One of the most pressing issues we confront when analysing the digital economy is a pronounced tendency towards oligopoly which makes a lie of an earlier generation’s utopian embrace of the Internet as a sphere of free competition and a driver of disintermediation. There are important lessons we can learn from platform studies about the reasons for this, concerning the architecture of platforms and the logic of their growth. But it’s important we don’t lose sight of how these dynamics are reliant upon existing legal and economic processes which predate the ‘digital revolution’. As Jonathan Taplin points out in Move Fast and Break Things, their competitive advantage was reliant upon a specific regulatory environment that was far from inevitable. From pg 79:

The economist Dean Baker has estimated that Amazon’s tax-free status amounted to a $ 20 billion tax savings to Bezos’s business. Baker notes, “In a state like New York, where combined state and local sales taxes average over 8.0 percent, Amazon could charge a price that was 1.0 percent below its brick and mortar competition, and still have an additional profit of 7 percent on everything it sold. That is a huge deal in an industry where profits are often just 2–3 percent of revenue.” Bezos, eager to preserve this subsidy, went to work in Washington, DC, and got Republican congressman Christopher Cox and Democratic senator Ron Wyden to author the Internet Tax Freedom Act. The bill passed and was signed by President Bill Clinton on October 21, 1998. Although not barring states from imposing sales taxes on ecommerce, it does prevent any government body from imposing Internet-specific taxes.

This is only one example. An adequate understanding of the digital economy requires that we identify the regulatory environments within which each category of tech firm operates and how this has contributed to their thriving or  struggling. When we combine this institutional analysis with platform dynamics, we can begin to account for the level of market concentration which Taplin summarises on pg 119-120:

In antitrust law, an HHI score —according to the Herfindahl-Hirschman Index, a commonly accepted measure of market concentration —is calculated by squaring the market share of each firm competing in a given market and then adding the resulting numbers. The antitrust agencies generally consider markets in which the HHI is between 1,500 and 2,500 to be moderately concentrated; markets in which the HHI is in excess of 2,500 are highly concentrated. The HHI in the Internet search market is 7,402. Off the charts.

He goes on to argue on pg 121-122 that this situation helps generate a cash glut with serious systemic consequences:

The problem is that the enormous productivity of these companies, coupled with their oligopolistic pricing, generates a huge and growing surplus of cash that goes beyond the capacity of the economy to absorb through the normal channels of consumption and investment. This is why Apple has $ 150 billion in cash on its balance sheet and Google has $ 75 billion. These enterprises cannot find sufficient opportunities to reinvest their cash because there is already overcapacity in many areas and because they are so productive that they are not creating new jobs and finding new consumers who might buy their products. As former treasury secretary Lawrence Summers has put it, “Lack of demand creates lack of supply.” Instead of making investments that could create new jobs, firms are now using their cash to buy back stock, which only increases economic inequality.

In other words: the inequality which digital capitalism generates is only contingently a function of technology.

Reluctantly cut from my digital sociology paper

Indeed, as Srnicek (2016) argues, this dynamics is integral to the nature of the platform itself, as a business model premised upon maximising opportunities for data extraction through situating itself as an intermediary between the interactions of existing actors. Each platform has an epistemic privilege in relation to the transactions taking place though it, the potential financial value of which encourages maximal data extraction from existing users and continued efforts to expand the user base. The more a platform grows, the more useful it is to its users and the greater the range and value of the data collected. The logic of platforms generates an ambition towards monopoly, which might manifest itself in a choice to pull out of areas where this seems impossible to achieve e.g. Uber in China (Stone 2017).

The explanatory challenge posed by platforms rests on the confluence of social, economic and technology factors within a rapidly changing environment, the intensification of which is being brought about in part by the platforms themselves. Srnicek’s (2016) work offers an account of how such an analysis could proceed, identifying the generic characteristics of platforms and the different forms they take. In the case of something like the ‘sharing economy’, we can see a clear business model: find a social interaction which already is or could be monetised, develop a digital platform which can be inserted as an intermediary within that interaction and rely on network develops to scale the new model in a way that will ultimately squeeze out any instances of the interaction which are unmediated or reliant on an older form of mediation. The precise character of these dynamics, as well as the changing situation of those caught up within them, is probably best pursued as a multi-disciplinary endeavour (Scholz 2016). But sociological thought offers powerful resources for making sense of the broader context within which this is taking place: how are the platforms scaling in this way? To what extent are they reliant upon declining social integration and to what extent are they contributing to it? How are social relations being transformed by increasingly large tracts of human activity being governed by the technical architecture and social imperative of large corporations based many thousands of mile away, whose local operations are concerned at most with recruiting new workers & safeguarding the platform against regulatory pushback? These questions are offered by way of example of the intellectual resources sociology offers for making sense of these changes.


In my copy of The Vocation Lectures, edited by David Owen and Tracy B. Strong, the editors helpfully annotate Weber’s description of the occupational realities of the German academic. From pg 2:

German students used to have a Studienbuch, a notebook in which they registered the coruses they were taking in their field. They then had to pay a fixed fee for each course. For staff on a full salary – that is, professors – these tuition fees were a welcome extra. For the unsalaried Privatdozent, these fees were the sole source of income. Science as a Vocation, pg 2. 

Is this where the Uberfication of the University could lead? I find it easy to imagine a Digital Studienbuch, the killer app of educational disrupters, dispersed throughout the university system. Universities would still exist to manage the ‘student experience’, control the academics and provide infrastructure. Perhaps there would still be paid professors to replenish the knowledge system and train the Privatdozent. But the university wouldn’t be the platform, instead it would be a whole series of arenas (with declining influence as the system became embedded), facilitating extraction from a relationship between teacher and taught on the part of a distant technology company.

Weber’s description of the academic career in Germany, “generally based on plutocratic premises”, seems eerily familiar from a contemporary vantage point:

For it is extremely risky for a young scholar without private means to expose himself to the conditions of an academic career. He must be able to survive at least for a number of years without knowing whether he has any prospects of obtaining ta position that will enable him to support himself.  Science as a Vocation, pg 2. 

In Platform Capitalism, Nick Srnicek seeks to address what he sees as a profound oversight in the existing literature on digital capitalism. One set of contributions focuses on emerging technologies and their implications for privacy and surveillance but ignores the economic analysis of ownership and profitability. Another set critically analyses the values embodied in corporate behaviour but neglects the broader context of a capitalist system. A further set addresses the ills of the ‘sharing economy’ but fails to situate these in terms of broader economic trends. Finally, there are those which analyse the emerging economic trends in the technology sector but treat them in a way which is decontextualised from wider historical changes.

In contrast, he intends to offer “an economic history of capitalism and digital technology, while recognising the diversity of economic forms and the competitive tensions inherent in the contemporary economy” (loc 155). This involves “abstracting from them as cultural actors defined by the values of the Californian ideology, or as political actors seeking to wield power” (loc 166) and instead simply taking “major tech companies” as “economic actors within a capitalist mode of production”. Such an undertaking requires that we distinguish the technology sector from the digital economy. The former is relatively small, employing around 2.5% of the US labour force and contributing around 6.8% of the value added by private companies (loc 157). In contrast, the digital economy has taken on a systemic importance that is obscured if we analyse it on a sectoral basis:

we can say that the digital economy refers to those businesses that increasingly rely upon information technology, data, and the internet for their business models. This is an area that cuts across traditional sectors –including manufacturing, services, transportation, mining, and telecommunications –and is in fact becoming essential to much of the economy today. Understood in this way, the digital economy is far more important than a simple sectoral analysis might suggest. In the first place, it appears to be the most dynamic sector of the contemporary economy –an area from which constant innovation is purportedly emerging and that seems to be guiding economic growth forward. The digital economy appears to be a leading light in an otherwise rather stagnant economic context. Secondly, digital technology is becoming systematically important, much in the same way as finance. As the digital economy is an increasingly pervasive infrastructure for the contemporary economy, its collapse would be economically devastating. Lastly, because of its dynamism, the digital economy is presented as an ideal that can legitimate contemporary capitalism more broadly. The digital economy is becoming a hegemonic model: cities are to become smart, businesses must be disruptive, workers are to become flexible, and governments must be lean and intelligent.

Loc 157-178

His analysis locates the nascent importance of the digital economy against a backdrop of a “long decline in manufacturing probability” across a “sluggish production sector”. Digitalisation has been seized upon a set of mechanisms through which these problems might be ameliorated, leading to the growth of the platform as the business model best able to ensure returns from these emerging opportunities (loc 178). This represents a historicisation of the platform, drawing out the linkages between the contemporary platforms which dominate the breathless discourse of ‘disruption’ and earlier upheavals in capitalism which digitalisation played an (often under-acknowledged) part in. For instance, consider the technological prerequisites which allowed a transition from Fordism to post-Fordism, driven by a crisis of overcapacity and overproduction in global markets:

Companies were increasingly told by shareholders and management consultants to cut back to their core competencies, any excess workers being laid off and inventories kept to a minimum. This was mandated and enabled by the rise of increasingly sophisticated supply chain software, as manufacturers would demand and expect supplies to arrive as needed. And there was a move away from the mass production of homogeneous goods and towards increasingly customised goods that responded to consumer demand.

Loc 294

The point is not that technology was the agent of these changes but rather that it facilitated new ways of organising production in time and space. Recognising the political agency involved in the onset of neoliberal ‘reforms’ shouldn’t detract from an appreciation of the role technology played in allowing the reorganisation of production. Historicising the digital economy necessitates that we understand this interplay between digitalisation and financialisation from the outset, something which of course came to the fore with the dot com boom. 

Astonishingly, nearly 1% of US GDP consisted of VC invested in tech companies at the height of the sector in the late 1990s, with 50,000 companies formed and over $256 billion invested in them. This influx of capital facilitated a ‘growth before profits’ model which is still familiar today, licensed by the expectations of immense wealth to be generated if enough market share was captured in a still hazily envisioned digital economy. This speculative boom led to a vast investment in digital infrastructure through which our contemporary digital economy was able to emerge:

This excitement about the new industry translated into a massive injection of capital into the fixed assets of the internet. While investment in computers and information technology had been going on for decades, the level of investment in the period between 1995 and 2000 remains unprecedented to this day. In 1980 the level of annual investment in computers and peripheral equipment was $ 50.1 billion; by 1990 it had reached $ 154.6 billion; and at the height of the bubble, in 2000, it reached an unsurpassed peak of $ 412.8 billion. 16 This was a global shift as well: in the low-income economies, telecommunications was the largest sector for foreign direct investment in the 1990s –with over $ 331 billion invested in it. Companies began spending extraordinary amounts to modernise their computing infrastructure and, in conjunction with a series of regulatory changes introduced by the US government, 18 this laid the basis for the mainstreaming of the internet in the early years of the new millennium. Concretely, this investment meant that millions of miles of fibre-optic and submarine cables were laid out, major advances in software and network design were established, and large investments in databases and servers were made.

Loc 314-333

Coping with the eventual crash through lowering mortgage rates in turn sowed the seeds of the future housing bubble. The story is one of a continued ‘asset-price Keynesianism’ where interest rate reductions were used to encourage continued rises in asset prices, seeking to encourage investment and consumption in the absence of deficit financed stimulus or any resurgence in the manufacturing sector. This low interest rate environment within the global economy has, argues Srnicek, provided “a  key enabling condition for parts of today’s digital economy to arise” (loc 377) by reducing returns on a range of assets and encouraging investors to seek higher yields elsewhere. This is the context within which platforms emerged and were readily able to find vast investment, even in the absence of profitability. But what are platforms?

What are platforms? At the most general level, platforms are digital infrastructures that enable two or more groups to interact. They therefore position themselves as intermediaries that bring together different users: customers, advertisers, service providers, producers, suppliers, and even physical objects. More often than not, these platforms also come with a series of tools that enable their users to build their own products, services, and marketplaces. Microsoft’s Windows operating system enables software developers to create applications for it and sell them to consumers; Apple’s App Store and its associated ecosystem (XCode and the iOS SDK) enable developers to build and sell new apps to users; Google’s search engine provides a platform for advertisers and content providers to target people searching for information; and Uber’s taxi app enables drivers and passengers to exchange rides for cash. Rather than having to build a marketplace from the ground up, a platform provides the basic infrastructure to mediate between different groups. This is the key to its advantage over traditional business models when it comes to data, since a platform positions itself between users, and as the ground upon which their activities occur, which thus gives it privileged access to record them.

Loc 596-618

He identifies three key characteristics of platforms which are interconnected:

  1. Platforms mediate interaction between groups, providing an epistemic privilege in relation such interactions (and their potential monetisation). They are a mechanism for producing and extracting data from interactions.
  2. Platforms are reliant on network effects, such that their value to users grows in line with the number of such users. This leads to a ‘winner-takes-all’ or ‘winner-takes-most’ dynamic. The more a platform grows, the easier it is for it to grow more and the potential value of its epistemic privilege increases in line with this.
  3. Platforms often use cross-subsidisation to encourage more users on to the network, exhibiting a dynamic pricing structure often entailing free products and services because of the gains that can be made elsewhere. This helps encourage more users on to the platform.

The mediating character of platforms means they “gain not only access to more data but also control and governance over the rules of the game” (loc 636). With this comes the challenge of facilitating continued growth within a competitive environment, using cross-subsidisation and leveraging network effects to position oneself as the central platform within a domain of activity. However in spite of these shared characteristics, different types of platform have emerged within different spheres of social life. Srnicek identifies 5 types:

The first type is that of advertising platforms (e.g. Google, Facebook), which extract information on users, undertake a labour of analysis, and then use the products of that process to sell ad space. The second type is that of cloud platforms (e.g. AWS, Salesforce), which own the hardware and software of digital-dependent businesses and are renting them out as needed. The third type is that of industrial platforms (e.g. GE, Siemens), which build the hardware and software necessary to transform traditional manufacturing into internet-connected processes that lower the costs of production and transform goods into services. The fourth type is that of product platforms (e.g. Rolls Royce, Spotify), which generate revenue by using other platforms to transform a traditional good into a service and by collecting rent or subscription fees on them. Finally, the fifth type is that of lean platforms (e.g. Uber, Airbnb), which attempt to reduce their ownership of assets to a minimum and to profit by reducing costs as much as possible.

Loc 657

Much of his subsequent analysis concerns the competitive conditions under which each type of platform operates, as well as how this is shaping the emerging field and platform capitalism as a whole. I don’t agree with all of it but it’s definitely worth reading in full. I understand his core points to be the following:

  1. The necessity of ‘data extraction’ has a basis in a longer term crisis of profitability within capitalism. These are, in effect, technical fixes for a systemic deterioration afflicting manufacturing and platforms represent a formalisation of these into a new emergent form.
  2. The financial conditions under which this platform economy has been able to emerge were historically specific and should not be assumed to continue indefinitely. The infrastructure through which ‘data extraction’ become technically viable, as well as the emergence of platforms as operating businesses were deeply dependent upon this.
  3. Platforms as emergent forms exhibit characteristics which shape competition between them, as well as guiding the unfolding of the digital economy as a whole. The fierce competition between them, the competitive challenges specific to categories of platforms, the dynamics of network effects and the affordances of their cash hoarding are leading to platform isomorphism. They have an inevitable drive towards monopoly, further incentivised by the dynamics of accruing investment, which leaves them orientated towards becoming owners of the infrastructure of society.

The analysis of platform tendencies is probably my favourite part of the book. He talks about expansion of extraction, positioning as a gatekeeper, convergence of markets and enclosure of ecosystems. These are analysed in the final chapter in some detail and offer a convincing meso-level account of the claimed macro tendency towards monopoly or oligopoly.

There’s an interesting article in the Wall Street Journal about the threat index funds are increasingly seen to pose to the global economy. I’d like to understand this more than I do because I’m intrigued by the technological preconditions for this form of investing and the competitive advantage this use of technology offers:

And a group of researchers at the University of Amsterdam in the Netherlands recently argued that the rise of giant index-fund managers, including BlackRock and Vanguard Group, could lead to a concentration of ownership unrivaled since the days when John Pierpont Morgan and John D. Rockefeller controlled vast tracts of the economy.

Of course, those titans primarily represented their own interests, while the index funds are legally bound to act on behalf of the millions of savers who own them.

The machines that run index funds slash the costs of investing by 90% or more by skipping most of the research and trading their human rivals engage in, instead owning essentially all the stocks or bonds in a market basket all the time.

Furthermore, if I’ve understood correctly, index funds are parasitical upon evaluation taking place elsewhere. To use the terms from my recent paper, it opens up the possibility of the algorithmic imperative swamping the curatorial imperative, with potentially catastrophic results for financial markets.

From Alan Jacobs (Via Audrey Watters):

The megatech companies’ ability to convince us that they are not Big Business but rather just open-minded, open-hearted, exploratory technological creators is perhaps the most powerful and influential — and radically misleading — sales jobs of the past 25 years. The Californian ideology has become our ideology. Which means that many people cannot help seeing skepticism about the intentions some of the biggest companies in the world as “blaming technology.” But that way Buy n Large lies.

From Rise of the Robots, by Martin Ford, loc 1053-1069:

Virtually all of the financial innovations that have arisen in recent decades—including, for example, collateralized debt obligations (CDOs) and exotic financial derivatives—would not have been possible without access to powerful computers. Likewise, automated trading algorithms are now responsible for nearly two-thirds of stock market trades, and Wall Street firms have built huge computing centers in close physical proximity to exchanges in order to gain trading advantages measured in tiny fractions of a second. Between 2005 and 2012, the average time to execute a trade dropped from about 10 seconds to just 0.0008 seconds, 64 and robotic, high-speed trading was heavily implicated in the May 2010 “flash crash” in which the Dow Jones Industrial Average plunged nearly a thousand points and then recovered for a net gain, all within the space of just a few minutes. Viewed from this perspective, financialization is not so much a competing explanation for our six economic trends; it is rather—at least to some extent—one of the ramifications of accelerating information technology.

Really interesting looking conference organised by Chris Till:

Digital Health/Digital Capitalism One Day Conference CfP 4th July 2016

Digital technologies have had a profound impact on the ways in which people live their lives, relate to one another and think about themselves and their capacities. This event will bring together scholars who are interested in the impacts of the digital on ideas and practices of health and the workings of capitalist economies and how the two come together.

Questions addressed at this event will include but not be limited to:

  • How has the digital changed the ways in which bodies and health are understood, managed and experienced?
  • How does the management of health data by commercial enterprises (public-private partnerships and sharing and collaborative websites such as PatientsLikeMe) impact on health outcomes and peoples’ engagement with themselves, others and their health and bodies?
  • In what ways are digital technologies affecting work practices which themselves impact on wellbeing, physical and mental health?
  • How has the blurring of work and non-work life through an “always on” digital culture created new health problems and new potential strategies for managing health?
  • What can existing theories tell us about the changes brought about by digital health and digital capitalism? What theoretical innovations are needed?
  • Does the commercial monitoring of health and wellbeing (through areas as diverse as corporate wellness initiatives and telehealth) enable greater freedom and stimulation for healthier lives or intensify surveillance?
  • What potential is there in digital management of health and work for increasing or decreasing existing health inequalities or producing new ones? Will the digital divide transfer to these arenas or be minimised?

If you would like to talk at this one day event please send a title and abstract of no more than 250 words to Chris Till by Monday 15th February 2016. Registration will open 1st March.

The event will take place on Monday 4th July, 2016 at Leeds Beckett University.

Follow this link for more information.

This BSA Digital Sociology Group and BSA MedSoc Yorkshire event is supported by a grant from Leeds Beckett University.

As we enter the second machine age, it’s easy to assume that coding skills will be in ever increasing demand. But this TechCrunch feature suggests both that the skills shortage will likely prove fleeting, due to the impending automation of much coding, as well as that bullshit abounds in schemes which aim to address the contemporary shortfall:

It’s an ingenious business model. There’s a dearth of skilled coders in the marketplace to fill the five million computing jobs available in this country. For somewhere between free and $36,000, you learn to program computers in less than a year. If you’re one of the lucky few, you will hit your aha moment with programming and develop a personal passion for it, as well land a real job.

In 15 years, those hard-won skills will be obsolete — if they ever stuck in the first place. Despite their promises, coding academies don’t manufacture coders. They cast wide nets to discover new talent that has not yet been exposed to code. Most people don’t find coding enthralling or interesting enough to continue to pursue it as a career. Given the changing nature of software, they probably shouldn’t.

I see coding shrinking as a widespread profession. Not because software is going away, but because the way we build software will fundamentally change. Technology for software creation without code is already edging toward mainstream use. Visual content creation tools such as Scratch, DWNLD and Telerik will continue to improve until all functionality required to build apps is available to consumers — without having to write a line of code.

Who needs to code when you can use visual building blocks or even plain English to describe intent? Advances in natural-language processing and conceptual modeling will remove the need for traditional coding from app development. Software development tools will soon understand what you mean versus what you say. Even small advances in disambiguating intent will pay huge dividends. The seeds are already planted, from the OpenCog project to NLTK natural-language processing to MIT’s proof that you can order around a computer in your human language instead of code.

Granted, the author has a vested interest as the developer of one of these automation tools. But he makes an interesting point about the growth of what might be conceived as a coding skills bubble, with 175 percent growth in US academies between 2013-14 and an estimated $2.2 million revenue per school. There’s a lot of money to be made here, with a growing stream of people gravitating towards one of the few occupations popularly understood to be entering a boom time. But those willing and able to make money from providing such an education are disproportionally able to shape public perceptions of coding as an occupation, as well as having an obvious financial incentive to do so. Why entertain challenging questions about the future of coding as an occupation when there’s so much money to be made now?

The way myths about coding spread seems strikingly similar to other spheres of the digital economy. The TechCrunch author has a wonderfully succinct phrase: publicize successful outliers to propagate the illusion. The visibility of those who have ‘made it’, who are living the dream and doing what they love, licenses commitment and investment by a much greater cohort who have no prospect of doing so. Sound familiar? I’d love to explore this more rigorously at a later date, because the prospect of a homologous opportunity structure between coding and much of the creative industries is a fascinating hypothesis. Now what about data science? Is there an equivalent data science bubble developing?

From The Big Short by Michael Lewis, pg 172. This is a part of the story of the financial crisis which has received too little attention: ‘innovations’ in finance were driven by the ‘disruption’ the established figures in the industry were subject to as a result of new online competitors:

One of the reasons Wall Street had cooked up this new industry called structured finance was that its old- fashioned business was every day less profitable. The profits in stockbroking, along with those in the more conventional sorts of bond broking, had been squashed by Internet competition. The minute the market stopped buying subprime mortgage bonds and CDOs backed by subprime mortgage bonds, the investment banks were in trouble.

I’ve been thinking recently about forms of moral self-understanding amongst elites and how they change over time. I’m particularly interested in how those in the tech sector make sense of their own actions. But there’s a broader background here, in which ‘globalisation’ is seen and justified in explicitly moral terms. For instance, this passage from Plutocrats: The Rise of the New Global Super-Rich pg 26-27:

The irony today is that the real internationalists are no longer the bleeding- heart liberals; they are the cutthroat titans of capital. Here, for instance, is what Steve Miller, the chairman of insurance giant AIG and one of Detroit’s legendary turnaround bosses (he wrote a bestselling memoir called The Turnaround Kid ), had to say to me at Davos about globalization and jobs: “ Well, first off, as a citizen of the world , I think everyone around the world, no matter what country they’re in, should have the opportunities that we have gotten used to in the United States. Globalization is here. It’s a fact of life; it’s not going away. And it does mean that for different levels of skill there’s going to be something of a leveling out of pay scales that go with it, particularly for jobs that are mobile, if the products can be moved, which is not everything.”

And from page 29:

Mr. O’Neill concludes his book with a heartfelt rebuttal of the gloomsters, with their emphasis on rising national income inequality and the hollowing out of the Western middle class: This is an exciting story . It goes far beyond business and economics. We are in the early years of what is probably one of the biggest shifts of wealth and income disparity ever in history. It irritates me when I hear and read endless distorted stories of how only a few benefit and increase their wealth from the fruits of globalization, to the detriment of the marginalized masses. Globalization may widen inequality within certain national borders, but on a global basis it has been a huge force for good, narrowing inequality among people on an unprecedented scale. Tens of millions of people from the BRICs and beyond are being taken out of poverty by the growth of their economies. While it is easy to focus on the fact that China has created so many billionaires, it should not be forgotten that in the past fifteen or so years, 300 million or more Chinese have been lifted out of poverty. … We at Goldman Sachs estimate that 2 billion people are going to be brought into the global middle class between now and 2030 as the BRIC and N- 11 economies develop. … Rather than be worried by such developments, we should be both encouraged and hopeful. Vast swaths of mankind are having their chance to enjoy some of the fruits of wealth creation. This is the big story.

From pg 45:

The global capitalist boom has allowed some people at the bottom of even the most traditionally stratified societies to rise to the top. Consider the small but growing community of plutocratic Dalits, the Indian caste once known as the untouchables. In some parts of rural India, Dalits are still not allowed to drink from the village well, and Dalit children are segregated in a special corner of their schoolrooms, lest their spiritual taint contaminate their higher- caste classmates. But India now has Dalit multimillionaires, like Ashok Khade, owner of a company that builds and refurbishes offshore drilling rigs, and subject of a recent front- page profile in the New York Times . As one Dalit businessman told a reporter, “ We are fighting the caste system with capitalism .”

And a slightly different spin on this on pg 55-56:

Another way to believe our plutocrats are heroes battling for the collective good is to think of capitalism as a liberation theology— free markets equal free people, as the editorial page of the Wall Street Journal asserts. One of the most convincing settings for this vision is Moscow, where in October 2010 you could hear it ringingly delivered by Pitch Johnson, one of the founders of the venture capital business in Silicon Valley, in a public lecture to business school students about capitalism and innovation. Johnson, who was a fishing buddy of Hewlett- Packard cofounder Bill Hewlett, is a genial octogenarian with a thick white head of hair, glasses, and a Santa Claus waistline. He has made something of a project of Russia, having traveled there twenty times since 1990 (he got a particular kick out of flying his private jet into what was then still Soviet airspace). As Johnson tells it, capitalism is about more than making money for yourself— it is about liberating your country. “ Those of you who practice economic freedom will also cause your country to have more political freedom,” Johnson promised with great enthusiasm. “I would call you the revolutionaries of this era of your country.”