibility, 44, 47, 76–79, 106, 107, 115, 118, 136 Retail sector, 87, 137 Retirement, 19, 67, 78 Ricardo, David, 2, 13–17 Robinson, James, 194 Robotisation, 21, 94, 95, 192 Robots carers, 106 Romantic (view of work), 34, 35 Ruskin, John, 34 S Safety nets, 67, 68 Sahlins, Marshall, 26, 158 Salazar-Xirinachs, Jose M., 198 Schumpeter, Joseph, 190, 194 Scientific management, 30 Scott, James C., 28 210 Index Searle, John, 100–103 Self-employment, 69–70, 75 Self-realisation, 57, 165 Sennett, Richard, 3 Services/service sector low frequency vs. high frequency, 134 work, 40, 68, 161, 163 Singularity, 116 Skidelsky, Edward, 60, 176 Skidelsky, Robert, 60, 176 Skills acquisition, 33, 70 skilled vs. unskilled labour/jobs, 67 Slavery, 11, 29, 30, 45 Smartphones, 140 Smiles, Samuel, 28 Smith, Adam, 12, 13, 27, 35, 54, 55, 65 Smith, Rob, 177 Social drawing rights, 70 Social interaction, 53, 88, 91 Social media, 77, 138, 168 Societal knowledge base, 196–197 Sociology (of work), 166 Spencer, David, 4, 54, 59, 61 Spinning mills (cotton industry?)
The Stack: On Software and Sovereignty by Benjamin H. Bratton
1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, Ethereum, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Joan Didion, John Markoff, Joi Ito, Jony Ive, Julian Assange, Khan Academy, liberal capitalism, lifelogging, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Robert Bork, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, undersea cable, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator
This may insert machine control at almost any point, amplifying or diverting human control over any machine in which the User happens to be installed, or even of the whole infrastructural landscape in which those machines swarm together. For example, the integrated design of driverless cars includes navigation interfaces, computationally intensive and environmentally aware rolling hardware, and street systems that can stage the network effects of hundreds of thousands speeding robots at once. The next stable form of the “automobile” (a description that will become perhaps more and more accurate) may be as a mobile Cloud platform inside of which Users navigate the City layer of a larger Stack according to augmented scenery Interfacial overlays and powered by grids of electrons as well as bits. Planetary-scale computation involves the whole Earth from which silica, steel, and all manner of conflict minerals are drawn.
Those models may represent a rigorously discrete view of the platform's internal operations, its external environment, or, most likely, some combination of the internal and the external that measures platform performance according to metrics evaluating its outward-facing systems.10 6.. Platforms’ mediation of User-input information may result in an increase in the value of that information for the User. Platform network effects absorb and train that information, making it more visible, more structured, and more extensible for the individual User or in relation to other Users who make further use of it, thereby increasing its social value. At the same time, it is likely the platform itself that derives the most significant net profit from these circulations in total. Each time a User interacts with a platform's governing algorithms, it also trains those decision models, however incrementally, to better evaluate subsequent transactions.
Platforms that organize existing systems and information tend to achieve generative entrenchment more quickly than those that seek to introduce new systems from scratch. Users will make tactical use of some platform interfaces to link some existing systems, and in doing so they are incentivized to incorporate more of their own interests within these systems. Subsequent Users are incentivized to link their systems to benefit from the network effects set in motion by earlier Users, who in turn enjoy increasing network benefits as more User systems are incorporated over time. The platform is able to realize platform surplus value from this generative entrenchment. 15.. Platforms link actors, information, and events across multiple spatial and temporal scales at once. Platform ubiquity makes it more robust in relation to some threats, both intrinsic and extrinsic, and more vulnerable in relation to others.
The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly
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, Mitch Kapor, 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, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Review, zero-sum game
This perfect storm of cheap parallel computation, bigger data, and deeper algorithms generated the 60-years-in-the-making overnight success of AI. And this convergence suggests that as long as these technological trends continue—and there’s no reason to think they won’t—AI will keep improving. As it does, this cloud-based AI will become an increasingly ingrained part of our everyday life. But it will come at a price. Cloud computing empowers the law of increasing returns, sometimes called the network effect, which holds that the value of a network increases much faster as it grows bigger. The bigger the network, the more attractive it is to new users, which makes it even bigger and thus more attractive, and so on. A cloud that serves AI will obey the same law. The more people who use an AI, the smarter it gets. The smarter it gets, the more people who use it. The more people who use it, the smarter it gets.
“akin to building a rocket ship”: Caleb Garling, “Andrew Ng: Why ‘Deep Learning’ Is a Mandate for Humans, Not Just Machines,” Wired, May 5, 2015. In 2006, Geoff Hinton: Kate Allen, “How a Toronto Professor’s Research Revolutionized Artificial Intelligence,” Toronto Star, April 17, 2015. he dubbed “deep learning”: Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature 521, no. 7553 (2015): 436–44. the network effect: Carl Shapiro and Hal R. Varian, Information Rules: A Strategic Guide to the Network Economy (Boston: Harvard Business Review Press, 1998). famous man-versus-machine match: “Deep Blue,” IBM 100: Icons of Progress, March 7, 2012. rather than competes against them: Owen Williams, “Garry Kasparov—Biography,” KasparovAgent.com, 2010. freestyle chess matches: Arno Nickel, Freestyle Chess, 2010.
., 70–71 and platform synergy, 122–25 and real-time on demand, 114–17 and renting, 117–18 and right of modification, 124–25 accountability, 260–64 Adobe, 113, 206 advertising, 177–89 aggregated information, 140, 147 Airbnb, 109, 113, 124, 172 algorithms and targeted advertising, 179–82 Alibaba, 109 Amazon and accessibility vs. ownership, 109 and artificial intelligence, 33 cloud of, 128, 129 and on-demand model of access, 115 as ecosystem, 124 and filtering systems, 171–72 and recommendation engines, 169 and robot technology, 50 and tracking technology, 254 and user reviews, 21, 72–73 anime, 198 annotation systems, 202 anonymity, 263–64 anthropomorphization of technology, 259 Apache software, 69, 141, 143 API (application programming interface), 23 Apple, 1–2, 123, 124, 246 Apple Pay, 65 Apple Watch, 224 Arthur, Brian, 193, 209 artificial intelligence (AI), 29–60 ability to think differently, 42–43, 48, 51–52 as accelerant of change, 30 as alien intelligence, 48 in chess, 41–42 and cloud-based services, 127 and collaboration, 273 and commodity consumer attention, 179 and complex questions, 47 concerns regarding, 44 and consciousness, 42 corporate investment in, 32 costs of, 29, 52–53 data informing, 39 and defining humanity, 48–49 and digital storage capacity, 265, 266–67 and emergence of the “holos,” 291 as enhancement of human intelligence, 41–42 and filtering systems, 175 of Google, 36–37 impact of, 29 learning ability of, 32–33, 40 and lifelogging, 251 networked, 30 and network effect, 40 potential applications for, 34–36 questions arising from, 284 specialized applications of, 42 in tagging book content, 98 technological breakthroughs influencing, 38–40 ubiquity of, 30, 33 and video games, 230 and visual intelligence, 203 See also robots arts and artists artist/audience inversion, 81 and augmented reality, 232 and authenticity, 70 and creative remixing, 209 and crowdfunding, 156–61 and low-cost reproduction, 87 and patronage, 72 public art, 232 attention, 168–69, 176, 177–89 audience, 88, 148–49, 155, 156–57 audio recording, 249.
A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind
3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, blue-collar work, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Khan Academy, Kickstarter, low skilled workers, lump of labour, Marc Andreessen, Mark Zuckerberg, mea