Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins

Category: Print Issues

Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins

Geoffrey Manne & Dirk Auer

Volume 28

, Issue 4

28 GEO. MASON L. REV. 1279 (2021)

Introduction

The dystopian novel is a powerful literary genre. It has given us such masterpieces as Nineteen Eighty-Four,¹ Brave New World,² Fahrenheit 451,³ and Animal Farm.⁴ Though these novels often shed light on some of the risks that contemporary society faces and the zeitgeist of the time when they were written, they almost always systematically overshoot the mark (whether intentionally or not) and severely underestimate the radical improvements commensurate with the technology (or other causes) that they fear. Nineteen Eighty-Four, for example, presciently saw in 1949 the coming ravages of communism, but it did not guess that markets would prevail, allowing us all to live freer and more comfortable lives than any preceding generation.⁵ Fahrenheit 451 accurately feared that books would lose their monopoly as the foremost medium of communication, but it completely missed the unparalleled access to knowledge that today’s generations enjoy.⁶ And while Animal Farm portrayed a metaphorical world where increasing inequality is inexorably linked to totalitarianism and immiseration, global poverty has reached historic lows in the twenty-first century,⁷ and this is likely also true of global inequality.⁸ In short, for all their literary merit, dystopian novels appear to be terrible predictors of the quality of future human existence. The fact that popular depictions of the future often take the shape of dystopias is more likely reflective of the genre’s entertainment value than of society’s impending demise.⁹

But dystopias are not just a literary phenomenon; they are also a powerful force in policy circles. For example, in the early 1970s, the so-called Club of Rome published an influential report titled The Limits to Growth.¹⁰ The report argued that absent rapid and far-reaching policy shifts, the planet was on a clear path to self-destruction:

If the present growth trends in world population, industrialization, pollution, food production, and resource depletion continue unchanged, the limits to growth on this planet will be reached sometime within the next one hundred years. The most probable result will be a rather sudden and uncontrollable decline in both population and industrial capacity.¹¹

Halfway through the authors’ 100-year timeline, however, available data suggests that their predictions were way off the mark. While the world’s economic growth has continued at a breakneck pace,¹² extreme poverty,¹³ famine,¹⁴ and the depletion of natural resources¹⁵ have all decreased tremendously.

For all its inaccurate and misguided predictions, dire tracts such as The Limits to Growth perhaps deserve some of the credit for the environmental movements that followed. But taken at face value, the dystopian future along with the attendant policy demands put forward by works like The Limits to Growth would have had cataclysmic consequences for, apparently, extremely limited gain. The policy incentive is to strongly claim impending doom. There’s no incentive to suggest “all is well,” and little incentive even to offer realistic, caveated predictions.

As we argue in this Article, antitrust scholarship and commentary is also afflicted by dystopian thinking. Today, antitrust pessimists have set their sights predominantly on the digital economy—“big tech” and “big data”—alleging a vast array of potential harms. Scholars have argued that the data created and employed by the digital economy produces network effects that inevitably lead to tipping and more concentrated markets.¹⁶ In other words, firms will allegedly accumulate insurmountable data advantages and thus thwart competitors for extended periods of time. Some have gone so far as to argue that this threatens the very fabric of western democracy.¹⁷ Other commentators have voiced fears that companies may implement abusive privacy policies to shortchange consumers.¹⁸ It has also been said that the widespread adoption of pricing algorithms will almost inevitably lead to rampant price discrimination and algorithmic collusion.¹⁹ Indeed, “pollution” from data has even been likened to the environmental pollution that spawned The Limits to Growth: “If indeed ‘data are to this century what oil was to the last one,’ then—[it’s] argue[d]—data pollution is to our century what industrial pollution was to the last one.”²⁰

Some scholars have drawn explicit parallels between the emergence of the tech industry and famous dystopian novels. Professor Shoshana Zuboff, for instance, refers to today’s tech giants as “Big Other.”²¹ In an article called “Only You Can Prevent Dystopia,” one New York Times columnist surmised:

The new year is here, and online, the forecast calls for several seasons of hell. Tech giants and the media have scarcely figured out all that went wrong during the last presidential election—viral misinformation, state-sponsored propaganda, bots aplenty, all of us cleaved into our own tribal reality bubbles—yet here we go again, headlong into another experiment in digitally mediated democracy.

I’ll be honest with you: I’m terrified . . . There’s a good chance the internet will help break the world this year, and I’m not confident we have the tools to stop it.²²

Parallels between the novel Nineteen Eighty-Four and the power of large digital platforms were also plain to see when Epic Games launched an antitrust suit against Apple and its App Store in August 2020.²³ Indeed, Epic Games released a short video clip parodying Apple’s famous “1984” ad (which upon its release was itself widely seen as a critique of the tech incumbents of the time).²⁴

Similarly, a piece in the New Statesman, titled “Slouching Towards Dystopia: The Rise of Surveillance Capitalism and the Death of Privacy,” concluded that: “Our lives and behaviour have been turned into profit for the Big Tech giants—and we meekly click ‘Accept.’ How did we sleepwalk into a world without privacy?”²⁵

Finally, a piece published in the online magazine Gizmodo asked a number of experts whether we are “already living in a tech dystopia.”²⁶ Some of the responses were alarming, to say the least:

I’ve started thinking of some of our most promising tech, including machine learning, as like asbestos: . . . it’s really hard to account for, much less remove, once it’s in place; and it carries with it the possibility of deep injury both now and down the line.

. . . .

We live in a world saturated with technological surveillance, democracy-negating media, and technology companies that put themselves above the law while helping to spread hate and abuse all over the world.

Yet the most dystopian aspect of the current technology world may be that so many people actively promote these technologies as utopian.²⁷

Antitrust pessimism is not a new phenomenon, and antitrust enforcers and scholars have long been fascinated with—and skeptical of—high tech markets. From early interventions against the champions of the Second Industrial Revolution (oil, railways, steel, etc.)²⁸ through the mid-twentieth century innovations such as telecommunications and early computing (most notably the RCA, IBM, and Bell Labs consent decrees in the US)²⁹ to today’s technology giants, each wave of innovation has been met with a rapid response from antitrust authorities, copious intervention-minded scholarship, and waves of pessimistic press coverage.³⁰ This is hardly surprising given that the adoption of antitrust statutes was in part a response to the emergence of those large corporations that came to dominate the Second Industrial Revolution (despite the numerous radical innovations that these firms introduced in the process).³¹ Especially for unilateral conduct issues, it has long been innovative firms that have drawn the lion’s share of cases, scholarly writings, and press coverage.

Underlying this pessimism is a pervasive assumption that new technologies will somehow undermine the competitiveness of markets, imperil innovation, and entrench dominant technology firms for decades to come. This is a form of antitrust dystopia. For its proponents, the future ushered in by digital platforms will be a bleak one—despite abundant evidence that information technology and competition in technology markets have played significant roles in the positive transformation of society.³² This tendency was highlighted by economist Ronald Coase:

[I]f an economist finds something—a business practice of one sort or another—that he does not understand, he looks for a monopoly explanation. And as in this field we are very ignorant, the number of ununderstandable practices tends to be rather large, and the reliance on a monopoly explanation, frequent.³³

“The fear of the new—and the assumption that ‘ununderstandable practices’ emerge from anticompetitive impulses and generate anticompetitive effects—permeates not only much antitrust scholarship, but antitrust doctrine as well.”³⁴ While much antitrust doctrine is capable of accommodating novel conduct and innovative business practices, antitrust law—like all common law-based legal regimes—is inherently backward looking: it primarily evaluates novel arrangements with reference to existing or prior structures, contracts, and practices, often responding to any deviations with “inhospitality.”³⁵ As a result, there is a built-in “nostalgia bias” throughout much of antitrust that casts a deeply skeptical eye upon novel conduct.

“The upshot is that antitrust scholarship often emphasizes the risks that new market realities create for competition, while idealizing the extent to which previous market realities led to procompetitive outcomes.”³⁶ Against this backdrop, our Article argues that the current wave of antitrust pessimism is premised on particularly questionable assumptions about competition in data-intensive markets.

Part I lays out the theory and identifies the sources and likely magnitude of both the dystopia and nostalgia biases. Having examined various expressions of these two biases, the Article argues that their exponents ultimately seek to apply a precautionary principle within the field of antitrust enforcement, made most evident in critics’ calls for authorities to shift the burden of proof in a subset of proceedings.

Part II discusses how these arguments play out in the context of digital markets. It argues that economic forces may undermine many of the ills that allegedly plague these markets—and thus the case for implementing a form of precautionary antitrust enforcement. For instance, because data is ultimately just information, it will prove exceedingly difficult for firms to hoard data for extended periods of time. Instead, a more plausible risk is that firms will underinvest in the generation of data. Likewise, the main challenge for digital economy firms is not so much to obtain data, but to create valuable goods and hire talented engineers to draw insights from the data these goods generate. Recent empirical findings suggest, for example, that data economy firms don’t benefit as much as often claimed from data network effects or increasing returns to scale.

Part III reconsiders the United States v. Microsoft Corp.³⁷84 F. Supp. 2d 9 (D.D.C. 1999), aff’d in part and rev’d in part, 253 F.3d 34 (D.C. Cir. 2001). antitrust litigation—the most important precursor to today’s “big tech” antitrust enforcement efforts—and shows how it undermines, rather than supports, pessimistic antitrust thinking. It shows that many of the fears that were raised at the time didn’t transpire (for reasons unrelated to antitrust intervention). Rather, pessimists missed the emergence of key developments that greatly undermined Microsoft’s market position, and greatly overestimated Microsoft’s ability to thwart its competitors. Those circumstances—particularly revolving around the alleged “applications barrier to entry”—have uncanny analogues in the data markets of today. We thus explain how and why the Microsoft case should serve as a cautionary tale for current enforcers confronted with dystopian antitrust theories.

In short, the Article exposes a form of bias within the antitrust community. Unlike entrepreneurs, antitrust scholars and policy makers often lack the imagination to see how competition will emerge and enable entrants to overthrow seemingly untouchable incumbents. New technologies are particularly prone to this bias because there is a shorter history of competition to go on and thus less tangible evidence of attrition in these specific markets. The digital future is almost certainly far less bleak than many antitrust critics have suggested and yet the current swath of interventions aimed at reining in “big tech” presume. This does not mean that antitrust authorities should throw caution to the wind. Instead, policy makers should strive to maintain existing enforcement thresholds, which exclude interventions that are based solely on highly speculative theories of harm.

I. The Precautionary Principle and Antitrust

Much of the momentum to reform antitrust enforcement for the twenty-first century is firmly rooted in the precautionary principle. The precautionary principle can be thought of as a form of cost-benefit analysis that gives significantly more weight to potential harms than potential benefits. In its most extreme form, the precautionary principle might even give an infinite weight to potential harms, implying that no action should be taken unless it is certain that it will cause no harm at all, however large its countervailing benefits may be.³⁸

Precautionary reasoning is often, but not always, reserved for situations that involve an element of uncertainty rather than mere risk.³⁹ As economist Frank Knight observed, risk describes situations where the outcome of an action is unknown, but where the probabilities and harms associated with that action are known.⁴⁰ Risk thus lends itself to precise expected gain computations. Conversely, uncertainty is present when the payoffs or probabilities of an action are unknown.⁴¹ Precise expected gain calculations are thus impossible. The precautionary principle has often been cited as a method of dealing with uncertainty.⁴² Proponents of mild versions of the precautionary principle thus tend to circumscribe its application to situations of uncertainty, whereas proponents of stronger versions of the principle also urge authorities to apply it to situations of risk.⁴³

The distinction between risk and uncertainty is critical. It is widely accepted that embracing localized risk (properly defined) is not just acceptable, but eminently desirable.⁴⁴ Driving a car may increase a person’s risk of mortality compared to sitting at home, but the benefits probably outweigh the costs. More importantly, when only risk is involved, good and bad outcomes can and should be weighed against each other, accounting for both sides of the coin. There is thus no obvious reason to reject cost-benefit analysis as a basis for risky decision-making.

Real questions—and ensuing policy debates—arise in situations of uncertainty.⁴⁵ Uncertainty gives rise to two interrelated policy questions. First, how should decisionmakers account for hypothetical costs and benefits (although the latter side of the equation is often ignored by critics)? Second, who should bear the burden of proving which outcomes are plausible or most likely?

Undergirding these two questions is a fear that uncertainty may conceal harmful, fat-tailed outcomes (i.e., low probability/high impact events, sometimes referred to as “Black Swans”).⁴⁶ For instance, the seemingly never-ending policy debates surrounding the use of genetically modified organisms for human consumption have this characteristic. On one side of the debate, commentators cite significant possible risks, while proponents point towards a veritable cornucopia of potential upsides.⁴⁷ Whatever one’s views on this matter, the key challenge for policy makers clearly lies in weighing harms (and benefits) that are often hypothetical against tangible upsides (and drawbacks).

In order to address these questions, policy makers and scholars have put forward various iterations of the precautionary principle.⁴⁸ At one end of the spectrum, proponents argue that the precautionary principle should preclude any activity that is not proven to be safe (though, taken literally, this command is impossible), and that parties undertaking an activity should have the burden of proving its safety.⁴⁹ On the other side, proponents argue that absence of scientific uncertainty is not sufficient to preclude regulation and that the burden of proving potential risks should fall upon those who seek to impose precautionary measures.⁵⁰

The goal of this Article is certainly not to provide an exhaustive survey of these various embodiments of the precautionary principle, and much less to argue in favor of one, or none of them. Instead, we wish to point out that precautionary principle-type reasoning has increasingly permeated antitrust policy discourse.⁵¹

This is best evidenced by two phenomena, which we refer to as “Antitrust Dystopia” and “Antitrust Nostalgia.” Antitrust Dystopia is the pessimistic tendency for competition scholars and enforcers to assert that novel business conduct will cause technological advances to have unprecedented, anticompetitive consequences. This is almost always grounded in the belief that “this time is different”—that, despite the benign or positive consequences of previous, similar technological advances, this time those advances will have dire, adverse consequences absent enforcement to stave off abuse.

Complementary to Antitrust Dystopia is Antitrust Nostalgia: the biased assumption—often built into antitrust doctrine itself—that change is bad. Antitrust Nostalgia holds that because a business practice has seemingly benefited competition before, changing it will harm competition going forward. Thus, antitrust enforcement is often skeptical of, and triggered by, various deviations from status quo conduct and relationships (i.e., “non-standard” business arrangements) when, to a first approximation (and at the very least in digital marketplaces), change is the hallmark of competition itself.⁵²

The following Sections illustrate these two tendencies. Section A discusses Antitrust Dystopia. Section B focuses on Antitrust Nostalgia. Both Sections draw from evidence within scholarship that calls for heightened antitrust enforcement in the digital sphere, particularly against firms that have access to large datasets of personal information concerning their users. We show that dystopia and nostalgia biases cause proponents to resort to precautionary reasoning; however, there is currently no evidence to warrant this precautionary approach. Indeed, while there is undoubtedly some level of uncertainty at play in digital markets, there is no evidence to suggest that the uncertainty could give rise to the type of fat-tailed situations where precautionary reasoning is arguably appropriate.

A. Dystopia

Recent antitrust scholarship and commentary has routinely voiced dystopian concerns, leading to several recurring tropes. One such trope is the oft-repeated mantra that the digital future will be bleak if governments do not act now. Similarly, it is often said that the emergence of large digital platforms poses threats to democracy that must, at least in part, be addressed through government intervention. Finally, critics often claim that the consolidation of digital markets will significantly slow innovation. Undergirding all of these is the claimed condition of fundamental uncertainty: reasoning from past experience is unavailing because this time will be different.

1. This Time is Different

As both the dystopia and nostalgia tendencies show, antitrust scholarship relating to digital competition often seems fearful of change. Scholars readily assume that new business realities complicate the task of antitrust authorities. Novel market features are often seen as a threat rather than an opportunity for authorities, consumers, and innovative rivals. The report on digital competition ordered by the European Commission neatly illustrates this view:

Despite the many benefits that digital innovation has brought, much of the enthusiasm and idealism that were so characteristic of the early years of the Internet has given way to concerns and scepticism.⁵³

. . . .

Digitisation is profoundly changing our economies, societies, access to information, and ways of life. It has brought welcome innovation, new products and new services, and has become an integral part of our daily lives. However, there is increasing anxiety about its ubiquity, political and societal impact and, more relevant to our focus, about the concentration of power by a few very large digital firms.⁵⁴

From a more technical and economic standpoint, commentators routinely conclude that digital markets display several features that arguably increase the likelihood of anticompetitive outcomes. They often ignore or minimize how these same features also benefit consumers, however, instead asserting that this time negative effects will predominate.

In a European Union committee report tasked with analyzing the competitive functioning of the digital economy, several prominent scholars concluded that:

The cost of production of digital services is much less than proportional to the number of customers served. While this aspect is not novel as such (bigger factories or retailers are often more efficient than smaller ones), the digital world pushes it to the extreme and this can result in a significant competitive advantage for incumbents.⁵⁵

The economic phenomenon just described is commonly referred to as “increasing returns to scale.”⁵⁶ The European Commission’s report on digital competition further noted the following about these increasing returns:

[T]he specificities of many digital markets have arguably changed the balance of error cost and implementation costs, such that some modifications of the established tests, including the allocation of the burden of proof and the definition of the standard of proof, may be called for. In particular, in the context of highly concentrated markets characterised by strong network effects and subsequently high barriers to entry (a setting where impediments to entry which will not be easily corrected by markets), one may want to err on the side of disallowing types of conduct that are potentially anticompetitive, and to impose the burden of proof for showing pro-competitiveness on the incumbent. This may be even more true where platforms display a tendency to expand their dominant positions in ever more neighbouring markets, growing into digital ecosystems which become ever more difficult for users to leave. In such cases, there may, for example, be a presumption in favour of a duty to ensure interoperability.⁵⁷

And Margrethe Vestager, the current head of DG Competition (the European Union’s main antitrust authority), made similar claims in a recent speech:

It’s not just that digitisation has made economies of scale more important than before. It’s also that the huge amount of data that some platforms have, and the huge networks behind them, can give them an edge that smaller rivals can’t match.⁵⁸

But while these increasing returns can cause markets to become more concentrated, they also imply that it is often more efficient to have a single firm serve the entire market.⁵⁹ For instance, to a first approximation, network effects, which are one potential source of increasing returns, imply that it is more valuable—not just to the platform, but to the users themselves—for all users to be present on the same network or platform.⁶⁰ In other words, fragmentation—de-concentration—may be more of a problem than monopoly in markets that exhibit network effects and increasing returns to scale.⁶¹ Given this, it is far from clear that antitrust authorities should try to prevent consolidation in markets that exhibit such characteristics, nor is it self-evident that these markets somehow produce less consumer surplus than markets that do not exhibit such increasing returns.⁶² Unfortunately, however, would-be antitrust reformers routinely overlook these important counterarguments or assume that they are meritless.

The idea that “this time is different” has also led scholars to argue that some industries deserve special protection against competitive disruption. A report published by the University of Chicago’s Stigler Center, for instance, argues that:

Digital Platforms are devastating the newspaper industry: Newspapers are a collateral damage of the digital platform revolution. Craigslist destroyed the lucrative newspaper classified ads, and Google and Facebook dramatically reduced the revenues newspapers could get from traditional advertising. Local newspapers have been hit particularly hard: At least 1800 newspapers closed in the United States since 2004, leaving more than 50% of US counties without a daily local paper. Every technological revolution destroys pre-existing business models. Creative destruction is the essence of a vibrant economy. In this respect, there is nothing new and nothing worrisome about this process. Yet, a vibrant, free, and plural media industry is necessary for a true democracy. The newspapers of yesteryear played an essential function in a democratic system.⁶³

In this case, it is not that the market dynamics that are presumed to play out differently, but that the consequences in this market or for this industry will be uniquely dire.

The upshot is that antitrust scholarship often emphasizes the unique risks that new market realities create for competition, while idealizing the extent to which previous market realities led to procompetitive outcomes. The critics who mobilize these sentiments generally call for the introduction of precautionary measures to keep anticompetitive concerns at bay. As argued below, however, there is little reason to believe that such measures are necessary, or that they would even be beneficial.

2. The Future is Bleak

Building on the “this time is different” sentiment is the first big dystopian trope: that our digital future will be miserable if governments do not act now. This is well illustrated by a quote from Tim Wu’s The Curse of Bigness, in which he discusses the rise of “big tech”:

We have managed to recreate both the economics and politics of a century ago—the first Gilded Age—and remain in grave danger of repeating more of the signature errors of the twentieth century. As that era has taught us, extreme economic concentration yields gross inequality and material suffering, feeding an appetite for nationalistic and extremist leadership. Yet, as if blind to the greatest lessons of the last century, we are going down the same path. If we learned one thing from the Gilded Age, it should have been this: The road to fascism and dictatorship is paved with failures of economic policy to serve the needs of the general public.⁶⁴

The Stigler Center Report offers another example. The report cautions governments against the severe economic and social consequences that digital industries will purportedly give rise to, offering its suggested brand of state intervention as a necessary corrective: “[T]his report is offered in the spirit of ensuring a future of continued technological and economic progress and social well-being as we move further forward into the Digital Age.”⁶⁵

Along similar lines, some scholars have suggested the digital world is not living up to its full potential. This is perhaps best illustrated by a passage written by economist Jason Furman and his co-authors, in a report commissioned by the UK Treasury:

Digital technology is providing substantial benefits to consumers and the economy. But digital markets are still not living up to their potential. A set of powerful economic factors have acted both to limit competition in the market at any point in time and also to limit sequential competition for the market in which new companies would overthrow the currently dominant ones. This means that consumers are missing out on the full benefits and innovations competition can bring.⁶⁶

The view that governments must act now was also echoed by the Australian Competition and Consumer Commission:

Important for Governments to act now, responding to current problems and anticipating future issues

We are at a critical time in the development of digital platforms and their impact on society. Digital platforms have fundamentally changed the way we interact with news, with each other, and with governments and business. It is also clear that the markets in which digital platforms and news media businesses operate will continue to evolve. It is very important that governments recognise the role digital platforms perform in our individual and collective lives, be responsive to emerging issues, and be proactive in anticipating challenges and problems.⁶⁷

In short, there is a long strand of antitrust scholarship that concludes governments must act immediately in the digital sphere or face the prospect of severe economic and social consequences. But while it is manifestly true that the future brings new risks and uncertainties and that the present is far from perfect, these critics often ignore the flipside of the same coin: things could also be worse today and, more importantly, there is no guarantee that the contemplated government interventions will lead to welfare improvements going forward.⁶⁸

3. Our Democracy Is at Stake

Tightly linked to these first two tropes is the oft-repeated claim that, if left unchecked, the rise of large digital platforms may threaten the very fabric of western democracies. For instance, Columbia Professor Tim Wu—and now member of President Biden’s National Economic Council—has argued that big tech firms currently exert too much control over our daily lives:

Big tech is ubiquitous, seems to know too much about us, and seems to have too much power over what we see, hear, do, and even feel. It has reignited debates over who really rules, when the decisions of just a few people have great influence over everyone. Their power feels like “a kingly prerogative, inconsistent with our form of government” in the words of Senator John Sherman, for whom the Sherman Act is named.⁶⁹

The Stigler Center Report reached a similar conclusion:

This concentration of economic, media, data, and political power is potentially dangerous for our democracies . . . . To make matters worse, as more of our lives move online, the more commanding these companies will become. We are currently placing the ability to shape our democracies into the hands of a couple of unaccountable individuals. It is clear that something has to be done.⁷⁰

The Stigler Center Report also claims that Google and Facebook are in a unique position to thwart the democratic forces:

Digital platforms are uniquely powerful political actors: Google and Facebook may be the most powerful political agents of our time. They congregate five key characteristics that normally enable the capture of politicians and that hinder effective democratic oversight[.]

. . . .

In sum, Google and Facebook have the power of ExxonMobil, the New York Times, JPMorgan Chase, the NRA, and Boeing combined. Furthermore, all this combined power rests in the hands of just three people.⁷¹

Finally, Federal Trade Commission (“FTC”) Chair Lina Khan famously argued that the way Amazon operates its retail platform excludes rivals, thus increasing economic concentration and threatening media freedom:

The political risks associated with Amazon’s market dominance also implicate some of the major concerns that animate antitrust laws. For instance, the risk that Amazon may retaliate against books that it disfavors— either to impose greater pressure on publishers or for other political reasons— raises concerns about media freedom.⁷²

Even specific elements of these platforms’ behaviors are sometimes said to imperil our polity: “Digital platform self-preferencing threatens the American Dream. When digital platforms pick the winners and losers of our economy, we lose the American promise of upward mobility based on merit.”⁷³

To summarize, critics routinely conclude that the advent of “big tech” today jeopardizes individual freedoms because the high levels of economic concentration that these markets exhibit allegedly translates inexorably into greater political power.⁷⁴ What these critics miss (aside from the tenuousness of the asserted causal relationship between economic concentration and political power) is that the type of heavy-handed intervention against big tech firms that many of these critics prescribe may pose equal if not greater threats to individual freedoms.⁷⁵ In other words, even if protecting individuals from the influence of powerful entities was a valid goal of antitrust policy, it is not clear that increased government intervention would achieve this end rather than the opposite.

4. Innovation Will Dwindle

Another recurring theme in Antitrust Dystopia is the idea that big tech firms will cause innovation to slow down if governments do not intervene. The intuition is that, because of their powerful market positions, big tech firms can either capture the profits of their rivals (by using their bottleneck positions to squeeze them) or snuff out budding competitors through so-called killer acquisitions. This, in turn, is said to reduce both rivals’ and incumbents’ incentives to innovate: the former because their expected profits from innovation are reduced, and the latter because they no longer need to innovate in order to best their competition.

These fears are perhaps best encapsulated by economist Hal Singer’s comments during the 2019 FTC hearings, as well as the paper on which these claims are based⁷⁶:

Dominant tech platforms can also exploit the vast amounts of user data made available only to them by monitoring what their users do both on and off their platforms and then appropriating the best performing ideas, functionality, and nonpatentable products pioneered by independent providers. If these practices are left unchecked, the resulting competitive landscape could become so inhospitable that independents might throw in the towel, leading to less innovation at the platform’s edges.⁷⁷

On a more dramatic note, in a piece promoting the Stigler Center Report, Professor Luigi Zingales referred to big tech firms as “robber barons” that levy a tax on innovation:

[B]y positioning themselves as a mandatory bottleneck between new entrants and customers, digital platforms play the role of the traditional robber barons, who exploited their position of gatekeepers to extract a fee from all travelers. Not only does this fee represent a tax on innovation, it also reduces the value new entrants can fetch alone and thus the price at which they would be acquired.⁷⁸

Merger enforcement in particular has become a key area of focus for these critics, who accuse large firms of buying startups to kill off future competition.⁷⁹ With this in mind, some scholars have called on authorities to implement far-reaching measures that would allegedly protect innovation from the depredations of “big tech.” For instance, a report ordered by the European Commission concluded that digital mergers should be much more heavily scrutinized:

Where network effects and strong economies of scale and scope lead to a growing degree of concentration, competition law must be careful to ensure that strong and entrenched positions remain exposed to competitive challenges. The test proposed here would imply a heightened degree of control of acquisitions of small start-ups by dominant platforms and/or ecosystems, as they would be analysed as a possible defensive strategy against partial user defection from the ecosystem as a whole. Where an acquisition plausibly is part of such a strategy, the burden of proof is on the notifying parties to show that the adverse effects on competition are offset by merger-specific efficiencies.⁸⁰

Along similar lines, the Stigler Center Report cautions that so-called “kill zone” mergers may entrench dominant incumbents and harm innovation:

While investment in innovation will continue, the type of innovation that will be funded will be broadly determined by the incumbent and its strategies. Disruptive innovation in markets that are characterized by high concentration levels and network effects is likely to be reduced compared to a competitive market. One of the few sources of entry in digital platforms comes from rival platforms that enter each other’s markets, as these large firms are more able to overcome entry barriers of all kinds.⁸¹

To be clear, this Article’s claim is not that digital platforms can never slow down innovation. The effect that market concentration and firm size have on innovation is ambiguous in theory and a hotly contested empirical question.⁸² The above critics do not merely argue that large digital platforms sometimes slow down innovation, however. Instead, they posit that this effect is so systematic and severe that it is necessary to impose precautionary measures—for instance, shifting the burden of proof in merger cases—whatever the cost.

To make matters worse, the evidence these critics cite to support their claims is highly conjectural. Economists Kevin Caves and Hal Singer, for example, put forward only weak anecdotal evidence to support their call for tougher antitrust enforcement against digital platforms, as they even acknowledge: “The empirical evidence that edge innovation has been diminished by dominant tech platforms is partially anecdotal and not dispositive . . . .”⁸³

Likewise, Professors Giulio Federico, Fiona Scott Morton and Carl Shapiro dismiss a large strand of economic literature concerning the link between competition and innovation by claiming that its authors did not specifically address the effect of mergers.⁸⁴ While this may be a useful caveat, it does nothing to support their own view that increased product market competition systematically boosts innovation. Indeed, the approach taken by Giulio Federico and his coauthors (like most of the relevant economic literature) is myopically committed to a model of innovation that ties innovation to market structure, and (unlike the literature it criticizes) even assumes that the relationship is unidirectional: changes in market structure affect incentives to innovate, but not the other way around. But the reality is far more complicated:

To summarize, the basic framework employed in discussions about innovation, technology policy, and competition policy is often remarkably naïve, highly incomplete, and burdened by a myopic focus on market structure as the key determinant of innovation. Indeed, it is common to ﬁnd a debate about innovation policy among economists collapsing into a rather narrow discussion of the relative virtues of competition and monopoly, as if they were the main determinants of innovation. Clearly, much more is at work.⁸⁵

Again, our argument is not that these authors’ claims about competition and innovation are necessarily wrong—only that they put forward little to no theoretical or empirical evidence to support their dire claims of curtailed innovation absent far-reaching precautionary measures.

B. Nostalgia

The antitrust literature surrounding digital competition is also beset by a strong and often-problematic sense of nostalgia. Scholars (and certain aspects of antitrust doctrine) are skeptical or fearful of change, even though change is a hallmark of digital industries where disruption has been the norm for decades.⁸⁶ This nostalgia can take several forms.

For a start, although it has no direct bearing on the development or interpretation of the law itself, nostalgic antitrust scholars have devoted extraordinary attention to dissecting and reinterpreting the historical origins of early US antitrust legislation (largely the Sherman Act). This sort of “meta-nostalgia” is consistent with a resurgent effort on the part of some activists and scholars to reinvigorate the populist sentiments of the late nineteenth century and progressive sentiments of the early twentieth century more broadly—including what some hold to be the apotheosis of those movements: the 1890 Sherman Act and the 1914 Clayton and FTC Acts. Although borne out of this broader political movement, such writing is more pointedly employed in an attempt to discredit subsequent judicial interpretations of antitrust law and the general common-law-like approach to antitrust jurisprudence.

Bearing more directly on the antitrust enterprise, nostalgic scholars tend to assume that markets were less problematic in the past, and that new business realities fundamentally alter the optimal balance of antitrust enforcement. This is nothing new, for as early as 1942 Joseph Schumpeter spoke dismissively of scholars advocating a view that “involves the creation of an entirely imaginary golden age of perfect competition that at some time somehow metamorphosed itself into the monopolistic age[.]”⁸⁷

But the problem is exacerbated in digital markets where change often takes the form of technological or business process innovation, both of which policy should generally encourage. “The critical point here is that innovation is closely related to antitrust error. The argument is simple. Because innovation involves new products and business practices, courts and economists’ initial understanding of these practices will skew initial likelihoods that innovation is anticompetitive and the proper subject of antitrust scrutiny.”⁸⁸ We simply do not know enough—especially about the relationship between firm structure, market structure, and innovation—to view change with the skepticism that some scholars do.⁸⁹

Nevertheless, a host of scholars, regulators, politicians, and, of course, competitors tend to confront novel business structures and practices as if they undermine presumptively efficient markets, thus counseling enforcement to thwart these practices—precisely the tendency to condemn “ununderstandable practices” decried by Ronald Coase.⁹⁰

Many further argue in favor of more aggressive interventions in digital markets in order to “restore” markets to the presumptively preferable state that existed before allegedly anticompetitive conduct occurred. In the past, antitrust enforcers mostly relied on cease-and-desist orders and deterrent sanctions (such as fines, treble damages for victims, and criminal prosecution) to police anticompetitive conduct.⁹¹ Such sanctions are mostly forward-looking (with the partial exception of damages awarded to victims of anticompetitive conduct). Authorities intended only to prevent anticompetitive harm from occurring in the future, whether by putting an end to a given practice or by deterring other firms from adopting a similar course of conduct. Today, however, many scholars consider these intervention methods to be insufficient in the realm of digital competition. They urge antitrust authorities to go one step further and impose remedies that “restore” markets to the presumptively efficient state that existed before the challenged conduct occurred. The underlying assumption is that the state of competition before an infringement took place is necessarily better than that which exists in the aftermath of a firm’s conduct, and that the differences that exist between both states are caused by this conduct.

These nostalgic inclinations are based on premises that are far from self-evident, however. In fact, as explained below, the error-cost framework that heavily influenced antitrust enforcement in the US (and to a lesser extent in the EU) is designed, among other things, to avoid the social costs that might stem from overly “nostalgic” enforcement. This makes it particularly ironic that nostalgic advocates for more aggressive intervention base their arguments in large measure on a rejection of the error-cost framework.

1. Progressive and Populist Nostalgia Aimed at Furthering a Broader “Democratization” Movement

The current resurgence of activist antitrust—variously labeled “populist antitrust,” “neo-Brandeisian antitrust,” or “hipster antitrust”—rests in considerable part (as its monikers suggest) on a deeply nostalgic sentiment. The hearkening back to activists’ preferred political and legal historical eras is part of a broader movement in both the academy⁹² and in political discourse⁹³ to (re-)build a “more genuine democracy that also takes seriously questions of economic power and racial subordination; . . . the displacement of concentrated corporate power and rooting of new forms of worker power; [and] . . . the challenges posed by emerging forms of power and control arising from new technologies . . . .”⁹⁴ “This [antitrust] moment is part of a larger one in which settled orthodoxies in many other areas of law and policy, particularly those that shape economic life, have been ruptured and new constructive projects have begun.”⁹⁵ “A necessary, though not sufficient, condition of [democratizing the economy] is to right the balance of law-making power, in antitrust law, in favor of the democratic branches of government.”⁹⁶

The antitrust prong of this “democratization” movement is focused on reviving what proponents see as the “true” antitrust tradition, which was illegitimately subverted by the neoliberalism of the late twentieth century. “Antitrust laws historically sought to protect consumers and small suppliers from noncompetitive pricing, preserve open markets to all comers, and disperse economic and political power. The Reagan administration—with no input from Congress—rewrote antitrust to focus on the concept of neoclassical economic efficiency.”⁹⁷

A significant part of this effort is a continual hearkening back to the origins of early US antitrust legislation, particularly the legislative history surrounding the enactment of the Sherman Act.⁹⁸ Recent scholarship has sought to justify what amounts to a sea change in antitrust law by suggesting that such a shift would merely reinstate the original intent behind federal antitrust legislation. In particular, this approach would supplant, and rescind, the judicially directed contours of antitrust law over the past 100 years with a “claimed” more fulsome and direct congressional mandate:

The legislative record shows that Congress . . . . made the affirmative purpose of the legislation quite clear. That purpose was to respond to the recent rise of concentrated corporate power by means of an overall decision rule that would disperse economic coordination rights rather than further concentrate them. Thus, Congress already set specific normative criteria for allocating economic coordination rights under the Sherman Act; it did not leave it to the courts to do so.⁹⁹

Interestingly, a significant number of scholars who advocate substantial precautionary antitrust reform are nevertheless deeply critical of these nostalgia-infused arguments. Professor Herbert Hovenkamp, for example, offers a strong critique of progressive antitrust, concluding that:

Not only have progressives been expansionist in antitrust policy, they also pursued policies that did not fit well into any coherent vision of the economy, often in ways that hindered rather than furthered competitiveness and economic growth—all while injuring the very interest groups the policies were designed to protect.

. . . .

[A]lthough the progressive state’s expanded ideas about the role of regulation may be justified, these views should not spill into antitrust policy. Rather, the country is best served by a more-or-less neoclassical antitrust policy with consumer welfare, or output maximization, as its guiding principle. Not only is such a policy consistent with overall economic growth, it is also more likely to provide resistance against special-interest capture, which is a particular vulnerability of the progressive state.¹⁰⁰

Indeed, although these progressive movement activists are surely the most nostalgic of all, their revivalist approach does not factor in any significant way into the central discussion of this Article. While some of these writers do also engage with the substantive debate over the proper antitrust treatment of data and digital platforms (and thus some of this work is discussed below), the effort to justify their preferred outcomes with an originalist appeal to nineteenth century legislative intent is simply irrelevant to the contemporary discussion. The broader political movement of which it is a part may be worth taking seriously, but as a matter of political science, not of antitrust law and economics. It is worth mentioning, but not worthy of further consideration here.

2. Much Antitrust Doctrine is Inherently Backward-Looking

The application of longstanding antitrust doctrine to digital platform technologies is often difficult:

[C]ompetition law instruments, such as market shares or concentration ratios, used for traditional markets in order to assess dominance cannot be easily used when it comes to platforms because of the multiplicity of services they offer simultaneously to different groups of consumers. It also means that market power indicators based on a comparison of price and cost . . . cannot be used on each side of the platform to assess its market power. It finally means that the characterization of an abusive practice of a platform may be either different or more complex than in the case of traditional markets.¹⁰¹

But the disconnect is made even more stark because much antitrust doctrine is inherently backward-looking. Antitrust Nostalgia is not simply a function of overly precautionary scholars advocating against change; it is also embedded within much antitrust process and doctrine.

This is not to say that the nostalgic elements of antitrust doctrine are inherently bad; indeed, as noted above, every common law-based legal system is inherently conservative in this respect.¹⁰² But it does mean that antitrust is often particularly skeptical of novel business conduct, absent the doctrinal correctives to this tendency¹⁰³—precisely the correctives dystopian antitrust scholars advocate rescinding.¹⁰⁴

Market definition offers a fitting example. Market definition is a key part of modern antitrust proceedings, not least in digital markets.¹⁰⁵ And yet, it is primarily backward-looking and nostalgic. Innovation often enables firms to disrupt rivals in new markets. Retrospective market definition exercises that focus on firms’ past and present competitors are thus likely to minimize where competition is going, locking even fast-evolving digital competitors out of the “relevant market.” As Judge Douglas Ginsburg and Professor Joshua Wright put it, “[E]conomics provides no reason to believe innovation ordinarily will come from within a ‘market’ as defined for the purpose of static antitrust analysis; hence, there is little reason to believe proxies for dynamic competition will be positively correlated with innovative activity observed in such a market.”¹⁰⁶

Indeed, traditional market definition analysis that infers future substitution possibilities from existing or past market conditions will systematically lead to overly narrow markets and an increased likelihood of erroneous market power determinations. This is the problem with viewing Google as a “search engine,” Amazon as an “online retailer,” Facebook and Instagram as “social networks,” and excluding each from the others’ markets. Because these products and services are constantly evolving, such firms can likely be viewed as “dominant” (if at all) only with backward-looking market definitions, superficially defined by legacy products that do not reflect the actual behavior of users or businesses.

Google and Amazon are direct competitors in search, for example—it makes no difference that one is nominally a “general search engine” and the other an “online retailer.” Indeed, today, over two-thirds of all product searches occur on Amazon, not Google.¹⁰⁷ Here, as in other nominal product markets, the reality is that the large platforms—as well as many other companies—are increasingly and significantly in direct competition with each other across those nominal market boundaries for users and advertisers.

In reality, digital platforms compete vigorously in a different and more competitive market—the “market for eyeballs,” perhaps—and arguably none is truly dominant, at least not for long. At the same time, the competition for users’ attention is constantly evolving. Today, the battle rages over digital assistants and smart-home devices—a sector characterized by intense competition among Google’s Assistant, Amazon’s Alexa, and Apple’s Siri, as well as failed or flailing combatants like Facebook’s virtual assistant named M, Microsoft’s Cortana, and Samsung’s Bixby. Surely this is not the last stage of competition among these firms, nor the last time that the relevant market(s) in which they compete will shift.

Assessing competition among these firms with reference to superficial markets defined by the specific functionality they employ in order to do so is circular. Rather, an important component of getting market definition right, especially in high tech markets, may be an expansion of the role of supply-side substitution in market definition and market power calculations, and especially from potential entrants.

The US Horizontal Merger Guidelines significantly downplay the role of supply-side substitution.¹⁰⁸ But demand-side substitution imposes on the analysis a static, backward-looking conception of competition driven significantly by consumer prices. Supply-side substitution, on the other hand (or some other, even less-traditional approach, perhaps), is better able to capture the dynamics of markets driven by product innovation and in which competitive constraints are importantly imposed by potential entrants from outside demand-defined “markets.”¹⁰⁹ This is an implication of the importance of competition via product innovation, rather than price, in these markets.¹¹⁰ This also means that seemingly unrelated suppliers and seemingly unrelated markets should often properly be counted in the same market.

Because of these relatively static, backward-looking market definitions, innovation or other procompetitive conduct may be systematically misidentified as anticompetitive. And the benefits of innovation aimed at competing with rivals outside an improperly narrow market, or procompetitive effects conferred on users elsewhere on the platform or in another market, will be relatively, if not completely, neglected. But at the same time, such innovation may seem to impose outsized constraints on firms or consumers within the improperly defined market, leading to more complaints and to more readily identifiable, apparent harm. These problems are likely to be particularly acute in rapidly changing digital markets.

It has to be recognized that some things that are excluded from the market because they seem to differ in superficial ways may actually be at least as similar, and at least as likely to operate as substitutes, as any number of items that are included in the market. Most obviously, this is true when it comes to digital platforms. If we think of them as competing for user attention, it is apparent, for example, that Google and Facebook are direct and significant competitors. But when the market is defined as “search” or “social media,” the market is being defined in a way that disregards the relevant competitive effects:

However, market definition is an entirely artificial construct that has been called an incoherent process as a matter of basic economic principles. Real markets do not come defined. Market definition is an exercise that serves to establish the group of products that are sufficiently substitutable with one another.¹¹¹

The bigger problem, perhaps, is that such market definitions are, as noted, inherently backward-looking. Yet, as Professors Thomas Jorde and David Teece note, true competition in high tech markets tends to come from the future:

It is especially in assessing potential competition that a departure must be made from orthodox approaches when new technologies and new products are at issue. The reason is that potential competition from new technologies can destroy a firm’s position in a particular market and its underlying competences. Price competition, on the other hand, may erode profit margins but is less likely to completely destroy the value of a firm’s underlying technological, physical, and human assets. Accordingly, potential competition from new products and processes is the more powerful form of competition.¹¹²

Google is a paradigmatic example. When Google evolved from offering “10 blue links” directing users only to other sites to offering direct answers or developing its own content and directing users there, it was probably responding primarily to the threat from Amazon (where two-thirds of product searches now originate¹¹³) and Facebook (which now accounts for approximately twenty-five percent of external page referrals on the Web¹¹⁴), rather than the “threat” from, say, Foundem—a UK-based comparison shopping search engine that built its business on the assumption that it would always be able to get all the product search traffic it wanted from Google’s links.¹¹⁵ But based on a narrow market definition that includes Foundem but not Amazon or any of the myriad other channels of distribution available (like direct navigation, mobile apps, links from other sites, etc.), Google’s innovation gets characterized (by Foundem, at least) as foreclosure, not healthy competition. Ultimately, this is exactly what the European Commission held in its Google Shopping decision.¹¹⁶

In the few instances where enforcers have taken a more forward-looking approach to market definition—the FTC’s Nielsen-Arbitron merger challenge, for example¹¹⁷—they have often resorted to even more speculative nostalgia given the inherent absence of data upon which to assess future markets. Thus, in Nielsen, the FTC asserted the risk of future anticompetitive harm based on rank speculation and “a general presumption that economic theory teaches that an increase in market concentration implies a reduced incentive to invest in innovation.”¹¹⁸

The essential facilities doctrine—at least as it is interpreted under US antitrust law—has the same nostalgic bent. Take Aspen Skiing Co. v. Aspen Highlands Skiing Corp.¹¹⁹472 U.S. 585 (1985).—the “outer boundary of [section] 2 liability” in the US—and the most prominent “essential facilities” case.¹²⁰ There, liability is defined by giving up a previously profitable, voluntary enterprise.¹²¹ In other words, a change in behavior by moving on to new modes of business or marketing can actually create liability. In that case, as it happened, it may well have been an effort to innovate that caused the defendant to abandon the prior arrangement and led to the case in the first place.¹²²

The Google-Foundem dispute again provides an example. Such a nostalgic approach—which bases a challenge to Google on the change in its practice away from one that favored Foundem—would end up protecting the decidedly non-innovative Foundem’s bad business model—one that made itself dependent on Google never changing—at the expense of Google’s efforts to evolve with technology, competition, and consumer demand. This turns essential facilities on its ear. Under this theory, Google is “essential” only because Foundem decided to put all its eggs in one basket. It is not that Google is the only way for consumers to reach Foundem and vice versa; it is that Foundem chose to acquire customers this way. If it is Foundem’s bad business model that turns Google into an essential facility, prevented from developing new products or new modes of business, then competition law will be doing the opposite of what it is supposed to do.

3. Static Nostalgia Meets Dynamic, Innovative Markets

The problem of nostalgia-driven false positives is particularly acute in dynamic markets.¹²³ Particularly in oligopoly markets (like those predominated by platforms), “[a] stable outcome will require restrictions on the freedom of market participants; that is, stability will require some sort of coordination. These restrictions look like the bread and butter of antitrust lawsuits—cartels, tacit collusion, vertical restrictions, and merger.”¹²⁴ “Clearly, when no competitive equilibrium is possible, something else has to take its place. Since the problems arise from too much competition and too little cooperation, the institutions that solve these problems necessarily imply a variety of arrangements that look ‘anticompetitive.’”¹²⁵

Large, complex systems like digital platforms are incongruous with perfect competition-informed antitrust doctrine and the traditional, linear supply-chain relationship upon which it is built. The Supreme Court’s recent Ohio v. American Express Co ¹²⁶ decision—much reviled by dystopian antitrust thinkers—is a nod in this direction: a realization that something more than the “local” efficiency notions endemic to traditional antitrust is required if adjudication of competition disputes relating to these businesses is to avoid systematic error. What the literature and the Amex Court make clear is that the systemic efficiency associated with platform ecosystems entails elements of coordination and control in various places in order to optimize the whole.¹²⁷ As the Supreme Court notes (with specific reference to the payment card platform at issue in the Amex case):

Focusing on merchant fees alone misses the mark because the product that credit-card companies sell is transactions, not services to merchants, and the competitive effects of a restraint on transactions cannot be judged by looking at merchants alone. Evidence of a price increase on one side of a two-sided transaction platform cannot by itself demonstrate an anticompetitive exercise of market power.¹²⁸

Properly assessing the competitive effects of business conduct in platform ecosystems entails a recognition that the systemic benefits from an efficiently operating platform can greatly exceed the costs of any localized restraint:

Systemic efﬁciencies involve and affect multiple, dispersed parts of a large complex system whose components are interconnected intricately in such a way that changes in one part might trigger readjustments in other parts (for example, as seen in electronic communications networks and operating system ecosystems). Because systemic efﬁciencies are drawn from multiple parts, understanding them requires a holistic overview of the system in which they are present, which makes them more difﬁcult to identify and appreciate. However, because systemic efﬁciencies are so integrative and extensive, that also means that they can bring about dramatic innovations in the industry that would otherwise not occur, especially in smaller-scale, insular environments. Systemic efﬁciencies and innovations therefore generate unique value for both the introducing ﬁrm and the industry as a whole, and deserve to be identiﬁed as a distinct type of efﬁciency.¹²⁹

Realizing systemic efficiencies across platform users’ disparate incentives and characteristics, the satisfaction of which is interrelated with those of every other group of users, entails “pervasive control,” which may demand “potentially exclusionary practices, such as refusing to supply, tying, and discrimination, among others. These practices aim at creating the necessary conditions for the efﬁciency to materialize, as they arguably ensure the involvement and proper interaction of only suitable parts, actors, and components.”¹³⁰ In certain contexts, of course, these mechanisms of control can be anticompetitive. But especially in complex systems, “authorities and courts should not underestimate the indispensable role control plays in achieving coordination and coherence in the context of systemic efﬁciencies. Without it, the attempted novelties and strategies might collapse under their own complexity.”¹³¹

In and of itself, this dynamic is likely to lead to mistaken over-enforcement against some procompetitive conduct. But the nostalgia bias amplifies this error by increasing the skepticism directed against the evolution of such practices out of arrangements where they may previously be absent or directed elsewhere.

The Court of Appeals for the Second Circuit’s United States v. Apple, Inc.¹³²791 F.3d 290 (2d Cir. 2015). case regarding Apple’s e-books suggests how nostalgia-infused antitrust enforcement against platforms can hinder the adoption of novel business practices, potentially stifling beneficial business model competition. In a nutshell, the case centered on most-favored-nation (“MFN”) clauses agreed upon by Apple and several book publishers. The record suggests these clauses were part of a concerted strategy to move the e-book industry from a retail model, where Amazon purchased e-books from publishers and sold them at its chosen price, to an agency model, where publishers set the price of e-books, and platforms (Apple and Amazon) took a percentage fee. Plaintiffs and courts framed these agreements as naked price fixing, thereby bringing them within the Sherman Act’s rule of per se liability. Whether or not this harsh treatment was appropriate is debatable. However, the case presented important implications for the evolution of the e-book industry (and competition in digital platforms, more generally) that mostly seem to have eluded enforcers and the court’s majority at the time.

Consider the following sentence from the majority opinion:

More importantly, even if there were such evidence, the fact that a competitor’s entry into the market is contingent on a horizontal conspiracy to raise prices only means (absent monopolistic conduct by the market’s dominant firm, which cannot lawfully be challenged by collusion) that the competitor is inefficient, i.e., that its entry will not enhance consumer welfare.¹³³

While the court’s analysis might arguably be correct from a static point of view, it misses key questions pertaining to dynamic competition. Though Apple’s agency model may have led to higher short-run e-book prices, and though bringing it about may have entailed control over and effective collusion of otherwise independent competitors, it also had the potential to shake up the e-book industry and enable firms with differentiated business models to gain traction (notably Apple and its then-brand-new iPad).¹³⁴

Indeed, by essentially shifting competition from price to non-price parameters, the clauses arguably enabled firms to experiment with business models that, in an “unfettered” environment, would have been thwarted by market imperfections. As the dissent appropriately noted:

As to the pro-competitive effects, the rule of reason must take account primarily of the deconcentrating of the e-book retail market . . . . As the district court found, Apple was weighing its entry into the retail e-book market, and the agency structure was the only way Apple would enter the market. Nobody has proposed—before or since Apple’s entry—any “less restrictive means” by which Apple could have achieved the same competitive benefits. Apple’s challenged conduct broke Amazon’s monopoly, immediately deconcentrated the e-book retail market, added a platform for reading e-books, and removed barriers to entry by others. And removal of a barrier to entry reduces for the long term a market’s vulnerability to monopolization. These effects sound in the basic goals of antitrust law . . . . (Judge Livingston’s opinion discounts this pro-competitive effect by noting the open question whether “below-cost pricing is unlawfully anti-competitive,” thereby suggesting that Apple’s dismantling of the entry barrier could be pro-competitive only if the barrier was itself a Sherman Act violation. But it is no matter whether the insuperable barrier that Apple tore down had been raised lawfully or not.).¹³⁵

While, on its face, this recognition that less “perfectly” competitive market structures (“non-standard contracting”)¹³⁶ may be optimal devices for overcoming market failures endemic to imperfect markets highlights the nostalgia bias, the fact that the case was brought in the context of a change in conduct is even more telling.

In this case, the court found it damning that Apple’s entry resulted in Amazon charging higher prices for certain e-books. While the court acknowledged that “[n]o court can presume to know the proper price of an ebook,”¹³⁷ its analysis rested on the presumption that Amazon’s prices before Apple’s entry were competitive.¹³⁸ The record, however, offered no support for that presumption, and thus no support for the inference that post-entry price increases were anticompetitive. In fact, a restraint might increase prices precisely because it overcomes a market failure.¹³⁹ Here, the change in Amazon’s pricing scheme may simply have reflected the fact that Amazon’s business model resulted in artificially low prices akin to market failure—not that Apple sought or obtained supra-competitive prices.

The Court of Appeals also focused erroneously on the effect of Apple’s entry on the e-book prices of a single competitor, Amazon, instead of on the e-book marketplace as a whole. The court found problematic that Apple’s entry “stiffened the spines” of the publishers¹⁴⁰ and enabled them to “demand new terms from Amazon,” including the use of the agency model.¹⁴¹ But that is exactly what competition from new entrants does: it empowers parties to obtain better products and more favorable terms from their suppliers. The fact that an incumbent firm—particularly a market leader such as Amazon—had to respond to the rigors of competition is hardly ground for condemning a new entry.

In other words, antitrust law may sometimes prevent firms from collectively moving towards a superior long-term equilibrium (on account that, in the short run, this marks an apparent competitive decline compared to the status quo), even though such arrangements might have been deemed unproblematic if they had been introduced ab initio (i.e., antitrust authorities would likely not have intervened in the e-book industry if the firms had immediately adopted the agency model with MFN clauses when the market emerged).

By the same token, there is a strong tendency among scholars to challenge platform evolution that may harm some complementors of these platforms to the benefit of others or enable platform appropriability where it was previously unavailable. Scholars have notably alleged this to be the case when Amazon enters business segments previously occupied by third-party retailers that use its platform.¹⁴² This relatively static, nostalgic analysis that infers harm from a change that imposes costs on complementors essentially assumes that any given complementor that succeeded in the past “should” succeed in the future (especially against competition from a platform’s own, integrated product). Doing so is mistaken.¹⁴³

The Google Shopping¹⁴⁴Commission Decision, Case AT.39740, Google Search (Shopping) (June 26, 2017). decision essentially turned this sort of shift in fortunes into an antitrust problem, inferring harm from the fact of a change, bolstered by the fact that the platform—Google—appeared to be appropriating more of the platform’s value than it had previously when it permitted comparison shopping services to operate without competition from Google itself.¹⁴⁵ Yet doing so is particularly likely to give rise to false positives:

The relatively static, “nostalgic” analysis that essentially assumes that any given complementor that succeeded in the past “should” succeed in the future (especially against competition from a platform’s own, integrated product) is deeply flawed. Past success under a particular set of platform constraints is no reason to assume that a complementor would provide any measure of innovation in the future under different constraints, nor is it an argument for insisting that the platform’s constraints cannot change. Indeed, if platform discrimination is rampant, the fact that a complementor previously succeeded under different, discriminatory conditions offers no reason to think that that there was an “effective competition structure” in the first place and thus that its previous success was in any way “merited.”¹⁴⁶

The upshot is that platforms are rarely static; there is expansive literature on the evolution of platforms both toward and away from greater “openness” depending on their place in the lifecycle, changing demand, evolving technology, experimentation, and the like.¹⁴⁷ And yet, for better or worse, antitrust enforcers routinely view such changes with circumspection. The pressing policy question is thus whether or not the idiosyncrasies of digital markets (discussed in Part II) warrant a reinforcement of these nostalgic inclinations (for instance, by shifting the burden the burden of proof in antitrust proceedings or by imposing restorative remedies).

4. Restorative Remedies

Another form of nostalgia can be seen in the repeated calls for authorities and courts to impose “restorative” measures upon firms that have infringed the antitrust laws. Contrary to other antitrust remedies that are mostly forward-looking (seeking to prevent further infringements from occurring), restorative measures, as their name suggests, attempt to restore the market to the state in which it was—or purportedly would have been—absent a firm’s anticompetitive behavior.

These calls for restorative remedies are driven by the idea that digital markets move fast, and that authorities are often late to the scene. Scholars thus argue that it is necessary to restore the competition that might have been lost in the interim. As the Stigler Center Report puts it:

Effective antitrust enforcement requires effective remedies. Treble damages and financial penalties can compensate for past harms and deter future bad conduct, but they do not restore competition to markets in which competition has been harmed. Even an injunction to forbear from the same or similar anticompetitive conduct going forward will not restore the lost competition if entry barriers are high. For example, if the market has tipped and network externalities are very strong, the firm that became a monopolist through violations of the antitrust laws could stop the conduct at issue and yet retain its monopoly position and the associated stream of profits.¹⁴⁸

The Stigler Report goes on to list several potential restorative remedies: “Data sharing, full protocol interoperability, non-discrimination requirements, and the unbundling of content from a platform are all tools that the regulator, in conjunction with the antitrust authority, could apply and monitor over time in order to restore competitive markets.”¹⁴⁹

The European Commission’s digital markets report makes a similar argument:

[W]here self-preferencing has significantly benefitted a platform’s subsidiary in improving its market position vis-à-vis competitors, such remedies might include a restitutive element (“restorative” remedies). In order to enable formerly disadvantaged competitors to regain strength, it may, for example, be necessary to give them access to the dominant platform’s competitively relevant data resources or otherwise compensate for their reduced visibility or lack of data access in the past.¹⁵⁰

Finally, a commonly cited solution would be for authorities to break up firms, thus reversing the supposedly harmful effects of industry consolidation. Tim Wu, for example, has argued that this should be the case for Facebook and Instagram:

As this analysis suggests, the case for the breakup should be relatively clear. Today, we can measure the effects of the lack of competitors to Facebook, in terms of higher prices and lower quality.

It is true that sometimes a breakup can undo benefits and efficiencies achieved by a merger. But when it comes to breaking off Instagram, it is hard to see what those might be. What seems more obvious is that an independent Instagram would be in a position to fashion itself into the full-fledged competitor to Facebook it was on track to becoming six years ago.¹⁵¹

While these proposals might sound good on paper, they overlook the very real challenges that antitrust authorities and courts would face in designing and enforcing such remedies. In doing so, these commentators exhibit significant nostalgia bias.

Take the most obvious question: what competition would authorities restore? Would their goal be to recreate the market conditions that prevailed before an infringement took place, or those that would currently prevail absent the challenged conduct? Because markets are constantly evolving, these two settings will often be very different.

Restoring the competitive conditions that prevailed before an infringement arbitrarily assumes that all, or most, of the changes that took place in the interim were ultimately to the detriment of consumers. But everything that occurs after an infringement is not necessarily caused by it. In seeking to restore markets to their prior state, policy makers might also lose any positive effects stemming from industry consolidation and vertical integration that occurred between the infringement and authorities’ decision.

Similarly, this approach implicitly assumes that less concentrated, less controlled markets (relative to subsequent concentration or reinforcement of monopoly) are implicitly more efficient and preferable to markets with structures that deviate from this norm. But this is a mistaken assumption. Deviations from perfect model assumptions are not necessarily expressions of market power; rather, they are often corrections of underlying market failures:

Reliance on the perfect competition model, I submit, accounts for the failure of modern scholars to offer any account of the formation and enforcement of non-standard contracts that does not depend on the possession or exercise of market power. By focusing solely on the propensity of non-standard contracts to reduce “transaction costs, ” these scholars ignore the fact that such agreements also reverse market failures by internalizing externalities and thus altering the costs faced by parties to such agreements. Thus, such restraints naturally produce prices or output different from what would obtain in an unbridled market.¹⁵²

Attempting to recreate a counterfactual world where an infringement never took place would not be any easier, and, for similar reasons, how could authorities distinguish those market evolutions that were caused by the infringement and which presumably harmed consumers, from those that emerged organically and were potentially beneficial?

More concretely, imagine that a vertically integrated platform excluded its downstream rivals by self-preferencing its own services. Imagine further that, months or even years down the line, an antitrust authority or court decided to challenge this practice and impose a restorative remedy. During that time span, the market may have tipped in favor of the monopolist. Authorities may thus be tempted to break up the firm, or to force it to share data with its rivals. But doing so opens a can of worms.

For a start, breaking up the dominant platform would potentially destroy valuable network externalities,¹⁵³ while data-sharing remedies may raise important data security issues.¹⁵⁴ More fundamentally, there is simply no telling whether the market would, or would not, have consolidated towards a single player in any case. In trying to reverse market consolidation, nostalgia-driven policy makers might ultimately be trying to reconstruct a state of competition that would have disappeared anyway. The breakup of the Bell Telephone Company offers a fitting case in point. After the breakup, in 1984, most of the so-called “baby Bells” either exited the market or merged together.¹⁵⁵

Finally, while it is easy to cite hypothetical reasons why breaking up companies would improve competition, as Tim Wu does when talking about Facebook and Instagram,¹⁵⁶ it is much harder to identify the benefits that were achieved thanks to market consolidation (be it by merger or competition). This could be the case for synergies,¹⁵⁷ economies of scale,¹⁵⁸ superior management,¹⁵⁹ etc. These benefits may be lost when a breakup remedy is imposed. Critics who dismiss this difficulty are ultimately just guessing that the market conditions were better for consumers before—the epitome of Antitrust Nostalgia.

In short, restorative remedies, though superficially tempting, would give rise to tremendous practical difficulties. Professor Richard Epstein summarizes these well:

There is something to be said for modest “behavioral” remedies limiting certain kinds of contractual provisions. The best evaluation of these is that they do little good, but, by the same token, do little harm as well. The same cannot be said of the more ambitious effort to impose “structural” remedies intended to break up the unitary corporate structure of firms.¹⁶⁰

5. Nostalgia and the Error-Cost Framework

The irony of the “this time is different” mindset¹⁶¹ is that, in neglected ways that do not fit the dystopian narrative, this time really is different. Modern digital platforms present a challenge for modern antitrust doctrine—not because it is too permissive (as some contend),¹⁶² but because it is overly nostalgic, based on presumptions and tools that, on the margin, point back to “blackboard models” of perfect competition as their touchstone. These models do not describe platform competition well, and many aspects of antitrust doctrine are incongruous.

Ironically, critics of the more permissive “Chicago School” of antitrust contend that this same defect underlies their opponents’ views.¹⁶³ And it is, of course, correct that economic science has evolved since the early days of the Chicago School and provided lessons that have, to a limited extent, been incorporated into antitrust law and economics.¹⁶⁴ But the proper corrective to nostalgia is not wild speculation. While the economic literature in this vein is both important and influential, it offers very little to substantiate more interventionist approaches to transforming antitrust doctrine.

While additional theoretical sophistication and complexity is useful, reliance on untested and in some cases untestable models can create indeterminacy, which can retard rather than advance knowledge.

. . . .

As with almost all monopolization strategies, one cannot distinguish an anticompetitive use of RRC from competition on the merits, absent a detailed factual inquiry. . . . [T]here is very little empirical evidence based on in-depth industry studies that RRC is a significant antitrust problem.

. . . .

[Thus, b]ecause of this literature’s focus on theoretical possibility theorems, little evidence exists regarding the empirical relevance of these theories. Absent specific evidence regarding the plausibility of these theories, the courts . . . properly ignore such theories.¹⁶⁵

As explained in the introductory section of this Article, the fears that underpin both the dystopia and nostalgia biases are as old as antitrust enforcement itself. These biases have thus had ample time to permeate through antitrust enforcement. This raises two possibilities. On the one hand, policy makers may have bought into these concerns and thus adapted the antitrust toolkit so as to address them (for instance, by making antitrust law more readily applicable to novel conduct). Conversely, decisionmakers may have perceived these fears as undesirable biases and thus attempted to make antitrust law impervious to them. There is at least some reason to believe that the error-cost framework¹⁶⁶ that guides US antitrust enforcement was, in part, designed so as to minimize the occurrence of what we have referred to as nostalgia bias.

Let us take a step back. The possibility that, despite its age, antitrust law may be fairly well calibrated to address the novel characteristics of digital markets is routinely overlooked by commentators and scholars alike. A piece recently published in Bloomberg, for instance, argued that “100-year-old antitrust laws are no match for big tech.”¹⁶⁷ The implication is clear: antitrust law is outdated and it needs to adapt. Scholars and advocates have voiced similar concerns; FTC Chair Khan has argued that the lack of antitrust enforcement against Amazon is evidence that the law is out of date.¹⁶⁸

While it is plausible that antitrust law is out of touch with the realities of digital markets, critics often overlook an important alternative explanation: the antitrust framers, and courts since then, may have consciously designed antitrust law so as to preclude the type of enforcement that nostalgic scholars are contemplating. In other words, antitrust may be powerless against big tech’s behavior not because it is outdated, but because policy makers decided to limit enforcement to what they perceived to be clear-cut infringements, deliberately making it relatively hard for plaintiffs to bring cases against novel forms of business conduct.

There is some reason to believe that the second of these two explanations is the most likely. This is, arguably, apparent from US antitrust law’s adoption of the error-cost framework as a guide to policy making.¹⁶⁹ At its core, the error-cost framework attempts to structure antitrust enforcement to “minimize the total costs of (1) anticompetitive practices that escape condemnation; (2) competitive practices that are condemned or deterred; and (3) the system itself.”¹⁷⁰

Readers might question how this relates to antitrust nostalgia. The answer is that proponents of the error-cost framework, be they courts or scholars, tend to assume that the costs stemming from false convictions are the most significant—because unlike most market failures, they are not self-correcting—and thus warrant particular caution from policy makers.¹⁷¹ In turn, this translates into a commandment that anticompetitive presumptions are appropriate only when enforcers have acquired enough familiarity with a given practice¹⁷²—something that is rarely the case for the novel business conducts that lie at the heart of the current antitrust upheaval. The error-cost framework thus tends to be relatively favorable to novel forms of conduct—the very opposite of Antitrust Nostalgia, which tends to view such conduct in a negative light.

Underpinning this policy orientation is a belief that policy makers are often quick to find a monopoly explanation for behavior that they fail to understand.¹⁷³ This is particularly common in innovative markets where business practices evolve rapidly.¹⁷⁴ This tendency is compounded by a sense that economics always lags behind business practice.¹⁷⁵ As a result, entrepreneurs will often struggle to articulate their reasons for adopting a given course of conduct, and thus fail to convey the practice’s redeeming virtues to courts and juries.¹⁷⁶ The error-cost framework accounts for these biases by making antitrust cases marginally harder to bring in such instances, most notably by precluding the application of per se prohibitions when courts are unfamiliar with the underlying conduct.¹⁷⁷

The upshot is that nostalgic scholars are wrong to assume that antitrust enforcement in the digital sphere has been sparse because the law is too dated to tackle the novel realities of these markets. To the contrary, it is precisely because innovative markets often feature novel forms of conduct that antitrust law proceeds with caution. By setting up the antitrust apparatus in this way, legislators and courts effectively recognized that emerging markets are, generally, no more prone to anticompetitive conduct than their traditional counterparts, but that it is harder to reliably identify anticompetitive behavior in these contexts.

C. Shifting the Burden of Proof

Antitrust dystopia and nostalgia would not be so problematic were it not for one of the key policy proposals that almost invariably accompanies them: shifting the burden of proof in antitrust proceedings, or enacting legislation that would achieve a similar result. Going down either of these paths would effectively transpose the precautionary principle into the world of antitrust enforcement—especially as it relates to digital markets. However, as the rest of this Article explains, there is no evidence to suggest that this precautionary approach is at all justified in antitrust doctrine.

Calls for antitrust enforcers to shift the burden of proof when dealing with digital markets have become increasingly common on both sides of the Atlantic. The European Commission’s digital competition report, for instance, concluded that shifting the burden of proof would be appropriate in both unilateral conduct and merger proceedings concerning digital markets:

The test proposed here would imply a heightened degree of control of acquisitions of small start-ups by dominant platforms and/or ecosystems, as they would be analysed as a possible defensive strategy against partial user defection from the ecosystem as a whole. Where an acquisition plausibly is part of such a strategy, the burden of proof is on the notifying parties to show that the adverse effects on competition are offset by merger-specific efficiencies.¹⁷⁸

. . . .

[O]ne may want to err on the side of disallowing potentially anticompetitive conducts, and impose on the incumbent the burden of proof for showing the pro-competitiveness of its conduct. This may be true especially where dominant platforms try to expand into neighbouring markets, thereby growing into digital ecosystems, which become ever more difficult for users to leave. In such cases, there may be, for example, a presumption in favour of a duty to ensure interoperability.¹⁷⁹

This conclusion was echoed by other influential thinkers. The Stigler Center Report surmised that:

[W]hen an acquisition involves a dominant platform, authorities should shift the burden of proof, requiring the company to prove that the acquisition will not harm competition.¹⁸⁰

. . . .

Antitrust law might be revised to relax the proof requirements imposed upon antitrust plaintiffs in appropriate cases or to reverse burdens of proof. Burdens of proof might be switched by adopting rules that will presume anticompetitive harm on the basis of preliminary showings by antitrust plaintiffs and shift a burden of exculpation to the defendant or by ensuring that plaintiffs are not required to prove matters to which the defendants have greater knowledge and better access to relevant information.¹⁸¹

And while it did not wholeheartedly endorse this approach, the UK’s “Furman Report” considered that shifting the burden of proof in merger cases should at least be on the table:

The principal alternative considered by the Panel has been the introduction of a legal presumption against acquisitions by large digital companies, with the burden placed on parties involved to provide proof that the merger will not be anti-competitive.¹⁸²

Finally, Tim Wu and a group of antitrust scholars have put forward various proposals that would effectively amount to shifting the burden of proof in antitrust proceedings:

1. Vertical coercion, vertical restraints, and vertical mergers should enjoy no presumption of benefit to the public;

. . . .

5. The Berkey Photo standard for establishing monopoly leveraging should be restored [thereby substantially lowering the evidentiary threshold required to bring Section 2 leveraging cases];

. . . .

6. The broad structural concerns expressed by Congress in its enactment of the 1950 Anti-Merger Act, including due concern for the economic and political dangers of excessive industrial concentration, should drive enforcement of Section 7 of the Clayton Act;

7. Anticompetitive conduct harming one party or class should never be justifiable by offsetting benefits to another party or class. Netting harms and benefits across markets, parties, or classes should not be a method for assessing anticompetitive effects[.]¹⁸³

These proposals are starting to gain traction with policy makers on both sides of the Atlantic. A draft bill introduced by US Senator Klobuchar proposes to shift the burden of proof in merger proceedings.¹⁸⁴ The draft European Digital Markets Act goes one step further. It lays out a series of practices that would be per se prohibited when they are implemented by so-called “gatekeeper” platforms.¹⁸⁵

This approach is highly problematic. For a start, it is not the same to argue that certain practices can harm competitors in the digital economy (as the reports cited above ostensibly do), as it is to demonstrate that such conduct is, on balance, harmful to consumer welfare. To the best of our knowledge, there is no evidence to support this second claim. If it is indeed the case that there is no evidence to support this claim, then the problem becomes one of proof: can defendants and plaintiffs realistically be expected to show the innocuousness or harmfulness of practices on a case-by-case basis? And, if not, what is the appropriate default presumption? As we have discussed, there are strong reasons to believe that plaintiffs are prone to mischaracterize efficient behavior as exclusionary, and that defendants will struggle to show that conduct is innocuous. And given the unlikelihood of fat-tailed antitrust harms in digital markets, plans to shift the burden of proof in digital antitrust proceedings appear ill-advised.

In short, the above proposals would effectively establish what amounts to a precautionary principle within the antitrust laws. From the moment authorities or plaintiffs meet some very limited evidentiary thresholds, a whole series of practices would be presumed harmful or banned outright and the onus of proving the lack of harm would fall upon defendants—who may be uniquely unsuited to meeting this burden.¹⁸⁶

D. Partial Conclusion: When is Precaution Appropriate?

The previous sections have shown that numerous policy makers and scholars—guided by dystopian fears and a sense of nostalgia—have called for the introduction of precautionary measures in antitrust proceedings. This raises two interrelated questions. First, as a matter of general principle, when is the resort to precautionary measures appropriate? Second, are precautionary measures required to deal with antitrust enforcement in digital markets?

Conclusively answering the first question is ultimately beyond the scope of this Article. Instead, we proceed under the assumption that precautionary measures are called for only when a given course of conduct brings about a credible (if hypothetical) risk of total ruin.¹⁸⁷ This is not to say that we endorse this version of the precautionary principle (or any version for that matter)—as some of if its implications are far from uncontroversial.¹⁸⁸ However, it is a solid benchmark against which to assess the claim that proponents of heightened antitrust enforcement have not produced sufficient evidence to warrant the imposition of precautionary measures within antitrust enforcement.

As the rest of this Article will explain, current calls for heightened antitrust intervention in digital markets do not pass the low precautionary principle benchmark set out above. More precisely, critics’ claims that digital markets are particularly prone to market failure appear unfounded. Proponents of heightened antitrust intervention cite digital markets’ strong reliance on user data and the presence of network effects and returns to scale in order to support their assertions. However, as argued below, theoretical and empirical evidence fails to support such claims. There is thus nothing to suggest that digital markets are inherently more problematic than the rest of the economy, and that they should, as result, be subject to a higher level of scrutiny from antitrust and/or regulatory authorities.¹⁸⁹ Finally, we show that extremely similar claims have been raised before, most notably during the Microsoft antitrust interventions in the late-1990s and early 2000s. Although the evidence is only anecdotal, the history of this case suggests that, if anything, severe market failures are less likely in digital markets than in the rest of the economy.¹⁹⁰

II. The Case of Big Data Competition

The idea that digital markets are inherently more problematic than their traditional counterparts—if there even is a meaningful distinction—is perhaps best encapsulated by the catchphrase that “data is the new oil.” This trope is routinely repeated by policy makers, business investors, and reporters alike.¹⁹¹ Behind the slogan lies the fear that left to their own devices, today’s dominant digital platforms will become all powerful—protected by an impregnable “data barrier to entry”—akin to the industrial giants of the Gilded Age and specifically the Standard Oil Company. These comparisons are not just implicit. The Economist and other press outlets have routinely used Standard Oil Company-related imagery to depict the rise of digital platforms (see Figure 1).

Against this alarmist backdrop, nostalgic antitrust scholars have argued for aggressive antitrust intervention against the inherently non-standard business models and contractual arrangements that characterize these markets. Yet, as this Part demonstrates, a proper assessment of the attributes of data-intensive digital markets does not support either the dire claims or proposed interventions.

Section A shows that data is not exclusive, making it hard for incumbents to appropriate the benefits that might stem from a superior access to data. Section B argues that firm-level capabilities, notably those which stem from talented research and development (“R&D”) teams, are likely far more relevant from a competitive standpoint than is the access to large datasets. This assertion is borne out by empirical evidence that suggests that, contrary to critics’ claims, the marginal value of data decreases rapidly. Finally, Section C shows that most successful platforms emerge and overthrow incumbents, despite—or maybe even thanks to—their initially inferior access to data (“necessity is the mother of invention”). In other words, even if data ultimately plays a large role in the monetization of digital platforms, it does not appear to be necessary for their creation, and thus for the emergence of new competitors.

The upshot is that “data is the new oil” is a highly misleading trope that perfectly illustrates the many misapprehensions that exist about competition in data-intensive markets.¹⁹² Instead, a closer look at these markets suggests that competition may indeed flourish. Andrei Hagiu and Julian Wright summarize this well: “These developments make data-enabled learning much more powerful than the customer insights companies produced in the past. They do not, however, guarantee defensible barriers.”¹⁹³

Figure 1: Top left, Top Right and Bottom left: Depictions of Google, Amazon, and Facebook (Contemporary). Bottom Right, Depiction of the Standard Oil Company (1904).¹⁹⁴

A. Data is Information

One of the most salient features of the data that online firms create and consume is that, jargon aside, it is just information. As with other types of information, it thus tends to have at least some traits that are usually associated with public goods (i.e., goods that are non-rivalrous in consumption and not readily excludable).¹⁹⁵ “[D]ata has near-zero marginal cost of production and distribution even over long distances[,]”¹⁹⁶ making it very difficult to exclude others from accessing it. Meanwhile, multiple economic agents can simultaneously use the same data, making it non-rivalrous in consumption.

This is not to say that data requires some special protection in order to be provided by the market: far from it. As Ronald Coase famously showed, public goods are a theoretical construct—like perfect competition or monopoly—that rarely exist outside of economic textbooks.¹⁹⁷ Instead, the public good analogy shows that data bears some traits which make it almost irreconcilable with the alleged hoarding and dominance that came to be associated with the oil industry of the late nineteenth and early twentieth centuries.

Moreover, data, broadly speaking, is useful to all industries. Collecting data on consumers is not a new phenomenon restricted to online companies. The market for data, even if narrowly described as data for targeted advertising, is much broader than the online world. Offline retailers have long used data about consumers to better serve them. Through devices like coupons and loyalty cards (to say nothing of targeted mailing lists and the age-old practice of data mining check-out receipts), brick-and-mortar retailers have long tracked purchase data and used it to better serve consumers.¹⁹⁸ Not only do consumers receive better deals as a result, but retailers know better what products to stock and advertise and when and on what products to run sales.

1. Access to Data Is Not Exclusive

Data tends to be non-rivalrous, or at least, the cost of producing an additional copy of some piece of data is usually close to zero.¹⁹⁹ For this reason, one agent’s use of a given piece of information does not automatically preclude its rivals from using the same information.

The non-rivalrous nature of information seriously undermines the views of critics who have compared digital platforms to Standard Oil and argued that government authorities need to step in to limit the platforms’ control over data.²⁰⁰ To say that data is like oil betrays a serious misunderstanding. Google knowing a person’s birthday doesn’t limit the ability of Facebook to know a person’s birthday as well. While databases may be proprietary, the underlying data usually is not.

In other words, most data is non-exclusive. Not only can the same data be used by many different economic agents, but there are also numerous ways in which it can be obtained through different platforms. As discussed in more detail below, antitrust authorities should thus be highly skeptical about claims that rivals will be unable to independently generate equivalent data to that which is held by dominant platforms.

2. Data is Hard to Appropriate

[W]e expect a free enterprise economy to underinvest in invention and research (as compared with an ideal) because it is risky, because the product can be appropriated only to a limited extent, and because of increasing returns in use.²⁰¹

The second key feature of information is that is it hard to appropriate. In practice, this means that companies that have acquired a valuable piece of data will struggle both to prevent their rivals from obtaining the same data as well as to derive competitive advantage from the data. For these reasons, it also means that firms may be more reluctant to invest in data generation than is socially optimal.²⁰² In fact, to the extent this is true there is arguably more risk of companies under-investing in data generation than of firms over-investing in order to create data troves with which to monopolize a market. This again contrasts with oil where complete excludability is the norm. The fact that appropriating data is a complicated task can be seen in a number of instances.

First, specific pieces of data can usually be obtained through a variety of channels. This undermines oft-repeated claims that large online platforms such as Google and Facebook have acquired an insurmountable data advantage over their competitors.²⁰³ In other words, it is almost impossible to build an insurmountable data advantage because there will generally be an alternative way (or, more likely, a multitude of ways) to amass the same data. To take just one example, mobile internet service providers (“ISPs”) like Verizon have access to considerable data about their users, likely at least comparable to what Google and Facebook have. What’s more, mobile ISPs have uniquely good access to location data, increasingly the coin of the realm in a world where the most important and valuable consumer interactions are shifting to mobile. This may not be identical information, and even where it overlaps it is certainly a somewhat different dataset. Yet there can be no doubt that advertisers, among others, can use Verizon’s data for the same purposes as they use data from Google and Facebook.

Another important example concerns the ubiquity of data scraping on the internet. Contrary to popular belief, numerous firms in data-heavy industries do not rely solely on proprietary data to improve and market their products. Instead, these firms routinely “scrape” the internet in order to obtain the data they require.²⁰⁴ This practice has led to a blossoming industry. Critically, this is one space where dominant firms arguably have little advantage over more nimble rivals. Indeed, stories abound of startups going head-to-head with large incumbents and generating more useful insights from the same publicly accessible data.²⁰⁵

The upshot is that the ease with which data can be obtained—especially by identifying or creating new sources of information or by using publicly accessible information—suggests that it is an unlikely tool for firms to perpetuate monopoly power over lasting periods of time. A monopoly that relies on data to cement its position is thus built on sand, because any data-related advantage can be eroded the moment rivals come up with an alternative way of attaining comparable information.

It is important not to overstate the fungibility of data. While fungibility is the norm for many types and uses of data, it certainly will not always be. But, properly understood, the uniqueness of data is not a strong argument for antitrust enforcement against firms successfully using big data. First, unique agglomerations of data for which comparable substitutes do not (yet) exist inevitably reflects unique entrepreneurial foresight into the value of certain data, superior data processing abilities, and a particularly innovative mechanism for generating unique data.²⁰⁶ In all of these cases, there potentially are considerable consumer advantages from the underlying conduct that enables the unique appropriation of data, and penalizing the successful use of data means also penalizing broader innovative activities. Indeed, the inseparability of data from the products or services that generate or use it is one of the key problems of calls for antitrust intervention against big data: we do not use our antitrust laws (in the US, at least) against effective competition, but only against abuse of market power.

Second, data use by multi-sided platforms may often appear competitively unique when looking at only one side of the platform. But any anticompetitive significance may also be mitigated or undermined by the fungibility of the data on the other side of the platform. To take one obvious example, the data used and generated by Google Search is significantly different than that used and generated by Facebook. And, not coincidentally, on the user side of the platform Google and Facebook offer substantially different products, used primarily for divergent purposes. But on the advertising side, of course, the distinctions are substantially less relevant. Both Google and Facebook collect, generate, and process data to help advertisers identify and reach likely customers. The mechanisms by which they do this are quite different, but the purpose and aggregate content of the data is likely not very different at all.²⁰⁷ The lack of advertising-side differentiation is no doubt bolstered by user multi-homing and the increasing ability of users to transfer data between platforms.²⁰⁸

3. Data as a Simulacrum and Information Asymmetry

A third important feature of data is that, in general, it is extremely difficult for parties to determine its value. For instance, economists have long theorized that it is hard for would-be purchasers to know how much a given piece of data is worth, ex ante. As economist Kenneth Arrow argued: “[T]here is a fundamental paradox in the determination of demand for information; its value for the purchaser is not known until he has the information, but then he has in effect acquired it without cost.”²⁰⁹

But the problem cuts even deeper. It can be hard for platforms or other intermediaries to know the value of underlying information at all—even once they have acquired access to a data set. Indeed, data’s true value typically is revealed only when firms apply high-quality processing to a data set.

Data is a simulacrum. Platforms are locked in an ever-evolving battle to identify, collect, process, interpret, and use data in order to figure out user preferences or conduct. There is no silver bullet with respect to the amount or kind of data to accomplish this. Every data set represents some collection of pieces of information that are an effort to guess at the user’s mind, as is every aggregate set of data about a large group of people (for which errors are more likely to cancel out, but for which the representational value of the data is less likely to be very accurate or useful because it encompasses significant noise relative to signal). Big data sets do, however, allow for pattern recognition. For example, in order to plot out likely traffic issues, mapping apps don’t really need to know where a given user is per se, but only whether a large mass of drivers are likely to be in the same place at the same time.

This information asymmetry point is important. It is commonly said or assumed that platforms have much more information than users and can use it to their advantage. The same is said for incumbents against new entrants. But is this really true? Users know far more about themselves than any platform ever will. If a new entrant were to ask the right questions, or buy the right data, it could easily know more than whatever Google knows about a user.

This is a key reason why Amazon is such a threat to Google: it knows what users shop for, what they buy, at what price, etc. That data is manifestly of enormous value to advertisers. Whatever Google knows about how often users search for terms like “Stigler entry barriers,” it pales in importance to what Amazon knows about what products users buy, or what Facebook knows about who their friends are and how they interact with them. And who knows what will be most relevant tomorrow? Even today, if big data were so good at predicting users’ behavior, then tech firms would be very good at, for example, predicting what future products and R&D projects will be most profitable. They, of course, are not.

The notion that data’s value is inherently tied to subsequent processing efforts also suggests that incumbent platforms may actually be important facilitators of new entry. Without generalizing, there are some obvious examples, like Amazon Web Services, which reduces the cost to smaller entrants of obtaining scale in backbone technology, or Google Search, which makes it easier for users to find new entrants that otherwise have to overcome the problem of anonymity. In fact, to the extent that lack of information is a real entry barrier, the role of incumbent intermediaries in reducing search and other information costs (like providing reputation markets, etc.) can actually operate to overcome entry barriers. It is crucial in assessing the extent to which data might operate as a barrier to also assess the mechanisms it enables for reducing barriers, even for a company’s direct competitors.²¹⁰

As suggested by the US Microsoft court, however, the relevant question concerns not the “initial acquisition of monopoly power”; it concerns a company’s “efforts to maintain this position through means other than competition on the merits.”²¹¹ It is, presumably, possible for a company to deploy, use, or limit access to data in order to impede competition at the platform level, rather to compete—but this does not convert data into an entry barrier per se.²¹²

B. Data is Not Scarce; Expertise Is

Another important feature of data is that it is ubiquitous. Contrary to oil, the predominant challenge for firms is not so much obtaining data but rather drawing useful insights from it. This has two important implications as far as antitrust policy is concerned. First, although data does not have the same self-reinforcing characteristics as network effects, there is a sense that acquiring a certain amount of data and expertise is necessary to compete in data-heavy industries. It is (or should be) equally apparent, however, that this “learning by doing” advantage rapidly reaches a point of diminishing returns. Second, it is firms’ capabilities, rather than the data they own, that lead to success in the marketplace. Critics who argue that firms such as Amazon, Google, and Facebook are successful because of their superior access to data might thus have causality in reverse: arguably, it is because these firms have come up with successful industry-defining paradigms that they have amassed so much data, and not the other way around.

1. Increasing Returns to Scale, Data Network Effects, and Learning by Doing

Much of the impetus for antitrust enforcement in digital markets is premised on the assumption that the firms operating in these industries necessarily present some combination of increasing returns to scale, network effects, and data-related incumbency advantages. However, critics fail to provide anything more than anecdotal evidence to support such claims.²¹³

A first question concerns increasing returns to scale in the digital economy, in particular those relating to the use of data.²¹⁴ Indeed, critics often argue that digital platforms benefit from increasing returns to scale on all ranges of output. In other words, because of their scale, they require fewer economic inputs per unit of output, which allegedly gives them an unassailable advantage over rivals. The Stigler Center Report, for instance, concludes that:

Typically, information goods involve increasing returns to scale because their production requires a fixed cost and no or little variable cost

. . . .

The increasing returns to scale create barriers to entry: New firms cannot offer the quality of the incumbent without the same large-scale operation to pay for the fixed costs. But the firm can only achieve a large scale if quality is high. Thus, a potential entrant, foreseeing that it will not be profitable at the smaller scale, will not enter the market to challenge the incumbent.²¹⁵

In practice, however, the evidence for these increasing returns is particularly thin. A look at the annual reports of many big tech firms is revealing in this regard.

Google’s most recent 10-K, for example, shows that many of the company’s costs are unlikely to become smaller (in relative terms) as its output increases. This cuts against the existence of extreme returns to scale. For a start, more than fifty percent of Google’s total expenditures concern so-called “cost of revenues.”²¹⁶ Of these, roughly half involve traffic acquisition costs (“TAC”), whereby Google pays other firms for the placement of its contents (be it advertisements or access points).²¹⁷ In Google’s own words, “TAC which are paid to Google Network Members primarily for ads displayed on their properties and amounts paid to our distribution partners who make available our search access points and services. Our distribution partners include browser providers, mobile carriers, original equipment manufacturers, and software developers.”²¹⁸ At first glance, there is nothing to suggest that these expenditures become relatively less burdensome as a company increases in scale. In fact, the opposite may well be true. It is probably more costly to gain access to marginal users than inframarginal ones. This might explain why, for years, Google’s TAC-related expenditures have been steadily increasing.²¹⁹ Google’s single largest expenditure thus fails to fit the increasing returns to scale pattern. And much the same can be said about many of the company’s other large outlays. These include the acquisition of bandwidth, the operation of data centers (which rivals can outsource²²⁰), general administration, sales and marketing costs.²²¹ This leaves R&D, which represents roughly a fifth of Google’s expenditures, as a potential source of increasing returns to scale. But, here too, the case for increasing returns to scale is far from clear-cut. Much of the economic literature on the topic considers that R&D leads to decreasing—and not increasing—returns to scale.²²² At the very least, there can be no presumption that Google’s heavy reliance on R&D is necessarily a source of increasing returns. In short, for most of Google’s expenditures, there is no obvious reason to believe that greater economic output would necessarily require a less than proportionate increase in economic inputs.

The story is much the same for other big tech firms, such as Amazon and Facebook. Roughly three-quarters of Amazon’s expenditures involve “cost of sales” and “fulfillment” costs.²²³ These primarily include costs associated with the purchase and shipping of goods. There is little to suggest that either of these is a source of “extreme” returns to scale. They mostly involve the same capacity utilization challenges that brick-and-mortar retailers must contend with.²²⁴ If these potential returns to scale were indeed insurmountable, Amazon would arguably have been unable to effectively compete against incumbent brick-and-mortar retailers in the first place. Likewise, roughly two-thirds of Facebook’s expenditures fall under the “cost of revenue,” “marketing and sales,” and “general and administrative” categories.²²⁵ Again, there is little to suggest that increased output would be associated with a less-than-proportional increase in inputs for these expenses.

So, if they exist, where might big tech’s increasing returns to scale originate? One common suggestion is that they stem from the use of data. Critics sometimes point to the existence of so-called “data network effects” (although economies of scale and scope might be a more appropriate terminology²²⁶).²²⁷ The argument goes that superior access to data allows firms to improve their products and gain more users. This then leads to even more data, thereby creating a self-reinforcing circle that eventually causes one firm to dominate the market. Take, for example, Google, which has become the poster child for unsophisticated “data network effects” arguments. In the words of Nathan Newman:

While there are a number of network effects that come into play with Google, [“its intimate knowledge of its users contained in its vast databases of user personal data”] is likely the most important one in terms of entrenching the company’s monopoly in search advertising.

. . . .

Google’s overwhelming control of user data . . . . might make its dominance nearly unchallengeable.²²⁸

Although the intuition is appealing, it has, to the best of our knowledge, neither been translated into a rigorous economic model nor been established empirically. In fact, the anecdotal evidence that has often been used to support this naïve assertion merely shows that learning-by-doing plays an important role in the tech industry, just as it does in the rest of the economy.

For a start, the existence of data-driven increasing returns to scale (or other data-related incumbency advantages) is not borne out by the burgeoning empirical literature on the topic. Summarizing these empirical findings, Professor Catherine Tucker concludes that “empirically there is little evidence of economies of scale and scope in digital data in the instances where one would expect to find them.”²²⁹

There are numerous pieces of evidence to support this claim.²³⁰ For instance, economist Patrick Bajari and his co-authors use data from Amazon to show that (1) data on a wider range of products does not improve demand forecasting, and (2) increasing the timescale of data improves forecasting, but with diminishing returns.²³¹ Likewise, in a paper co-authored with economist Lesley Chiou, Catherine Tucker finds that storing search engine results for shorter periods does not affect the accuracy of subsequent search results.²³² Again, this cuts against the existence of increasing returns to scale. In another paper, Catherine Tucker and her co-authors cast doubts on the overall accuracy of digital profiling, and thus the competitive edge that firms might obtain by acquiring larger amounts of data.²³³ Finally, a recent study argues that additional data improves algorithmic prediction with decreasing returns to scale.²³⁴ Using data from a large German news outlet, the authors show that the number of times a user visits a website improves the site’s prediction algorithm with decreasing returns (the algorithm optimizes the news articles that are presented to each individual user).²³⁵

As our survey of the empirical literature in the following table shows, additional pieces of data are usually beneficial, but these benefits systematically entail diminishing marginal returns.

Table 1: Survey of Empirical Papers That Analyze the Marginal Benefits of Data

Author	Year	Method	Source of data	Effect of “More Data”
de Fortuny, Martens, & Provost	2013	Multivariate event model	Data drawn from nine different predictive modeling applications, from book reviews to banking transactions	Increasing with diminishing returns “One should note, however, that the curves do seem to show some diminishing returns to scale.”²³⁶ “The marginal increase in generalization accuracy decreases with more data for several reasons.”²³⁷
Chiou & Tucker	2017	Difference-in-differences	Data from Experian Hitwise (outgoing traffic from Google, Yahoo! and Bing search engines)	Flat Longer periods of data storage (6 to 18 months; and 3 to 13 months) “do not confer advantages in search quality.”²³⁸
Schaefer, Sapi & Lorincz	2018	Local polynomial regression	Yahoo! search logs (i.e., users’ click behavior)	Increasing with diminishing returns “[Q]uality of search results improve with more data on previous searches” (cookie length)”; with decreasing returns. Personalized data is the most valuable.²³⁹
Bajari, Chernozhukov, Hortaçsu & Suzuki	2019	Linear regression	Amazon’s retail forecasting system	Flat, or increasing with diminishing returns Cookie length is robustly helpful in improving forecast quality. The effect of increasing number of products in the same category is robustly flat (with a few exceptions). When the estimated effects are not flat, “they exhibit diminishing returns to scale, with the exception of T effects in the model without time controls.”²⁴⁰
Neumann, Tucker & Whitfield	2019	Field experiment	Nielsen ad ratings data	Sometimes negative from a cost/benefit point of view “In comparison with random audience selection, the use of black box data profiles, on average, increased identification of a user with a desired single attribute by 0%–77%. Audience identification can be improved, on average, by 123% when combined with optimization software. However, given the high extra costs of targeting solutions and the relative inaccuracy, we find that third-party audiences are often economically unattractive.”²⁴¹
Claussen, Peukert & Sen	2019	Randomized experiment	Website of large German news outlet	Increasing with diminishing returns “Additional data helps algorithmic performance with rapidly decreasing returns.”²⁴²

In short, available evidence suggests that claims of “extreme” returns to scale in the tech sector are greatly overblown. Not only are the largest expenditures of digital platforms unlikely to become proportionally less important as output increases, but empirical research strongly suggests that even data does not give rise to increasing returns to scale, despite routinely being cited as the source of this effect.

2. Misapplying the Theory of Network Effects

At a more theoretical level, the “data network effects” framing also misapplies the theory of network effects. Network effects occur when a consumer’s utility for a good is, at least in part, a function of the expected number (and quality) of other agents using the same product.²⁴³ These valuable users may be located on the same side (direct network effects) or on the opposite side (indirect network effects) of a platform.²⁴⁴ In both cases, the bottom line is that consumers place a premium on utilizing a product whose network contains a large number of users (or higher quality ones). To a first approximation, however, this means that network effects are a benefit to users, not a cost.²⁴⁵

In the case of “data networks effects,” however, the theoretical model is weak at best. Because users likely do not attach any standalone value to platforms with more data,²⁴⁶ there is literally an infinite number of ways in which firms may offer a superior product without having the same or as much data as their rivals. Firms can differentiate themselves on a variety of features, ranging from price, to quantity and invasiveness of ads to which users are exposed, to the degree of privacy protection afforded to users. This notably has been the case for search engines.²⁴⁷

Given this theoretical distinction, telling a story of problematic “data network effects” for a company like Google is difficult. For instance, some critics have argued that Bing lost out to Google because of inferior data. The claim is perhaps best encapsulated by Nathan Newman who writes: “That Microsoft, with nearly half of Google’s user base, still generated $2.6 billion in losses compared to its costs shows the height of the competitive barrier.”²⁴⁸

Although it is possible that data-related incumbency advantages stymied Microsoft’s success relative to Google, it seems far more likely that Microsoft simply offered an inferior product. That proponents of a data network effects story ignore relative product quality along multiple dimensions and assume that the quantity of data alone is outcome determinative highlights the paucity of the argument. Data matters to the extent that it is used to provide value to users within a product or service that is also attractive, functional, and usable. The quality of the underlying algorithm, informed in part by data derived from users, certainly contributes to that, but it is far from the only factor.

And even if one were to assume that data has a significant impact on the quality of a digital firm’s products, critics would still need to prove that smaller rivals cannot obtain comparable datasets. Returning to the criticism that has been voiced against Google, proponents of the data network effects theory often propose that Google’s access to data is unparalleled and unsurmountable: “[T]he gain for Google from its network of users is not just data on each individual user, but the cumulative data that can reveal how similar users behave.”²⁴⁹

But the relevant information is as available to Bing as it is to Google: observable patterns of users’ interactions with readily indexable, web-connected content. Google certainly makes observations about its greater number of users’ behaviors that it uses to improve its product. But Bing also has that capability, as well as the support structure of one of the most valuable companies in the world (Microsoft) and teams of talented programmers. Roughly a quarter of all US searches were performed on Bing in 2018.²⁵⁰ Surely, given its resources and teams of programmers, Bing (or another well-funded and technologically savvy competitor) is capable of competing away Google’s gains. There’s no indication that any more than a significantly smaller volume of data is required to train a search algorithm.²⁵¹ After all, the power of machine learning is that it can make useful inferences about user behavior based on (relatively) small sample sizes—one doesn’t need “all the data” to make useful machine learning algorithms. Bing has “enough” data—it indexes the same public web and has access to a very large share of user activity. It just so happens that users prefer Google’s results and other features of its product.

Moreover, synthetic data sets can cheaply, easily, and sufficiently replicate real-world data to enable even a data-poor competitor (at least one with comparable data processing capabilities) to compete with a data-rich incumbent. As Kelvin Yu has observed:

One of the most significant advantages [AI researcher] Kai-Fu [Lee] claims that top companies have—the ability to continuously collect proprietary data—may not be such a skewed advantage in the near future. Synthetic data generation is the creation of artificial data for the purposes of testing and improving AI models. . . . [S]tartups are already offering Data-Generation-as-a-Service. The technique is already being used by companies like Waymo and Tesla to simulate autonomous driving. As of July 2019, Waymo had 10 billion simulated miles and only 10 million physical miles driven, demonstrating the scalability and speed of simulating data.²⁵²

In fact, there is already a company, Usearch, offering search engine technology built on synthetic data.²⁵³

A final point concerns the difference between network effects and “learning by doing.” When it comes to data, it is more appropriate to consider the growth of firms and the size of their networks as a function of the latter.²⁵⁴

Network effects occur when a consumer’s utility for a good is, at least in part, a function of the expected number (and quality) of other agents using the same product.²⁵⁵ These valuable users may be located in the same market or on the opposite side of a platform.²⁵⁶ From a policy standpoint, some scholars have voiced fears that these network effects may lead to highly concentrated markets when incumbents are impossible to overthrow.²⁵⁷ And yet, in practice, this intuition often turns out to be false. For instance, economists Stan Liebowitz and Stephen Margolis show that one of the most commonly cited examples of “excess inertia”—the failure of Dvorak keyboards to displace the allegedly less-efficient QWERTY layout—did not withstand empirical scrutiny.²⁵⁸ The authors conclude that:

The trap constituted by an obsolete standard may be quite fragile. Because real-world situations present opportunities for agents to profit from changing to a superior standard, we cannot simply rely on an abstract model to conclude that an inferior standard has persisted. Such a claim demands empirical examination.²⁵⁹

The upshot is that there is a theoretical, though empirically debatable, case to be made for network effects leading to potential market failures.²⁶⁰

In contrast, learning-by-doing is the idea that a firm’s productivity improves with experience.²⁶¹ The advantage of learning-by-doing is usually found to be much less pronounced than network effects. For instance, in his seminal paper about learning-by-doing, Kenneth Arrow cites empirical literature indicating that “to produce the Nth airframe of a given type, counting from the inception of production, the amount of labor required is proportional to N^-1/3.”²⁶² Contrary to network effects, learning-by-doing is thus generally assumed to involve decreasing marginal benefits, and to become almost irrelevant beyond a point.²⁶³ In other words, learning-by-doing generates significant advantages in the early stages of improving a process, but these incremental advantages drop off sharply after a certain point because firms have picked all the low hanging fruit and because knowledge spills over to rival firms that can imitate the learned process improvements. For this reason, for data-driven platforms, growth more commonly follows a “learning curve” and is not subject to the winner-takes-all effect implied by the conventional network effect assumption.²⁶⁴

Another important difference is that, in the case of learning by doing, success is, by definition, a function of superior capabilities (and/or efficiency because of increased productivity). Large returns can (and do) exist in industries in which learning-by-doing is important (arguably in proportion to the technological complexity of the industry²⁶⁵). But it makes no sense to attack such firms, even where they may enjoy large profits and market power as a result of their superior skill; this is precisely the type of benefit that the antitrust laws were designed to promote.²⁶⁶ And there is even less of an argument that learning-by-doing constitutes a barrier to entry than do network effects because incumbents and entrants must bear roughly the same costs to move down their respective learning curves. Moreover, initial advantages are typically dissipated over time as information spills over. This contrasts with the widely accepted definition of barriers to entry, which holds that a barrier to entry is any cost that must be borne by entrants but not incumbents.²⁶⁷

Although these may seem like abstract distinctions, they have very real consequences. Take a presentation given by the Chief Economist of the European Commission in late 2018. In a nutshell, the Commission official held that a positive feedback loop allowed dominant platforms to extract even more data from its users.²⁶⁸ The intuition is that a platform with more users generates more data, and this allegedly leads to superior targeted advertisements. These, in turn, allegedly lead to more users because the platform can reinvest the added revenue they generate, etc.²⁶⁹ But this is precisely the conceptual trap that competition authorities should avoid.

This flawed reasoning implies that there is a linear, or even super-linear (e.g., quadratic) relationship between the data owned by a firm and the money it can extract from targeted advertisements. Putting aside the fact that the revenue required to fund platform growth can come from any source, not just advertising itself,²⁷⁰ this leaves out consideration of two crucial questions: (1) when does additional data cease to markedly improve ad targeting, and (2) at what point does superior ad targeting no longer significantly increase revenues? As argued above, early empirical evidence suggests that data used for ad targeting exhibits diminishing returns to scale, and that it does so at a fairly modest threshold.²⁷¹ Moreover, although this area of research is still in its infancy, there is at least some evidence that highly targeted advertisements might not always be effective because consumers perceive them to be overly intrusive.²⁷² This suggests that there may indeed be a point at which more data used to improve ad targeting no longer provides any meaningful benefits.

The bottom line is that so-called “data network effects” are better framed as a form of learning-by-doing. They thus raise relatively little antitrust concern and should be embraced by policy makers because they ultimately lead to superior efficiency—the very goal of antitrust law.

3. Dynamic Capabilities

This leads us to a third important point. The challenge for firms in data-reliant industries is multidimensional. Not only must they acquire data (and this is not merely a matter of “data network effects”), but just as importantly they must also develop the expertise to analyze this data, draw useful insights from it, and turn these insights into successful products. In doing so, acquiring the right data and getting the best out of a firm’s engineers is at least as important as controlling a large amount of data or engineering expertise. In other words, there is no single ingredient that mechanically leads to success. Instead, it is up to firms to identify and seize upon emerging business opportunities. Under this light, the resounding success of certain technology platforms appears to be down to their respective “dynamic capabilities” rather than the operation of positive feedback loops.

Dynamic capabilities can be defined as the “capacity business enterprises have to shape, reshape, configure, and reconfigure those assets so as to create and respond to changing technologies, competition, and market developments.”²⁷³ Critically, David Teece adds that “[t]he dynamic capabilities framework recognizes that the business enterprise is shaped but not necessarily trapped by its past.”²⁷⁴

This is of great importance for antitrust authorities. Though it may seem obvious, not all firms will possess the requisite capabilities to compete and flourish in these dynamic marketplaces. And evolving market realities imply that some prosperous firms will fall out of favor with consumers for no other reason than the firms’ failure to adapt to new market realities (these firms will often find themselves in situations where it is too late to turn the ship and opt for another business strategy). Antitrust enforcers may often be tempted to try and prop-up these failing firms—under the faulty premise that their demise is due to anticompetitive behavior rather than a mix of poor decisions and bad luck. But forcing successful firms to share their assets will often only delay the inevitable.

These factors can be seen at play in the early days of the search engine market. In 2013, The Atlantic ran a piece titled “What the Web Looked Like Before Google.”²⁷⁵ By comparing the websites of Google and its rivals in 1998 (when Google Search was launched), the article shows how the current champion of search marked a radical departure from the status quo (see Figure 2):

Figure 2: Above, Homepages of Yahoo!, AltaVista, and AOL, 1998. Below, Homepage and First Result Page of Google, 1998.

These images reveal critical differences between Google and its rivals. Even if it stumbled upon it by chance (and although it was not necessarily apparent at the time), Google immediately identified a winning formula for the search engine market. It ditched the complicated classification schemes favored by its rivals and opted, instead, for a clean page with a single search box. This ensured that users could access the information they desired in the shortest possible amount of time (thanks in part to Google’s PageRank algorithm).²⁷⁶

It is hardly surprising that Google’s rivals struggled to keep up with this shift in the search engine industry. The theory of dynamic capabilities tells us that firms that have achieved success by indexing the web will struggle when the market rapidly moves toward a new paradigm (in this case, Google’s single search box and ten blue links). During the time it took these rivals to identify their weaknesses and repurpose their assets, Google kept on making successful decisions (notably, the introduction of Gmail,²⁷⁷ its acquisitions of YouTube and Android,²⁷⁸ and the introduction of Google Maps,²⁷⁹ among others). All these products tied in with one of Google’s key capabilities: to provide users with information through whatever platform they are using (desktop or mobile) and regardless of the medium in which it is stored (be it web pages, online videos, maps, emails, etc.). Seen from this evolutionary perspective, Google thrived because its capabilities were perfect for the market at that time, while rivals were ill-adapted.

According to this interpretation, Google’s meteoric rise had nothing to do with “data network effects” and everything to do with its specific capabilities and the strategy it deployed, over many years, to capture latent consumer demand in the search engine market. In fact, it overcame a tremendous data disadvantage to catch-up with—and overtake—firms such as Yahoo! and AltaVista (which had entered the search engine market long before Google).²⁸⁰

This capabilities approach—distinctly absent from traditional antitrust economics and the decisions it has produced—should at the very least give pause to proponents of the “data network effects” theory. Indeed, it is hard to take such claims seriously when they are so deeply incongruous with one of the most significant competitive events in the history of the search engine. If the data network effects fable were true, and search engines with more data inevitably prosper compared to data-poor rivals, then Yahoo! and AltaVista should have obliterated Google. The reality is that competition in the search engine industry (and other digital markets) is about far more than data.

The theory of dynamic capabilities also sheds light on the European Union’s recent Google Search and Google Android decisions.²⁸¹ In these cases, the European Commission concluded that Google had excluded its rivals from the search engine market. On closer inspection, however, it seems at least plausible that these rivals simply failed because of poor business judgement.

In the Google Search case, Foundem (one of the complainants) based its entire business on comparison shopping services.²⁸² In so doing, it took no steps to protect itself from potential changes to the Google Search engine on which it depended, despite a clear industry trend towards single search boxes leading to all results. As Geoffrey Manne put it:

Google’s purpose is not to send traffic away from its site; it’s “to bring all the world’s information to users seeking answers.” It just happens that sending users away from its site was the best and quickest way to provide answers on the Web in, say, 1999. But as Google’s technological abilities and resources grew, and as users sought even quicker answers—especially ones provided by voice or on mobile devices—its mechanisms for serving its users evolved.²⁸³

Much the same can be said about Yandex, a Russian search engine that was a complainant in the Android case.²⁸⁴ Yandex argued that it was being excluded from the search market as a result of Google’s dominance of the Android mobile platform. Regardless of the merits of the underlying case, two facts are particularly relevant: (1) Yandex never attempted to launch its own mobile operating system, and (2) with the rise of virtual assistants, the market for search will likely become less and less distinct from the mobile operating system market. Though Yandex has not been excluded from the Russian market (proof that the theory of data network effects is greatly exaggerated), its market share has slowly declined (it nevertheless remains roughly on par with Google in Russia).²⁸⁵

The upshot is that, in both of these cases, enforcers struggled to distinguish exclusion resulting from anticompetitive conduct from shifts in the marketplace that incidentally caused some firms to fall by the wayside due to their failure to adapt to new circumstances. When deciding on such matters, it is crucial that authorities not ignore the important role that dynamic capabilities may play during these industry transition periods. In contrast to the claims made by those who allege that “data network effects” account for the success of some firms and failure of others, these capabilities actually do appear to be a key predictor of a firm’s success or failure. In short, business model competition necessarily implies that some firms will be left out, not because they don’t have data, but because they have chosen a strategy that either left them with too few users to generate relevant data or collecting the wrong type(s) of data. It is thus particularly inapposite for proponents of more aggressive intervention to adhere to relatively nostalgic antitrust economics in defense of their claims.

C. Data as a Byproduct and Path to Monetization, Not Creation, of Platforms

Policy makers should also bear in mind that platforms must often go to great lengths in order to create data about their users—data which these same users often do not know about themselves. Under this framing, data is a by-product of firms’ activity rather than an input that is necessary for rivals to launch a business. This is especially clear when one looks at the formative years of numerous online platforms. Most of the time, these businesses were started by entrepreneurs who did not own much data but, instead, had a brilliant idea for a service that consumers would value. Even if data ultimately plays a role in the monetization of these platforms, it does not appear to be necessary for their creation. According to economist Andres Lerner:

While data collected from users can be important to online providers in improving the services offered and their ability to monetize, user data is only one of many inputs into providing online services. The quality of services offered by online providers, and the ability to monetize effectively, is driven by much more than user data. There are many other sources of data, inputs into providing high quality services, dimensions of quality, and means of attracting users (such as distribution arrangements). Online providers can make investments in quality and distribution that are independent of its scale of users. And, through these investments, a provider can attain scale. Thus, it is incorrect to assert that an online platform lacking scale today can never attain scale. The fact that online providers can gain user scale in ways that do not involve user data weakens the claimed user data-service quality feedback-loop.²⁸⁶

1. Platforms Create Data

Possessors of information are assumed to benefit from the private use of information. But, while this is undoubtedly true for some data, it is often the case that information has no realizable value unless and until a mechanism is created for using it. At the extreme, for example, there is no intrinsic value to a consumer in the knowledge that she likes music by the Grateful Dead. There is value to her, however, in others knowing and using this information—most obviously, music recommendation services and music sellers (but also, in the noncommercial sense, friends and social communities). There is thus also value to the consumer from making sure that others know this information about her.

That applies to information that is known to the consumer. But there is also information that does not even “exist” in any real sense (or at least is not known) until the mechanism is created to elicit it. Indeed, “[i]t is questionable whether wants, as conscious motives to conduct, ever exist unless we are in a position of having to choose, to adopt one line of conduct and renounce another.”²⁸⁷ Whether or not someone likes her brother’s latest photo of his dog isn’t “information” in any meaningful sense until the photo exists, it is shared with her, and she considers her reaction to it. In this sense, the vast majority of (actionable) information exists only because of some activity that creates the mechanism for the information to be created (or coalesced).²⁸⁸

This type of information is extremely important—but routinely overlooked—in discussions of big data. It is, in fact, arguably the most important sort of data employed by these platforms, and it does not exist absent the platforms on which it is created. Crucially, data of this sort is most obviously the manifestation of users’ preferences. A user’s preferences may be, in some philosophical sense, pre-existing. But the user may not even know what they are until asked, and certainly external users of that information cannot know it without it being communicated either directly (e.g., “I like the Grateful Dead”) or indirectly (e.g., through a user’s music purchase history). Thus, information about users’ preferences can perhaps be known, but, more to the point, it must typically be elicited. “[The consumer] does not know what he will want, and how much, and how badly; consequently, he leaves it to producers to create goods and hold them ready for his decision when the time comes.”²⁸⁹

And users have an interest in that information being elicited and shared. Moreover, users have no particular comparative advantage in the eliciting or interpreting of that information: as noted, it may not even be known ex ante, and, even if it is, in many cases it is virtually useless. As a result, the mechanisms that elicit and share that information with others who do have a comparative advantage in using it are of great value to users—not only because the information, once processed, may be used by others in ways that ultimately impart value to the user, but also because the very act of eliciting and sharing the information imparts knowledge to the user directly.

This is a crucial and overlooked aspect of policy discussions surrounding data-intensive platforms. The value of a user’s interactions with Facebook and Google, for example, is not, as commonly assumed, only in the platform’s aggregation and use of the data generated through those interactions, but also in the user’s own immediate access to information that either didn’t exist or wasn’t known to her beforehand. An enormous quantity of the data at issue in these policy discussions is of this sort: it is non-existent, unknown, or useless, even for personal use by the user, until it is made manifest through some activity by which the user interacts with the platform. Thus, the value of those activities is not just in the sharing of information with others, but in the creation of information in the first place.

Why is this so important? Because, as discussed above,²⁹⁰ it turns the generally assumed “platform information asymmetry” on its head. To begin with, there is information that a user does often know about herself that the platform does not: her preferences. Any information that the platform gleans about her preferences is necessarily incomplete and indeterminate, and the platform can make only inferences—inferences that, even when accurate, can quickly become obsolete. Information asymmetry in this regard runs in favor of the individual user, not the platform.

In addition, there is information that even the user does not know about herself and that becomes known only because of the platform. Even though the user (unlike the platform) does not know the aggregate information from many users of which her data is only a minuscule part, the private use value of that information is better known to the user than to the platform. While the user knows whether the information is accurate and valuable, and while there is no limitation on what the user can do with that information, the platform is able to use it only to make inferences about its relevance and importance to the user, with limited accuracy.

To be sure, a platform can also combine this information with other data to create yet more information and to derive value inaccessible to the individual user. But the relative magnitudes of these different types of information and their value to different users and to the platform itself is uncertain. It cannot simply be assumed that there is “asymmetry” or that it flows in only one direction. Indeed, this dispels any notion that “if you’re not paying for it, you are the product” or “the price for ‘free’ services is your data.” In truth, much of the information we share is shared because it is only by doing so that its value can be realized. More importantly, much of the data we share with platforms does not even exist (or is not known) separately from our interactions with these platforms. In this sense it is not data that is the “price” users pay for platform services; it is platform services that are the “price” platforms pay for data.

Of course, none of this is especially new; it is simply overlooked. The great UCLA economist, Jack Hirshleifer, noted many of these dynamics as long ago as the 1970s:

The possessor can in general benefit simply by private use of the information for his own productive or consumptive decisions. But in a market context it might also be possible for him to profit from sale of the information to others. The information-seeker might correspondingly find it advantageous to produce socially “new” information by direct inquiry of Nature (research) or to purchase “secondhand” information in the market. Viewed as a tradeable commodity, information has (as we shall see) a number of special features. . . . In the market process information can be regarded as “pulled” from the possessor by purchase, i.e., by payment of an explicit price. But what is surprising, the possessor may find it preferable to give away this valuable commodity, to disseminate it without pull of compensation. Indeed it may be highly profitable for him to incur costs so as to gratuitously “push” information to potential recipients! As for the information-seeker, his knowing that the possessors are so motivated may lead to adoption of a monitoring or listening mode of learning behavior.²⁹¹

2. Most Platform Businesses Started Without Any Data

Another important point is that data often becomes significant only at a relatively late stage in these businesses’ development. A quick glance at the digital economy is particularly revealing in this regard. Google and Facebook, in particular, both launched their platforms under the assumption that building a successful product would eventually lead to significant revenues. It took five years from its launch (and 300 million users) for Facebook to start making a profit. But even then, it was not entirely clear whether the social network would generate most of its income from app sales or online advertisements.²⁹² It was another three years before Facebook started to cement its position as one of the world’s leading providers of online ads.²⁹³ During this eight-year timespan, it seems that Facebook’s first concern was not so much the monetization of its platform, but user growth.

Facebook thus appears to have concluded (correctly, it turns out) that once its platform attracted enough users, it would surely find a way to make it highly profitable. This suggests that data might not have been of critical importance during the formative years of the Facebook platform (or at least not for its monetization). This might explain how Facebook managed to build a highly successful platform, despite a large data disadvantage over rivals like MySpace.²⁹⁴ The upshot is that, in the case of Facebook, data does not seem to have been a prerequisite for building a successful platform.

And Facebook is no outlier. Other successful technology firms have similar origins. For instance, Snapchat managed to build a successful platform that has 280 million daily active users.²⁹⁵ Snapchat achieved this feat without much, if any, user data, and despite entering the market later than numerous high-profile rivals,²⁹⁶ including Facebook,²⁹⁷ Instagram,²⁹⁸ and WhatsApp.²⁹⁹ Like Facebook, Snapchat chose to build its network without a clear monetization strategy, deferring this question to a later stage, when it would have an established user base.³⁰⁰ Granted, Snapchat may yet succumb to larger rivals (at the time of writing, Instagram seems to be winning the battle and may ultimately drive Snapchat out of the market). But these rivals’ success does not appear to have anything to do with superior access to data.³⁰¹ Instead, Snapchat’s possible decline appears to be due to Instagram having introduced more attractive features to its app.³⁰² Far from being suggestive of data-related market failures, Snapchat’s decline at the hands of Instagram appears to be a sign of healthy competition. It thus shows that competition between digital platforms is about much more than data, and that it is perfectly feasible for innovative companies to enter these markets despite significant data disadvantages.

And consider companies like Uber, Lyft, and Sidecar that have taken over the personal transport sector. They too had no customer data when they began to challenge established cab companies that did possess such data. If data were really so significant, they could never have competed successfully. But Uber, Lyft, and Sidecar have been able to effectively compete because they built products that users wanted to use³⁰³—they came up with an idea for a better mousetrap. The data they have accrued came after they innovated, entered the market, and mounted their successful challenges—not before.

The list of companies that prevailed despite starting with little to no data, and before they implemented (or even identified) a data-dependent monetization strategy, is vast. Other examples include Airbnb, Amazon, Twitter, PayPal, etc. These abundant illustrations severely undermine ideas that data constitutes a barrier to entry, that “data network effects” inevitably lead to tech platform tipping, or that data constitutes an essential facility.

A more apt economic parallel can be made with regard to the economic literature on two-sided markets. In these markets, it is well established that firms face a “chicken and egg problem.” Because the success of their business hinges on attracting two complementary groups of users, these platforms must often decide which group of users to favor early on in the hope that this will then kickstart any positive feedback loops that may exist between users on both sides of the platform.³⁰⁴ One particularly relevant strategy for ad-supported business is what economist David Evans refers to as “sequential entry”:³⁰⁵

In some cases it is possible to get one group of agents on board over time and then make these agents available to the other group of agents later in time. That is the situation with advertising-supported media. One can use content to attract viewers and then bring advertisers on board later. This dynamic works because there are non-positive indirect network effects between the two sides: viewers do not care about advertisers (and may dislike advertising) but come to the platform for the content.³⁰⁶

The ubiquity of the sequential entry strategy, which is relevant for many internet firms (including, Google, Facebook, and their rivals), contradicts arguments that access to data is necessary to compete in these industries. Granted, at some point firms may need data to earn profits and reinvest in their platforms. But saying that this dynamic somehow interferes with competition is merely a repeat of the “deep pocket” fallacy that plagued early predatory pricing theory.³⁰⁷ In the case of online platforms, there is no reason to believe that firms that earn less profits will invest less in their products. Instead, all these firms need to do is convince investors they will ultimately have the best product and the most users. If capital markets work properly—and there are literally billions of dollars flowing to Silicon Valley tech startups every year³⁰⁸—then being able to immediately monetize data offers little to no advantage over rivals that must call upon capital markets.

The inevitable conclusion is that, in reality, those who complain about data facilitating unassailable competitive advantages have it exactly backwards. Companies need to innovate to attract consumer data, otherwise consumers will switch to competitors, including both new entrants and established incumbents. As a result, the desire to make use of more and better data drives competitive innovation, with manifestly impressive results: the continued explosion of new products, services, and apps is evidence that data is not a bottleneck to competition but a spur to drive it.

D. Partial Conclusion: Data-Intensive Markets Should Not Alter the Balance of Antitrust Enforcement

We started this Part by referring to the popular metaphor that “data is the new oil.” As we have shown, while this metaphor may be rhetorically appealing, the comparison could hardly be any less apt. Indeed, it overlooks the key economic features of data. In doing so, it is symptomatic of wider misapprehensions about competition in data-intensive markets and the proper application of antitrust law and economics to them.

As we have shown, unlike oil, data is ultimately a form of information and as such is non-rivalrous and, in many cases, non-exclusive. Moreover, the value of a given dataset hinges critically on the expertise that firms can bring to bear in order to analyze the data. Unfortunately, this combination of learning-by-doing and firmwide capabilities in data-intensive markets has often been mislabeled as a “data network effect.” Finally, unlike an oil company that must first drill and refine oil before it can make sales, large amounts of data often become important only in later stages of a digital platform’s development. At the same time, much of the data used by platforms does not, in any meaningful sense, pre-exist platforms’ interactions with their users; rather, it is created by those interactions. As we have discussed, firms routinely build successful businesses without having access to pre-existing data. Instead, they hope that a strong product on the user side of the market will eventually translate into substantial revenues, notably by leveraging the data that is eventually generated on the platform.

In short, we argue that the advent of data-enabled markets does not support the calls for a significant expansion of antitrust enforcement being made in its name. Contrary to what has sometimes been claimed, data does not present unique—or even uniquely large—anticompetitive risks. Data is not irrelevant, of course, but it is just one amongst a plethora of factors that enforcement authorities and courts should consider when they analyze firms’ behavior.

III. We’ve Been Here Before: The Microsoft Antitrust Saga

Dystopian and nostalgic discussions concerning the power of successful technology firms are nothing new. Throughout recent history, there have been repeated calls for antitrust authorities to reign in these large companies. These calls for regulation have often led to increased antitrust scrutiny of some form. The Microsoft antitrust cases—which ran from the 1990s to the early 2010s on both sides of the Atlantic—offer a good illustration of the misguided “Antitrust Dystopia.”

In the mid-1990s, Microsoft was one of the most successful and vilified companies in America. After it obtained a commanding position in the desktop operating system market, the company sought to establish a foothold in the burgeoning markets that were developing around the Windows platform (many of which were driven by the emergence of the internet.)³⁰⁹ These included the internet browser and media player markets. The business tactics employed by Microsoft to execute this transition quickly drew the ire of the press and rival firms, ultimately landing Microsoft in hot water with antitrust authorities on both sides of the Atlantic.

This Part analyzes the antitrust cases that were brought against Microsoft and focuses on four main issues. First, Section A addresses the fears that commentators expressed before and during these cases. Second, Section B explores whether antitrust authorities echoed these same fears. Third, Section C analyzes how the market evolved after the antitrust cases and whether the critics’ fears came to transpire. Fourth, Section D addresses whether the market’s positive evolution could reasonably be attributed to antitrust intervention. Finally, Section E suggests that Microsoft’s own behavior might have caused rival entry, ultimately preventing it from extending its desktop advantage to adjacent markets.

This Article’s analysis shows that, although there were numerous calls for authorities to adopt a precautionary principle-type approach when dealing with Microsoft, and antitrust enforcers were more than receptive to these calls, critics’ worst fears never came to be. This positive outcome is unlikely to be the result of the antitrust cases that were brought against Microsoft. In other words, the markets in which Microsoft operated seem to have self-corrected (or were misapprehended as competitively constrained) and, today, are generally seen as being unproblematic. This is not to say that antitrust interventions against Microsoft were necessarily misguided. Instead, our critical point is that commentators and antitrust decisionmakers routinely overlooked or misinterpreted the existing and non-standard market dynamics that ultimately prevented the worst anticompetitive outcomes from materializing.

A. Popular Fears

From the mid-1990s onwards, a rapidly growing chorus of commentators started drawing the public’s attention to Microsoft’s business practices. For example, The Guardian published an article with the title “Microsoft’s Plan for World Domination.”³¹⁰ A commentator in the Wall Street Journal intimated that Microsoft was “a threat to everybody in the industry.”³¹¹ And somewhat comically, a piece in the Harvard Crimson called for the company to be broken up (and mocked Bill Gates for having dropped out of Harvard).³¹² But the prize for the most alarmist article, without doubt, goes to the New York Times for a piece titled “Making Microsoft Safe for Capitalism.” The piece concluded that “[i]f the software giant has its way, it will soon be in a position to collect a charge from every airline ticket you buy, every credit card purchase you make, every fax you send, every picture you download, every website you visit. It’s time to draw the line.”³¹³

So, what was it about Microsoft’s market position that frightened these commentators? A central theme was that Microsoft would leverage its strong position in the consumer operating system market to dominate competitors in adjacent markets, particularly the blossoming online space. Having overthrown these competitors, Microsoft could then levy a tax on the entire digital ecosystem, exploiting consumers who had come to rely on its products. The aforementioned New York Times piece, written by James Gleick, nicely encapsulated these fears:

By making connections among all these levels of modern computing, and by exerting control over the architectures that govern those connections, Microsoft is in the process of transforming the very structure of the world’s computer businesses. “Microsoft is imposing a new verticality on the industry,” says Gary Reback, a Silicon Valley technology lawyer who represented a group of anonymous Microsoft rivals in the antitrust proceedings. “Bill’s been able to exploit the market far better than anybody else has, and I think that’s because he intuitively understands what enormous power he has and how to exploit that power.” . . . With its new Microsoft Network, providing both an on-line service and Internet access, it is focusing on electronic financial-transaction processing—which is to say, all electronic commerce; which is to say, at least in some visions of the future, pretty much all commerce. “Basically what Microsoft is trying to do is tax every bit transition in the whole world,” says a senior executive of a competing software company. “When a bit flips, they will charge you.”³¹⁴

The implication was perfectly clear: Microsoft was allegedly using its strong position in the consumer operating system market to become the leading gateway to the internet, and thus have the ability to tax every internet user on the planet. Microsoft would purportedly achieve this goal by integrating its “Microsoft Network” dial-up service and web portal (also referred to as “MSN”) into its Windows operating system (these attempts ultimately ended in failure).³¹⁵ In short, as the same New York Times article concluded:

The Department of Justice does not need to break Microsoft apart. It need only—a far-reaching step in itself—require Microsoft to make its operating system, and the web of standards surrounding it, truly and permanently open. Other companies should be allowed to clone it if they could; Microsoft should be restricted from taking internal advantage of new changes until they were published to the rest of the market.³¹⁶

This alarmist article offers a perfect illustration of Antitrust Dystopia in action. Its author, James Gleick, hastily concluded that the market would fail, Microsoft would come to dominate online markets, and the internet would inexorably become a closed system. The only solution was government-mandated openness, because market forces allegedly could do nothing to prevent Microsoft from dominating adjacent markets. Of course, Gleick was not alone in reaching this conclusion. For example, another New York Times article found that:

Few people are more familiar with the real-world nature of Microsoft’s market power than Douglas P. Colbeth, the 42-year-old chief executive of Spyglass Inc. His Internet software company has been a supplier to Microsoft, providing the software engine for its Internet Explorer browser. Spyglass has also been a Microsoft competitor, briefly, in the browser market. Both roles proved to be sobering for Spyglass. So much so that the company’s current strategy is mainly to stay out of Microsoft’s path, finding specialized markets for Internet software and services that the software giant ignores.³¹⁷

And numerous other publications marched to the same beat.³¹⁸ For instance, a Harvard Crimson article concluded that:

Gates and company follow an “embrace-and-extend’ strategy: incorporate the features of competitors’ products into your operating system and core applications, essentially giving them away, then make them slightly better to keep customers from buying the stand-alone version.

In market after market, Microsoft has followed this strategy successfully. Text editors. Drive compression. Screen savers. Disk tool software. 3-D graphics acceleration. And soon, speech and handwriting recognition. If Microsoft wins against Justice, add Internet browsers to the list of conquests.

Microsoft’s reach into many software markets gives it other advantages that work against a competitive market.

. . . .

The problem is that when Microsoft is done driving its competitors out of business, innovation tends to stop and prices usually rise. Recent hikes in the price of Office and a surprise end to the company’s concurrent licensing policy show what Microsoft will do when it gains hegemony over a market.³¹⁹

There are uncanny similarities between the claims being made in these articles and those that are routinely raised against contemporary tech firms. The calls for Microsoft to open up its platform are almost identical to those that are now being brought against Google and Amazon, twenty years later. For instance, a report by the Stigler Center called for authorities to create a mandatory access regime covering digital platforms’ data.³²⁰ Likewise, the assertion that Microsoft copied the products of rivals in adjacent markets is analytically identical to claims that Amazon cannibalizes its online retailers by offering Amazon-branded products that copy their goods.³²¹ Similarly, claims that Google’s online advertising business is premised on taxing all news outlets are reminiscent of assertions that Microsoft would exert a tax on all online markets.³²² Moreover, just as past critics did with Microsoft, today’s critics ultimately argue that innovation will grind to a halt if dominant tech platforms are left to their own devices.³²³ Last but not least, the idea that startups should have avoided those markets where Microsoft operated is similar to today’s “kill zone” claims, in which critics argue that the presence of large digital platforms in a given market deters rivals from investing in that same space.³²⁴

One immediate reaction might be that newspapers at the time were ill-equipped to understand the complexities of these rapidly evolving markets, and that their preoccupations were far-removed from those of sophisticated antitrust authorities and courts. But nothing could be further from the truth. There were, in fact, significant overlaps between some of the more extreme claims raised by newspapers, before and during the Microsoft antitrust investigations, and the conclusions of some antitrust decisionmakers and courts.

B. Antitrust Intervention

It was not only press outlets that espoused a highly pessimistic view of Microsoft’s market position; antitrust decisionmakers were no different. After the first version of Windows was released in 1985,³²⁵ Microsoft’s sales rapidly took off. By 1993 it was the world’s most used personal computer operating system.³²⁶ This led several antitrust decisionmakers and courts to the conclusion that, absent antitrust intervention, Microsoft would inexorably crush competitors in adjacent markets.

The first case was brought against Microsoft by the US Department of Justice (“DOJ”) in 1993, leading to a consent decree in 1994.³²⁷ This early case mostly focused on Microsoft’s licensing terms with the original equipment manufacturers (“OEMs”) that assembled Windows-based desktop computers. The DOJ argued that these terms excluded competing operating systems.³²⁸ In a nutshell, Microsoft licensed its operating system on a “per processor” basis.³²⁹ As a result, OEMs paid a fee to Microsoft for each PC they sold, regardless of the operating system that was ultimately installed on it.³³⁰ Microsoft and various OEMs also entered into long-term agreements, sometimes with minimum volume commitments.³³¹

With the benefit of hindsight, some of the conclusions reached by authorities and scholars at the time of the case seem particularly severe.³³² For instance, both the DOJ and Amici Curiae agreed that the desktop operating system market was beset by increasing returns to scale and network effects (a mantra that is often cited by recent reports calling for greater antitrust enforcement in the tech sector).³³³ They therefore surmised that barriers to entry precluded more efficient rivals from entering the market.³³⁴ As the DOJ observed in its competitive impact assessment:

Development, testing, and marketing of a new PC operating system involves considerable time and expense. A new operating system faces additional barriers to entry, including the absence of a variety of high quality applications to run on the system; the small number of people trained on and using the system, which discourages customers from buying it and software companies from writing applications to run on it; and, since the overwhelming majority of PCs are sold with a pre-installed operating system, the difficulty of convincing OEMs to offer and promote the system.³³⁵

And although he did not entirely agree with the normative stance of the DOJ and Amici Curiae, Nobel-winning economist Kenneth Arrow concurred that:

[T]he software market is peculiarly characterized by increasing returns to scale and therefore natural barriers to entry. Large-scale operation is low-cost operation and also conveys advantages to the buyer. Virtually all the costs of production are in the design of the software and therefore independent of the amount sold, so that marginal costs are virtually zero. There are also fixed costs in the need to risk large amounts of capital and the costs associated with developing a reputation as a quality supplier. Further, there are network externalities, in particular, the importance of an established product with a large installed base and the related advantage of a product that is compatible with complementary applications.³³⁶

In short, at the time of the first Microsoft investigation in the US, there was a relatively widespread consensus that operating systems were a natural monopoly. The actions of antitrust authorities were thus premised on the assumption that market forces alone were unlikely to restore competitive outcomes in these software markets.³³⁷ As this Article discusses below, however, the competition that has since emerged in these markets suggests that the natural monopoly fears expressed at the time were likely overblown.³³⁸

Much the same can be said about the second antitrust case brought against Microsoft in the US. The case centered on claims of both monopolization and attempted monopolization.³³⁹ On appeal, Microsoft was found guilty of maintaining its monopoly over the operating system market by deploying a series of measures destined to slow the uptake of Java and the rival Netscape Navigator browser.³⁴⁰ This was sometimes referred to as the “platform threat” theory of harm.³⁴¹ Microsoft was also accused of attempting to monopolize the internet browser market by tying Internet Explorer to the Windows operating system (although this part of the district court’s ruling was vacated and remanded on appeal).³⁴² The findings of the district court are a perfect example of Antitrust Dystopianism. The final paragraph of Judge Jackson’s ruling is perhaps the most revealing:

Most harmful of all is the message that Microsoft’s actions have conveyed to every enterprise with the potential to innovate in the computer industry. Through its conduct toward Netscape, IBM, Compaq, Intel, and others, Microsoft has demonstrated that it will use its prodigious market power and immense profits to harm any firm that insists on pursuing initiatives that could intensify competition against one of Microsoft’s core products. Microsoft’s past success in hurting such companies and stifling innovation deters investment in technologies and businesses that exhibit the potential to threaten Microsoft. The ultimate result is that some innovations that would truly benefit consumers never occur for the sole reason that they do not coincide with Microsoft’s self-interest.³⁴³

In light of these stark findings, the district court ordered that Microsoft should be broken up (the court euphemistically referred to this as a “structural reorganization”).³⁴⁴ It also imposed various behavioral commitments on the firm.³⁴⁵ The structural remedy would effectively have severed Microsoft’s operating systems business from its other products, such as word processors, internet browsers, etc.³⁴⁶ The court and plaintiffs’ reasoning was clear: absent structural separation and strict non-discrimination commitments, Microsoft inevitably would have leveraged its operating system dominance into adjacent markets. There are striking similarities between this stance and some of the claims that have been levelled against today’s successful tech platforms.³⁴⁷

Furthermore, according to Microsoft’s critics, structural separation was not only necessary because of the underlying market conditions, but also because of the company’s “untrustworth[iness].”³⁴⁸ For instance, Judge Jackson’s memorandum and order severely criticized the firm’s track record with regulators:

Microsoft has proved untrustworthy in the past. In earlier proceedings in which a preliminary injunction was entered, Microsoft’s purported compliance with that injunction while it was on appeal was illusory and its explanation disingenuous. If it responds in similar fashion to an injunctive remedy in this case, the earlier the need for enforcement measures becomes apparent the more effective they are likely to be.³⁴⁹

On a more substantive front, on appeal, the DOJ and several State Attorneys General argued that breaking up Microsoft was essential to restore competition. Endorsing a precautionary principle–type approach to antitrust enforcement, they argued that:

Microsoft’s operating system monopoly and dominance in applications (including IE) give it the incentive and ability to undertake the forms of anticompetitive conduct proved at trial and to pursue future variants of its past anticompetitive strategies that are impossible to predict. . . . Injunctive relief crafted for the long term necessarily would involve complex and highly intrusive restrictions on Microsoft’s conduct, might result in regulation rather than consumer choice determining market outcomes, and would require continued monitoring of Microsoft’s future activities.³⁵⁰

And it was not just US antitrust authorities that espoused a highly dystopian view of Microsoft. Throughout two long-running competition cases, the European Commission was similarly bullish about the threat posed by Microsoft.³⁵¹ Mario Monti, the Commissioner responsible for competition policy at the time of the investigation, notably argued that “Microsoft is leveraging its overwhelmingly dominant position from the PC into low-end servers, the computers which provide core services to PCs in corporate networks.”³⁵²

This pessimism regarding rivals’ ability to compete head-on with Microsoft was also plain to see in the first decision that the European Commission adopted against Microsoft (which dealt with Microsoft’s alleged tying of Windows Media Player to the Windows operating system, and Microsoft’s purported refusal to supply interoperability information to its key rivals.)³⁵³ The European Commission concluded that:

Microsoft’s tying of WMP [i.e., Windows Media Player] also sends signals which deter innovation in any technologies which Microsoft could conceivably take interest in and tie with Windows in the future. Microsoft’s tying instils actors in the relevant software markets with a sense of precariousness thereby weakening both software developers’ incentives to innovate in similar areas and venture capitalists’ proclivity to invest in independent software application companies. A start-up intending to enter or raise venture capital in such a market will be forced to test the resilience of its business model against the eventuality of Microsoft deciding to bundle its own version of the product with Windows.³⁵⁴

Finally, the Commission cited equally alarming press articles and comments from rivals to support the above conclusion:

“The presence of the WM player on top of the Windows XP operating system [is] a major threat to the emergence of competitive technologies. Such problems are common to other applications shipped with Windows XP, such as instant messaging services, which are subject to the same threat.”

. . . .

“‘The mood among venture capitalists gathered two weeks ago was somber,’ says Larry Marcus, partner with the WaldenVC firm in San Francisco, as Microsoft presented details of Office XP, the newest version of its word-processing and spreadsheet software. ‘They continue to expand the number of businesses that they’re going after,’ Marcus says. ‘As an investor in early-stage and private companies, the dance with Microsoft is becoming more important and more dangerous.’ He assumes that dozens of business plans were scrapped after the Office XP demonstration.”³⁵⁵

All the above shows that antitrust authorities and courts on both sides of the Atlantic often bought in to the dystopian reasoning that was being voiced against Microsoft in popular news outlets at the time of their respective investigations. Indeed, just like their lay counterparts, antitrust policy makers readily concluded Microsoft would inevitably maintain its operating system monopoly and extend it to adjacent markets, thus deterring rival investments and stifling innovation. However, as explained in the following section, this is not how competition ultimately played out.

C. What Happened Afterward

The previous sections have shown that mainstream media, antitrust authorities, and courts repeatedly made strong claims about the threats posed by Microsoft. This raises a critical question: did these gloomy prophecies come to pass, or was there alarmism at play?

Answering this query is no easy task. If critics’ predictions did not materialize, this failure could reasonably be attributed to the antitrust interventions that took place. Critics would thus have been right when they urged authorities to act. Moreover, even if critics’ worst fears were unfounded, this does not necessarily mean that antitrust intervention was uncalled for. We simply do not know what a counterfactual world with stronger (or weaker) antitrust or regulatory intervention against Microsoft would have looked like.

Mindful of these difficulties, this Article narrows the discussion to the following two questions. First, did critics’ most severe predictions materialize? And second, in the event that they did not, is there any evidence to suggest this was due to antitrust intervention? Accordingly, this Article does not second-guess the antitrust interventions that took place against Microsoft. Others have already thoroughly dissected that topic.³⁵⁶ Instead, this Article’s goal is to determine whether the criticism that was levelled against Microsoft was overblown or not.

This Article’s analysis shows that critics’ dire predictions mostly failed to materialize and focuses on three main lines of argument (discussed in detail in the previous sections). First, Subsection 1 explores critics claims that, because of increasing returns to scale and network effects, more efficient rivals would be unable to enter the operating system market and that operating system and software markets were natural monopolies. Second, Subsection 2 analyzes the arguments that Microsoft would leverage its operating system dominance to overthrow more efficient rivals in adjacent markets, especially online ones. Finally, Subsection 3 explores detractors opinions that innovation would grind to a halt, especially in products that relied on Microsoft’s platform and were thus purportedly vulnerable to leveraging. This Article finds little evidence that any of these predictions were accurate.

1. Natural Monopoly and Barriers to Entry

Let us start with the claims that operating systems were effectively a natural monopoly and that entry barriers would prevent more efficient rivals from competing in the markets where Microsoft was already present. Although it is true that Microsoft still commands a strong market share in the desktop and laptop segments, critics’ predictions were mostly off the mark.

At the time of writing this Article, Microsoft’s strongest segment is still desktop operating systems, where it currently commands a global market share of roughly 60% to 88%, according to various sources.³⁵⁷ At best, this marks only a slight decrease compared to the US district court’s ruling (which found that Microsoft had a greater than 80% market share, with MacOS included in the market) and the European Commission’s first decision (which concluded that Microsoft had a market share of 93.8% in the year 2002, also including MacOS).³⁵⁸

Although, at first sight, these numbers might appear to confirm critics’ fears, they mask a very different competitive reality. For a start, if one avoids static and overly nostalgic market definitions and instead accounts for the emergence of mobile operating systems, Microsoft’s market share has decreased to a far less impressive 31%, behind the 42% commanded by Google Android.³⁵⁹ This is no small detail.

The biggest implication is that Microsoft’s operating system is no longer a bottleneck though which users must pass to access innovative software (and vice versa). Developers can now choose from a vast array of platforms to reach users. For example, Epic Game’s highly popular “Fortnite Battle Royale” is available on Windows, MacOS, PlayStation 4, Xbox One, Nintendo Switch, iOS, and Android.³⁶⁰

Similarly, Microsoft’s Windows operating system is no longer the sole gateway through which mainstream users can access the internet. Today, a majority use a smartphone running iOS or Android, a computer running Windows or MacOS, or a tablet running iOS or Android (though there are countless alternatives, as well).³⁶¹ Readers will recall that Microsoft being the gateway to the internet was an important concern for its critics.³⁶² This is simply no longer the case.

Finally, the success of rival operating systems, albeit on different platforms, contradicts critics’ claims that network effects and economies of scale ran across platforms.³⁶³ In other words, dominating the desktop operating system segment did not give Microsoft a clear advantage when it competed in other segments, such as mobile operating systems (where it has struggled to maintain a significant foothold).³⁶⁴ The upshot is that Microsoft’s market position in its strongest and most secure segment was, and still is, much frailer than its critics could have ever imagined.

Perhaps more important, Microsoft’s market position has also been severely eroded in the server operating system market. Estimates place Microsoft’s market share in the web server segment somewhere between 1.8% and 25% (various iterations of Linux occupy almost all of the remaining market).³⁶⁵ Microsoft’s share of the market also seems to be declining at a steady pace; according to one report, it decreased from 27% in the year 2000, to 5% in 2019.³⁶⁶ More importantly, “Apache” and Microsoft, which once occupied a combined 85% of the market, have both been overtaken by competitors.³⁶⁷ If increasing returns to scale, insurmountable network effects, and barriers to entry and expansion were really endemic, the opposite should have occurred.

Things are less clear in the workgroup server segment. The best estimate found places Microsoft’s share at 72.8% of the global workgroup market, slightly up compared to the 61% to 64% that it commanded in Europe at the time of the European Commission’s investigation.³⁶⁸ As we discuss below, this tends to suggest that one of the remedies imposed by the European Commission was not particularly effective.³⁶⁹

In short, Microsoft’s critics made two important miscalculations. First, operating systems do not appear to have been as close to natural monopolies as they believed—otherwise, Microsoft would not have steadily lost market shares in the web server operating system market and failed to gain traction with its Windows Mobile operating system.³⁷⁰ Second, critics failed to recognize that the emergence of new platforms would allow users and developers to bypass Microsoft’s desktop operating system bottleneck, and that new technologies, such as cloud computing, would make the desktop operating system segment far less relevant because many calculations would no longer need to be made on a user’s device.³⁷¹ The emergence of these new platforms also seems to have prevented the tipping that critics feared.³⁷² In sum, Microsoft might not have been entirely disrupted—its desktop operating system and workgroup server market shares are still high—but it was defanged. It effectively lost the strong base that purportedly would have enabled it to coerce consumers and developers into adopting its products in adjacent markets.

2. Leveraging

The picture gets worse for Microsoft and its critics when one looks at potential leveraging in adjacent markets. Microsoft’s critics mainly focus on three adjacent markets: web browsers, media players, and search engines. At the time of writing, Microsoft is a secondary figure in all three of them.³⁷³ Moreover, Microsoft failed to dominate almost all of the adjacent markets in which it is currently active—the biggest potential exception being office suites.

In the internet browser market, Microsoft’s share has plunged from roughly 67% in 2007 to about 8% in 2019.³⁷⁴ But even this 8% figure is misleading. Microsoft’s Edge browser now runs on the open-source Chromium technology.³⁷⁵ It could thus be replicated by rivals with relative ease. Much the same is true in the media player segment. Although Microsoft still bundles a version of its media player with Windows 10, the video streaming market has moved on.³⁷⁶ Most video streaming takes place either through internet browsers (thanks in part to HTML5³⁷⁷), or through dedicated apps that are available on most popular platforms (think Netflix, HBO Max, Amazon Prime Video, YouTube, and Disney+). Microsoft is not an important player in this space. Finally, Microsoft failed to convert its operating system dominance into a strong search engine market share. Bing currently hovers around the 6% mark³⁷⁸ (although some estimates place it as high as 21% when the market is narrowed to desktop search engines in the US³⁷⁹Comscore Releases February 2016 U.S. Desktop Search Engine Rankings, Comscore (Mar. 16, 2016), https://perma.cc/TUU9-XMYG.).

Microsoft’s other services do not appear to have benefited from cross-platform leveraging either. For instance, Azure (Microsoft’s cloud computing service) is only the second largest player in the market (behind Amazon), with a market share of roughly 16%.³⁸⁰ More importantly, there is no sign that Microsoft achieved this position by “forcing” users of its other services to choose Azure. The “Azure Portal,” which allows users access to Microsoft’s cloud, runs on all major desktop and tablet devices and is compatible with the latest versions of all the major web browsers.³⁸¹ Put simply, Microsoft is not particularly dominant in cloud computing and there is no evidence it has strongly benefited from some type of self-preferencing.

Similarly, Microsoft has apparently not attempted to coerce users of its wider ecosystem into joining the LinkedIn social network (which it acquired in 2016).³⁸² For example, the LinkedIn app does not come pre-installed on Windows 10.³⁸³ And although LinkedIn has a large market share in its narrow segment of the social networking industry, Microsoft captures only a tiny share of online advertising. To give readers some idea, LinkedIn and Microsoft’s other websites captured an estimated 4.1% of online advertising spend in 2019, compared to 38.2% for Google, 21.8% for Facebook, and 6.8% for Amazon.³⁸⁴

Microsoft also failed to dominate the game console market. Granted, at the time of writing, it has a comfortable market share of 42% (excluding mobile gaming and platforms such as Steam).³⁸⁵ Though this is undoubtedly a profitable segment for Microsoft,³⁸⁶ it is certainly not the type of dominance its critics’ feared. Nor, again, is there any apparent sense that Microsoft somehow leveraged its strong position in desktop operating systems to maintain a strong position in the game console market.

And although Microsoft has been more aggressive in its attempts to convert users to its messaging services, it has not managed to thwart its competitors in those markets, either. Skype is pre-installed on Windows 10³⁸⁷ and Microsoft Teams is bundled with Office 365.³⁸⁸ However, despite Microsoft’s best efforts, competition in these markets has remained fierce. In the business chat segment, Skype competes against Google Hangouts, Slack, and Facebook, among many others.³⁸⁹ In video calls, it competes against the likes of Zoom, Google Hangouts, and WhatsApp. Finally, in the instant messaging segment, Skype competes against Facebook (WhatsApp, Facebook Messenger, and Instagram), and Snapchat.³⁹⁰ And a broader market definition might even include differentiated services such as Twitter, SMS, and TikTok. All of these are robust rivals.

Finally, Microsoft’s position has been somewhat eroded in the market for office suites (which partly overlaps with the services described in the previous paragraph). Estimates place Microsoft’s Office 365 suite at roughly 40% of the market, behind Google’s “G Suite” of productivity apps (now known as Google Workplace), which takes up roughly 60% of the market.³⁹¹ And although G Suite is free for private users, which is not the case of Office 365,³⁹² both firms charge comparable fees to businesses (Office 365 is priced between $5 and $12.50 per month/per user,³⁹³ while G Suite costs between $6 to $18 per user/per month³⁹⁴). However, Microsoft’s position is stronger if one looks at the market in terms of revenue. In 2016, Office 365 generated roughly ten times more revenue than the G suite.³⁹⁵ Although Google’s suite has grown significantly since 2016, it likely still lags behind Microsoft in terms of revenue. But even with this caveat, there is little sense that Microsoft has anything close to a monopoly in the market for office suites.

What do all of these numbers tell us? Microsoft’s critics claimed it would use its dominance in the operating system market to crush competitors in adjacent markets.³⁹⁶ Looking at the most important business segments in which it operates reveals that this simply has not been the case. Microsoft has not managed to establish a durable monopoly position, or anything close to it, in any of these markets. Of course, this is not to say that Microsoft has not created highly profitable services in these adjacent markets. But critics’ fears that it would leverage its desktop operating system dominance into adjacent markets failed to transpire (the important question, which we address in Section D, is why).

3. Innovation

A final important claim was that innovation would slow down because of Microsoft’s size and behavior. For instance, the European Commission concluded that:

Microsoft’s tying instils actors in the relevant software markets with a sense of precariousness thereby weakening both software developers’ incentives to innovate in similar areas and venture capitalists’ proclivity to invest in independent software application companies.³⁹⁷

Unfortunately, it is impossible to reproduce counterfactual worlds in which Microsoft might have been subjected to tougher or looser antitrust scrutiny, or where it might have decided not to enter adjacent markets. This Article therefore cannot exclude the possibility that innovation could have been superior if Microsoft’s desktop operating system position had been further reduced, just as we cannot exclude the opposite.

Despite this limitation, it is worth noting that the period leading up to and following the Microsoft antitrust cases has been particularly rich in innovation and startup activity. At the very least, this Article can show innovation most certainly did not grind to a halt, contrary to what some of Microsoft’s critics feared.

The best evidence for this conclusion comes from significant increases in venture capital spending in the United States, the upward movement of the Nasdaq index, and a steady (if not record-breaking) trend of Initial Public Offerings (“IPOs”). This Article also complements that analysis by identifying a series of groundbreaking technologies that have emerged in the information technology sphere since the mid-1990s.

A first sign that innovation has continued to progress at a strong pace is that venture capital spending in the United States has been steadily increasing since the mid-1990s.³⁹⁸ According to some sources, this spending reached an all-time high in 2018.³⁹⁹ These claims are based on a report by PitchBook and the National Venture Capital Association, which found that the total capital invested in 2018 was $131 billion, compared to the previous record of $105 billion in 2000 (just before the Dotcom bubble burst).⁴⁰⁰ Similarly, PWC’s MoneyTree report places 2018 just beneath 2000, with $116 billion and $120 billion, respectively.⁴⁰¹ Whether or not 2018 was the biggest year on record, it is clear that venture capital investment has not declined since Microsoft’s prime in the mid-1990s.

Likewise, the Nasdaq Composite index, which is heavily weighted towards information technology companies, reached an all-time high in the summer of 2019.⁴⁰² With the notable exception of the dotcom bubble which burst in the year 2000, it has been steadily increasing since its launch in 1971.⁴⁰³

The picture is a little less clear when one looks at IPOs.⁴⁰⁴ The years 1999 and 2000 (leading up to the end of the dotcom bubble) remain the largest on record, both in terms of aggregate proceeds and number of IPOs.⁴⁰⁵ During each of these two years, aggregate IPO proceeds were around $64 billion, compared to roughly $33 billion in 2018.⁴⁰⁶ The expectation is that 2021 will beat those numbers.⁴⁰⁷ This gap is even larger if one adjusts for inflation.

Some have hypothesized that the slowdown of IPOs might simply be due to more companies choosing to remain private.⁴⁰⁸ This idea is partly backed by data, which shows that the median IPO size has increased significantly since the end of the 1990s.⁴⁰⁹ Nevertheless, the fact that IPO volumes have not (or have only recently) reached their late 1990s high does not mean that innovation has ground to a halt. For a start, it is worth mentioning that the records set in 1999 and 2000 were the product of what is arguably one of the largest economic bubbles in history.⁴¹⁰ In other words, the IPO values that took place during those years likely overstate the technological contributions of the underlying firms. Second, and more importantly, critics did not claim that innovation would fail to return to its record highs, they argued that Microsoft would cause innovation to considerably slow down.⁴¹¹ The healthy IPO activity recorded every year since then suggests they were wrong.

Of course, not all of these venture capital deals, IPOs, and stock market increases took place in the industries where Microsoft was active. Could it be that the rest of the economy was highly innovative while the information technology sector lagged behind? A qualitative analysis rapidly dispels this idea. The first two decades of the twenty-first century have witnessed the commercialization of, among others: the iPod; smartphones and tablets; smartwatches and other connected devices; the Blu-Ray disc; online video and music streaming services; social networks (including Facebook, Twitter, LinkedIn, and Slack); e-books; online video conferencing; the Wikipedia encyclopedia (which easily prevailed over Microsoft’s failed “Encarta”⁴¹²); high-performance search engines; online maps; online shopping; Wi-Fi; Bluetooth; 3G, 4G, and 5G; crowdfunding; rudimentary self-driving vehicle technology; tremendous increases to processing power; and blockchains. These are all fields that are closely linked to the markets where Microsoft operates, and yet the innovation in these fields has been nothing short of incredible.⁴¹³

From a more quantitative standpoint, Susan Houseman, Timothy Bartik, and Timothy Sturgeon, among others, have shown that the computer and electronics sector has witnessed unprecedented productivity growth from the mid-1990s onwards.⁴¹⁴ In their words:

[T]he extraordinary growth in real value-added in manufacturing and the accompanying productivity growth in the computer and electronic products industry results largely from two sets of products, computers and semiconductors, that, when adjusted for quality improvements, have prices that are falling rapidly.⁴¹⁵

In short, claims that Microsoft would cause innovation to significantly slow down appear to have been extremely off the mark. Innovation did not slow down, and even if it had, critics would still have to show that this was attributable to Microsoft’s size or its behavior.

D. Was Antitrust Intervention Against Microsoft Responsible for These Positive Outcomes?

This leaves us with one important question to answer. Could the failure of critics’ predictions regarding Microsoft reasonably be attributed to the antitrust interventions that took place between the mid-1990s and today, or simply the general level of deterrence that existing antitrust laws achieve? This Article argues that neither of these is a convincing explanation. Instead, more plausible theories include the fact that Microsoft ultimately lost its bottleneck position, and that its incentives and ability to exclude rivals in adjacent markets were greatly exaggerated.

1. Actual Remedies Were Relatively Weak

A first important point is that the remedies that were imposed against Microsoft by antitrust authorities on both sides of the Atlantic were ultimately quite weak. It is thus unlikely that these remedies, by themselves, prevented Microsoft from dominating its competitors in adjacent markets.

The European Commission imposed three remedies upon Microsoft. In a 2004 decision, the Commission required Microsoft (1) to sell a version of Windows without its media player bundled, and (2) to provide server interoperability information to its rivals.⁴¹⁶ In 2010, the Commission also required that Microsoft introduce a browser choice screen within new versions of the Windows operating system.⁴¹⁷ But all three of these remedies were, to some extent, failures.

For a start, Microsoft did sell a version of Windows without the media player: “Windows XP N.”⁴¹⁸ During the first nine months following its introduction, Microsoft sold just 1,787 copies of this version (compared to 35.5 million copies of the regular version)⁴¹⁹—clearly too few to have any kind of competitive effect. Second, the browser choice screen remedy was so ineffective that, when Microsoft illegally stopped implementing it, it took authorities and consumers a full fourteen months to notice.⁴²⁰ Finally, it took almost four years for Microsoft and the Commission to agree upon the exact contours of the interoperability remedy.⁴²¹ Microsoft’s workgroup server market position remained roughly constant from the start of the Commission’s investigation until the time of writing.⁴²² This suggests that the Commission’s remedy did not significantly boost Microsoft’s competitors, as it had hoped (although we do not know the exact counterfactual).

The remedies imposed against Microsoft in the US do not appear to have had a much more meaningful effect. As explained above, from the mid-1990s to the early 2000s, Microsoft was the subject of two high-profile antitrust suits in the US.⁴²³ Both proceedings resulted in the adoption of consent decrees that attempted to limit Microsoft’s ability to foreclose rivals.⁴²⁴ However, with hindsight, it is unlikely that either of these proceedings ultimately had a meaningful impact on Microsoft’s market position.

The first Microsoft case, in the mid-1990s, led to a consent decree that entered into force in 1995.⁴²⁵ The original deal agreed upon by Microsoft and the DOJ’s antitrust division essentially sought to facilitate the entry of rivals in the desktop operating system market. It attempted to do this by preventing Microsoft from agreeing to “per processor” licenses or imposing “requirement contracts” on OEMs, and by limiting the duration of Microsoft’s license agreements with OEMs to one year.⁴²⁶

It is worth noting that these remedies focused solely on the desktop operating system market.⁴²⁷ And yet, Microsoft’s desktop operating system market position remained more or less unchanged until 2010, fifteen years after the remedy was implemented.⁴²⁸ It thus seems highly unlikely that this first case meaningfully affected the course of competition in the desktop operating system market. More importantly, the first consent decree did not hinder Microsoft’s ability to compete in adjacent markets.⁴²⁹ In other words, it did nothing to assuage fears that Microsoft might leverage its strong desktop operating system market position to foreclose rivals in adjacent markets. In fact, it is this “oversight” that ultimately led the DOJ and State Attorneys General to bring a second, and far broader, case against Microsoft.⁴³⁰

The remedies imposed following the second major antitrust case do not appear to have been any more effectual. The breakup remedy that was initially imposed by Judge Jackson in June 2000 was overruled on appeal.⁴³¹ Subsequent remand proceedings led Microsoft and the plaintiffs to agree upon two almost identical consent decrees.⁴³² These consent decrees included several noteworthy provisions.⁴³³ A first set of requirements aimed to prevent Microsoft from foreclosing rival “middleware” providers, such as Java and Netscape.⁴³⁴ Microsoft was also made to share some of its APIs (i.e., application programming interfaces that effectively enable different applications to talk to each other) and communications protocols.⁴³⁵ Finally, a technical committee was appointed to monitor Microsoft’s compliance with the remedies.⁴³⁶

While it is of course difficult to ascertain exactly how much of an effect these measures actually had on competition in the software industry, the general consensus appears to be that they were particularly weak. For instance, Professor David McGowan opined that:

The case produced a peculiar mishmash of liability and remedies, in which the acts that did the most to reinforce Microsoft’s market power were found lawful while the acts found unlawful were effectively trivial. The net result was a tepid tapioca pudding of a consent decree, which almost certainly will do nothing to reduce Microsoft’s market power.⁴³⁷

Other commentators have been more positive, though they have largely stopped short of extolling the virtues of the remedies. For instance, former Assistant Attorney General Renata Hesse (who oversaw much of the Antitrust Division’s work on the remedial aspects of the case) noted that, while the remedies may have been relatively positive overall, some of their most important aspects lacked teeth:

[I]t is not terribly surprising that Section III.E has formed the principal basis for claims by some that the Final Judgments have “failed.” The provision attempts to anticipate and address unknown threats to Microsoft’s operating system dominance. No matter how well drafted or vigilantly enforced the provision might be, that goal is difficult to accomplish.

. . . .

Section III.E imposed an affirmative obligation on Microsoft to make technology that it had not previously licensed available via a broad licensing program. As such, the focus on this particular provision was substantial. With each difficulty reported by the plaintiffs regarding Microsoft’s compliance, the perception intensified that the most significant part of the remedy would end up accomplishing nothing.

. . . .

This perception had grown so strong that, in late 2007, certain state plaintiffs advocated extending the Final Judgments in their entirety for an additional five years, in part to account for Microsoft’s delay in implementing Section III.E.

. . . .

It may yet be some time before all parts of the Final Judgments operate together as intended, as the district court—and the government enforcers—hoped would happen.⁴³⁸

Reading between the lines, one of Hesse’s main points seems to be that it is hard to design truly forward-looking remedies, and that the Microsoft litigation was no exception. As was the case with the first Microsoft consent decree, the new wave of remedies mainly addressed the issues identified in the case at hand (namely the challenges that Netscape and Java faced when trying to compete on the Windows platform). However, they did little to prevent Microsoft from flexing its muscles in other adjacent markets. Today, dystopian scholars would likely perceive this as a failure—what was there to stop Microsoft from using its desktop position to dominate the mobile sector, for instance—but it would be far more accurate to characterize this as a (possibly unintended) success. History tells us that Microsoft never did manage to transfer its desktop market position into other adjacent markets,⁴³⁹ despite the absence of remedies preventing it from doing so. Perhaps courts at the time wisely recognized that it is almost impossible to snuff out as yet unknown threats, and that it is better to leave the job of guarding this competition to market forces. More likely, however, given the underlying analysis that myopically concentrated on backward-looking relevant markets and very limited conceptions of future innovative entry, such an outcome was an inadvertent byproduct.

The upshot is that the remedies that were imposed upon Microsoft in the EU and the US did very little to prevent the type of fears critics had been voicing,⁴⁴⁰ and did not hamper Microsoft’s ability to compete in the mobile internet segment. Yet, as this Article explains in the following subsection, this is precisely where Microsoft ultimately lost its grip over online markets.

2. Microsoft Lost Its Bottleneck Position

A second important factor suggests that it was not antitrust enforcement that enabled Microsoft’s competitors to flourish. As the previous subsection already alluded, one of the biggest changes that took place in the digital space was the emergence of alternative platforms through which consumers could access the internet. Because Microsoft was unable to beat competitors in all of these markets, it is no longer a bottleneck to the internet.

According to one source, roughly 94% of all internet traffic came from Windows-based computers in January 2009.⁴⁴¹ Eleven years later, this number has fallen to about 31%.⁴⁴² Android, iOS, and OS X have shares of roughly 41%, 16% and 7%, respectively.⁴⁴³ Consumers can thus access the web via a large number of platforms. The emergence of these alternatives surely reduced the extent to which Microsoft could use its bottleneck position in order to force its services upon consumers in online markets. The result is well known: the early twenty-first century witnessed the emergence of innovative and successful internet companies such as Google, Amazon, Facebook, eBay, Spotify, and TikTok. Microsoft could only sit and watch as other firms conquered these new online markets.

This again raises the critical question: would this market outcome have been substantially different without the antitrust cases that were brought against Microsoft? While any answer will necessarily be speculative, the short answer appears to be no. And the reason, once again, lies in the importance of capabilities—something that antitrust analysis simply ignores.

As Ben Thompson has argued, Microsoft did not have the right capabilities to compete in the search and mobile phone markets—two markets with enormous strategic importance.⁴⁴⁴ For a start, a company built on providing high-end software was unlikely to successfully compete with Apple in the mobile handset industry. Apple had a track record for producing outstanding hardware—culminating with the groundbreaking iPhone—that Microsoft simply couldn’t match.⁴⁴⁵ Similarly, by the time Microsoft decided to enter the search engine industry, Google had already acquired vast experience in this area.⁴⁴⁶ This is not to say that subsequent entrants can never overthrow industry incumbents. But, in this case, there is little evidence to suggest that Microsoft ever came close to doing so. In the words of Benedict Evans:

The end of Microsoft’s dominance of tech actually came in two phases. First, as above, it lost the development environment to the web, but it still had the client (the Windows PC) and it then provided lots and lots of clients to access the web and so became a much bigger company. But second, a decade or so later, Apple proposed a better client model with the iPhone, and Google picked that up and made a version for every other manufacturer to use. Microsoft lost dominance of development to the web, and then lost dominance of the client to smartphones.⁴⁴⁷

In response to these arguments, some former Microsoft executives, including Bill Gates, have argued that antitrust intervention distracted Microsoft, thus preventing it from effectively competing in the mobile phone industry.⁴⁴⁸ Benedict Evans, however, offers three compelling counterarguments to that theory.⁴⁴⁹ First, Microsoft competed aggressively in the mobile phone segment before and after the launch of the iPhone.⁴⁵⁰ However, none of its offerings came anywhere close to the successful formula employed by Apple.⁴⁵¹ Second, many other large firms failed to successfully navigate the mobile internet market, even though they had no antitrust cases to worry about.⁴⁵² Why should Microsoft have fared differently? Finally, the successful formula identified by Google—relying heavily on widely compatible open-source software—was completely at odds with Microsoft’s entire corporate culture.⁴⁵³

In short, it is very difficult to make the case that antitrust intervention caused Microsoft to lose its strong “gateway to the internet” position.

3. If Antitrust Deterrence Was Sufficient for Microsoft, Then It Also Applies to Today’s Tech Firms

More fundamentally, if this Article is wrong, and antitrust enforcement did indeed prevent Microsoft from dominating online markets, then there is arguably no need to reform the antitrust laws on both sides of the Atlantic nor even to adopt a particularly aggressive enforcement position. As argued above, the remedies that were imposed on Microsoft were relatively localized. Accordingly, if antitrust enforcement did indeed prevent Microsoft from dominating other online markets, then it is antitrust enforcement’s deterrent effect that is to thank, and not the remedies that were actually imposed.

This raises an inconvenient prospect for those who would reform antitrust laws today. As this Article has argued above, the antitrust fears that were voiced about Microsoft bear a strong resemblance to those that are levelled against other big tech firms today. More importantly, many of the market features that are said to cause failures in digital markets were already present during Microsoft’s heyday. For example, Microsoft could also call upon network effects and potentially increasing returns to scale. However, under antitrust laws that still exist today, the result was not the total domination of the internet but thriving competition.

In short, if antitrust laws did indeed prevent Microsoft from harming burgeoning competitors, they still have that same effect today. At the very least, critics have not shown how the characteristics of the tech firms they would target—for instance, Google and Facebook—markedly differ from Microsoft’s. They thus fail to make the case that antitrust law is in need of reform.

E. Microsoft, Pathways to Entry, and Lessons for Antitrust Scrutiny of Data-Intensive Firms Today

Finally, it is important to consider the alternative: it is possible that Microsoft’s own behavior ultimately sowed the seeds of its relative demise. In particular, the alleged barriers to entry (rooted in nostalgic market definitions and skeptical analysis of “ununderstandable” conduct) that were essential to establishing the antitrust case against the company may have been pathways to entry as much as barriers.

There is a fundamental underlying error in the entire barriers to entry enterprise: it is rooted in the idea that barriers tend to determine the number of firms, and the number of firms determines competitiveness. But this is a far too simplistic view:

[A]s markets grow in size, the industry structure that can emerge is not one of atomistic competition with constant quality but rather one where concentration remains high but product quality increases. Therefore, competition along nonprice dimensions can explain why concentration does not necessarily diminish as industries grow. The significance of this point cannot be overstated. Models that focus on only price competition may fail miserably to correctly predict industry concentration and consumer welfare when there are other product dimensions along which competition occurs. This is likely to be particularly true in industries requiring investment and creation of new products. It is no coincidence that many of the most controversial antitrust and regulatory cases have arisen in high technology industries (e.g., computers and telecommunications) where competition in research and development and new products is paramount.⁴⁵⁴

The confusion surrounding the meaning of “barriers to entry” often results because the precise consequence of conduct characterized as an entry barrier is actually unclear. If there are such “barriers,” is anticompetitive conduct a result of the barriers? The proper analysis doesn’t end with entry barriers; it starts with an analysis of what would happen without them, and then assesses whether barriers change anything. In so doing, it must also account for the benefits of existing conduct, including barriers. Where it does not, it again tends the assessment toward protection of the status quo.

A key status quo bias problem in the analysis of entry barriers is the assumption of essentiality of inputs or other relationships created by the early movers. Consider this error in the Microsoft court’s analysis of entry barriers: the court pointed out that new entrants faced a barrier that Microsoft didn’t face, in that Microsoft didn’t have to contend with a powerful incumbent impeding its entry by tying up application developers.⁴⁵⁵

But while this may be true, Microsoft did face the absence of any developers at all, and had to essentially create (or encourage the creation of) businesses that didn’t previously exist. In fact, although the court dismissed this argument in a slightly different context, it noted that, “[a]ccording to Microsoft, it had to make major investments to convince software developers to write for its new operating system, and it continues to ‘evangelize’ the Windows platform today.”⁴⁵⁶ Yet, the court also noted:

Because the applications barrier to entry protects a dominant operating system irrespective of quality, it gives Microsoft power to stave off even superior new rivals. The barrier is thus a characteristic of the operating system market, not of Microsoft’s popularity, or, as asserted by a Microsoft witness, the company’s efficiency.⁴⁵⁷

The point about quality may be true, and it may even be true that the extent of the purported barrier didn’t correlate with Microsoft’s popularity or efficiency. But it is not true that the applications barrier to entry was independent of Microsoft’s efforts or investment; it was not merely a “characteristic of the operating system market,” as if exogenous to any conduct undertaken by Microsoft in order to obtain its scale in the first place. Rather, as noted, Microsoft invested heavily to create the network of developers in the first place.

Crucially, having done so, Microsoft created a huge positive externality for new entrants: existing knowledge and organizations devoted to software development, industry knowledge, reputation, awareness, and incentives for schools to offer courses. It could well be that new entrants in fact faced lower barriers with respect to app developers than did Microsoft when it entered. In other words, whatever Microsoft’s intent with its efforts to create and capture the app developer input market, the effect was to create an entire ecosystem supportive of app and software development. Microsoft failed to internalize this externality (or it did not have the capability to do so), and subsequent entrants were able to build their platforms more expeditiously and at less relative expense than Microsoft.

This dynamic is arguably crucial in considering the distinction between data pre- and post-entry. Much of the discussion of data as a barrier to entry casually speaks as if, because an incumbent has data, new entrants must also have data in order to compete. But the reality is that incumbents entered without data and produced it subsequent to entry—again, sometimes creating entirely new businesses and business models around it. Facebook is an obvious example of this dynamic, but so are Uber and Google and many others.

Data in this respect is like reputation. Nearly all new entrants suffer reputational disadvantages. And yet new entry happens all the time. Likewise, the more successful the incumbent—the larger its network, the stronger its reputation, the better its product—the more difficult is new entry for rivals. In the US, courts have consistently rejected the idea that reputation operates as a barrier to entry. The Court of Appeals for the Ninth Circuit, for example, has noted:

We agree with the unremarkable proposition that a competitor with a proven product and strong reputation is likely to enjoy success in the marketplace, but reject the notion that this is anticompetitive. It is the essence of competition.⁴⁵⁸

Or the Court of Appeals for the Third Circuit, for example, noted:

New entrants and customers in virtually any market emphasize the importance of a reputation for delivering a quality good or service. . . . [Plaintiff’s] argument, without some limiting principle (that it fails to supply), implies that there are barriers to entry, significant in an antitrust sense, in all markets. We find this proposition implausible and . . . precluded by Supreme Court precedent.⁴⁵⁹

It is possible that, under some conditions, reputation or product differentiation can operate as a barrier to entry.⁴⁶⁰ But there must be special circumstances for that to be true: most notably it has arisen in cases where incumbents undertook actions to prevent or preclude new entrants from developing their own brand reputation in order to compete.⁴⁶¹ But it can’t be always and everywhere true, or else every market would be characterized by anticompetitive barriers.

The same holds true for data. Data is typically generated by companies after they enter markets, as a by-product or intended consequence of their operations, or in some cases it is purchased beforehand.⁴⁶² It cannot be the case that doing so in the abstract creates an entry barrier, or else every market would be marked by entry barriers and the risk of antitrust liability for incumbents—including offline markets. By definition, data produced as a consequence of ongoing market operations is something only incumbents will have, and incumbents will always have. Defining the possession of data in this context as an entry barrier would be tantamount to inviting antitrust challenges on the basis of a company’s mere existence (and even more so, success) in a market that competitors wish to enter.

What seems to be required in order that data may be treated as a potential entry barrier is that the data at issue be some combination of essential, unique, exclusive, and rivalrous. If a suitable dataset can be created by new entrants or obtained elsewhere, or if other data can be used in its stead, or if alternatives other than data can be used (e.g., synthetic data or artificial intelligence), then it is hard to see any relevant competitive significance from data, regardless of the amount.

A key aspect of the mistake here is an availability heuristic (another form of nostalgia bias): it is often assumed that the successful way something has been done, and is done today, is the only way to do it, or the only way new entrants can do it and be competitive. But, of course, that is never actually true. As noted, Facebook uses a very different method and different data than does Google to match advertisers and users, and yet it entered the online advertising/matchmaking market and became enormously successful without adopting Google’s model (and without obtaining Google’s, or anyone else’s, existing data). Uber entered the transportation network market with a business model that didn’t require capital outlay on a large fleet of vehicles. Digital cameras made film irrelevant and didn’t need to rely on suppliers of film to enter. Fax machines went through a series of improvements, until email and cloud services completely replaced them.

The examples are endless. But they are key to understanding the non-essentiality of data: For some entrants—those adopting incumbents’ business models, minimizing their own innovations, or even piggybacking on incumbents—it seems indispensable. And they may find a willing ear at some antitrust agencies. But innovation has never required implementation of the same business model as incumbents, and especially not access to the particular proprietary inputs incumbents have created.

But most importantly, as noted above, new entrants may face even more welcoming environments because of incumbents. Consider how much Google contributed to the creation of the online advertising industry, consumer acceptance of advertising-financed websites, and web page and app developers’ expectations that advertising would need to be accommodated. Whatever the data used to deliver it, there can be no doubt that a new provider of online advertising today faces an environment in which its product is known and even invited. That was not always true in the past. Knowing precisely how much benefit this advantage confers on new entrants is extremely difficult. The point, however, is that there is no basis for assuming it is simply irrelevant, yet the fact that it is simply ignored by traditional antitrust analysis implies that conclusion. And, of course, in some cases it could well be more significant than the impediment that accompanies it.

Conclusion

This Article has argued that dystopian antitrust prophecies are generally doomed to fail, just like those belonging to the literary world. The reason is simple: while it is easy to identify what makes dominant firms successful in the present day (i.e., what enables them to hold off competitors in the short-term), it is almost impossible to cognize the near infinite ways in which the market could adapt. Indeed, it is today’s supra-competitive profits that spur the efforts of competitors. Surmising that the economy will come to be dominated by a small number of successful firms is thus the same as believing that all market participants can be outsmarted by a few successful ones. This might occur in some cases (or for some time), but, as this Article has argued, it is bound to happen far less often than pessimists fear.

Dystopian and nostalgic commentators reject this premise. Today, they have set their sights on the digital economy. The crux of their case is that data-intensive markets share common features that, purportedly, pose severe threats to competition and innovation. These features include the ubiquity of network effects, increasing returns to scale and data-related network effects. This has led some to call for a complete overhaul of the current antitrust paradigm, including a move away from the consumer welfare standard and the error-cost framework. The proposals that have, so far, been put forward would notably involve blanket prohibitions against self-preferencing by dominant platforms, mandatory data access regimes that would purportedly prevent big tech firms from leveraging their market positions, shifting the burden of proof in merger proceedings that involve digital markets, and upending antitrust law’s light-touch approach to vertical restraints. All of these changes would be achieved by either amending existing antitrust statutes or by adopting ad hoc regulations. And because they target harms that are merely hypothetical, these proposals would effectively lead enforcers to apply a form of precautionary principle within antitrust policy.

However, both this Article’s review of the economic literature and its case study of the Microsoft litigation suggest that dystopian scholars have not successfully made the case for precautionary antitrust. Indeed, the economic features of data make it highly unlikely that today’s tech giants could anticompetitively maintain their advantage for an indefinite amount of time—and much less leverage this advantage in adjacent markets. Because data is just information, it is almost impossible for a single firm to either hoard it for a prolonged period or prevent its competitors from obtaining similar data from alternative sources. Moreover, a burgeoning body of empirical evidence suggests that, while data can be very valuable, it also involves diminishing returns at the margin. Finally, data is of little value without complementary firm-level capabilities. As a result, there is little reason to believe that today’s tech giants have acquired a self-reinforcing competitive advantage. This might explain why these same firms managed to outcompete incumbents who had access to much vaster datasets.

The idea that dystopian scholars have overshot the mark is also supported by this Article’s case study of the Microsoft antitrust litigation. Indeed, the fears that were voiced at the time of the Microsoft antitrust proceedings are strikingly similar to those that are being raised about the digital economy today. Chief among them was a concern that Microsoft would use its desktop operating system market position to dominate adjacent online markets. These fears did not pan out. While the reasons for this are hard to ascertain, there is a strong case to be made that this had little to do with antitrust enforcement and that competitive forces— particularly Microsoft’s inability to offer compelling products in the online space—were the chief cause of this “failure.” The bottom line is that critics have not successfully made the case for replacing current antitrust doctrine with something akin to the precautionary principle. This is not to say that antitrust authorities should never intervene in digital markets. Our argument is merely that competition authorities should focus on behavior that clearly harms consumer welfare, rather than try to prevent hypothetical future harms.

With this in mind, there is one dystopian novel that offers a fitting metaphor to end this Article. The Man in the High Castle tells the story of an alternate present, where Axis forces triumphed over the Allies during the second world war.⁴⁶³ This turns the dystopia genre on its head: rather than argue that the world is inevitably sliding towards a dark future, The Man in the High Castle posits that the present could be far worse. In other words, we should not take any of the luxuries we currently enjoy for granted. In the world of antitrust, critics routinely overlook that the emergence of today’s tech industry might have occurred thanks to, and not in spite of, existing antitrust doctrine. Changes to existing antitrust law should thus be dictated by a rigorous assessment of the various costs and benefits they would entail, rather than a litany of hypothetical concerns. The most recent wave of calls for antitrust reform have so far failed to clear this low bar.

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Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins

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