From Kleptopedia


{Jordan Hall @medium}

Social media is corroding society. It is degrading the strength and objective capacity of human populations.

Four foundational problems:

  • Supernormal stimuli;
  • Replacing strong link community relationships with weak link affinity relationships;
  • Training people on complicated rather than complex environments; and
  • The asymmetry of Human / AI relationships


The human organism is an evolved homeostatic system roughly adapted to a particular set of environmental characteristics. While humans are remarkably flexible and capable of both individual and group learning, we are nonetheless sitting on a mammalian, primate, homo- substrate that is relatively hard-wired in its response to its environment.

Supernormal stimuli (also called hypernormal stimuli) are inputs that hijack an animal’s instincts beyond their evolutionary purpose. Evolution is remarkably willing to make do with “just enough” and as a consequence, our evolved systems are vulnerable to stimuli that overwhelm our evolutionary heuristics. In an important way, at a biological level, we really can’t tell what is good for us.

For example, up until about thirty thousand years ago, equating “sweetness” with “healthy” was a useful error. It worked. If you equated sweetness with “good, healthy, nutritious, desirable”, then you picked that nice sweet fruit, ate it, survived and passed on your genes. But as human beings began to take over from raw nature and more and more of our lived environment was a human constructed environment, the gap in this error between what you really need and what your sensemaker is tuned to make you seek became an exploit.

It turns out, it is possible to refine the sensation of sweetness away from the context that associated it with nutrition and have the signal without the thing that it is supposed to deliver. Cotton candy is sweet, but not nutritious. Indeed, this is more than possible, it can be incredibly profitable — a spoon full of sugar makes everything go down.

This deliberate use of supernormal stimuli is a kind of black magic because it gets in behind your conscious sensemaker to lead you into all sorts of bad (self destructive, fitness diminishing) behaviours. And as “seeking sweetness” is a part of our evolved hardware layer, it is the sort of thing that is very hard for individuals to overcome.

We humans have become masters of supernormal stimuli. Our ability to give ourselves what we want has far outstripped our ability to sense what we really need. And in the accelerating win/lose game theoretic arms race that has characterized late 20th Century society, the use and abuse of supernormal stimuli has become an almost requisite tool in the product marketing toolkit.


In every possible market niche, we see an arms race for attention and choice making (purchasing). And in each case, a ruthless (and reckless) use of increasingly sophisticated understandings of human physio-emotional and psycho-cognitive systems (and their supernormal vulnerabilities) is part of any viable competitive strategy. Anyone who fails to take advantage of supernormal stimuli is selected against, and the general drift of the entire market is towards increasing disruption of our evolved homeostatic systems.

It is important to note that, at a social level (i.e. using “collective intelligence”), we have shown some capacity to develop defenses to these supernormal vulnerabilities (e.g., the emergence of social movements regulating sugar, nicotine, etc.). However, these social defenses tend to move relatively slowly and to be unevenly distributed. Moreover, the general rule that decentralized market-based mechanisms outcompete top down regulatory mechanisms seems to be in play here.

Now we come to supernormal stimuli in the context of social media. Here we see the gamification and hijacking of both the evolved systems for “attention allocation” (what we pay attention to) and for “social relationship.” Notifications (particularly bings and buzzes on our phone), likes, hearts, simple and explicit “friending,” even just the extraordinary pace and vastness of the news feed itself — all of these are supernormal stimuli that play havoc with our homeostatic systems (e.g., neurotransmitter feedback loops) and the adaptive capacities that rely on them (e.g., forming and maintaining real relationships, thinking about reality).

Importantly, while the application of supernormal stimuli to our eating choices or mating choices is certainly destructive, the use of supernormal stimuli in social media is particularly risky. This is because supernormal stimuli in social media directly undermine our capacity for individual and collective intelligence. In this context, the hijacking of our evolved functions presents the potential of disrupting our social capacity to respond to the problem itself. Not good.


The second primary risk associated with social media is that they serve to change the conditions under which we form and maintain human relationships in a fashion that leads to a meaningful reduction in the number and type of “strong-community” connections and a substantial shift of time and attention towards “weak-affinity” connections.

In a natural environment, the primary selection criteria for relationship formation is physical proximity. Simply put, you can only form relationships with people who are within relatively easy travel distance. Therefore this is who you form relationships with. Notably, while local communities will naturally tend to form shared sensibilities, the simple fact of diversity of experience and perspective will lead to significant heterogeneity of both ideas and values within any physical community. Even siblings in a family will naturally develop substantially heterogenous sensibilities and experiences.

In this natural environment, the exigencies of community require that all participants develop adequate personal and interpersonal skillfulness to navigate this heterogeneity: regardless of how much you might disagree with your uncle, if both of you are required to maintain the success of the hunt, you will learn how to get along.

When you combine high skillfulness at getting along with a lot of time in relationship with heterogenous perspectives, you get the kinds of “strong” links out of which we can fabricate real community.

By contrast, social media enables an entirely new kind of human relationship: the “weak affinity” bond. In the social media space, it is trivial to (a) find people who very closely share your own perspectives and preferences and to (b) avoid people who do not (up to and including simply “blocking” them from your perception with the click of a mouse).

These kinds of bonds are the “cotton candy” of relationship. On the one hand they are easy and pleasant. On the other hand, they build little of enduring value. In the context of “attention exploiting media” where there is a premium placed on getting as many eyeballs as possible — this new potential for weak affinity becomes an operational mandate. A social platform that lacks the ability to filter or block unpleasant participants will quickly be outcompeted by one that has that capacity.

As adaptive creatures (particularly developmentally during childhood and adolescence), we cannot help but respond and adapt to the signals of our physical and social environment. Weak affinity environments reward and punish behaviours very differently from strong community environments. Thus, as we spend more and more time in virtual social spaces and (by necessity) less and less time in physical social spaces, we observe the continual movement of virtual social space towards asymptotically superficial echo chambers and the participants in these echo chambers trained for skills like emotional fragility, virtue signaling, conformity policing, and / or neo-sociopathy. These are not the ingredients of an enduring society.


The deep problem here has to do with a distinction between “complicated” environments and “complex” environments and how participation in these kinds of environments trains for very different adaptive capacities.

A rich examination of complexity and complication is outside of the scope of this document, but in brief the distinction is that a complicated system is defined by a finite and bounded (unchanging) set of possible dynamic states, while a complex system is defined by an infinite and unbounded (growing, evolving) set of possible dynamic states. Thus, for example, a Boeing 777, while very complicated, is ultimately a bounded system. Given enough information about the Boeing 777, we can predict with precision how it will respond to given inputs.

By contrast, a humble bumble bee is intrinsically complex. In principle, while we might be able to get a good sense of how it will respond to given inputs, it is always possible that the system itself (the bee) will simply change and, therefore, no matter how much information we have, our ability to predict is always limited.

Note that is always possible to see a complicated system as complex by putting it into relationship with a complex system. Thus, if a Boeing 777 is struck by a bird while in flight or is flown into a mountain, these effects will lead to the destruction of the Boeing 777 as a complicated (predictable) system and its reconnection with unbounded complexity. The fact that complexity is the base case of the natural world and that complication is always a temporary and artificial condition is of singular importance.

In practice, this distinction shows up in two very different adaptive responses when one has an eye towards making good choices. In the case of complication, the optimal choice is to become an “expert”. That is, to grasp the whole of the system such that one can make precise predictions about how it will respond to inputs.

In the case of complexity, the optimal choice goes in a very different direction: to become responsive. Because complex systems change, and by definition change unexpectedly, the only “best” approach is to seek to maximize your agentic capacity in general. In complication, one specializes. In complexity, one becomes more generally capable.

In this context, we can say that a fundamental issue of something like the Facebook News Feed is that it is training our sense making systems to navigate a complicated space in a complicated manner (“browse and select”). And, because our attention is limited, the more time we spend training in this condition, the less time we spend training our sense making systems to explore an open complex space.

Moreover, we witness the same dynamic on the other side of the UI. If and when I encounter something (say a post on my News Feed) that motivates me to some action, the only actions that are available to me within the FB UI are:

  1. To select one of six emoticons to “like” the post;
  2. To comment on the post;
  3. To share the post;
  4. To write a post of my own (which will be separated from the original post by the News Feed algorithm in the attention stream of the FB audience).

Again, the deep problem here lies less in the specific actions that are possible within the Facebook UI, but in the basic fact of presenting an environment of radically simple (or complicated) choices rather than complex ones. Of replacing choice with selection.

In a truly complex environment, we are always empowered (and indeed often required) to generate novel (creative) actions in response to perceived circumstances. In other words, our field of choice is unbounded and, therefore, symmetric to the unbounded field potential of the complex system in which we are living. We are thus challenged to and trained to improve our responsive capacity to complex circumstances.

In a complicated environment, we are ultimately engaging in the very different mode of simply selecting the “right” or “best” action from a finite list. This is an optimization game, and while it can be extremely useful when competing in finite complicated environments (e.g., Chess) it is a capacity that is oblique to creative response. Therefore, again, the basic problem is that meaningful (and widespread) participation in this kind of platform is training our agency away from capacities that are truly adaptive and towards a narrow specialization for particular complicated games.

[Moreover, we can notice that even we select the relatively complex problem of commenting or writing our own post, the overall environment of Facebook serves to narrow even this choice into a relatively complicated game. The pace of change in Facebook and the almost complete erasure of what had presence even a moment ago, constrains “success” to that subset of expressions that satisfy the dual conditions of (a) will grab attention and (b) will drive actions of the sort that are perceived and upregulated by the attention mediating algorithm.]


The final problem at the root of social media is a bit more challenging to grasp. Perhaps because it is the most novel in our collective experience: the intrinsic asymmmetry between human and artificial (calculation-based closed system) intelligence.

Gary Kasparov is a much better chess player than you are. In 1996, the AI chess program Deep Blue beat him. Shortly thereafter chess AI became effectively unbeatable. Ke Ji is the human champion at the much more complex game Go. In 2017, AlphaGo beat him. His evaluation of the match: “Last year, it was still quite humanlike when it played,” Ke said. “But this year, it became like a god of Go.”

Later in 2017, a new version of the AI named “AlphaGo Zero” took it a step further. In three days it taught itself to go from knowing nothing about Go, to beating the version of AlphaGo that had bested Ke Ji by 100 games to 0. If Alpha Go was a god of Go, what in the world might we make of AlphaGo Zero? One thing is for sure: it is very much not human. We are rapidly moving into the Era of AI and we are going to have to get used to this fact and to its deep implications.


When we enter into a relationship with an entity like Facebook (or Google, or Apple, or . . .) we still have the basic expectation that we are entering into a vaguely symmetric, human, relationship. At worst, we unconsciously expect the sort of unpleasant bureaucratic relationship that we enter into with Walmart, IBM or General Motors.

Nothing could be further from the truth. No matter how devious you might imagine the suits of the corporate world being, they are still, ultimately, just human. These social media AI? When it comes to grabbing and holding our attention or to analyzing and profiling our data, the algorithms of social media stand in relationship to our human sensibilities as Alpha Go Zero to an average Go player. They are like gods. And gods that, for now at least, don’t have our best interests in mind.

Imagine if your spouse, your therapist and your priest all entered into a conspiracy with a team of world class con men to control and shape your behaviour. Sound a bit unsettling? Well consider what the Facebook algorithms alone know about you. Every conversation you have — even those that you type out but don’t send — are perceived by the Facebook AI, and then analyzed by technology designed by thousands of researchers schooled in the very cutting edge of psychology and cognitive neuroscience.

Every conversation you have — and every conversation the other 1.4 billion people on the platform have. In one second, the Facebook AI learns more about how people communicate and how they make choices as a result of their communication than an average person will learn in fifty years. The Facebook AI is Alpha Go. The equivalent of Alpha Go Zero is a few minutes in the future.

We need to get our heads around the fact that this kind of relationship, a relationship between humans and AI, is simply novel in our experience and that we cannot rely on any of our instincts, habits, traditions or laws to effectively navigate this new kind of relationship. At a minimum, we need to find a way to be absolutely dead certain that in every interaction, these gods of social media have our individual best interests in mind.

These problems are not limited only to social media, of course. Supernormal stimuli show up in our cell phones and video games. Training on complication at the expense of the complex is a major problem with our educational system too. And, of course, in the next few decades, AI is going to show up everywhere.

We are already suffering from a major breakdown in our collective intelligence. It's become challenging for most people to think clearly and objectively about anything at all — much less anything nuanced and tricky. Worse yet, our natural path of least resistance has been co-opted by snake-oil marketing practicality to train the public to associate nuance and complexity with snake-oil disingenuousness. How convenient! But also a recipe for trouble ahead for the homo sapiens experiment.


The emergence of economic inequality as a public policy issue grew out of the wreckage of the Great Recession. And while it was protest movements like Occupy Wall Street that brought visibility to America’s glaring income gap, academic economists have had a near-monopoly on diagnosing why it is that inequality has worsened in the decades since 1980.

Monopolies rarely deliver outstanding service, and this is no exception. The economics profession is fond of believing that its theorizing is an impartial, value-neutral endeavour. In actuality, mainstream (‘neoclassical’) economics is loaded with suppositions that have as much to do with ideology as with science.

Take the distribution of income, which economists argue is (in the final analysis) a consequence of production. Whether one earns $10 per hour or $10 million per year, the presumption is that individuals receive as income that which they contribute to societal output (their ‘marginal product’). In this vision, the free market is not only the best way to efficiently divide the economic pie, it also ensures distributive justice.

But what if income inequality is shaped, in part, by broad power institutions—oligopolistic corporations and labor unions being two examples—such that some are able to claim a greater share of national income, not through superior productivity, but through market power? In a study recently published with the Levy Economics Institute, I explore the power underpinnings of American income inequality over the past century. The key finding: corporate concentration exacerbates income inequality, while trade union power alleviates it.

Mass prosperity — the fabled ‘middle class’ — was largely built between the 1940s and the 1970s. When President Roosevelt created the New Deal in 1935 union density was just eight percent. Density soared to nearly 30 percent by the mid-1950s, and the period spanning the 1930s to the 1970s would bear witness two major strike waves.

The combined effect was a surge in the national wage bill. In 1935 the share of national income going to the bottom 99 percent of the workforce was 44 percent. In tandem with strong unions and intense strike activity, the wage bill rose to 54 percent by the 1970s. In the period after 1980, union density and work stoppages both plummeted, pulling the wage bill down with them. American unionization is now just 11 percent and the wage bill sits at 41 percent—a seven decade-low for both metrics.

The declining power of the labor movement has many causes, but a series of state policies in the early 1980s hastened the demise. President Regan’s penchant for union-busting and the crippling effects of overly restrictive monetary policy (the infamous ‘Volcker shock’) broke the back or organized labor. As trade union power declined, a crucial mechanism for progressively redistributing income began to fade in significance.

The decline of trade unions did not lead to an economic golden age, as some would have hoped. In the decades after 1980, business investment trended downward, job creation slowed and GDP growth decelerated—a phenomenon often referred to as ‘secular stagnation’. Many economists have wondered why, given business-friendly policies in Washington, investment declined so precipitously after 1980.

My study reveals that America does not suffer from a shortage of investment in the general sense. The American corporate sector has been spending more money than ever, but instead of ploughing resources into job creation and fixed asset investment, historically unprecedented resources are flowing into mergers and acquisitions (M&A) and stock repurchase, the combined effect of which has been slower growth and rising inequality (a finding which also applies to Canada—see here and here).

Unlike an investment in fixed assets, which is linked with job creation, M&A merely redistributes corporate ownership claims between proprietors. The motivation for M&A is straightforward: large firms absorb the income stream of the firms they acquire while reducing competitive pressure, which increases their market power.

In the century spanning 1895 through 1990, for every dollar spent on fixed-asset investment, American business spent an average of just 18 cents on M&A. In the period since 1990, for every dollar spent on fixed-asset investment an average of 68 cents was spent on M&A—a four-fold increase. The explosion of M&A since 1990 has led to the concentration of corporate assets (power, in other words). In 1990 the 100 largest American firms controlled 9 percent of total corporate assets. Asset concentration more than doubled over the next two decades, peaking at 21 percent. The creation of a concentrated market structure, which has gone largely unnoticed by the economics profession, is one reason inequality has worsened in recent decades.

With more market power-generated income at their disposal, large firms have paid comparatively more to shareholders in the form of dividends (the enclosed figure contrasts the income share of the richest 1 percent of Americans with the dividend share of national income). At the same time, the 100 largest firms have spent more repurchasing their own stock than they have on machinery and equipment. And because many executives have stock options in their contracts, the share price inflation associated with stock repurchase has led to soaring executive compensation.

It is in this manner that increasing corporate concentration has simultaneously slowed growth and exacerbated inequality. None of these developments are inevitable, but if we are to meaningfully confront the dual problem of secular stagnation and soaring inequality we must begin to understand the role that power plays in driving these trends.



The rise of the 1% was the result of interaction among several systems. It was not technologically determined, though technology helped implement and amplify some of its elements. It was not driven by a right-wing conspiracy of elite businesses, although business lobbying played an important role at critical junctures. It certainly built on the intellectual ascendance of neoliberalism, but also emerged from left-wing skepticism about regulation and consumer-oriented drives for deregulation. Changes in popular culture that tied social status to money more directly than had typified the prior three decades, particularly perceptions of superstars, their importance, and the legitimate levels of compensation they could expect played a critical role. The dynamic reflected both intended and unintended consequences.

And it introduced dynamics that likely reduced productivity growth, rather than enhancing it. The story is not one of skills and technology leading to winner-take-all markets that lifts all boats as long as we have enough redistribution. It is a story of power and rent extraction by those who were in the position to take advantage of broad social and intellectual dynamics, political shifts, and organizational transformations to capture the overwhelming majority of the gains from market production. Throughout the era of oligarchic capitalism claims that technology was the central cause of rising inequality—skills-biased technical change in the broad economy, and winner-take-all markets at the top of the income distribution—were the dominant explanation in economics and policy circles.

Arguments about technology, efficiency, and growth served to legitimate growing inequality and limit the range of policy responses to the massive extraction of value by a managerial and financial class at the expense of working families, consumers, fiscally constrained communities and government services, and even saver-investors and their retirement security. The economic insecurity that the policies so justified wrought for the majority of the population has now bled into political instability as large numbers of voters across the most established democratic market societies are turning to xenophobic finger-pointing to explain why they are on the losing end of an economy that fails to provide them with security and paths for growth.

A political economy of the rise of oligarchic capitalism suggests that the radical shift from an era of high productivity growth and lower inequality to a period of slower productivity growth, widespread economic insecurity, and extreme concentration of wealth reflected a shift in power across several dimensions—knowledge, institutions (politics, law, organizational practice, social norms, markets), and technology.

Academic Solution

Where do we go from here?

If we are to overcome the democratic crisis that mature Oligarchic Capitalism has wrought, we will need solutions that operate across all the various dimensions of power that built that system. Here's a brief sketch covering elements of an alternative approach.

One class of approaches that the analysis I offer here is intended to exclude is, broadly speaking, techno-liberalism. These mix libertarian and progressive ideals (although there is a more explicitly techno-libertarian version, most prominently embodied by Peter Thiel) that take the settlement of the past forty years as given, and project that with enough economic dynamism, technology will lead us to an age of abundance so that will eliminated economic insecurity. The primary institutional proposals shared by these approaches are a much deeper investment in education, particularly a belief that better educational technology will improve outcomes,1 and a universal basic income that will redistribute the gains from those who are the winners in a “naturally” winner-take-all economy, to those who lose out in it, so that those who lost are free to develop their own projects and continue to innovate, feeding the virtuous cycle.

There are deep divisions regarding just how generous the basic income should be; how public the education; how big a role technologically-enhanced municipal government can be and so forth. But part of what is interesting about this class of answer is that it is effectively a continuation of the elite détente of the past forty years (leave the institutional foundations of oligarchic extraction largely untouched but assure equal dignity to diverse ethnic, gender, race populations; and strive for equal opportunity to compete in the otherwise-unperturbed market structures), coupled with a Panglossian progressivism about the power of technology to liberate humanity from want.2

But there is another answer that assumes that scarcity will not be repealed, and yet we must find a model for an open social economy that will provide broad-based economic security without sacrificing dynamism and without resurrecting ethnic and patriarchal sources of solidarity. It combines insights that emerge from the mainstream of the economics profession under the moniker “inclusive growth” with foundational challenges from networks, commons, cooperation, and complexity aimed at creating an open social economy. It insists that markets are arenas of power, not spontaneous order; that economic security and equality are integral to the institutional design of markets, and that the two cannot be separated, analytically or practically; that diversity of institutions, motivations, organizational forms, and normative commitments is the normal state of affairs, and that there is no convergence on an efficient equilibrium on any of these dimensions.

We have seen remarkable victories in the form of the Fight for 15 movement through agile advocacy, networking collaborations across locations, sectors, and targets wherever it can be most effective. We have seen local victories, most clearly that of the Barcelona en Comu party, now translating into significant efforts at integrating municipal with non-governmental efforts to build a collaborative economy. These victories represent the feasibility of a combination of strategies for economic reorganization, including action focused on private firms, municipalities, and states, and perhaps most importantly a reshaping of broad social norms and the basic intellectual beliefs that govern public and private, political and economic decisions.

Just as managerial capitalism was based on progressivism, and oligarchic capitalism was based on neoliberalism, the open social economy is based on developments across a wide range of academic disciplines that offer micro, meso, and macro-level understanding of human motivation and action. These have not to date been articulated as a coherent alternative, but taken together provide a way of understanding economic production and growth that neither collapses back to the expertise-based command and control system that typified old progressivism nor perpetuate the myth of efficient markets that has been the legitimating force of oligarchic capitalism.

We have seen a shift in the nature of our understanding of rationality from homo economicus, a uniform model of self-interested rational action, to homo socialis, who has diverse motivations that are socially-oriented and respond to the social setting and situation. We have seen a move from competition as the sole organizing concept of economic activity, to seeing cooperation and competition as complements. We have seen a move from optimization based on property and contract as the fundamental institutions of interaction, to a mix of commons and property, or governance and participation rather than arms-length bargaining as the core model of organizing production. We have seen a shift from optimization to experimentation and learning as the core model of technical design—most clearly of the Internet itself—and organizational strategy. More generally, the past quarter-century marks a broad shift from the idea of uniformity of optimal solutions—of motivations, institutions, and organizational forms—to diversity and continuous experimentation.

Rather than understanding the investor-owned firm as the core economic organization in modern economy, we are seeing an explosion of experimentation with organizational forms. Firms themselves have persistently diverse organizational models—the management science literature is rich in examples of firms that sustain “good jobs” or “high-commitment, high-performance” strategies to outperform their competitors while offering higher wage, greater stability, and greater autonomy to workers, gaining in return a more knowledgeable workforce with higher initiative, a cooperative dyanmic, and the team gains they yield. Long ignored by mainstream economists and policymakers, the non-profit and government sectors have been absolutely central to the core growth areas of economy and society—healthcare, education, and innovation. On the flip side, we are seeing experimentation with using LLCs, B-corps, and other fully or partly for-profit forms instead of the purely for-profit form to attain social goals. We are seeing a resurgence of interest in cooperative ownership by workers or consumers. And we are actually seeing a range of unincorporated networks of individuals working together to organize productive activity, again, most clearly with free and open-source software, but now moving to real-world models like emerging makerspaces or urban farming.

In all these areas, from “hard-nosed” business disciplines and hard science evolutionary biology to ethically-driven activist practice, we are seeing that uncertainty and human fallibility cannot be solved by perfecting property and contract or getting self-interested incentives just right. We are seeing that identity and participation are central to the flourishing of business firms no less than they are to the flourishing of communities. We are seeing that values-orientation, flexibility, autonomy for self-motivated exploration and cooperation combined with economic security, rather than contingency and competitive self-interest drive functionally superior economic performance. From these building blocks we can, and must, synthesize a much more foundational alternative to both the settlement of the past forty years and the rising economic nationalism in the United States and Europe.

These foundational and social-practice changes must then be integrated with the emerging program that developed under the “inclusive growth” paradigm within more traditional economic work—covering reforms of labor and employment law, national and international tax regimes, and macro-economic policy-oriented equally toward labor market effects as towards inflation, rather than the present strict emphasis on inflation. Only be integrating some of these macro-policies that can only be implemented at national or even international scale, with the meso-organizational and micro-behavioral changes toward a more social economy, can we break the systemic effects that led to the rise of oligarchic capitalism. Failure means that continued broad economic insecurity and sustained identity threat to pluralities in the populations of market societies will continue to generate fertile ground for parties and leaders all too happy to exploit these anxieties to divert attention from oligarchic extraction to enemies of the state and the people, both internal and external.

1 This strong emphasis on technology as the solution to fundamental broad social problems is the core of Morozov's critique of Silicon Valley-centered progressivism. See Evgeny Morozov, To Save Everything, Click Here: The Folly of Technological Solutionism, Reprint edition (New York: PublicAffairs, 2014).

2 Gregory Ferenstein 11 08 15 11:00 AM, “The Politics of Silicon Valley,” Fast Company, November 8, 2015,