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Executive Summary

We thank the U.S. Justice Department Antitrust Division (DOJ) for this invitation to comment (ITC) on “Promoting Competition in Artificial Intelligence.”[1] The International Center for Law & Economics (ICLE) is a nonprofit, nonpartisan global research and policy center founded with the goal of building the intellectual foundations for sensible, economically grounded policy. ICLE promotes the use of law & economics methodologies to inform public-policy debates and has longstanding expertise in the evaluation of competition law and policy. ICLE’s interest is to ensure that competition law remains grounded in clear rules, established precedent, a record of evidence, and sound economic analysis.

In these comments, we express the view that policymakers’ current concerns about competition in AI industries may be unwarranted. This is particularly true of the notions that data-network effects shield incumbents in AI markets from competition; that Web 2.0’s most successful platforms will be able to leverage their competitive positions to dominate generative-AI markets; that these same platforms may use strategic partnerships with AI firms to insulate themselves from competition; and that generative-AI services occupy narrow markets that leave firms with significant market power.

In fact, we are still far from understanding the boundaries of antitrust-relevant markets in AI. There are three main things that need to be at the forefront of competition authorities’ minds when they think about market definition in AI products and services. First, understand that the “AI market” is not unitary, but is instead composed of many distinct goods and services. Second, and relatedly, look beyond the AI marketing hype to see how this extremely heterogeneous products landscape intersects with an equally variegated consumer-demand landscape.

In other words: AI products and services may, in many instances, be substitutable for non-AI products, which would mean that, for the purposes of antitrust law, AI and non-AI products contend in the same relevant market. Getting this relevant product-market definition right is important in antitrust because wrong market definitions could lead to wrong inferences about market power. While either an overly broad or overly narrow market definition could lead to both over and underenforcement, we believe the former currently represents the bigger threat.

Third, overenforcement in the field of generative AI could paradoxically engender the very harms that policymakers are seeking to avert. As we explain in greater detail below, preventing so-called “big tech” firms from competing in AI markets (for example, by threatening competition intervention whenever they forge strategic relationships with AI startups, launch their own generative-AI services, or embed such services in their existing platforms) may thwart an important source of competition and continued innovation. In short, competition in AI markets is important,[2] but trying naïvely to hold incumbent (in adjacent markets) tech firms back, out of misguided fears they will come to dominate the AI space, is likely to do more harm than good. It is essential to acknowledge how little we know about these nascent markets and that the most important priority at the moment is simply to ask the right questions that will lead to sound competition policy.

The comments proceed as follows. Section I debunks the notion that incumbent tech platforms can use their allegedly superior datasets to overthrow competitors in markets for generative AI. Section II discusses how policymakers should approach strategic partnerships among tech incumbents and AI startups. Section III outlines some of the challenges to defining relevant product markets in AI, and suggests how enforcers could navigate the perils of market definition in the nascent, fast-moving world of AI.

I. Anticompetitive Leveraging in AI Markets

Antitrust enforcers have recently expressed concern that incumbent tech platforms may leverage their existing market positions and resources (particularly their vast datasets) to stifle competitive pressure from AI startups. As this sections explains, however, these fears appear overblown, as well as underpinned by assumptions about data-network effects that are unlikely to play a meaningful role in generative AI. Instead, the competition interventions that policymakers are contemplating would, paradoxically, remove an important competitive threat for today’s most successful AI providers, thereby reducing overall competition in generative-AI markets.

Subsection A summarizes recent calls for competition intervention in generative-AI markets. Subsection B argues that many of these calls are underpinned by fears of data-related incumbency advantages (often referred to as “data-network effects”), including in the context of mergers. Subsection C explains why these effects are unlikely to play a meaningful role in generative-AI markets. Subsection D offers five key takeaways to help policymakers better weigh the tradeoffs inherent to competition-enforcement interventions in generative-AI markets.

A. Calls for Intervention in AI Markets

It was once (and frequently) said that Google’s “data monopoly” was unassailable: “If ‘big data’ is the oil of the information economy, Google has Standard Oil-like monopoly dominance—and uses that control to maintain its dominant position.”[3] Similar claims of data dominance have been attached to nearly all large online platforms, including Facebook (Meta), Amazon, and Uber.[4]

While some of these claims continue even today (for example, “big data” is a key component of the DOJ Google Search and adtech antitrust suits),[5] a shiny new data target has emerged in the form of generative artificial intelligence (AI). The launch of ChatGPT in November 2022, as well as the advent of AI image-generation services like Midjourney and Dall-E, have dramatically expanded the public’s conception of what is—and what might be—possible to achieve with generative-AI technologies built on massive datasets.

While these services remain both in the early stages of mainstream adoption and in the throes of rapid, unpredictable technological evolution, they nevertheless already appear to be on the radar of competition policymakers around the world. Several antitrust enforcers appear to believe that, by acting now, they can avoid the “mistakes” that purportedly were made during the formative years of Web 2.0.[6] These mistakes, critics assert, include failing to appreciate the centrality of data in online markets, as well as letting mergers go unchecked and allowing early movers to entrench their market positions.^[7] As Federal Trade Commission (FTC) Chair Lina Khan has put it: “we are still reeling from the concentration that resulted from Web 2.0, and we don’t want to repeat the mis-steps of the past with AI.”^[8]

This response from the competition-policy world is deeply troubling. Rather than engage in critical self-assessment and adopt an appropriately restrained stance, the enforcement community appears to be champing at the bit. Rather than assessing their prior assumptions based on the current technological moment, enforcers’ top priority appears to be figuring out how to rapidly and almost reflexively deploy existing competition tools to address the presumed competitive failures presented by generative AI.^[9]

It is increasingly common for competition enforcers to argue that so-called “data-network effects” serve not only to entrench incumbents in those markets where the data is collected, but also to confer similar, self-reinforcing benefits in adjacent markets. Several enforcers have, for example, prevented large online platforms from acquiring smaller firms in adjacent markets, citing the risk that they could use their vast access to data to extend their dominance into these new markets.^[10]

They have also launched consultations to ascertain the role that data plays in AI competition. For instance, in a recent consultation, the European Commission asked: “What is the role of data and what are its relevant characteristics for the provision of generative AI systems and/or components, including AI models?”^[11] Unsurprisingly, the FTC has likewise been hypervigilant about the risks ostensibly posed by incumbents’ access to data. In comments submitted to the U.S. Copyright Office, for example, the FTC argued that:

The rapid development and deployment of AI also poses potential risks to competition. The rising importance of AI to the economy may further lock in the market dominance of large incumbent technology firms. These powerful, vertically integrated incumbents control many of the inputs necessary for the effective development and deployment of AI tools, including cloud-based or local computing power and access to large stores of training data. These dominant technology companies may have the incentive to use their control over these inputs to unlawfully entrench their market positions in AI and related markets, including digital content markets.[12]

Recently, in the conference that prompts these comments, Jonathan Kanter, assistant U.S. attorney general for antitrust, claimed that:

We also see structures and trends in AI that should give us pause AI relies on massive amounts of data and computing power, which can give already dominant firms a substantial advantage. Powerful networks and feedback effects may enable dominant firms to control these new markets, and existing power in the digital economy may create a powerful incentive to control emerging innovations that will not only impact our economy, but the health and well-being of our society and free expression itself.[13]

On an even more hyperbolic note, Andreas Mundt, the head of Germany’s Federal Cartel Office, called AI a “first-class fire accelerator” for anticompetitive behavior and argued it “will make all the problems only worse.”[14] He further argued that “there’s a great danger that we’ll will get an even deeper concentration of digital markets and power increase at various levels, from chips to the front end.”[15] In short, Mundt is one of many policymakers who believes that AI markets will enable incumbent tech firms to further entrench their market positions.

Certainly, it makes sense that the largest online platforms—including Alphabet, Meta, Apple, and Amazon—should have a meaningful advantage in the burgeoning markets for generative-AI services. After all, it is widely recognized that data is an essential input for generative AI.^[16] This competitive advantage should be all the more significant, given that these firms have been at the forefront of AI technology for more than a decade. Over this period, Google’s DeepMind and AlphaGo and Meta’s NLLB-200 have routinely made headlines.[17] Apple and Amazon also have vast experience with AI assistants, and all of these firms deploy AI technologies throughout their platforms.[18]

Contrary to what one might expect, however, the tech giants have, to date, been largely unable to leverage their vast troves of data to outcompete startups like OpenAI and Midjourney. At the time of writing, OpenAI’s ChatGPT appears to be, by far, the most successful chatbot,[19] despite the large tech platforms’ apparent access to far more (and more up-to-date) data.

Moreover, it is important not to neglect the role that open-source models currently play in fostering innovation and competition. As former DOJ Chief Antitrust Economist Susan Athey pointed out in a recent interview, “[the AI industry] may be very concentrated, but if you have two or three high quality — and we have to find out what that means, but high enough quality — open models, then that could be enough to constrain the for-profit LLMs.”[20] Open-source models are important because they allow innovative startups to build upon models already trained on large datasets—therefore entering the market without incurring that initial cost. Apparently, there is no lack of open-source models, since companies like xAI, Meta, and Google offer their AI models for free.[21]

There are important lessons to glean from these developments, if only enforcers would stop to reflect. The meteoric rise of consumer-facing AI services should offer competition enforcers and policymakers an opportunity for introspection. As we explain, the rapid emergence of generative-AI technology may undercut many core assumptions of today’s competition-policy debates, which have focused largely on the rueful after-effects of the purported failure of 20^th-century antitrust to address the allegedly manifest harms of 21^st-century technology. These include the notions that data advantages constitute barriers to entry and can be leveraged to project dominance into adjacent markets; that scale itself is a market failure to be addressed by enforcers; and that the use of consumer data is inherently harmful to those consumers.

B. Data-Network Effects Theory and Enforcement

Proponents of more extensive intervention by competition enforcers into digital markets often cite data-network effects as a source of competitive advantage and barrier to entry (though terms like “economies of scale and scope” may offer more precision).^[22] The crux of the argument is that “the collection and use of data creates a feedback loop of more data, which ultimately insulates incumbent platforms from entrants who, but for their data disadvantage, might offer a better product.”^[23] This self-reinforcing cycle purportedly leads to market domination by a single firm. Thus, it is argued, e.g., that Google’s “ever-expanding control of user personal data, and that data’s critical value to online advertisers, creates an insurmountable barrier to entry for new competition.”^[24]

But it is important to note the conceptual problems these claims face. Because data can be used to improve products’ quality and/or to subsidize their use, if possessing data constitutes an entry barrier, then any product improvement or price reduction made by an incumbent could be problematic. This is tantamount to an argument that competition itself is a cognizable barrier to entry. Of course, it would be a curious approach to antitrust if competition were treated as a problem, as it would imply that firms should under-compete—i.e., should forego consumer-welfare enhancements—in order to inculcate a greater number of firms in a given market, simply for its own sake.[25]

Meanwhile, actual economic studies of data-network effects have been few and far between, with scant empirical evidence to support the theory.^[26] Andrei Hagiu and Julian Wright’s theoretical paper offers perhaps the most comprehensive treatment of the topic to date.[27] The authors ultimately conclude that data-network effects can be of differing magnitudes and have varying effects on firms’ incumbency advantage.^[28] They cite Grammarly (an AI writing-assistance tool) as a potential example: “As users make corrections to the suggestions offered by Grammarly, its language experts and artificial intelligence can use this feedback to continue to improve its future recommendations for all users.”^[29]

This is echoed by economists who contend that “[t]he algorithmic analysis of user data and information might increase incumbency advantages, creating lock-in effects among users and making them more reluctant to join an entrant platform.”^[30] Crucially, some scholars take this logic a step further, arguing that platforms may use data from their “origin markets” in order to enter and dominate adjacent ones:

First, as we already mentioned, data collected in the origin market can be used, once the enveloper has entered the target market, to provide products more efficiently in the target market. Second, data collected in the origin market can be used to reduce the asymmetric information to which an entrant is typically subject when deciding to invest (for example, in R&D) to enter a new market. For instance, a search engine could be able to predict new trends from consumer searches and therefore face less uncertainty in product design.^[31]

This possibility is also implicit in Hagiu and Wright’s paper.[32] Indeed, the authors’ theoretical model rests on an important distinction between “within-user” data advantages (that is, having access to more data about a given user) and “across-user” data advantages (information gleaned from having access to a wider user base). In both cases, there is an implicit assumption that platforms may use data from one service to gain an advantage in another market (because what matters is information about aggregate or individual user preferences, regardless of its origin).

Our review of the economic evidence suggests that several scholars have, with varying degrees of certainty, raised the possibility that incumbents may leverage data advantages to stifle competitors in their primary market or in adjacent ones (be it via merger or organic growth). As we explain below, however, there is ultimately little evidence to support such claims. Policymakers have nonetheless been keenly receptive to these limited theoretical findings, basing multiple decisions on these theories, often with little consideration given to the caveats that accompany them.^[33]

Indeed, it is remarkable that, in its section on “[t]he data advantage for incumbents,” the “Furman Report” created for the UK government cited only two empirical economic studies, and they offer directly contradictory conclusions with respect to the question of the strength of data advantages.[34] The report nevertheless concluded that data “may confer a form of unmatchable advantage on the incumbent business, making successful rivalry less likely,”[35] and it adopted without reservation what it deemed “convincing” evidence from non-economists that have no apparent empirical basis.[36]

In the Google/Fitbit merger proceedings, the European Commission found that the combination of data from Google services with that of Fitbit devices would reduce competition in advertising markets:

Giving [sic] the large amount of data already used for advertising purposes that Google holds, the increase in Google’s data collection capabilities, which goes beyond the mere number of active users for which Fitbit has been collecting data so far, the Transaction is likely to have a negative impact on the development of an unfettered competition in the markets for online advertising.^[37]

As a result, the Commission cleared the merger only on the condition that Google refrain from using data from Fitbit devices for its advertising platform.^[38] The Commission also appears likely to focus on similar issues in its ongoing investigation of Microsoft’s investment in OpenAI.[39]

Along similar lines, in its complaint to enjoin Meta’s purchase of Within Unlimited—makers of the virtual-reality (VR) fitness app Supernatural—the FTC relied on, among other things, the fact that Meta could leverage its data about VR-user behavior to inform its decisions and potentially outcompete rival VR-fitness apps: “Meta’s control over the Quest platform also gives it unique access to VR user data, which it uses to inform strategic decisions.”^[40]

The DOJ’s twin cases against Google also implicate data leveraging and data barriers to entry. The agency’s adtech complaint charges that “Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry.”[41] Similarly, in its Google Search complaint, the agency argued that:

Google’s anticompetitive practices are especially pernicious because they deny rivals scale to compete effectively. General search services, search advertising, and general search text advertising require complex algorithms that are constantly learning which organic results and ads best respond to user queries; the volume, variety, and velocity of data accelerates the automated learning of search and search advertising algorithms.[42]

Finally, updated merger guidelines published in recent years by several competition enforcers cite the acquisition of data as a potential source of competition concerns. For instance, the FTC and DOJ’s 2023 guidelines state that “acquiring data that helps facilitate matching, sorting, or prediction services may enable the platform to weaken rival platforms by denying them that data.”^[43] Likewise, the UK Competition and Markets Authority warned against incumbents acquiring firms in order to obtain their data and foreclose other rivals:

Incentive to foreclose rivals…

7.19(e) Particularly in complex and dynamic markets, firms may not focus on short term margins but may pursue other objectives to maximise their long-run profitability, which the CMA may consider. This may include… obtaining access to customer data….^[44]

In short, competition authorities around the globe have taken an increasingly aggressive stance on data-network effects. Among the ways this has manifested is in enforcement decisions based on fears that data collected by one platform might confer decisive competitive advantages in adjacent markets. Unfortunately, these concerns rest on little to no empirical evidence, either in the economic literature or the underlying case records.

C. Data-Incumbency Advantages in Generative-AI

Given the assertions detailed in the previous section, it would be reasonable to assume that firms such as Google, Meta, and Amazon should be in pole position to meet the burgeoning demand for generative AI. After all, these firms have not only been at the forefront of the field for the better part of a decade, but they also have access to vast troves of data, the likes of which their rivals could only dream when they launched their own services. Thus, the authors of the Furman Report caution that “to the degree that the next technological revolution centres around artificial intelligence and machine learning, then the companies most able to take advantage of it may well be the existing large companies because of the importance of data for the successful use of these tools.”[45]

To date, however, this is not how things have unfolded (although it bears noting that these technologies remain in flux and the competitive landscape is susceptible to change). The first significantly successful generative-AI service was arguably not from either Meta—which had been working on chatbots for years and had access to, arguably, the world’s largest database of actual chats—or Google. Instead, the breakthrough came from a previously unknown firm called OpenAI.

OpenAI’s ChatGPT service currently accounts for an estimated 60% of visits to online AI tools (though reliable numbers are somewhat elusive).[46] It broke the record for the fastest online service to reach 100 million users (in only a couple of months), more than four times faster than TikTok, the previous record holder.^[47] Based on Google Trends data, ChatGPT is nine times more popular worldwide than Google’s own Bard service, and 14 times more popular in the United States.^[48] In April 2023, ChatGPT reportedly registered 206.7 million unique visitors, compared to 19.5 million for Google’s Bard.^[49] In short, at the time we are writing, ChatGPT appears to be the most popular chatbot. The entry of large players such as Google Bard or Meta AI appear to have had little effect thus far on its leading position.^[50]

The picture is similar in the field of AI-image generation. As of August 2023, Midjourney, Dall-E, and Stable Diffusion appear to be the three market leaders in terms of user visits.^[51] This is despite competition from the likes of Google and Meta, who arguably have access to unparalleled image and video databases by virtue of their primary platform activities.^[52]

This raises several crucial questions: how have these AI upstarts managed to be so successful, and is their success just a flash in the pan before Web 2.0 giants catch up and overthrow them? While we cannot answer either of these questions dispositively, we offer what we believe to be some relevant observations concerning the role and value of data in digital markets.

A first important observation is that empirical studies suggest that data exhibits diminishing marginal returns. In other words, past a certain point, acquiring more data does not confer a meaningful edge to the acquiring firm. As Catherine Tucker put it, following a review of the literature: “Empirically there is little evidence of economies of scale and scope in digital data in the instances where one would expect to find them.”^[53]

Likewise, following a survey of the empirical literature on this topic, Geoffrey Manne and Dirk Auer conclude that:

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.^[54]

In other words, being the firm with the most data appears to be far less important than having enough data. Moreover, this lower bar may be accessible to far more firms than one might initially think possible. Furthermore, obtaining sufficient data could become easier still—that is, the volume of required data could become even smaller—with technological progress. For instance, synthetic data may provide an adequate substitute to real-world data,[55] or may even outperform real-world data.[56] As Thibault Schrepel and Alex Pentland surmise:

[A]dvances in computer science and analytics are making the amount of data less relevant every day. In recent months, important technological advances have allowed companies with small data sets to compete with larger ones.^[57]

Indeed, past a certain threshold, acquiring more data might not meaningfully improve a service, where other improvements (such as better training methods or data curation) could have a large impact. In fact, there is some evidence that excessive data impedes a service’s ability to generate results appropriate for a given query: “[S]uperior model performance can often be achieved with smaller, high-quality datasets than massive, uncurated ones. Data curation ensures that training datasets are devoid of noise, irrelevant instances, and duplications, thus maximizing the efficiency of every training iteration.”[58]

Consider, for instance, a user who wants to generate an image of a basketball. Using a model trained on an indiscriminate range and number of public photos in which a basketball appears surrounded by copious other image data, the user may end up with an inordinately noisy result. By contrast, a model trained with a better method on fewer, more carefully selected images could readily yield far superior results.[59] In one important example:

The model’s performance is particularly remarkable, given its small size. “This is not a large language model trained on the whole Internet; this is a relatively small transformer trained for these tasks,” says Armando Solar-Lezama, a computer scientist at the Massachusetts Institute of Technology, who was not involved in the new study…. The finding implies that instead of just shoving ever more training data into machine-learning models, a complementary strategy might be to offer AI algorithms the equivalent of a focused linguistics or algebra class.[60]

Platforms’ current efforts are thus focused on improving the mathematical and logical reasoning of large language models (LLMs), rather than maximizing training datasets.^[61] Two points stand out. The first is that firms like OpenAI rely largely on publicly available datasets—such as GSM8K—to train their LLMs.^[62] Second, the real challenge to creating innovative AI lies not so much in collecting data, but in creating innovative AI-training processes and architectures:

[B]uilding a truly general reasoning engine will require a more fundamental architectural innovation. What’s needed is a way for language models to learn new abstractions that go beyond their training data and have these evolving abstractions influence the model’s choices as it explores the space of possible solutions.

We know this is possible because the human brain does it. But it might be a while before OpenAI, DeepMind, or anyone else figures out how to do it in silicon.^[63]

Furthermore, it is worth noting that the data most relevant to startups in a given market may not be those held by large incumbent platforms in other markets. They might instead be data specific to the market in which the startup is active or, even better, to the given problem it is attempting to solve:

As Andres Lerner has argued, if you wanted to start a travel business, the data from Kayak or Priceline would be far more relevant. Or if you wanted to start a ride-sharing business, data from cab companies would be more useful than the broad, market-cross-cutting profiles Google and Facebook have. Consider companies like Uber, Lyft and Sidecar that 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 use—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.[64]

The bottom line is that data is not the be-all and end-all that many in competition circles make it out to be. While data may often confer marginal benefits, there is little evidence that these benefits are ultimately decisive.[65] As a result, incumbent platforms’ access to vast numbers of users and troves of data in their primary markets might only marginally affect their competitiveness in AI markets.

A related observation is that firms’ capabilities and other features of their products arguably play a more important role than the data they own.[66] Examples of this abound in digital markets. Google overthrew Yahoo in search, despite initially having access to far fewer users and far less data. Google and Apple overcame Microsoft in the smartphone operating-system market, despite having comparatively tiny ecosystems (at the time) to leverage. TikTok rose to prominence despite intense competition from incumbents like Instagram, which had much larger userbases. In each of these cases, important product-design decisions (such as the PageRank algorithm, recognizing the specific needs of mobile users,^[67] and TikTok’s clever algorithm) appear to have played far more significant roles than the firms’ initial user and data endowments (or lack thereof).

All of this suggests that the early success of OpenAI likely has more to do with its engineering decisions than with what data it did or did not possess. Going forward, OpenAI and its rivals’ relative abilities to offer and monetize compelling use cases by offering custom versions of their generative-AI technologies will arguably play a much larger role than (and contribute to) their ownership of data.[68] In other words, the ultimate challenge is arguably to create a valuable platform, of which data ownership is a consequence, not a cause.

It is also important to note that, in those instances where it is valuable, data does not just fall from the sky. Instead, it is through smart business and engineering decisions that firms can generate valuable information (which does not necessarily correlate with owning more data). For instance, OpenAI’s success with ChatGPT is often attributed to its more efficient algorithms and training models, which arguably have enabled the service to improve more rapidly than its rivals.^[69] Likewise, the ability of firms like Meta and Google to generate valuable data for advertising arguably depends more on design decisions that elicit the right data from users, rather than the raw number of users in their networks.

Put differently, setting up a business so as to gather and organize the right information is more important than simply owning vast troves of data.[70] Even in those instances where high-quality data is an essential parameter of competition, it does not follow that having vaster databases or more users on a platform necessarily leads to better information for the platform. Indeed, if data ownership consistently conferred a significant competitive advantage, these new AI firms would not be where they are today.

This does not, of course, mean that data is worthless. Rather, it means that competition authorities should not assume that the mere possession of data is a dispositive competitive advantage, absent compelling empirical evidence to support such a finding. In this light, the current wave of decisions and competition-policy pronouncements that rely on data-related theories of harm are premature.

D. Five Key Takeaways: Reconceptualizing the Role of Data in Generative-AI Competition

As we explain above, data network effects are not the source of barriers to entry that they are sometimes made out to be. The picture is far more nuanced. Indeed, as economist Andres Lerner demonstrated almost a decade ago (and the assessment is only truer today):

Although the collection of user data is generally valuable for online providers, the conclusion that such benefits of user data lead to significant returns to scale and to the entrenchment of dominant online platforms is based on unsupported assumptions. Although, in theory, control of an “essential” input can lead to the exclusion of rivals, a careful analysis of real-world evidence indicates that such concerns are unwarranted for many online businesses that have been the focus of the “big data” debate.[71]

While data can be an important part of the competitive landscape, incumbents’ data advantages are far less pronounced than today’s policymakers commonly assume. In that respect, five primary lessons emerge:

1. Data can be (very) valuable, but beyond a certain threshold, those benefits tend to diminish. In other words, having the most data is less important than having enough;
2. The ability to generate valuable information does not depend on the number of users or the amount of data a platform has previously acquired;
3. The most important datasets are not always proprietary;
4. Technological advances and platforms’ engineering decisions affect their ability to generate valuable information, and this effect swamps those that stem from the amount of data they own; and
5. How platforms use data is arguably more important than what data or how much data they own.

These lessons have important ramifications for policy debates over the competitive implications of data in technologically evolving areas.

First, it is not surprising that startups, rather than incumbents, have taken an early lead in generative AI (and in Web 2.0 before it). After all, if data-incumbency advantages are small or even nonexistent, then smaller and more nimble players may have an edge over established tech platforms. This is all the more likely given that, despite significant efforts, the biggest tech platforms were unable to offer compelling generative-AI chatbots and image-generation services before the emergence of ChatGPT, Dall-E, Midjourney, etc.

This suggests that, in a process akin to Clayton Christensen’s “innovator’s dilemma,”[72] something about the incumbent platforms’ existing services and capabilities might have been holding them back in this emerging industry. Of course, this does not necessarily mean that those same services or capabilities could not become an advantage when the generative-AI industry starts addressing issues of monetization and scale.[73] But it does mean that assumptions about a firm’s market power based primarily on its possession of data are likely to be off the mark.

Another important implication is that, paradoxically, policymakers’ efforts to prevent Web 2.0 platforms from competing freely in generative-AI markets may ultimately backfire and lead to less, not more, competition. Indeed, OpenAI is currently acquiring a sizeable lead in generative AI. While competition authorities might like to think that other startups will emerge and thrive in this space, it is important not to confuse those desires with reality. While there currently exists a vibrant AI-startup ecosystem, there is at least a case to be made that significant competition for today’s AI leaders will come from incumbent Web 2.0 platforms—although nothing is certain at this stage.

Policymakers should beware not to stifle that competition on the misguided assumption that competitive pressure from large incumbents is somehow less valuable to consumers than that which originates from smaller firms. This is particularly relevant in the context of merger control. An acquisition (or an “acqui-hire”) by a “big tech” company does not only, in principle, entail a minor risk to harm competition (it is not a horizontal merger),[74] but could create a stronger competitor to the current market leaders.

Finally, even if there were a competition-related market failure to be addressed in the field of generative AI (which is anything but clear), the remedies under contemplation may do more harm than good. Some of the solutions that have been put forward have highly ambiguous effects on consumer welfare. Scholars have shown that, e.g., mandated data sharing—a solution championed by EU policymakers, among others—may sometimes dampen competition in generative AI.[75] This is also true of legislation like the General Data Protection Regulation (GDPR), which makes it harder for firms to acquire more data about consumers—assuming such data is, indeed, useful to generative-AI services.[76]

In sum, it is a flawed understanding of the economics and practical consequences of large agglomerations of data that has led competition authorities to believe data-incumbency advantages are likely to harm competition in generative AI—or even in the data-intensive Web 2.0 markets that preceded it. Indeed, competition or regulatory intervention to “correct” data barriers and data network and scale effects is liable to do more harm than good.

II. Merger Policy and AI

Policymakers have expressed particular concern about the anticompetitive potential of deals wherein AI startups obtain funding from incumbent tech firms, even in cases where these strategic partnerships cannot be considered mergers in the antitrust sense (because there is no control exercised by one firm over the other). To date, there is no evidence to support differentiated scrutiny for mergers involving AI firms or, in general, firms working with information technology. The view that so-called “killer acquisitions,” for instance, pose a significant competition risk in AI markets is not supported by solid evidence.[77] To the contrary, there is reason to believe these acquisitions bolster competition by allowing larger firms to acquire capabilities relevant to innovation, and by increasing incentives to invest for startup founders.[78]

Companies with “deep pockets” that invest in AI startups may provide those firms the resources to compete with prevailing market leaders. Firms like Amazon, Google, Meta, and Microsoft, for instance, have been investing to create their own microchips capable of building AI systems, aiming to be less dependent on Nvidia.[79] The tributaries of this flow of funds could serve to enhance competition at all levels of the AI industry.[80]

A. Existing AI Partnerships Are Unlikely to Be Anticompetitive

Some jurisdictions have also raised concerns regarding recent partnerships among big tech firms and AI “unicorns,”[81] in particular, Amazon’s partnership with Anthropic; Microsoft’s partnership with Mistral AI; and Microsoft’s hiring of former Inflection AI employees (including, notably, founder Mustafa Suleyman) and related arrangements with the company. Publicly available information, however, suggests that these transactions may not warrant merger-control investigation, let alone the heightened scrutiny that comes with potential Phase II proceedings. At the very least, given the AI industry’s competitive landscape, there is little to suggest these transactions merit closer scrutiny than similar deals in other sectors.

Overenforcement in the field of generative AI could paradoxically engender the very harms that policymakers are seeking to avert. Preventing big tech firms from competing in these markets (for example, by threatening competition intervention as soon as they build strategic relationships with AI startups) may thwart an important source of competition needed to keep today’s leading generative-AI firms in check. In short, while competition in AI markets is important,[82] trying naïvely to hold incumbent (in adjacent markets) tech firms back, out of misguided fears they will come to dominate this space, is likely to do more harm than good.

At a more granular level, there are important reasons to believe these kinds of agreements will have no negative impact on competition and may, in fact, benefit consumers—e.g., by enabling those startups to raise capital and deploy their services at an even larger scale. In other words, they do not bear any of the prima facie traits of “killer acquisitions,” or even of the acquisition of “nascent potential competitors.”[83]

Most importantly, these partnerships all involve the acquisition of minority stakes and do not entail any change of control over the target companies. Amazon, for instance, will not have “ownership control” of Anthropic. The precise amount of shares acquired has not been made public, but a reported investment of $4 billion in a company valued at $18.4 billion does not give Amazon a majority stake or sufficient voting rights to control the company or its competitive strategy. [84] It has also been reported that the deal will not give Amazon any seats on the Anthropic board or special voting rights (such as the power to veto some decisions).[85] There is thus little reason to believe Amazon has acquired indirect or de facto control over Anthropic.

Microsoft’s investment in Mistral AI is even smaller, in both absolute and relative terms. Microsoft is reportedly investing just $16 million in a company valued at $2.1 billion.[86] This represents less than 1% of Mistral’s equity, making it all but impossible for Microsoft to exert any significant control or influence over Mistral AI’s competitive strategy. There have similarly been no reports of Microsoft acquiring seats on Mistral AI’s board or any special voting rights. We can therefore be confident that the deal will not affect competition in AI markets.

Much the same applies to Microsoft’s dealings with Inflection AI. Microsoft hired two of the company’s three founders (which currently does not fall under the scope of merger laws), and also paid $620 million for nonexclusive rights to sell access to the Inflection AI model through its Azure Cloud.[87] Admittedly, the latter could entail (depending on deal’s specifics) some limited control over Inflection AI’s competitive strategy, but there is currently no evidence to suggest this will be the case.

Finally, none of these deals entail any competitively significant behavioral commitments from the target companies. There are no reports of exclusivity agreements or other commitments that would restrict third parties’ access to these firms’ underlying AI models. Again, this means the deals are extremely unlikely to negatively impact the competitive landscape in these markets.

B. AI Partnerships Increase Competition

As discussed in the previous section, the AI partnerships that have recently grabbed antitrust headlines are unlikely to harm competition. They do, however, have significant potential to bolster competition in generative-AI markets by enabling new players to scale up rapidly and to challenge more established players by leveraging the resources of incumbent tech platforms.

The fact that AI startups willingly agree to the aforementioned AI partnerships suggests this source of funding presents unique advantages for them, or they would have pursued capital through other venues. The question for antitrust policymakers is whether this advantage is merely an anticompetitive premium, paid by big tech platforms to secure monopoly rents, or whether the investing firms are bringing something else to the table. As we discussed in the previous section, there is little reason to believe these partnerships are driven by anticompetitive motives. More importantly, however, these deals may present important advantages for AI startups that, in turn, are likely to boost competition in these burgeoning markets.

To start, partnerships with so-called big tech firms are likely a way for AI startups to rapidly obtain equity financing. While this lies beyond our area of expertise, there is ample economic literature to suggest that debt and equity financing are not equivalent for firms.[88] Interestingly for competition policy, there is evidence to suggest firms tend to favor equity over debt financing when they operate in highly competitive product markets.[89]

Furthermore, there may be reasons that AI startups to turn to incumbent big tech platforms to obtain financing, rather than to other partners (though there is evidence these firms are also raising significant amounts of money from other sources).[90] In short, big tech platforms have a longstanding reputation for deep pockets, as well as a healthy appetite for risk. Because of the relatively small amounts at stake—at least, relative to the platforms’ market capitalizations—these firms may be able to move faster than rivals, for whom investments of this sort may present more significant risks. This may be a key advantage in the fast-paced world of generative AI, where obtaining funding and scaling rapidly could be the difference between becoming the next GAFAM or an also-ran.

Partnerships with incumbent tech platforms may also create valuable synergies that enable startups to extract better terms than would otherwise be the case (because the deal creates more surplus for parties to distribute among themselves). Potential synergies include better integrating generative-AI services into existing platforms; several big tech platforms appear to see the inevitable integration of AI into their services as a challenge similar to the shift from desktop to mobile internet, which saw several firms thrive, while others fell by the wayside.[91]

Conversely, incumbent tech platforms may have existing infrastructure that AI startups can use to scale up faster and more cheaply than would otherwise be the case. Running startups’ generative-AI services on top of this infrastructure may enable much faster deployment of generative-AI technology.[92] Importantly, if these joint strategies entail relationship-specific investments on the part of one or both partners, then big tech platforms taking equity positions in AI startups may be an important facilitator to prevent holdup.[93] Both of these possibilities are perfectly summed up by Swami Sivasubramanian, Amazon’s vice president of Data and AI, when commenting on Amazon’s partnership with Anthropic:

Anthropic’s visionary work with generative AI, most recently the introduction of its state-of-the art Claude 3 family of models, combined with Amazon’s best-in-class infrastructure like AWS Tranium and managed services like Amazon Bedrockfurther unlocks exciting opportunities for customers to quickly, securely, and responsibly innovate with generative AI. Generative AI is poised to be the most transformational technology of our time, and we believe our strategic collaboration with Anthropic will further improve our customers’ experiences, and look forward to what’s next.[94]

All of this can be expected to have a knock-on effect on innovation and competition in generative-AI markets. To put it simply, a leading firm like OpenAI might welcome the prospect of competition authorities blocking the potential funding of one of its rivals. It may also stand to benefit if incumbent tech firms are prevented from rapidly upping their generative-AI game via partnerships with other AI startups. In short, preventing AI startups from obtaining funding from big tech platforms could not only arrest those startups’ growth, but also harm long-term competition in the burgeoning AI industry.

III. Market Definition in AI

The question of market definition, long a cornerstone of antitrust analysis, is of particular importance and complexity in the context of AI. The difficulty in defining relevant markets accurately stems not only from the novelty of AI technologies, but from their inherent heterogeneity and the myriad ways they intersect with existing markets and business models. In short, it is not yet clear how to determine the boundaries of markets for AI-powered products. Indeed, traditional approaches to market definition will ultimately provide the correct tools to accomplish this task, but, as we discuss below, we do not yet know the right questions to ask.

Regulators and policymakers must develop a nuanced understanding of AI markets, one that moves beyond broad generalizations and marketing hyperbole to examine the specific characteristics of these emerging technologies and their impacts on various product and service markets.

There are three main things that need to be at the forefront of competition authorities’ minds when they think about market definition in AI products and services. First, they must understand that AI is not a single thing, but is a composite category composed of many distinct goods and services. Second, and related to looking beyond the AI marketing hype, they must recognize how the extremely heterogeneous products landscape of “AI” intersects with an equally variegated consumer-demand landscape. Finally, they must acknowledge how little we know about these nascent markets, and that the most important priority at the moment is simply to ask the right questions that will lead to sound competition policy.

A. AI Is Difficult to Define and Not Monolithic

The task of defining AI for the purposes of antitrust analysis is fraught with complexity, stemming from the multifaceted nature of AI technologies and their diverse applications across industries. It is imperative to recognize that AI does not constitute a monolithic entity or a singular market, but rather encompasses a heterogeneous array of technologies, techniques, and applications that defy simplistic categorization.[95]

At its core, the “AI Stack” comprises multiple layers of interrelated yet distinct technological components. At the foundational level, we find specialized hardware such as semiconductors, graphics processing units (GPUs), and tensor processing units (TPUs), as well as other specialized chipsets designed to accelerate the computationally intensive tasks associated with AI. These hardware components, while critical to AI functionality, also serve broader markets beyond AI applications (e.g., crypto and gaming), complicating efforts to delineate clear market boundaries.

The data layer presents another dimension of complexity. AI systems rely on vast quantities of both structured and unstructured data for training and operation.[96] The sourcing, curation, and preparation of this data constitute distinct markets within the AI ecosystem, each with its own competitive dynamics and potential barriers to entry.

Moving up the stack, we encounter the algorithmic layer, where a diverse array of machine-learning techniques—including, but not limited to, supervised learning, unsupervised learning, and reinforcement learning[97]—are employed. These algorithmic approaches, while fundamental to AI functionality, are not uniform in their application or market impact. Different AI applications may utilize distinct combinations of these techniques,[98] potentially serving disparate markets and consumer needs.

At the application level, the heterogeneity of AI becomes most apparent. From natural-language processing and computer vision to predictive analytics and autonomous vehicles, AI technologies manifest in a multitude of forms, each potentially constituting a distinct relevant market for antitrust purposes. Moreover, these AI applications can intersect with and compete against non-AI solutions, further blurring the boundaries of what might be considered an “AI market.”

The deployment models for AI technologies add yet another layer of complexity to the task of defining antitrust-relevant markets. Cloud-based AI services, edge-computing solutions, and on-premises AI deployments may each serve different market segments and face distinct competitive pressures. The ability of firms to make “build or buy” decisions regarding AI capabilities further complicates the delineation of clear market boundaries.[99]

B. Look Beyond the Marketing Hype

The application of antitrust principles to AI markets necessitates a rigorous analytical approach that transcends superficial categorizations and marketing rhetoric. It is imperative for enforcement authorities to eschew preconceived notions and popular narratives surrounding AI, and to focus instead on empirical evidence and careful economic analysis, in order to accurately assess competitive dynamics in AI-adjacent markets.

The allure of AI as a revolutionary technology has led to a proliferation of marketing claims and industry hype[100] that often may obscure the true nature and capabilities of AI systems. This obfuscation presents a significant challenge for antitrust authorities, who must disentangle factual competitive realities from speculative or exaggerated assertions about AI’s market impact. This task is further complicated by the rapid pace of technological advancement in the field, which can render even recent market analyses obsolete.

A particularly pernicious misconception that must be addressed is the notion that AI technologies operate in a competitive vacuum, distinct from and impervious to competition from non-AI alternatives. This perspective risks leading antitrust authorities to define markets too narrowly, potentially overlooking significant competitive constraints from traditional technologies or human-driven services.

Consider, for instance, the domain of natural-language processing. While AI-powered language models have made significant strides in recent years, they often compete directly with human translators, content creators, and customer-service representatives. Similarly, in the realm of data analysis, AI systems may vie for market share not only with other AI solutions, but also with traditional statistical methods and human analysts. Failing to account for these non-AI competitors in market-definition exercises could result in a distorted view of market power and competitive dynamics.

Moreover, the tendency to treat AI as a monolithic entity obscures the reality that many AI-powered products and services are, in fact, hybrid solutions that combine AI components with traditional software and human oversight.[101] This hybridization further complicates market-definition efforts, as it becomes necessary to assess the degree to which the AI element of a product or service contributes to its market position and substitutability.

C. Current Lack of Knowledge About Relevant Markets

It is crucial to acknowledge at this juncture the profound limitations in our current understanding of how AI technologies will ultimately shape competitive landscapes across various industries. This recognition of our informational constraints should inform a cautious and empirically grounded approach to market definition in the context of AI.

The dynamic nature of AI development renders many traditional metrics for market definition potentially unreliable or prematurely restrictive. Market share, often a cornerstone of antitrust analysis, may prove particularly volatile in AI markets, where technological breakthroughs can rapidly alter competitive positions. Moreover, the boundaries between distinct AI applications and markets remain fluid, with innovations in one domain frequently finding unexpected applications in others, and thereby further complicating efforts to delineate stable market boundaries.

In this context, Jonathan Barnett’s observations regarding the dangers of preemptive antitrust approaches in nascent markets are particularly salient.[102] Barnett argues persuasively that, at the early stages of a market’s development, uncertainty concerning the competitive effects of certain business practices is likely to be especially high.[103] This uncertainty engenders a significant risk of false-positive error costs, whereby preemptive intervention may inadvertently suppress practices that are either competitively neutral or potentially procompetitive.[104]

The risk of regulatory overreach is particularly acute in the realm of AI, where the full spectrum of potential applications and competitive dynamics remains largely speculative. Premature market definition and subsequent enforcement actions based on such definitions could stifle innovation and impede the natural evolution of AI technologies and business models.

Further complicating matters is the fact that what constitutes a relevant product in AI markets is often ambiguous and subject to rapid change. The modular nature of many AI systems, where components can be combined and reconfigured to serve diverse functions, challenges traditional notions of product markets. For instance, a foundational language model might serve as a critical input for a wide array of downstream applications, from chatbots to content-generation tools, each potentially constituting a distinct product market. The boundaries between these markets, and the extent to which they overlap or remain distinct, are likely to remain in flux in the near future.

Given these uncertainties, antitrust authorities must adopt a posture of epistemic humility when approaching market definition in the context of AI. This approach of acknowledged uncertainty and adaptive analysis does not imply regulatory paralysis. Rather, it calls for a more nuanced and dynamic form of antitrust oversight, one that remains vigilant to potential competitive harms while avoiding premature or overly rigid market definitions that could impede innovation.

Market definition should reflect our best understanding of both AI and AI markets. Since this understanding is still very much in an incipient phase, antitrust authorities should view their current efforts not as definitive pronouncements on the structure of AI markets, but as iterative steps in an ongoing process of learning and adaptation. By maintaining this perspective, regulators can hope to strike a balance between addressing legitimate competitive concerns and fostering an environment conducive to continued innovation and dynamic competition in the AI sector.

D. Key Questions to Ask

Finally, the most important function for enforcement authorities to play at the moment is to ask the right questions that will help to optimally develop an analytical framework of relevant markets in subsequent competition analyses. This framework should be predicated on a series of inquiries designed to elucidate the true nature of competitive dynamics in AI-adjacent markets. While the specific contours of relevant markets may remain elusive, the process of rigorous questioning can provide valuable insights and guide enforcement decisions.

Two fundamental questions emerge as critical starting points for any attempt to define relevant markets in AI contexts.

First, “Who are the consumers, and what is the product or service?” This seemingly straightforward inquiry belies a complex web of considerations in AI markets. The consumers of AI technologies and services are often not end-users, but rather, intermediaries that participate in complex value chains. For instance, the market for AI chips encompasses not only direct purchasers like cloud-service providers, but also downstream consumers of AI-powered applications. Similarly, the product or service in question may not be a discrete AI technology, but rather a bundle of AI and non-AI components, or even a service powered by AI but indistinguishable to the end user from non-AI alternatives.

The heterogeneity of AI consumers and products necessitates a granular approach to market definition. Antitrust authorities must carefully delineate between different levels of the AI value chain, considering the distinct competitive dynamics at each level. This may involve separate analyses for markets in AI inputs (such as specialized hardware or training data), AI development tools, and AI-powered end-user applications.

Second, and perhaps more crucially, “Does AI fundamentally transform the product or service in a way that creates a distinct market?” This question is at the heart of the challenge in defining AI markets. It requires a nuanced assessment of the degree to which AI capabilities alter the nature of a product or service from the perspective of consumers.

In some cases, AI’s integration into products or services may represent merely an incremental improvement, not warranting the delineation of a separate market. For example, AI-enhanced spell-checking in word-processing software might not constitute a distinct market from traditional spell-checkers if consumers do not perceive a significant functional difference.

Conversely, in other cases, AI may enable entirely new functionalities or levels of performance that create distinct markets. Large language models capable of generating human-like text, for instance, might be considered to operate in a market separate from traditional writing aids or information-retrieval tools (or not, depending on the total costs and benefits of the option).

The analysis must also consider the potential for AI to blur the boundaries between previously distinct markets. As AI systems become more versatile, they may compete across multiple traditional product categories, challenging conventional market definitions.

In addressing these questions, antitrust authorities should consider several additional factors:

1. The degree of substitutability between AI and non-AI solutions, from the perspective of both direct purchasers and end-users.
2. The extent to which AI capabilities are perceived as essential or differentiating factors by consumers in the relevant market.
3. The potential for rapid evolution in AI capabilities and consumer preferences, which may necessitate dynamic market definitions.
4. The presence of switching costs or lock-in effects, which could influence market boundaries.
5. The geographic scope of AI markets, which may transcend traditional national or regional boundaries.

It is crucial to note that these questions do not yield simple or static answers. Rather, they serve as analytical tools to guide ongoing assessment of AI markets. Antitrust authorities must be prepared to revisit and refine their market definitions as technological capabilities evolve and market dynamics shift.

Moreover, the process of defining relevant markets in the context of AI should not be viewed as an end in itself, but as a means to understand competitive dynamics and to inform enforcement decisions. In some cases, traditional market-definition exercises may prove insufficient, necessitating alternative analytical approaches that focus on competitive effects or innovation harms.

By embracing this questioning approach, antitrust authorities can develop a more nuanced and adaptable framework for market definition in AI contexts. This approach would acknowledge the complexities and uncertainties inherent in AI markets, while providing a structured methodology to assess competitive dynamics. As our understanding of AI markets deepens, this framework will need to evolve further, ensuring that antitrust enforcement remains responsive to the unique challenges posed by artificial-intelligence technologies.

[1] Press Release, Justice Department and Stanford University to Cohost Workshop “Promoting Competition in Artificial Intelligence”, U.S. Justice Department (May 21, 2024), https://www.justice.gov/opa/pr/justice-department-and-stanford-university-cohost-workshop-promoting-competition-artificial.

[2] Artificial intelligence is, of course, not a market (at least not a relevant antitrust market). Within the realm of what is called “AI,” companies offer myriad products and services, and specific relevant markets would need to be defined before assessing harm to competition in specific cases.

[3] Nathan Newman, Taking on Google’s Monopoly Means Regulating Its Control of User Data, Huffington Post (Sep. 24, 2013), http://www.huffingtonpost.com/nathan-newman/taking-on-googlesmonopol_b_3980799.html.

[4] See, e.g., Lina Khan & K. Sabeel Rahman, Restoring Competition in the U.S. Economy, in Untamed: How to Check Corporate, Financial, and Monopoly Power (Nell Abernathy, Mike Konczal, & Kathryn Milani, eds., 2016), at 23. (“From Amazon to Google to Uber, there is a new form of economic power on display, distinct from conventional monopolies and oligopolies…, leverag[ing] data, algorithms, and internet-based technologies… in ways that could operate invisibly and anticompetitively.”); Mark Weinstein, I Changed My Mind—Facebook Is a Monopoly, Wall St. J. (Oct. 1, 2021), https://www.wsj.com/articles/facebook-is-monopoly-metaverse-users-advertising-platforms-competition-mewe-big-tech-11633104247 (“[T]he glue that holds it all together is Facebook’s monopoly over data…. Facebook’s data troves give it unrivaled knowledge about people, governments—and its competitors.”).

[5] See, generally, Abigail Slater, Why “Big Data” Is a Big Deal, The Reg. Rev. (Nov. 6, 2023), https://www.theregreview.org/2023/11/06/slater-why-big-data-is-a-big-deal; Amended Complaint at ¶36, United States v. Google, 1:20-cv-03010- (D.D.C. 2020); Complaint at ¶37, United States v. Google, 1:23-cv-00108 (E.D. Va. 2023), https://www.justice.gov/opa/pr/justice-department-sues-google-monopolizing-digital-advertising-technologies (“Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry.”).

[6] See, e.g., Press Release, Commission Launches Calls for Contributions on Competition in Virtual Worlds and Generative AI, European Commission (Jan. 9, 2024), https://ec.europa.eu/commission/presscorner/detail/en/IP_24_85; Krysten Crawford, FTC’s Lina Khan Warns Big Tech over AI, SIEPR (Nov. 3, 2020), https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai (“Federal Trade Commission Chair Lina Khan delivered a sharp warning to the technology industry in a speech at Stanford on Thursday: Antitrust enforcers are watching what you do in the race to profit from artificial intelligence.”) (emphasis added).

[7] See, e.g., John M. Newman, Antitrust in Digital Markets, 72 Vand. L. Rev. 1497, 1501 (2019) (“[T]he status quo has frequently failed in this vital area, and it continues to do so with alarming regularity. The laissez-faire approach advocated for by scholars and adopted by courts and enforcers has allowed potentially massive harms to go unchecked.”); Bertin Martins, Are New EU Data Market Regulations Coherent and Efficient?, Bruegel Working Paper 21/23 (2023), https://www.bruegel.org/working-paper/are-new-eu-data-market-regulations-coherent-and-efficient (“Technical restrictions on access to and re-use of data may result in failures in data markets and data-driven services markets.”); Valéria Faure-Muntian, Competitive Dysfunction: Why Competition Law Is Failing in a Digital World, The Forum Network (Feb. 24, 2021), https://www.oecd-forum.org/posts/competitive-dysfunction-why-competition-law-is-failing-in-a-digital-world.

[8] See Rana Foroohar, The Great US-Europe Antitrust Divide, Financial Times (Feb. 5, 2024), https://www.ft.com/content/065a2f93-dc1e-410c-ba9d-73c930cedc14.

[9] See, e.g., Press Release, European Commission, supra note 6.

[10] See infra, Section I.B. Commentators have also made similar claims; see, e.g., Ganesh Sitaram & Tejas N. Narechania, It’s Time for the Government to Regulate AI. Here’s How, Politico (Jan. 15, 2024) (“All that cloud computing power is used to train foundation models by having them “learn” from incomprehensibly huge quantities of data. Unsurprisingly, the entities that own these massive computing resources are also the companies that dominate model development. Google has Bard, Meta has LLaMa. Amazon recently invested $4 billion into one of OpenAI’s leading competitors, Anthropic. And Microsoft has a 49 percent ownership stake in OpenAI — giving it extraordinary influence, as the recent board struggles over Sam Altman’s role as CEO showed.”).

[11] Press Release, European Commission, supra note 6.

[12] Comment of U.S. Federal Trade Commission to the U.S. Copyright Office, Artificial Intelligence and Copyright, Docket No. 2023-6 (Oct. 30, 2023), at 4, https://www.ftc.gov/legal-library/browse/advocacy-filings/comment-federal-trade-commission-artificial-intelligence-copyright (emphasis added).

[13] Jonathan Kanter, Remarks at the Promoting Competition in AI Conference (May 30, 2024), https://youtu.be/yh–1AGf3aU?t=424.

[14] Karin Matussek, AI Will Fuel Antitrust Fires, Big Tech’s German Nemesis Warns, Bloomberg (Jun. 26, 2024), https://www.bloomberg.com/news/articles/2024-06-26/ai-will-fuel-antitrust-fires-big-tech-s-german-nemesis-warns?srnd=technology-vp.

[15] Id.

[16] See, e.g., Joe Caserta, Holger Harreis, Kayvaun Rowshankish, Nikhil Srinidhi, & Asin Tavakoli, The Data Dividend: Fueling Generative AI, McKinsey Digital (Sep. 15, 2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-data-dividend-fueling-generative-ai (“Your data and its underlying foundations are the determining factors to what’s possible with generative AI.”).

[17] See, e.g., Tim Keary, Google DeepMind’s Achievements and Breakthroughs in AI Research, Techopedia (Aug. 11, 2023), https://www.techopedia.com/google-deepminds-achievements-and-breakthroughs-in-ai-research; see, e.g., Will Douglas Heaven, Google DeepMind Used a Large Language Model to Solve an Unsolved Math Problem, MIT Technology Review (Dec. 14, 2023), https://www.technologyreview.com/2023/12/14/1085318/google-deepmind-large-language-model-solve-unsolvable-math-problem-cap-set; see also, A Decade of Advancing the State-of-the-Art in AI Through Open Research, Meta (Nov. 30, 2023), https://about.fb.com/news/2023/11/decade-of-advancing-ai-through-open-research; see also, 200 Languages Within a Single AI Model: A Breakthrough in High-Quality Machine Translation, Meta, https://ai.meta.com/blog/nllb-200-high-quality-machine-translation (last visited Jan. 18, 2023).

[18] See, e.g., Jennifer Allen, 10 Years of Siri: The History of Apple’s Voice Assistant, Tech Radar (Oct. 4, 2021), https://www.techradar.com/news/siri-10-year-anniversary; see also Evan Selleck, How Apple Is Already Using Machine Learning and AI in iOS, Apple Insider (Nov. 20, 2023), https://appleinsider.com/articles/23/09/02/how-apple-is-already-using-machine-learning-and-ai-in-ios; see also, Kathleen Walch, The Twenty Year History Of AI At Amazon, Forbes (Jul. 19, 2019), https://www.forbes.com/sites/cognitiveworld/2019/07/19/the-twenty-year-history-of-ai-at-amazon.

[19] See infra Section I.C.

[20] Josh Sisco, POLITICO PRO Q&A: Exit interview with DOJ Chief Antitrust Economist Susan Athey, Politico Pro (Jul. 2, 2024), https://subscriber.politicopro.com/article/2024/07/politico-pro-q-a-exit-interview-with-doj-chief-antitrust-economist-susan-athey-00166281.

[21] Belle Lin, Open-Source Companies Are Sharing Their AI Free. Can They Crack OpenAI’s Dominance?, Wall St. J. (Mar. 21, 2024), https://www.wsj.com/articles/open-source-companies-are-sharing-their-ai-free-can-they-crack-openais-dominance-26149e9c.

[22] See, e.g., Cédric Argenton & Jens Prüfer, Search Engine Competition with Network Externalities, 8 J. Comp. L. & Econ. 73, 74 (2012).

[23] John M. Yun, The Role of Big Data in Antitrust, in The Global Antitrust Institute Report on the Digital Economy (Joshua D. Wright & Douglas H. Ginsburg, eds., Nov. 11, 2020) at 233, https://gaidigitalreport.com/2020/08/25/big-data-and-barriers-to-entry/#_ftnref50; see also, e.g., Robert Wayne Gregory, Ola Henfridsson, Evgeny Kaganer, & Harris Kyriakou, The Role of Artificial Intelligence and Data Network Effects for Creating User Value, 46 Acad. of Mgmt. Rev. 534 (2020), final pre-print version at 4, http://wrap.warwick.ac.uk/134220) (“A platform exhibits data network effects if, the more that the platform learns from the data it collects on users, the more valuable the platform becomes to each user.”); see also, Karl Schmedders, José Parra-Moyano, & Michael Wade, Why Data Aggregation Laws Could be the Answer to Big Tech Dominance, Silicon Republic (Feb. 6, 2024), https://www.siliconrepublic.com/enterprise/data-ai-aggregation-laws-regulation-big-tech-dominance-competition-antitrust-imd.

[24] Nathan Newman, Search, Antitrust, and the Economics of the Control of User Data, 31 Yale J. Reg. 401, 409 (2014) (emphasis added); see also id. at 420 & 423 (“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.”).

[25] See also Yun, supra note 23 at 229 (“[I]nvestments in big data can create competitive distance between a firm and its rivals, including potential entrants, but this distance is the result of a competitive desire to improve one’s product.”).

^[26] For a review of the literature on increasing returns to scale in data (this topic is broader than data-network effects) see Geoffrey Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 Geo Mason L. Rev. 1281, 1344 (2021).

[27] Andrei Hagiu & Julian Wright, Data-Enabled Learning, Network Effects, and Competitive Advantage, 54 RAND J. Econ. 638 (2023).

^[28] Id. at 639. The authors conclude that “Data-enabled learning would seem to give incumbent firms a competitive advantage. But how strong is this advantage and how does it differ from that obtained from more traditional mechanisms… .”

^[29] Id.

^[30] Bruno Jullien & Wilfried Sand-Zantman, The Economics of Platforms: A Theory Guide for Competition Policy, 54 Info. Econ. & Pol’y 10080, 101031 (2021).

^[31] Daniele Condorelli & Jorge Padilla, Harnessing Platform Envelopment in the Digital World, 16 J. Comp. L. & Pol’y 143, 167 (2020).

[32] See Hagiu & Wright, supra note 27.

^[33] For a summary of these limitations, see generally Catherine Tucker, Network Effects and Market Power: What Have We Learned in the Last Decade?, Antitrust (2018) at 72, available at https://sites.bu.edu/tpri/files/2018/07/tucker-network-effects-antitrust2018.pdf; see also Manne & Auer, supra note 26, at 1330.

[34] See Jason Furman, Diane Coyle, Amelia Fletcher, Derek McAuley, & Philip Marsden (Dig. Competition Expert Panel), Unlocking Digital Competition (2019) at 32-35 (“Furman Report”), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_review_web.pdf.

[35] Id. at 34.

[36] Id. at 35. To its credit, it should be noted, the Furman Report does counsel caution before mandating access to data as a remedy to promote competition. See id. at 75. That said, the Furman Report maintains that such a remedy should remain on the table because “the evidence suggests that large data holdings are at the heart of the potential for some platform markets to be dominated by single players and for that dominance to be entrenched in a way that lessens the potential for competition for the market.” Id. The evidence, however, does not show this.

^[37] Case COMP/M.9660 — Google/Fitbit, Commission Decision (Dec. 17, 2020) (Summary at O.J. (C 194) 7), available at https://ec.europa.eu/competition/mergers/cases1/202120/m9660_3314_3.pdf, at 455,

^[38] Id. at 896.

[39] See Natasha Lomas, EU Checking if Microsoft’s OpenAI Investment Falls Under Merger Rules, TechCrunch (Jan. 9, 2024), https://techcrunch.com/2024/01/09/openai-microsoft-eu-merger-rules.

^[40] Amended Complaint at 11, Meta/Zuckerberg/Within, Fed. Trade Comm’n. (2022) (No. 605837), available at https://www.ftc.gov/system/files/ftc_gov/pdf/D09411%20-%20AMENDED%20COMPLAINT%20FILED%20BY%20COUNSEL%20SUPPORTING%20THE%20COMPLAINT%20-%20PUBLIC%20%281%29_0.pdf.

[41] Amended Complaint (D.D.C), supra note 5 at ¶37.

[42] Amended Complaint (E.D. Va), supra note 5 at ¶8.

^[43] Merger Guidelines, US Dep’t of Justice & Fed. Trade Comm’n (2023) at 25, available at https://www.ftc.gov/system/files/ftc_gov/pdf/2023_merger_guidelines_final_12.18.2023.pdf.

[44] Merger Assessment Guidelines, Competition and Mkts. Auth (2021) at ¶7.19(e), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1051823/MAGs_for_publication_2021_–_.pdf.

[45] Furman Report, supra note 34, at ¶4.

[46] See, e.g., Chris Westfall, New Research Shows ChatGPT Reigns Supreme in AI Tool Sector, Forbes (Nov. 16, 2023), https://www.forbes.com/sites/chriswestfall/2023/11/16/new-research-shows-chatgpt-reigns-supreme-in-ai-tool-sector/?sh=7de5de250e9c; Sujan Sarkar, AI Industry Analysis: 50 Most Visited AI Tools and Their 24B+ Traffic Behavior, Writerbuddy (last visited, Jul. 15, 2024), https://writerbuddy.ai/blog/ai-industry-analysis.

^[47] See Krystal Hu, ChatGPT Sets Record for Fastest-Growing User Base, Reuters (Feb. 2, 2023), https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01; Google: The AI Race Is On, App Economy Insights (Feb. 7, 2023), https://www.appeconomyinsights.com/p/google-the-ai-race-is-on.

^[48] See Google Trends, https://trends.google.com/trends/explore?date=today%205-y&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited Jan. 12, 2024) and https://trends.google.com/trends/explore?date=today%205-y&geo=US&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited Jan. 12, 2024).

^[49] See David F. Carr, As ChatGPT Growth Flattened in May, Google Bard Rose 187%, Similarweb Blog (Jun. 5, 2023), https://www.similarweb.com/blog/insights/ai-news/chatgpt-bard.

^[50] See Press Release, Introducing New AI Experiences Across Our Family of Apps and Devices, Meta (Sep. 27, 2023), https://about.fb.com/news/2023/09/introducing-ai-powered-assistants-characters-and-creative-tools; Sundar Pichai, An Important Next Step on Our AI Journey, Google Keyword Blog (Feb. 6, 2023), https://blog.google/technology/ai/bard-google-ai-search-updates.

^[51] See Ion Prodan, 14 Million Users: Midjourney’s Statistical Success, Yon (Aug. 19, 2023), https://yon.fun/midjourney-statistics; see also Andrew Wilson, Midjourney Statistics: Users, Polls, & Growth [Oct 2023], ApproachableAI (Oct. 13, 2023), https://approachableai.com/midjourney-statistics.

^[52] See Hema Budaraju, New Ways to Get Inspired with Generative AI in Search, Google Keyword Blog (Oct. 12, 2023), https://blog.google/products/search/google-search-generative-ai-october-update; Imagine with Meta AI, Meta (last visited Jan. 12, 2024), https://imagine.meta.com.

^[53] Catherine Tucker, Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility, 54 Rev. Indus. Org. 683, 686 (2019).

^[54] Manne & Auer, supra note 26, at 1345.

[55] See, e.g., Stefanie Koperniak, Artificial Data Give the Same Results as Real Data—Without Compromising Privacy, MIT News (Mar. 3, 2017), https://news.mit.edu/2017/artificial-data-give-same-results-as-real-data-0303 (“[Authors] describe a machine learning system that automatically creates synthetic data—with the goal of enabling data science efforts that, due to a lack of access to real data, may have otherwise not left the ground. While the use of authentic data can cause significant privacy concerns, this synthetic data is completely different from that produced by real users—but can still be used to develop and test data science algorithms and models.”).

[56] See, e.g., Rachel Gordon, Synthetic Imagery Sets New Bar in AI Training Efficiency, MIT News (Nov. 20, 2023), https://news.mit.edu/2023/synthetic-imagery-sets-new-bar-ai-training-efficiency-1120 (“By using synthetic images to train machine learning models, a team of scientists recently surpassed results obtained from traditional ‘real-image’ training methods.).

[57] Thibault Schrepel & Alex ‘Sandy’ Pentland, Competition Between AI Foundation Models: Dynamics and Policy Recommendations, MIT Connection Science Working Paper (Jun. 2023), at 8.

[58] Igor Susmelj, Optimizing Generative AI: The Role of Data Curation, Lightly (last visited Jan. 15, 2024), https://www.lightly.ai/post/optimizing-generative-ai-the-role-of-data-curation.

[59] See, e.g., Xiaoliang Dai, et al., Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack, ArXiv (Sep. 27, 2023) at 1, https://ar5iv.labs.arxiv.org/html/2309.15807 (“[S]upervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality.”); see also, Hu Xu, et al., Demystifying CLIP Data, ArXiv (Sep. 28, 2023), https://arxiv.org/abs/2309.16671.

[60] Lauren Leffer, New Training Method Helps AI Generalize like People Do, Sci. Am. (Oct. 26, 2023), https://www.scientificamerican.com/article/new-training-method-helps-ai-generalize-like-people-do (discussing Brendan M. Lake & Marco Baroni, Human-Like Systematic Generalization Through a Meta-Learning Neural Network, 623 Nature 115 (2023)).

^[61] Timothy B. Lee, The Real Research Behind the Wild Rumors about OpenAI’s Q* Project, Ars Technica (Dec. 8, 2023), https://arstechnica.com/ai/2023/12/the-real-research-behind-the-wild-rumors-about-openais-q-project.

^[62] Id.; see also GSM8K, Papers with Code (last visited Jan. 18, 2023), https://paperswithcode.com/dataset/gsm8k; MATH Dataset, GitHub (last visited Jan. 18, 2024), https://github.com/hendrycks/math.

^[63] Lee, supra note 61.

[64] Geoffrey Manne & Ben Sperry, Debunking the Myth of a Data Barrier to Entry for Online Services, Truth on the Market (Mar. 26, 2015), https://truthonthemarket.com/2015/03/26/debunking-the-myth-of-a-data-barrier-to-entry-for-online-services (citing Andres V. Lerner, The Role of ‘Big Data’ in Online Platform Competition (Aug. 26, 2014), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2482780.).

[65] See Catherine Tucker, Digital Data as an Essential Facility: Control, CPI Antitrust Chron. (Feb. 2020), at 11 (“[U]ltimately the value of data is not the raw manifestation of the data itself, but the ability of a firm to use this data as an input to insight.”).

[66] Or, as John Yun put it, data is only a small component of digital firms’ production function. See Yun, supra note 23, at 235 (“Second, while no one would seriously dispute that having more data is better than having less, the idea of a data-driven network effect is focused too narrowly on a single factor improving quality. As mentioned in supra Section I.A, there are a variety of factors that enter a firm’s production function to improve quality.”).

^[67] Luxia Le, The Real Reason Windows Phone Failed Spectacularly, History–Computer (Aug. 8, 2023), https://history-computer.com/the-real-reason-windows-phone-failed-spectacularly.

[68] Introducing the GPT Store, Open AI (Jan. 10, 2024), https://openai.com/blog/introducing-the-gpt-store.

^[69] See Michael Schade, How ChatGPT and Our Language Models Are Developed, OpenAI, https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed; Sreejani Bhattacharyya, Interesting Innovations from OpenAI in 2021, AIM (Jan. 1, 2022), https://analyticsindiamag.com/interesting-innovations-from-openai-in-2021; Danny Hernadez & Tom B. Brown, Measuring the Algorithmic Efficiency of Neural Networks, ArXiv (May 8, 2020), https://arxiv.org/abs/2005.04305.

[70] See Yun, supra note 23 at 235 (“Even if data is primarily responsible for a platform’s quality improvements, these improvements do not simply materialize with the presence of more data—which differentiates the idea of data-driven network effects from direct network effects. A firm needs to intentionally transform raw, collected data into something that provides analytical insights. This transformation involves costs including those associated with data storage, organization, and analytics, which moves the idea of collecting more data away from a strict network effect to more of a ‘data opportunity.’”).

[71] Lerner, supra note 64, at 4-5 (emphasis added).

[72] See Clayton M. Christensen, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (2013).

[73] See David J. Teece, Dynamic Capabilities and Strategic Management: Organizing for Innovation and Growth (2009).

[74] Antitrust merger enforcement has long assumed that horizontal mergers are more likely to cause problems for consumers than vertical mergers. See: Geoffrey A. Manne, Dirk Auer, Brian Albrecht, Eric Fruits, Daniel J. Gilman, & Lazar Radic, Comments of the International Center for Law and Economics on the FTC & DOJ Draft Merger Guidelines, (Sep. 18, 2023), https://laweconcenter.org/resources/comments-of-the-international-center-for-law-and-economics-on-the-ftc-doj-draft-merger-guidelines.

[75] See Hagiu & Wright, supra note 27, at 27 (“We use our dynamic framework to explore how data sharing works: we find that it in-creases consumer surplus when one firm is sufficiently far ahead of the other by making the laggard more competitive, but it decreases consumer surplus when the firms are sufficiently evenly matched by making firms compete less aggressively, which in our model means subsidizing consumers less.”); see also Lerner, supra note 64.

[76] See, e.g., Hagiu & Wright, id. (“We also use our model to highlight an unintended consequence of privacy policies. If such policies reduce the rate at which firms can extract useful data from consumers, they will tend to increase the incumbent’s competitive advantage, reflecting that the entrant has more scope for new learning and so is affected more by such a policy.”); Jian Jia, Ginger Zhe Jin, & Liad Wagman, The Short-Run Effects of the General Data Protection Regulation on Technology Venture Investment, 40 Marketing Sci. 593 (2021) (finding GDPR reduced investment in new and emerging technology firms, particularly in data-related ventures); James Campbell, Avi Goldfarb, & Catherine Tucker, Privacy Regulation and Market Structure, 24 J. Econ. & Mgmt. Strat. 47 (2015) (“Consequently, rather than increasing competition, the nature of transaction costs implied by privacy regulation suggests that privacy regulation may be anti-competitive.”).

[77] See Jonathan M. Barnett, “Killer Acquisitions” Reexamined: Economic Hyperbole in the Age of Populist Antitrust, 3 U. Chi. Bus. L. Rev. 39 (2023).

[78] Id. at 85. (“At the same time, these transactions enhance competitive conditions by supporting the profit expectations that elicit VC investment in the startups that deliver the most transformative types of innovation to the biopharmaceutical ecosystem (and, in some cases, mature into larger firms that can challenge incumbents).)”

[79] Cade Metz, Karen Weise, & Mike Isaac, Nvidia’s Big Tech Rivals Put Their Own A.I. Chips on the Table, N.Y. Times (Jan. 29, 2024), https://www.nytimes.com/2024/01/29/technology/ai-chips-nvidia-amazon-google-microsoft-meta.html.

[80] See, e.g., Chris Metinko, Nvidia’s Big Tech Rivals Put Their Own A.I. Chips on the Table, CrunchBase (Jun. 12, 2024), https://news.crunchbase.com/ai/msft-nvda-lead-big-tech-startup-investment.

[81] CMA Seeks Views on AI Partnerships and Other Arrangements, Competition and Mkts. Auth. (Apr. 24, 2024), https://www.gov.uk/government/news/cma-seeks-views-on-ai-partnerships-and-other-arrangements.

[82] As noted infra, companies offer myriad “AI” products and services, and specific relevant markets would need to be defined before assessing harm to competition in specific cases.

[83] Start-ups, Killer Acquisitions and Merger Control, OECD (2020), available at https://web-archive.oecd.org/2020-10-16/566931-start-ups-killer-acquisitions-and-merger-control-2020.pdf.

[84] Kate Rooney & Hayden Field, Amazon Spends $2.75 Billion on AI Startup Anthropic in Its Largest Venture Investment Yet, CNBC (Mar. 27, 2024), https://www.cnbc.com/2024/03/27/amazon-spends-2point7b-on-startup-anthropic-in-largest-venture-investment.html.

[85] Id.

[86] Tom Warren, Microsoft Partners with Mistral in Second AI Deal Beyond OpenAI, The Verge (Feb. 26, 2024), https://www.theverge.com/2024/2/26/24083510/microsoft-mistral-partnership-deal-azure-ai.

[87] Mark Sullivan, Microsoft’s Inflection AI Grab Likely Cost More Than $1 Billion, Says An Insider (Exclusive), Fast Company  (Mar. 26, 2024), https://www.fastcompany.com/91069182/microsoft-inflection-ai-exclusive; see also, Mustafa Suleyman, DeepMind and Inflection Co-Founder, Joins Microsoft to Lead Copilot, Microsoft Corporate Blogs (Mar. 19, 2024), https://blogs.microsoft.com/blog/2024/03/19/mustafa-suleyman-deepmind-and-inflection-co-founder-joins-microsoft-to-lead-copilot; Krystal Hu & Harshita Mary Varghese, Microsoft Pays Inflection $ 650 Mln in Licensing Deal While Poaching Top Talent, Source Says, Reuters (Mar. 21, 2024), https://www.reuters.com/technology/microsoft-agreed-pay-inflection-650-mln-while-hiring-its-staff-information-2024-03-21; The New Inflection: An Important Change to How We’ll Work, Inflection (Mar. 19, 2024), https://inflection.ai/the-new-inflection; Julie Bort, Here’s How Microsoft Is Providing a ‘Good Outcome’ for Inflection AI VCs, as Reid Hoffman Promised, Tech Crunch (Mar. 21, 2024), https://techcrunch.com/2024/03/21/microsoft-inflection-ai-investors-reid-hoffman-bill-gates.

[88]  See, e.g., Paul Marsh, The Choice Between Equity and Debt: An Empirical Study, 37 The J. of Finance 121, 142 (1982) (“First, it demonstrates that companies are heavily influenced by market conditions and the past history of security prices in choosing between equity and debt. Indeed, these factors appeared to be far more significant in our model than, for example, other variables such as the company’s existing financial structure. Second, this study provides evidence that companies do appear to make their choice of financing instrument as though they had target levels in mind for both the long term debt ratio, and the ratio of short term to total debt. Finally, the results are consistent with the notion that these target levels are themselves functions of company size, bankruptcy risk, and asset composition.”); see also, Armen Hovakimian, Tim Opler, & Sheridan Titman, The Debt-Equity Choice, 36 J. of Financial and Quantitative Analysis 1, 3(2001) (“Our results suggest that, although pecking order considerations affect corporate debt ratios in the short-run, firms tend to make financing choices that move them toward target debt ratios that are consistent with tradeoff models of capital structure choice. For example, our findings confirm that more profitable firms have, on average, lower leverage ratios. But we also find that more profitable firms are more likely to issue debt rather than equity and are more likely to repurchase equity rather than retire debt. Such behavior is consistent with our conjecture that the most profitable firms become under-levered and that firms’ financing choices tend to offset these earnings-driven changes in their capital structures.”): see also, Sabri Boubaker, Wael Rouatbi, & Walid Saffar, The Role of Multiple Large Shareholders in the Choice of Debt Source, 46 Financial Management 241, 267 (2017) (“Our analysis shows that firms controlled by more than one large shareholder tend to rely more heavily on bank debt financing. Moreover, we find that the proportion of bank debt in total debt is significantly higher for firms with higher contestability of the largest controlling owner’s power.”).

[89] Sabri Boubaker, Walid Saffar, & Syrine Sassi, Product Market Competition and Debt Choice, 49 J. of Corp. Finance 204, 208 (2018). (“Our findings that firms substitute away from bank debt when faced with intense market pressure echo the intuition in previous studies that the disciplinary force of competition substitutes for the need to discipline firms through other forms of governance.”).

[90] See, e.g., George Hammond, Andreessen Horowitz Raises $7.2bn and Sets Sights on AI Start-ups, Financial Times (Apr. 16, 2024), https://www.ft.com/content/fdef2f53-f8f7-4553-866b-1c9bfdbeea42; Elon Musk’s xAI Says It Raised $6 Billion to Develop Artificial Intelligence, Moneywatch (May. 27, 2024), https://www.cbsnews.com/news/elon-musk-xai-6-billion; Krystal Hu, AI Search Startup Genspark Raises $60 Million in Seed Round to Challenge Google, Reuters (Jun. 18, 2024), https://www.reuters.com/technology/artificial-intelligence/ai-search-startup-genspark-raises-60-million-seed-round-challenge-google-2024-06-18; Visa to Invest $100 Million in Generative AI for Commerce and Payments, PMYNTS (Oct. 2, 2023), https://www.pymnts.com/artificial-intelligence-2/2023/visa-to-invest-100-million-in-generative-ai-for-commerce-and-payments.

[91] See, e.g., Eze Vidra, Is Generative AI the Biggest Platform Shift Since Cloud and Mobile?, VC Cafe (Mar. 6, 2023), https://www.vccafe.com/2023/03/06/is-generative-ai-the-biggest-platform-shift-since-cloud-and-mobile. See also, OpenAI and Apple Announce Partnership to Integrate ChatGPT into Apple Experiences, OpenAI (Jun. 10, 2024), https://openai.com/index/openai-and-apple-announce-partnership (“Apple is integrating ChatGPT into experiences within iOS, iPadOS, and macOS, allowing users to access ChatGPT’s capabilities—including image and document understanding—without needing to jump between tools.”). See also, Yusuf Mehdi, Reinventing Search With a new AI-powered Microsoft Bing and Edge, Your Copilot for the Web, Microsoft Official Blog (Feb. 7, 2023), https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web (“‘AI will fundamentally change every software category, starting with the largest category of all – search,’ said Satya Nadella, Chairman and CEO, Microsoft. ‘Today, we’re launching Bing and Edge powered by AI copilot and chat, to help people get more from search and the web.’”).

[92] See, e.g., Amazon and Anthropic Deepen Their Shared Commitment to Advancing Generative AI, Amazon (Mar. 27, 2024), https://www.aboutamazon.com/news/company-news/amazon-anthropic-ai-investment (“Global organizations of all sizes, across virtually every industry, are already using Amazon Bedrock to build their generative AI applications with Anthropic’s Claude AI. They include ADP, Amdocs, Bridgewater Associates, Broadridge, CelcomDigi, Clariant, Cloudera, Dana-Farber Cancer Institute, Degas Ltd., Delta Air Lines, Druva, Enverus, Genesys, Genomics England, GoDaddy, HappyFox, Intuit, KT, LivTech, Lonely Planet, LexisNexis Legal & Professional, M1 Finance, Netsmart, Nexxiot, Parsyl, Perplexity AI, Pfizer, the PGA TOUR, Proto Hologram, Ricoh USA, Rocket Companies, and Siemens.”).

[93] Ownership of another firm’s assets is widely seen as a solution to contractual incompleteness. See, e.g., Sanford J. Grossman & Oliver D. Hart, The Costs and Benefits of Ownership: A Theory of Vertical and Lateral Integration, 94 J. Polit. Econ. 691, 716 (1986) (“When it is too costly for one party to specify a long list of the particular rights it desires over another party’s assets, then it may be optimal for the first party to purchase all rights except those specifically mentioned in the contract. Ownership is the purchase of these residual rights of control.”).

[94] See Amazon Staff, supra note 92.

[95] As the National Security Commission on Artificial Intelligence has observed: “AI is not a single technology breakthrough… The race for AI supremacy is not like the space race to the moon. AI is not even comparable to a general-purpose technology like electricity. However, what Thomas Edison said of electricity encapsulates the AI future: “It is a field of fields … it holds the secrets which will reorganize the life of the world.” Edison’s astounding assessment came from humility. All that he discovered was “very little in comparison with the possibilities that appear.” National Security Commission on Artificial Intelligence, Final Report, 7 (2021), available at https://www.dwt.com/-/media/files/blogs/artificial-intelligence-law-advisor/2021/03/nscai-final-report–2021.pdf.

[96] See, e.g., Structured vs Unstructured Data, IBM Cloud Education (Jun. 29, 2021), https://www.ibm.com/think/topics/structured-vs-unstructured-data; Dongdong Zhang, et al., Combining Structured and Unstructured Data for Predictive Models: A Deep Learning Approach, BMC Medical Informatics and Decision Making (Oct. 29, 2020), https://link.springer.com/article/10.1186/s12911-020-01297-6 (describing generally the use of both structured and unstructured data in predictive models for health care).

[97] For a somewhat technical discussion of all three methods, see generally Eric Benhamou, Similarities Between Policy Gradient Methods (PGM) in Reinforcement Learning (RL) and Supervised Learning (SL), SSRN (2019), https://ssrn.com/abstract=3391216.

[98] Id.

[99] For a discussion of the “buy vs build” decisions firms employing AI undertake, see Jonathan M. Barnett, The Case Against Preemptive Antitrust in the Generative Artificial Intelligence Ecosystem, in Artificial Intelligence and Competition Policy (Alden Abbott and Thibault Schrepel eds., 2024), at 3-6.

[100] See, e.g., Melissa Heikkilä & Will Douglas Heaven, What’s Next for AI in 2024, MIT Tech. Rev. (Jan. 4, 2024), https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024 (Runway hyping Gen-2 as a major film-production tool that, to date, still demonstrates serious limitations). LLMs, impressive as they are, have been touted as impending replacements for humans across many job categories, but still demonstrate many serious limitations that may ultimately limit their use cases. See, e.g., Melissa Malec, Large Language Models: Capabilities, Advancements, And Limitations, HatchWorksAI (Jun. 14, 2024), https://hatchworks.com/blog/gen-ai/large-language-models-guide.

[101] See, e.g., Hybrid AI: A Comprehensive Guide to Applications and Use Cases, SoluLab, https://www.solulab.com/hybrid-ai (last visited Jul. 12, 2024); Why Hybrid Intelligence Is the Future of Artificial Intelligence at McKinsey, McKinsey & Co. (Apr. 29, 2022), https://www.mckinsey.com/about-us/new-at-mckinsey-blog/hybrid-intelligence-the-future-of-artificial-intelligence; Vahe Andonians, Harnessing Hybrid Intelligence: Balancing AI Models and Human Expertise for Optimal Performance, Cognaize (Apr. 11, 2023), https://blog.cognaize.com/harnessing-hybrid-intelligence-balancing-ai-models-and-human-expertise-for-optimal-performance; Salesforce Artificial Intelligence, Salesforce, https://www.salesforce.com/artificial-intelligence (last visited Jul. 12, 2024) (combines traditional CRM and algorithms with AI modules); AI Overview, Adobe, https://www.adobe.com/ai/overview.html (last visited Jul. 12, 2024) (Adobe packages generative AI tools into its general graphic-design tools).

[102] Barnett supra note 99.

[103] Id. at 7-8.

[104] Id.

For more on this topic, see the ICLE Issue Spotlight “Digital Competition Regulations Around the World.”

Executive Summary

Inspired by the European Union’s Digital Markets Act (DMA), a growing number of jurisdictions around the globe either have adopted or are considering adopting a framework of ex-ante rules to more closely regulate the business models and behavior of online platforms.

These digital competition regulations (“DCRs”) share two key features. The first is that they target so-called “gatekeepers” who control the world’s largest online platforms. Such regulations assume that these firms have accumulated a degree of economic and political power that allows them to harm competition, exclude rivals, exploit users, and possibly inflict a broader range of social harms in ways that cannot be adequately addressed through existing competition laws. Typically cited as examples of gatekeepers are the main platforms of Google, Amazon, Facebook/Meta, Apple, and Microsoft.

The second common features of these DCR regimes is that they impose similar, if not identical, per-se prohibitions and obligations on gatekeepers. These often include prohibitions on self-preferencing and the use of third-party party data, as well as obligations for interoperability and data sharing. These two basic characteristics set DCRs apart from other forms of “digital regulation”—e.g., those that concern with AI, privacy, or content moderation and misinformation.

This paper seeks to understand what digital competition regulations aim to achieve and whether a common rationale underpins their promulgation across such a broad swatch of territories.

A. Multiple and Diverging Goals?

We find that DCRs pursue multiple goals that may vary across jurisdictions. Some DCRs are guided by the same goals as competition law, and may even be embedded into such laws. Such is the case, e.g., in Germany and Turkey. Other regulations address competition concerns under differing or modified standards. Examples here include the “material-harm-to-competition” standard in the United States and, arguably, digital competition regulation in the UK and Australia—where traditional competition-law goals such as the protection of competition and consumer welfare comingle with an increased emphasis on “fairness.”

DCRs sometimes pursue a much broader set of goals. For instance, a prospective digital competition regulation in South Africa seeks greater visibility and opportunities for small South African platforms and increased inclusivity of historically disadvantaged peoples, along with other more competition-oriented objectives (this duality is a common feature of South African legislation). Similarly, a bill proposed in Brazil attempts to reduce regional and social inequality, as well as to widen social participation in matters of public interest, alongside its stated effort to protect competition.

In the United States, apart from protection of competition, proponents of the (now-stalled) DCR bills have invoked a broad set of potential benefits, including fairness; fair prices; a more level playing field; reduced gatekeeper power; protections for small and medium-sized enterprises (“SMEs”); reduced costs for consumers; and boosts to innovation.

Some DCRs, however, are not promulgated in pursuit of competition-oriented objectives at all—at least, not explicitly or not in the sense in which such objectives are understood in traditional competition law. The clearest example is the EU’s DMA itself, which openly eschews traditional competition-related goals and instead seeks to make digital markets “fair” and “contestable.”

B. A New Form of Competition Regulation

Regardless of the overarching goals, it is evident that DCRs incorporate themes and concepts familiar to the competition lawyer, such as barriers to entry, exclusionary conduct, competitive constraints, monopolistic outcomes, and, in some cases, even market power. This may, at first blush, hint at a close relationship between digital competition regulation and competition law. While not entirely incorrect, that assessment must come with a number of caveats.

DCRs diverge in subtle but significant ways from mainstream notions of competition law. We posit that DCRs are guided by three fundamental goals: wealth redistribution among firms, the protection of competitors of incumbent digital platforms, and the “leveling down” of those same digital platforms.

C. Rent Redistribution Among Firms

The notion of “gatekeepers” itself presumes asymmetrical power relations between digital platforms and other actors, which are further presumed both to lead to unfair outcomes and to be insurmountable without regulatory intervention. Thus, the first commonality among the DCRs we study is that they all seek to transfer rents directly from gatekeepers to rival firms, complementors, and, to a lesser extent, consumers. This conclusion follows inexorably from the DCRs’ stated goals, the prohibitions and obligations they promulgate, and the public statements of those who promote them.

While the extent to which various groups are intended to benefit from this rent re-allocation might not always be identical, all DCRs aim to redistribute rents generated on digital platforms away from gatekeepers and toward some other group or groups—most commonly the business users active on those platforms.

D. Protection of Competitors

Another important feature that DCRs share is the common goal not just to protect business users, but to directly benefit competitors—including, but not limited to, via rent redistribution. DCRs are concerned with ensuring that competitors—even if they are less efficient—enter or remain on the market. This is evidenced by the lack of overarching efficiency or consumer-welfare goals—at the very least, for those regulations not based on existing competition laws—that would otherwise enable enforcers to differentiate anticompetitive exclusion of rivals from those market exits that result from rivals’ inferior product offerings.

This focus on protecting competitors can also be seen in DCRs’ pursuit of “contestability.” As defined by DCRs, promoting contestability entails diminishing the benefits of network effects and the data advantages enjoyed by incumbents because they make it hard for other firms to compete, not because they are harmful in and of themselves or because they have been acquired illegally or through deceit. In other words, DCRs pursue contestability—understood as other firms’ ability to challenge incumbent digital platforms’ position—regardless of the efficiency of those challengers or the ultimate effects on consumers.

E. ‘Leveling Down’ Gatekeepers

The other way that DCRs seek to balance power relations and achieve fairness is by “leveling down” the status of the incumbent digital platforms. DCRs directly and indirectly worsen gatekeepers’ competitive position in at least three ways:

1. By imposing costs on gatekeepers not borne by competitors;
2. By negating gatekeepers’ ability to capitalize on key investments; and
3. By facilitating third parties’ free riding on those investments.

For example, prohibitions on the use of nonpublic (third-party) data benefit competitors, but they also negate the massive investments that incumbents have made in harvesting that data. Similarly, data-sharing obligations impose a cost on gatekeepers because data-tracking and sharing is anything but free. Gatekeepers are expected to aid and subsidize competitors and third parties at little or no cost, thereby diminishing their competitive position and dissipating their resources (and investments) for the benefit of another group. The same can be said, mutatis mutandis, for other staples of digital competition regulation, such as prohibitions on self-preferencing and sideloading mandates.

F. The Perils of Redistributive and Protectionist Competition Regulation

It should be noted, of course, that direct rent redistribution among firms is generally not the goal of competition law. Rent redistribution entails significant risks of judicial error and rent seeking. Regulators may require firms to supply their services at inefficiently low prices that are not mutually advantageous, and may diminish those same firms’ incentives to invest and innovate. Those difficulties are compounded in the fast-moving digital space, where innovation cycles are faster, and yesterday’s prices and other nonprice factors may no longer be relevant today. In short, rent redistribution is difficult to do well in traditional natural-monopoly settings and may be impossible to do without judicial error in the digital world.

Protecting competitors at the expense of competition, as DCRs aim to do, is equally problematic. Competition depresses prices, increases output, leads to the efficient allocation of resources, and encourages firms to innovate. By facilitating competitors—including those that may have fallen behind precisely because they have not made the same investments in technology, innovation, or product offerings—DCRs may dampen incentives to strive to become a so-called gatekeeper, to the ultimate detriment of consumers. Protecting competition benefits the public, but protecting competitors safeguards their special interests at the public’s expense.

This is not only anathema to competition law but also to free competition. As Judge Learned Hand observed 80 years ago in his famous Alcoa decision: “the successful competitor, having been urged to compete, must not be turned upon when he wins.” Critiques of digital competition regulation’s punitive impulse against incumbent platforms flow from this essential premise—which, we contend, is the cornerstone of good competition regulation. The multiplicity of alternative justifications put forward by proponents of such regulations are generally either pretextual or serve as a signal to the voting public. To paraphrase Aldous Huxley: “several excuses are always less convincing than one.”

We end by speculating that digital competition regulation could signal more than just a digression from established principles in a relatively niche, technical field such as competition law. If extended, the DCR approach could mark a new conception of the roles of companies, markets, and the state in society. In this “post-neoliberal” world, the role of the state would not be limited to discrete interventions to address market failures that harm consumers, invoking general, abstract, and reactive rules—such as, among others, competition law. It would instead be free to intercede aggressively to redraw markets, redesign products, pick winners, and redistribute rents; indeed, to function as the ultimate ordering power of the economy.

Ultimately, however, we conclude that it is too early to make any such generalizations, and that only time will tell whether digital competition regulation was truly a sign of things to come, or merely a small but ultimately insignificant abrupt dirigiste turn in the zig-zagging of antitrust history.

Introduction

Inspired by the European Union’s Digital Markets Act (“DMA”),[1] a growing number of jurisdictions around the globe either have adopted or are considering adopting a framework of ex-ante rules to more closely regulate the business models and behavior of online platforms.

These “digital competition regulations”[2] (“DCRs”) share two key features. The first is that they target so-called “gatekeepers” who control the world’s largest online platforms. Such regulations assume that these firms have accumulated a degree of economic and political power that allows them to harm competition, exclude rivals, exploit users, and possibly inflict a broader range of social harms in ways that cannot be adequately addressed through existing competition laws.[3] Typically cited as examples of gatekeepers are the main platforms of Google, Amazon, Facebook/Meta, Apple, and Microsoft.

The second common feature these DCR regimes share is that they impose similar, if not identical, per-se prohibitions and obligations on gatekeepers. These often include prohibitions on self-preferencing and the use of third-party party data, as well as obligations for interoperability and data sharing. These two basic characteristics set DCRs apart from other forms of “digital regulation”—e.g., those dealing with AI,[4] privacy,[5] or content moderation and misinformation.[6]

It is not, however, always entirely clear what DCRs aim to achieve. A cursory survey suggests that these rules pursue different goals, without an immediately apparent unifying theme. For example, some DCRs have been integrated into existing competition laws and ostensibly pursue the same goals: the protection of competition and consumer welfare. Others aim for a range of goals—including, but not limited to, competition—such as the protection of small and medium-sized enterprises (“SMEs”); regional equality; social participation; and improving the lot of business users who operate on online platforms. Some DCRs purposefully and explicitly sidestep competition-oriented considerations, aiming instead for such adjacent but ultimately distinct goals as “fairness” and “contestability.”[7]

What emerges is a seeming patchwork of goals and objectives. In this paper, we seek to assess those disparate goals and objectives, drawing on many of the major proposed and enacted DCRs.

Part I examines the goals that DCRs claim to pursue. It takes those goals at face value and offers a largely descriptive account of the objectives offered. Where necessary (such as, for example, where those goals are cryptic or not clearly articulated), reference is made to public statements by those who promulgated them.

Part II argues that DCRs are best understood as a new form of law, grounded in ideas that have found limited success in competition law itself. To some extent, DCRs are based on a common narrative that has transformed some of the core principles and themes of antitrust law. As such, DCRs partially jibe with antitrust law, but ultimately diverge from it in subtle but consequential ways.

Part III argues that, despite superficial differences, DCRs share three common goals. The first is a desire to redistribute rents from some companies to others. At the most fundamental level, DCRs all seek to address what are perceived to be extreme power imbalances between digital platforms and the rest of society—especially business users and competitors. Thus, they seek to redistribute rents away from so-called “gatekeepers” and toward the business users that operate on those platforms, and to promote competitors (including, but not limited to, via rent redistribution).

DCRs are particularly concerned with ensuring that competitors, even if they are less efficient, enter or remain in the market. This is evidenced by a lack of overarching efficiency or consumer-welfare goals—even in those regulations that are based on existing competition laws—that would otherwise enable enforcers to differentiate between anticompetitive exclusion of rivals and market exit that results from rivals’ inferior product offerings. The focus on protecting competitors also stems from DCRs’ pursuit of “contestability.” In this context, promoting contestability entails diminishing the benefits of the network effects and the data advantages enjoyed by incumbents on the theory that they make it difficult for other firms to compete—not because they are harmful to consumers or because they have been acquired illegally or through deceit.

The third way that DCRs seek to balance power relations and achieve fairness is by “leveling down” the status of the incumbent digital platforms. DCRs worsen the competitive position of gatekeepers in at least three ways:

1. By imposing costs on gatekeepers not borne by competitors;
2. By negating their ability to capitalize on key investments; and
3. By helping third parties to free ride on those investments.

Essentially, gatekeepers are expected to aid and subsidize competitors and third parties at little or no cost. This, in turn, diminishes their competitive position and dissipates their resources (and investments) for the benefit of another group.

Part IV concludes. It speculates that DCRs might signal the advent of a new paradigm in political economy: a redrawing of the existing lines and roles between states, markets, and firms, with greater emphasis on the role of the state as the ultimate ordering power of the economy. In hindsight, one expression of this could turn out to be the overturning (if only partial) of the essential principles of modern competition policy: the protection of competition rather than competitors, a policy emphasis on maximizing economic output rather than rent redistribution among firms, and a commitment to merit, rather than fairness and equity. It is difficult to overstate how deeply at loggerheads this conception of the role of competition is from the existing, predominant paradigm long found in competition law.

I. A Cacophony of Goals in Digital Competition Regulation

Most DCRs pursue multiple overlapping objectives. The global picture is even more complex, as there is only partial overlap among the various goals pursued by DCRs in different jurisdictions.

Some DCRs are an extension of competition-law frameworks and are sometimes even formally embedded into existing competition laws. In principle, this means that the standard goals and rationale of competition law apply. Germany, for instance, recently amended its Competition Act, emphasizing the need to “intervene at an early stage in cases where competition is threatened by certain large digital companies.”[8] According to the Bundeskartellamt:

The newly introduced Section 19a probably represents the most important change as the Bundeskartellamt will now be able to intervene at an early stage in cases where competition is threatened by certain large digital companies. As a preventive measure the Bundeskartellamt can prohibit certain types of conduct by companies which, due to their strategic position and their resources, are of paramount significance for competition across markets.[9]

Similarly, Turkey currently is looking to amend the Turkish Competition Act with the objectives of promoting competition and innovation in digital markets; protecting consumer and business rights; and ensuring that gatekeepers do not engage in anticompetitive practices.[10] Proponents argue that the current Turkish Competition Act is not adequately equipped to address anticompetitive conduct in digital markets—such as, e.g., that the process of defining relevant markets is inappropriate for dynamic and global digital ecosystems and that specific regulations are needed due to the network effects that digital platforms confer.[11] These are all nominally competition-related concerns.[12] Other proposed changes to the Turkish Competition Act similarly reflect an increased emphasis on competition. For instance, in merger analysis, the current “dominance test” would be substituted with a “significant impediment to effective competition test,” similar to that in the EU merger-control regime. A “de minimis” rule would also be added to Article 41 to exempt agreements “that do not significantly impede competition.”

Other DCRs appear, at least to some extent, to pursue competition-law-inspired goals, despite not being formally incorporated into existing competition laws. In South Korea, for example, the Korean Fair Trade Commission (“KFTC”) recently proposed a draft DMA-style bill, the Platform Competition Promotion Act,  whose purpose is establish ex-ante rules to restore competition rapidly in designated markets “without the tedious process of defining a relevant market through economic analysis.”[13] According to the KFTC, digital competition regulation is necessary to combat monopolization in digital markets, where monopolies tend to become entrenched.[14]  As some observers have noted,[15] the Platform Competition Promotion Act covers conduct already addressed by South Korea’s existing Monopoly Regulation and Fair Trade Act.[16] Thus, while the draft bill is likely to be passed as a separate piece of legislation, there appears to be a continuum between it and South Korean competition law.

In the United Kingdom, the 2023 Digital Markets, Competition, and Consumer Bill (“DMCC”) is in the final stages of legislative approval.[17] The DMCC aims to “provide for the regulation of competition in digital markets” and, in theory, dovetails with goals pursued by competition law (it even invokes familiar competition-law themes, such as market power).[18] The DMCC would grant the UK antitrust enforcer, the Competition and Markets Authority (“CMA”), power to take “pro-competition interventions” where it has reasonable grounds to believer there may be an adverse effect on competition.[19]

The DMCC has, however, also been touted as a tool to “stamp out unfairness in digital markets.”[20] This could refer to the bill’s consumer-protection provisions, which would prohibit, inter alia, unfair commercial practices.[21] But it may also suggest that the DMCC goes beyond the remit of traditional competition law, in which “unfairness” is generally not central, except within the relatively narrow confines of the abuse-of-dominance provision under S.18 of the Competition Act.[22]

Further, in a press release welcoming the DMCC draft, the CMA enumerated the bill’s benefits as falling into the three categories of “consumer protection,” “competition,” and “digital markets.”[23] The second category grants the CMA increased powers to “identify and stop unlawful anticompetitive conduct more quickly.”[24] The third, however, proposes that the bill will “[enable] all innovating businesses to compete fairly.”[25] This could imply that competition rules in “digital markets” would be governed by different principles than those that apply in “traditional” markets—that is, those that do not involve the purchase or sale of goods over the internet, or the provision of digital content.[26] The DMCC’s provisions on “digital markets” are also formally separate from those on “competition.”[27]

In Australia, the Australian Competition and Consumers Commission (“ACCC”) is conducting a five-year digital-platform-services inquiry (“DPS Inquiry”), set to be finalized in March 2025.[28] The ACCC recommended, as part of the inquiry’s fifth interim report, service-specific obligations (similar to the UK’s proposed ex-ante rules) for “designated” digital platforms.[29] These would serve to address “anticompetitive conduct, unfair treatment of business users and barriers to entry and expansion that prevent effective competition in digital platform markets.”[30] Thus, alongside competition law’s traditional concerns (e.g., harms and benefits to consumers, innovation, efficiency, and “effective competition”), the ACCC would also incorporate concerns over “fairness” and, especially, the protection of business users.

In the United States, several bills have been put forward that are formally separate from existing antitrust law, but cover some of the same conduct as would typically be addressed under U.S. antitrust law—albeit with seemingly different goals and standards. Some of these new goals and standards represent only slight variations on the usual goals of competition law. Three main pieces of legislation have so far been put forward: the American Innovation and Choice Online Act (“AICOA”),[31] the Open App Market Act (“OAMA”),[32] and the Augmenting Compatibility and Competition by Enabling Service Switch Act (“ACCESS Act”)[33] (together, “U.S. tech bills”).

Although the U.S. tech bills largely fail to describe their underlying goals, the titles of the bills and statements made by their sponsors suggest a set of overlapping concerns, such as preventing “material harm to competition,”[34] reducing “gatekeeper power in the app economy,”[35] and “increasing choice, improving quality, and reducing costs for consumers.”[36] These goals appear to fall relatively well within the traditional remit of antitrust law.

But there are others. According to U.S. Sen. Amy Klobuchar (D-Minn.), the primary sponsor or cosponsor of several of the U.S. tech bills, AICOA is intended to “restore competition online by establishing commonsense rules of the road,” “ensure small businesses and entrepreneurs still have the opportunity to succeed in the digital marketplace,” and “create a more even playing field,” all “while also providing consumers with the benefit of greater choice online.”[37] “Fairness,” “fair prices,” and “innovation” all have also been invoked by the bills’ supporters.[38]

At the same time, for three out of the 10 types of challenged conduct, AICOA would require demonstrating “material harm to competition,” which would suggest that one of that bill’s goals is to protect competition. As the American Bar Association’s Antitrust Section has observed, however, there is no “material harm to competition” standard in U.S. antitrust law.[39] This suggests that AICOA may posit a different interpretation of what it means to protect competition, or of what sort of competition should be protected, than does traditional U.S. antitrust law.

OAMA, on the other hand, aims to open competitive avenues for startup apps, third-party app stores, and payment services in existing digital ecosystems.[40] Its title reads: “to promote competition and reduce gatekeeper power in the app economy, increase choice, improve quality, and reduce costs for consumers.” Unlike AICOA, however, OAMA would not require a showing of harm to competition—material or otherwise—to establish liability, which appears to suggest that competition might be less of a concern than the bill’s title implies.

Finally, the ACCESS Act is intended to “promote competition, lower entry barriers and reduce switching costs for consumers and businesses online.”[41] U.S. Sen. Mark Warner (D-Va.), the bill’s primary sponsor, has said that the ACCESS Act will promote competition, allow startups to “compete on equal terms with the biggest social media companies,” and “level the playing field between consumers and companies” by giving them more control over who manages their privacy.[42] Again, these are antitrust-adjacent objectives, but with a flavor (“equal terms,” “level playing field,” etc.) that is largely foreign to U.S. antitrust law.

Other DCRs pursue a mix of competition and noncompetition goals. The South African Competition Commission’s (“SACC”) Final Report on the Online Intermediation Platforms Market Inquiry, for example, found that remedial actions similar to the ex-ante rules contemplated in the DMA and elsewhere are needed to grant “[g]reater visibility and opportunity for smaller South African platforms” to compete with international players; “[e]nabl[e] more intense platform competition,” offer “more choice and innovation”; reduce prices for consumers and business users; “[p]rovid[e] a level playing field for small businesses selling through these platforms, including fairer pricing and opportunities”; and “[p]rovid[e] a more inclusive digital economy” for historically disadvantaged peoples.[43]

In a similar vein, Brazil’s proposed law PL 2768/2022 (“PL 2768”) pursues an expansive grab-bag of social and economic goals.[44] Article 4 states that targeted digital platforms must operate based on the following principles: freedom of initiative, free competition, consumer protection, a reduction in regional and social inequality, combatting the abuse of economic power, and widening social participation in matters of public interest.[45] In addition, PL 2768 also states as objectives that it will enable access to information, knowledge, and culture; foster innovation and mass access to new technologies and access models; promote interoperability among apps; and enable data portability.[46]

Finally, there are those DCRs that claim not to pursue competition-oriented goals at all. The DMA has two stated goals: “fairness” and “contestability,”[47] and explicitly denies being bound by, or even pursuing, the traditional goals of competition law: protecting competition and consumer welfare.[48] According to the DMA, competition, consumer welfare, and efficiency considerations such as those that underpin antitrust law are not relevant under the new framework. This is, according to the DMA’s text, because the goals of competition law and the DMA “are complimentary but ultimately distinct.”[49]

Interestingly, however, few other DCRs have so steadfastly disavowed competition considerations, even those that copy the DMA’s provisions verbatim. India is a case in point. In 2023, a report by the Standing Committee on Finance argued that, if digital competition regulation was not passed, “interconnected digital markets will rapidly demonstrate monopolistic outcomes that prevent fair competition. This will restrict consumer choice, inhibit business users, and prevent the rise of dynamic new companies.”[50] These concerns jibe with traditional antitrust goals, as indicated inter alia by the report’s title (“anti-competitive practices by big tech companies”). Later, another report—the Report of the Committee on Digital Competition Law (“CDC Report”)—proposed a Draft Digital Competition Bill (“DCB”).[51] According to the CDC Report, DMA-style digital competition regulation was needed to supplement the 2002 Indian Competition Act (“ICA”),[52] which—and here is the interesting part—supposedly also aims to promote “fairness and contestability.”[53]

But the ICA’s stated aims were the protection of competition, the interests of consumers, and free trade.[54] The Report of the High-Powered Expert Committee on Competition Law and Policy (“Raghavan Committee Report”),[55] which served as the basis for the ICA, modernized Indian competition law by moving it away from the structure-based paradigm of the earlier Anti-Monopolies and Restrictive Trade Practices Act of 1969 and toward an economic-effects-based analysis. The Raghavan Committee Report was unequivocal in its support of consumer welfare as the system’s ultimate goal.[56] Moreover, the report advised against a plurality of goals, including, specifically, “bureaucratic perceptions”[57] of equity and fairness, which, it argued, were mutually contradictory, difficult to quantify, and potentially opposed to the sustenance of free, unfettered competition.[58] It is therefore curious, to say the least, that the CDC Report would now, in hindsight, recast the ICA’s goals to support essentially the opposite idea.

The multiplicity of goals and their unclear, partially overlapping relationship with competition law raises questions about how we should think about these laws and, indeed, whether we can even think of them as a coherent, unified group. In the next section, we seek to untangle the nature and classification of digital competition regulation.

II. A New Form of Competition Regulation

DCRs are likely best understood as a new form of competition regulation. As some authors have noted, the precise relationship between competition law and the EU’s DMA is difficult to pinpoint.[59] In a similar vein, it is evident that many DCRs incorporate themes and concepts familiar to the competition lawyer, such as barriers to entry, exclusionary conduct, competitive constraints, monopolistic outcomes, and, in some cases, even market power. At first blush, this may suggest a direct relationship between digital competition regulation and competition law. While not entirely incorrect, that assessment comes with considerable caveats.

In this section, we argue that DCRs are a new form of competition regulation that diverges in subtle but definitive ways from mainstream notions of competition law. In essence, DCRs take plausible competition-law themes and alter and subvert them in fundamental ways, creating what could be described as sector-specific[60] or enforcer-friendly[61] competition laws. Due to their blend of competition principles and prescriptive, top-down regulatory provisions, we have opted for the term “digital competition regulation.” To understand their nature, we must start with their underlying assumptions and the ills they claim to address.

A. The DCR Narrative

A starting assumption of all DCRs is that there is an extreme imbalance of power between large digital platforms and virtually every other stakeholder with whom they deal—from other industries to the businesses that operate on digital platforms to their competitors to, finally, end-users.[62] Even governments are often presumed to be virtually powerless in the face of the depredations of so-called “Big Tech.”[63] The adage that “big tech has too much power” has been almost universally endorsed by proponents of DCRs and strong antitrust enforcement;[64] is explicitly or implicitly embedded into those DCRs;[65] and now also permeates popular discourse, media, and entertainment.[66] The corollary is that asymmetric regulation is needed to help those other actors that have been “dispossessed” by big-tech platforms.

This notion is widespread and underpins a range of other policy proposals, not just DCRs. For example, the EU is considering a “Fair Share” regulation that would address the supposed power imbalance between tech companies and telecommunications operators, by forcing the former to pay for the infrastructure of the latter.[67] Similarly, various “bargaining codes” either already have been adopted or are currently under consideration to force tech companies to pay news publishers. In Australia, the Treasury Laws Amendment (News Media and Digital Platforms Mandatory Bargaining Code) Act 2021 (“Bargaining Code”) was put in place to address the supposed bargaining-power imbalance between digital platforms and news-media businesses.[68]  According to the ACCC, digital-advertisement regulation was necessary to support the sustainability of the Australian news-media sector, “which is essential to a well-functioning democracy.”[69] Laws with a similar rationale have also been passed or are under consideration in other jurisdictions.[70]

All these initiatives originate from the same foundational assumption, which is that tech companies are more powerful than anyone else, and are therefore able to get away with imposing draconian conditions unilaterally that allow them to benefit disproportionately at the expense of all other parties, business users, complementors, and consumers. While it is not always easy to identify a coherent thread running through the rules and prohibitions contained in DCRs and other initiatives to regulate “Big Tech,” a good rule of thumb to understand the unifying logic behind these initiatives is that digital platforms should have less “power,” and other stakeholders should have more “power.”

Sometimes—but by no means always—this also encompasses familiar notions of “market power,” i.e., firms’ ability to profitably raise prices because of the absence of sufficient competition. In fact, in most DCRs, “power” stems from the fact that an online platform is an important gateway for business users to reach consumers.[71] This is considered manifestly evident by the platform’s size, turnover, or “strategic” importance.[72] As Bundeskartellamt (the German competition authority) President Andreas Mundt has put it: “we shouldn’t talk about this narrow issue of price, we should talk about power.”[73]

DCRs embody this principle. They seek to extract better deals for the party or parties that are considered to suffer from an imbalance of bargaining power vis-à-vis digital platforms—such as, for instance, through interoperability and data-sharing mandates. As we argue in Section III, these beneficiaries are intended to be the platform’s business users and competitors.

The reasoning is as follows. The asymmetrical power relations between digital platforms and other actors are presumed to lead to unfair outcomes in how these stakeholders are treated and the ways that rents are allocated across the supply chain. As the DMA explains in its preamble:

The combination of those features of gatekeepers is likely to lead, in many cases, to serious imbalances in bargaining power and, consequently, to unfair practices and conditions for business users, as well as for end users of core platform services provided by gatekeepers, to the detriment of prices, quality, fair competition, choice and innovation in the digital sector.[74]

Once it is accepted that power relations between digital platforms and other stakeholders are unfairly skewed, any outcome resulting from the interaction of the two groups must also, by definition, be “unfair.” For example, under the DMA, “unfairness” is broadly defined as “an imbalance between the rights and obligations of business users where the gatekeeper obtains a disproportionate advantage.”[75] A “fair” outcome would be one in which market participants—including, but not limited to, business users—“adequately” capture the benefits from their innovations or other efforts, something the DMA assumes is currently not taking place due to gatekeepers’ superior bargaining power.

In the world of digital competition regulation, “unfairness” is a foregone conclusion. And, sure enough, the concept of “fairness” is the central normative value driving these regulations. Proponents liberally invoke it[76] and it features prominently in DCRs.[77] This narrative, however, is built on premises that differ markedly from those of antitrust law. We discuss these below.

B. Key Differences in First Principles

The DMA is the original blueprint for all digital competition regulation that has followed in its wake. The DMA’s text states that it is distinct from competition law:

This Regulation pursues an objective that is complementary to, but different from that of protecting undistorted competition on any given market, as defined in competition-law terms, which is to ensure that markets where gatekeepers are present are and remain contestable and fair, independently from the actual, potential or presumed effects of the conduct of a given gatekeeper covered by this Regulation on competition on a given market. This Regulation therefore aims to protect a different legal interest from that protected by those rules and it should apply without prejudice to their application.[78]

Other DCRs are rarely so candid about their break with competition law. On the contrary, some are even outwardly couched in competition-based terms. But in the end, DCRs replicate all or most of the prohibitions and obligations pioneered by the DMA.[79] DCRs also apply largely to the same companies as the DMA or, at the very least, use the same thresholds to establish which companies should be subject to regulation.[80]

This leads to a curious “Schrödinger’s DCR” scenario, where the same substantive rules simultaneously are and are not competition law. In the EU, for example, they are not; but in Turkey and Germany, they are. India’s DCB is a verbatim copy of the DMA, yet it is presented as a specific competition law.[81] This apparent contradiction is salvageable only if one thinks of digital competition regulation neither as competition law, strictu sensu, nor as an entirely separate regulation, but rather, as a partially overlapping tool that regulates competition and competition-related conduct in a different—and sometimes fundamentally different—manner.

Consider the example of the EU. EU competition law seeks to protect competition and consumer welfare. The DMA, on the other hand, is guided by the twin goals of “fairness” and “contestability.” As such, under the DMA (as under all other DCRs) the relevant standards are inverted. Under most DCRs, market power—understood as a firm’s ability to raise praises profitably—is either immaterial or not essential to establish whether a firm is a gatekeeper.[82] The competition-law practice of defining relevant markets on a case-by-case basis to determine whether a company has market power is, therefore, likewise moot.[83]

That approach is instead substituted for a list of pre-determined “core platform services,” which are thought to be sufficiently unique that they necessitate special and more stringent regulation.[84] Notably, and unlike in competition law, this presumption admits no evidence to the contrary. Once a good or service is marked as a core platform service, all a company can do to escape digital competition regulation is to argue either that it is not a gatekeeper, or that its services do not fall into the definition of a core platform service.

A corollary of this is that it is typically irrelevant whether a firm is dominant, or even a monopolist. Instead, DCRs apply to companies with high turnover and many business- or end-users—in other words, to “big” companies or companies people currently rely on or like to use.

Lastly, consumer-welfare considerations, which are central under competition law,[85] play only a marginal role in digital competition regulation, both in imposing prohibitions and mandates and in exempting companies from fulfilling those prohibitions or obligations.[86] While DCR supporters applaud this shift toward a broader conception of power,[87] it is important to understand how this approach differs from competition law.[88]

Competition law generally does not engage companies for being big or “important”—even if they are of “paramount importance”—except in very narrow instances, such as those prescribed by the essential-facilities doctrine.[89] Rather, antitrust targets conduct that restricts competition to the ultimate detriment of consumers. To establish whether a company has the ability and incentive to restrict competition, an assessment of market power is typically required, and definitions of relevant product and geographic markets are instrumental to that end.

Even the concept of dominance in competition law eschews crude arithmetic in favor of evidence-based analysis of market power, including the dynamics of the specific market; the extent to which products are differentiated; and shifts in market-share trends over time.[90] As one leading EU competition-law textbook puts it:

The assessment of substantial market power calls for a realistic analysis of the competitive pressure both from within and from outside the relevant market. A finding of a dominant position derives from a combination of several factors which, taken separately, are not necessarily determinative.[91]

Well-established competition-law principles—such as the prevention of free-riding,[92] the protection of competition rather than competitors,[93] and the freedom of even a monopolist to set its own terms and choose with whom it does business[94]—all preclude the imposition of hard-and-fast prohibitions and obligations without a robust case-by-case analysis or consideration of countervailing efficiencies. The narrow exceptions are those few cases where (substantive) experience shows that per-se prohibitions are warranted. But note that even cartels, “the cancers of the market economy,”[95] can generally be exempted under EU competition law.[96]

There exists no such consensus about the harms inflicted by the sort of gatekeeper conduct covered by DCRs.[97] Yet in digital competition regulation, strict (often per-se) prohibitions and obligations based on a company’s size are the norm.

C. The Transformation of Familiar Antitrust Themes

Even those DCRs that explicitly allude to competition-related objectives—such as the protection of competition and consumers—modify those objectives in subtle, but important ways. The U.S. tech bills are a case in point. AICOA would introduce a new “material harm to competition” standard. This facially sounds like it could be an existing standard under U.S. antitrust law, but it is not.[98]

DCRs also combine traditional competition-law objectives with considerations that would not be cognizable under antitrust law. For example, Brazilian competition law is guided by the constitutional principles of free competition, freedom of initiative, the social role of property, consumer protection, and prevention of the abuse of economic power.[99] PL 2768, however, would add two exogenous elements to these relatively mainstream antitrust goals: a reduction in regional and social inequality and increased social participation in matters of public interest.[100]

Other DCRs—like the UK’s or Australia’s prospective efforts to regulate digital platforms—also combine “fairness” goals with consumer welfare and competition considerations.[101] India’s DCB even offers an ex-post rationalization of competition law that brings it in line with the “fairness and contestability” goals of the new digital competition regulation.[102]

It is also questionable whether the protection of consumers and business users under DCRs accords with antitrust notions of “consumer welfare.” It should be noted that competition law, unlike consumer-protection law, protects consumers only indirectly, through the suppression of anticompetitive practices that may affect them through increased prices or decreased quality. Thus, antitrust law is generally uninterested in a company’s deceptive practices, unless they stem directly from a competitive restraint or the misuse of market power.[103] In this scenario, market power acts as a filter to determine where a company’s conduct can be corrected by market forces, and where intervention may be necessary.[104]

By contrast, most DCRs that claim to protect consumers[105] seek to do so through mandates of increased transparency, explicit consent, choice screens, and the like, imposed independently of market power.[106] While some of the focus on consumers remains (at least nominally), the ways in which DCRs protect consumers are more in line with consumer-protection law than competition law.

As for the protection of business users, according to some interpretations, antitrust law protects both consumers and other trading parties (customers).[107] This could, in principle, also include “business users.” Unlike digital competition regulation, however, antitrust law does not generally protect a predetermined group of businesses such that, for example, business users of online platforms would be afforded special protection. Any trading party—regardless of size, industry, or position in the supply chain, and whether a small developer or a large online platform—could theoretically benefit from the protection afforded by antitrust law to those harmed by the misuse of market power.

D. Partial Conclusion: When Failed Antitrust Doctrine Becomes ‘Groundbreaking’ New Regulation

While digital competition regulation’s approach to competition diverges from that of mainstream competition law, and may even be anathema to it, the arguments it espouses are not new. To the contrary, digital competition regulation, in many ways, codifies ideas that have been repeatedly tried and spurned by competition law.

The fountainhead of these ideas is that size alone should be the determining factor for antitrust action and liability.[108] On this historically recurring view—which is championed today most fervently by American “neo-Brandeisians” and European “ordoliberals”—big business inherently harms smaller companies, consumers, and democracy. It is therefore the role of antitrust law to combat this pernicious influence through structural remedies, merger control, and other interventions intended to disperse economic power.[109]

In a similar vein, digital competition regulation targets companies that, a priori, have little in common. Digital competition regulation applies to information-technology firms that specialize in online advertising, such as Google and Meta, but also to electronics companies that focus on hardware, such as Apple.[110] It covers voice assistants and social media, which are vastly different products. Cloud computing, another “core platform service,” is arguably not even a platform; yet, it was included in the DMA at the 11th hour.[111] In the end, what these “gatekeepers” have in common is that they all enjoy significant turnover, large user bases, are disruptors of legacy industries (such as, for example, news media), and are—possibly for these precise reasons—politically convenient targets.[112]

One corollary of this school of thought is that antitrust law should abandon (or, at least, drastically reduce) its reliance on the consumer-welfare standard as the lodestar of competition.[113] The law’s fixation on consumer welfare, the argument goes, has turned a blind eye to rampant economic concentration and to any form of abuse or exploitation that does not result in decreased output or higher prices.[114] Instead of this “myopic” focus on economic efficiency, proponents argue, antitrust law should strive to uphold a pluralistic market structure, which necessarily implies protecting companies from more efficient competitors.[115] This, they claim, was the Sherman Act’s original intent, which was subverted, in time, by the Chicago School’s emphasis on economic efficiency.[116]

Shunning consumer welfare also has implications for the role of market power in antitrust analysis. At the most fundamental level, competition law is concerned with controlling market power.[117] However, on the neo-Brandeisian view, antitrust’s historical concern with delineating efficient and inefficient market exit gives way to the unitary goal of controlling size and maintaining a certain market structure, regardless of companies’ ability to restrict competition and profitably raise prices.[118] This disenfranchises market power or, at the very least, redefines it as synonymous with size and market concentration.[119] This is familiar ground for digital competition regulation, which, as we have seen, generally does not target companies with market power, but companies with a certain size and “economic significance.”

Throughout antitrust law’s storied history, it has often been argued that antitrust law pursues, or should pursue, a plurality of goals and values.[120] Today, these arguments posit that antitrust law must look beyond a “narrow focus” on consumer welfare,[121] which is still enshrined as the dominant paradigm in most jurisdictions. Some of the alternative goals posited to inform the adjudication of competition-law cases include, but are not limited to, democracy, protection of competitors (especially SMEs), pluralism, social participation, combating undue corporate size, and equality. In turn, many of these goals are mentioned in digital competition regulation. In Section III, we argue that wealth redistribution (equality), the protection of competitors, and combatting size are truly shared goals of DCRs.

Digital competition regulation is a bridge between competition law and regulation. That bridge is built on old but persistent ideas that have found limited success in antitrust law and that have largely been precluded by decades of case-law and the progressively mounting exigencies of robust, effects-based economic analysis.[122] It is therefore perhaps unsurprising that digital competition regulation spurns both in favor or new legislation and per-se rules.

Its break with antitrust law, however, is not total, and was arguably never intended to be. Instead, digital competition regulation revises modern competition law to bring it in line with the regulatory philosophy it seeks to resuscitate, selectively plucking those bits and pieces that conform to that vision, and discarding those that do not.

The partial continuity between competition law and digital competition regulation is not merely hypothetical, either. Consider the example of the DMA. According to EU Commissioner of Competition Margrethe Vestager, “the Digital Markets Act is very different to antitrust enforcement under Article 102 TFEU. First, the DMA is not competition law. Its legal basis is Article 114 TFEU. Therefore, it pursues objectives pertaining to the internal market.”[123]

But observe that the DMA covers conduct identical to that which the Commission has pursued under EU competition law. For instance, Google Shopping is a self-preferencing case that would fall under Article 6(5) DMA.[124] Cases AT.40462 and AT.40703, which related to Amazon’s use of nonpublic trader data when competing on Marketplace, and its supposed bias when awarding the “Buy Box,” would now be caught by Articles 6(2) and 6(5) DMA.[125] The fine issued against Apple for its anti-steering provisions, which would be prohibited by Article 5(4) DMA, mere days before the law’s entry into force, is another case in point.[126]

This casts doubt on the assertion that the DMA and EU competition law are two distinctly different regimes. It suggests instead that the DMA is simply a more stringent, targeted, and enforcer-friendly form of competition regulation, intended specifically to cover certain products, certain companies, and certain markets. Or, as some have put it, “the DMA is just antitrust law in disguise.”[127] Indeed, Australia’s ACCC may have said the quiet part out loud when it contended that its proposed DCR would be both a “compliment to, and an expansion of, existing competition rules.”[128]

Or consider the example of India. In India, digital competition regulation would also be implemented though separate legislation. According to a 2023 report of the Standing Committee on Finance, a “Digital Competition Act”[129] is needed to prevent monopolistic outcomes and anticompetitive practices in “digital markets,” which are thought to differ in important ways from “traditional” markets:

India’s competition law must be enhanced so that it can meet the requirements of restraining anti-competitive behaviours in the digital markets. To that end, it is also necessary to strengthen the Competition Commission of India to take on the new responsibilities. India needs to enhance its competition law to address the unique needs of digital markets. Unlike traditional markets, the economic drivers that are rampant in digital markets quickly result in a few massive players dominating vast swathes of the digital ecosystem.[130]

But it seems that, based on the relevant Report of the Standing Committee on Finance, this new regime would be inspired by goals similar to Indian competition law. One important difference is that, according to Indian ministers, the new Digital Competition Act would adopt a “whole government approach.”[131] Pursuant to the  Digital Competition Act, the government would have the power to override any decisions taken by the Competition Commission of India on public-policy grounds. This, again, underscores the “subtle” but significant differences between the competition regimes that would essentially apply in parallel to digital platforms and all other companies, as India’s Competition Act does not otherwise adopt a “whole government approach” to anticompetitive conduct.[132]

A separate question, beyond the scope of this paper, is whether the sui generis logic of digital competition regulation will eventually be transferred to standard competition law. Now that they have the weight of the law—in jurisdictions like Turkey and Germany, even formally incorporated into competition law—ideas that have hitherto remained at the fringes of mainstream competition law may start to be seen as more respectable. Further, the goals of competition law may even be reconfigured, a posteriori, in accordance with the rationale of digital competition regulation.

This possibility cannot be discarded as entirely hypothetical. For example, Andreas Mundt recently remarked that competition law “has always been about fairness and contestability,”[133] thus de facto extrapolating the logic of the DMA’s sector-specific competition regulation to all competition law.

When populist arguments about equality, fairness, and “anti-bigness” previously have reared their head in competition law, they have largely (though not entirely) failed. It is thus somewhat ironic that such ideas should now be spurred by passage of the DMA, a regulation that is—by its own terms—not even a competition law, sensu proprio.

III. The Real Goals of Digital Competition Regulation

Notwithstanding certain differences, DCRs are largely animated by a common narrative and seek to achieve, on the whole, similar goals. At the most basic level, DCRs seek to tip the balance of power away from digital platforms (see Section IIA); to scatter rents, especially toward app developers and complementors; and to make it easier for potential competitors to contest incumbents’ positions. In this context, traditional antitrust conceptions of competition and consumer welfare are afforded, at best, a ceremonial role.

A. Redistributing Rents Among Firms

Despite the apparent discrepancies identified in Section I, it becomes evident on closer examination that DCRs share a common set of assumptions, rationales, and goals. The first of these goals is direct rent redistribution among firms.

The central conceit of DCRs is that asymmetrical power relations between digital platforms and virtually everyone else produce “unfair” outcomes where, in a zero-sum game, “big tech” gets a big slice of the piece of the pie at the expense of every other stakeholder.[134] Thus, DCRs must step in to reallocate rents across the supply chain, so that other actors receive a share of benefits in line with regulators’ understanding of what constitutes a “fair” distributive outcome.

Indeed, as the OECD has noted, the concept of “fairness” is strongly tied to redistribution.[135] As Pablo Ibanez Colomo wrote of the then-draft DMA: “the proposal is crafted to grant substantial leeway to restructure digital markets and re-allocate rents.”[136] This notion is accepted even by DCR proponents, who have admitted that “the regime is not designed to regulate infrastructure monopolies, but rather to create competition as well as to redistribute some rents.”[137]

As to whom should benefit principally from such interventions, the answer varies across jurisdictions, and may depend on the effectiveness of various groups’ rent-seeking efforts, or the particular country’s political priorities.[138] In countries like Korea and South Africa, there has been an explicit emphasis on SMEs, with attempts made to “equalize” their bargaining position vis-à-vis large digital platforms.[139] Other jurisdictions, such as the EU, emphasize competitors (see Section IIIB) and companies that “depend” on the digital platform to do business—such as, e.g., app developers and complementors that “depend” on access to users through iOS; logistics operators that “depend” on Amazon to reach customers; and shops that “depend” on Google for exposure.[140] Granted, these companies may also be SMEs, but they need necessarily not be.[141] In fact, many of the DMA’s expected beneficiaries, including Spotify, Booking.com, Epic, and Yelp,[142] are not small companies at all.[143]

Elsewhere, it is explicitly recognized that DCRs seek to abet the market position of national companies. Prior to the DMA’s adoption, many leading European politicians touted the act’s text as a protectionist industrial-policy tool that would hinder U.S. firms to the benefit of European rivals. As French Minister of the Economy Bruno Le Maire stated:

Digital giants are not just nice companies with whom we need to cooperate, they are rivals, rivals of the states that do not respect our economic rules, which must therefore be regulated…. There is no political sovereignty without technological sovereignty. You cannot claim sovereignty if your 5G networks are Chinese, if your satellites are American, if your launchers are Russian and if all the products are imported from outside.[144]

This logic dovetails neatly with the EU’s broader push for digital and technology sovereignty, a strategy intended to reduce the continent’s dependence on technologies that originate abroad. This strategy has already been institutionalized at different levels of EU digital and industrial policy.[145] In fact, the European Parliament’s 2020 briefing on “Digital Sovereignty for Europe” explicitly anticipated an ex-ante regulatory regime similar to the DMA as a central piece of that puzzle.[146]

The fact that no European companies were designated as gatekeepers lends credence to theories about the DMA’s protectionist origins.[147] But while protectionism is not explicitly embedded in EU law, it likely will be in South Africa’s digital competition regulation. The understanding of “free competition” that underpins the SACC’s DCR proposal hinges on forcing large, foreign digital platforms to elevate local competitors and complementors, even if it means granting them unique advantages.[148] Moreover, unlike other DCRs, SACC’s proposal explicitly notes that its proposed remedies are designed to redistribute wealth from the targeted digital companies or downstream business users toward certain social groups—namely, South African companies, historically disadvantaged peoples (“HDPs”), and SMEs, especially those owned by HDPs.

For instance, to address the “unfair” advantage enjoyed by larger competitors who are displayed more prominently in Google’s search results and are able to invest in search-engine optimization,[149] the SACC would oblige Google to introduce “new platform sites unit (or carousel) to display smaller SA platforms relevant to the search (e.g., travel platforms in a travel search) for free and augment organic search results with a content-rich display.”[150] In addition, Google would be forced to add a South African flag identifier and South African platform filter to “aid consumers to easily identify and support local platforms in competition to global ones.”[151]

The SACC’s proposal is chock full of similar, blatantly redistributive policies that—despite being formally integrated into competition law—flip its logic on its head by requiring distortions of competition in order to (putatively) preserve undistorted competition. Thus, the SACC’s proposal would require gatekeepers to give free credit to South African SMEs; offer promotional rebates; waive fees; provide direct funding for the identification, onboarding, promotion, and growth of SMEs owned by HDPs; force app stores to have a “local curation of apps” aimed at circumventing “automated curation based on sales and downloads for the SA storefronts, and some geo-relevance criteria”; and ban both volume-based discounts that benefit larger companies (relative to SMEs) and promotions that would otherwise “decimate” local competitors.[152]

One reading is that the SACC’s report deviates from the “standard” in digital competition regulation. Another is that the SACC is simply more forthright about accomplishing the goals implicit in the DMA. Indeed, the SACC targets the same types of digital platforms as the DMA, includes many of the same prohibitions and obligations (e.g., self-preferencing, interoperability, cross-use of data, price parity clauses), and openly references the DMA.[153]

In conclusion, despite some distributional differences, the overarching implication of digital competition regulation is generally the same: competitors and business-users (e.g., app store and app developers in the case of Apple’s iOS; sellers and logistics operators in the case of Amazon’s marketplace; competing search and service providers in the case of Google search) should be propped up by gatekeepers. These parties, DCR proponents argue, should get more and easier access to the platforms, feature more prominently therein, be entitled to a larger slice of the transactions facilitated by those platforms,[154] and pay gatekeepers less (or nothing at all).

In some countries, the beneficiaries are intended to be primarily national companies or SMEs. Ultimately, like many other questions surrounding digital competition regulation, the question of cui bono—who benefits?—is not an economic, but a political one, hinging on whatever parties lawmakers want to favor, and at the expense of whatever parties they wish to disfavor.[155] The bottom line, however, goes back to the same, simple idea: gatekeepers should get less, and other businesses should get more.

Consider, for example, the reaction to Apple’s DMA-compliance plan.[156] Most of the backlash concerned the (frustrated) expectations that Apple would, as a result of the obligations imposed by the DMA, take a smaller cut from in-app payments and paid downloads on its platform.[157] If one strips away the rhetoric, the reaction was not about competitive bottlenecks, competition, fairness, contestability, or any other such lofty ambitions, but about the very simple arithmetic of rent seeking, whereby those who invest in lobbying legislators expect a return on their investments.[158]

Or consider the UK’s DMCC. The DMCC includes a “final offer mechanism” that the CMA can use in some cases where a conduct requirement relating to fair and reasonable payment has been breached, and where the CMA considers other powers would not resolve the breach within a reasonable time period.[159] A key aspect of the mechanism is that the two parties to a transaction (at least one of them being a gatekeeper, or a firm with “strategic market status”) submit suggested payment terms for the transaction. The CMA then decides between the two offers, with no option to take a third or intermediate course.

Under the DMCC, however, this mechanism could be applied to any SMS business relationship with third parties. While, as the British government says, this does not involve “direct price setting,”[160] it does mean the CMA would be empowered to decide between two alternative offers and, thus, will determine the distribution of revenues between gatekeepers and, potentially, any third party.[161]

B. Facilitating Competitors and the Duality of Contestability

DCRs share a common aim not just to protect business users, but to benefit competitors directly.[162] In contrast with modern notions of competition law, which readily accept that protecting competition often forces less-efficient competitors to depart the market,[163] DCRs are chiefly concerned with ensuring that even inferior competitors enter or remain on the market. Simply put: if a designated digital platform acts “unfairly,” its actions are illegal. But it is generally—save limited exceptions—irrelevant whether its behavior is efficient or if it enhances consumer welfare. These are the very questions that typically serve to delineate pro-competitive from anti-competitive conduct in the context of competition law (and competition on the merits from anti-competitive conduct).[164]

This makes sense if one recognizes that digital competition regulation and competition law have fundamentally different goals: the former seeks to make it easier for nonincumbent digital platforms to succeed and stay on the market, regardless of the costs either to consumers or to the regulated platforms; the latter seeks to protect competition to the ultimate benefit of consumers, which often implies (and requires) weeding out laggard competitors (see Section II).[165]

As former Federal Trade Commission (“FTC”) Commissioner Maureen Ohlhausen has observed:

Some recent legislative and regulatory proposals appear to be in tension with this basic premise. Rather than focusing on protection of competition itself, they appear to impose requirements on some companies designed specifically to facilitate their competitors, including those competitors that may have fallen behind precisely because they had not made the same investments in technology, innovation or product offerings. For example, the Digital Markets Act (DMA) would force a ‘gatekeeper’ company to provide business users of its service, as well as those who provide complementary services, access to and interoperability with the same operating system, hardware, or software features that are available to or used by the gatekeeper. While this would restrain gatekeepers and presumably facilitate the interests of the gatekeeper’s rivals, it is not clear how this would protect consumers, as opposed to competitors.[166]

That is because the two kinds of legislation pursue mutually exclusive goals. DCRs aim to facilitate competitors by making covered digital markets more “contestable.” The assumption is that, because consumers consistently use certain dominant platforms, “digital markets” must not contestable, or not sufficiently contestable.[167] The putative reason for this low level of contestability allegedly lies in certain advantages that have accrued to incumbent platforms and that competitors purportedly cannot reasonably replicate, such as network effects, data accumulation, and data-driven economies of scale. Consumer cognitive biases and lock-in are asserted as further cementing incumbents’ positions. Because digital markets are also said to be “winner-takes-all,” the corollary is that currently dominant firms will remain dominant unless regulators intercede swiftly and decisively to bolster contestability.

DCRs seek to achieve this state of contestability by “equalizing” the positions of gatekeepers and competitors in two interconnected ways: by diminishing incumbents’ advantages and by forcing them to share some of those advantages with competitors. Making digital markets more contestable therefore requires undercutting the benefits of network effects and advantages enjoyed by “data-rich” incumbents,[168] not because data harvesting is inherently bad or because incumbents have acquired such data illegally or through deceit; but because it makes it hard for other firms to compete. Contestability—understood as other firms’ ability to challenge incumbent digital platforms’ positions—is therefore put forward as a goal in itself, regardless of those challengers’ relative efficiency or what effects the contestability-enhancing obligations have on consumers (see Section IIID).

It is not hard to see how the deontological focus on contestability is narrowly connected to the protection of competitors. Many, if not most, of the obligations and prohibitions in DCRs are best understood as attempts to improve contestability by facilitating competitors, while stifling incumbents. For instance, data-sharing obligations—such as those included in Article 19a of the German Competition Act and Art.6(j) DMA—make it harder for incumbents to accumulate data, while also forcing them to share the data they harvest with competitors. The objective is clearly not to tackle data harvesting because it is noxious, but to disperse users and data across smaller competitors and thereby make it easier for those competitors to stay on the market and contest the incumbents’ position.

Similarly, so-called “self-preferencing” provisions seek to prohibit designated companies from preferencing their own products’ position ahead of that granted to competitors, even if consumers ultimately benefit from such positioning (e.g., because the incumbent’s package is more convenient).[169]

Interoperability obligations likewise require incumbents to make their products and services compatible with those offered by competitors, often with very limited scope for affirmative defenses grounded solely in objective security and privacy considerations. The logic is that interoperability reduces switching costs and allows competitors to attract more easily the previously “locked-in” users.

There are also prohibitions on the use of data generated by a platform’s business users, which essentially ignore the potentially pro-competitive cost reductions and product improvements that may result from the cumulative use of such data. Instead, the goal is to preclude gatekeepers from outperforming—including through more vigorous competition, such as better products or more relevant offers—the third parties who have generated such data on gatekeepers’ platforms.

Ultimately, what all these provisions have in common is that they primarily seek to increase the number of competitors on the market and to enhance their ability to gain market share at the incumbent’s expense, regardless of the effects on the quality of competition, end products, or concerns related to free-riding on incumbents’ legitimate business investments, superior management decisions, or product design (all of which are considerations that would be cognizable under antitrust law—on which, see Section II).[170] “Contestability” in digital competition regulation thus means an erosion, through regulatory means, of incumbents’ competitive advantages, regardless of how those competitive advantages have been achieved.

Digital competition regulation is therefore inherently competitor-oriented, regardless of its stated goals, and this focus is often enshrined in law in other, subtler ways. For instance, the DMCC explicitly invites potential or actual competitors to provide testimony to the CMA before it imposes or revokes a conduct requirement. It requires the CMA to initiate consultations on the imposition or removal of such conduct requirements (S. 24), as well as on “procompetitive interventions” (S. 48).

The proposed ACCESS Act in the United States likewise gives competitors a privileged seat at the table.[171] According to Sec.4(e) of the bill, if a covered platform wishes to make any changes to its interoperability interface, it must ask the FTC for permission. In deciding the question, the FTC is to consult with a “technical committee” formed by, among others, representatives of businesses that utilize or compete with the covered platform.[172] Representatives of the covered platform also would sit on the technical committee, but have no vote.[173]

Importantly, the FTC’s decision in these matters would be dependent on whether competitors’ interests have been harmed—i.e., “that the change is not being made with the purpose or effect of unreasonably denying access or undermining interoperability for competing businesses or potential competing businesses.”[174] This is tantamount to asking competitors for permission to make product-design decisions on a company’s own platform, based on the vested interests of those competitors.

Finally, less than a month after the DMA’s entry into force, the European Commission launched investigations into four gatekeepers for noncompliance. Critical to the Commission’s decision to investigate these companies was feedback received from stakeholders,[175] most of whom are competing firms who hoped to benefit from its provisions.

C. ‘Levelling Down’ Gatekeepers

There are two ways to promote equality: one is to lift up Party A, the other is to drag down Party B.[176] DCRs typically do both, all in service of suppressing the presumably illegitimate levels of gatekeeper power. In the previous subsection, we argued that DCRs facilitate competitors. But it is just as important to note that they also—sometimes concomitantly and sometimes separately—seek to worsen gatekeepers’ competitive position in at least three ways: by imposing costs on gatekeepers that are not borne by competitors, by negating their ability to capitalize on key investments, and by facilitating free riding by third parties on those investments.

For example, prohibitions on the use of nonpublic third-party data benefit competitors, but they also negate the massive investments made by incumbents to harvest that data. They preclude gatekeepers from monetizing the investments made in their platforms by, say, using that data to improve their own products and product lineup in response to new information about users’ changing tastes. This directly undermines gatekeepers’ competitive position, which depends on their ability to improve and adapt their products (see Section IIID). But this is a feature, not a bug, of DCRs. DCRs seek to dissipate gatekeepers’ “power,” where power does not necessarily mean “market power,” but simply their ability to compete effectively. For example, even if allowing gatekeepers to use nonpublic data would improve their products, to consumers’ ultimate benefit, it would also “harm” competitors in the sense that it would make it harder for them to compete with the gatekeeper. In other words, it would not be anticompetitive, but it would be “unfair.” By contrast, in the moral lexicon of digital competition regulation, free riding and effectively expropriating gatekeepers’ investments is not considered “unfair.”

Nor are data-sharing obligations. Data-sharing obligations clearly impose costs on gatekeepers: tracking and sharing data is anything but free. Nonetheless, gatekeepers are expected to aid and subsidize competitors and third parties at little or no cost,[177] thereby diminishing their competitive position and dissipating their resources (and investments) for the benefit of another group.

Similar arguments can be made about the other prohibitions and obligations that form part of the standard DCR package. Sideloading mandates allow third parties to free ride on gatekeepers’ investments in developing popular and functioning operating systems.[178] Insofar as they worsen gatekeepers’ ability to curate content and monitor safety and privacy risks, they also deprecate platforms’ overall quality and integrity, thereby potentially harming even the very companies they seek to aid.[179] Sideloading and interoperability mandates also essentially turn closed platforms into open ones (or, at the very least, they bring the two much closer together), thus forcing closed platforms to forfeit their competitive benefits relative to the primary alternatives.[180]

Antitrust law is unequivocal in its preference for inter-brand over intra-brand competition.[181] But under digital competition regulation, this principle gives way to a de-facto harmonization toward the model preferred by regulators—i.e., the one that makes every successful platform as open and accessible to competitors as possible, regardless of tradeoffs.

For example, self-preferencing prohibitions destroy one of the primary incentives for (and benefits of) vertical integration, which is the ability to prioritize a company’s own upstream or downstream products.[182] Such prohibitions also allow third parties that without the foresight to invest in a platform to accrue the same benefits as those that have. They also limit a platform’s ability to offer goods whose quality and delivery it can more readily guarantee,[183] another bane for competitiveness recast as a desirable symptom of “fairness and contestability.”

Some DCRs are considerably more candid than others about their intent to hamstring gatekeepers. The Turkish E-Commerce Law includes some provisions that differ from the DMA, despite being evidently inspired by it.[184]  Among those provisions are regulations that would not only prevent electronic-commerce intermediary-service providers (“ECISPs”) from gaining significant market power, but also require that those already in a dominant position must lose this power.[185] Moreover:

Another example of atypical regulations envisaged in the E-Commerce Law is the limitations imposed on the advertising and discount budgets of large-scale ECISPs. Under Additional Article 2/3(a), the annual advertising budget of large-scale ECISPs is limited to the sum of 2% of the amount of 45 Billion Turkish Liras of the net transaction volume of the previous calendar year applied to the twelve-month average Consumer Price Index change rate for the same calendar year and 0.03% of the amount above 45 Billion Turkish Liras. This limit constitutes the total advertising budget for all ECISPs within the same economic unit and for all ECSPs operating in the e-commerce marketplace within the same economic unity.[186]

According to Kadir Bas and Kerem Cen Sanli:

The amended E-Commerce Law goes beyond prohibiting gatekeepers’ behavior that restricts fair and effective competition, and introduces provisions that prevent undertakings in the e-commerce sector from gaining market power through organic internal growth without distorting competition or committing any unfair practices. In this context, the E-Commerce Law gradually imposes obligations and restrictions on undertakings based on their transaction volumes, which are not directly related to market power, and some restrictions significantly limiting the ability to compete are imposed on all undertakings in the sector. When those features of the E-Commerce Law are evaluated together, it can be said that the legislator aims to structurally design the competition conditions and business models in the Turkish e-commerce sector.[187]

Bas and Sanli argue that this distinguishes the E-Commerce Directive from the DMA. While it technically true that the DMA does not impose measures that would, e.g., directly limit a firm’s advertising expenditure or tax additional transactions beyond a certain threshold, it does nevertheless “level down” gatekeepers’ ability to compete and grow organically in other ways. On this view, the Turkish E-Commerce Directive takes the DMA’s logic to its natural conclusion and, much like the SACC’s proposal, simply says the quiet part out loud.

Similarly, the UK’s DMCC is designed to foreclose activities that would otherwise bolster gatekeepers’ “strategic significance.”[188] A company with strategic significance is defined as one that fulfills one or several of the following conditions: has achieved significant size or scale; is used by a significant number of other undertakings in carrying out their business; has a position that allows it to determine or substantially influence the ways in which other undertakings conduct themselves; or is in a position to extend its market power to different activities. At least three of these conditions (the first three) can easily result from organic growth or procompetitive behavior. There are many investments and innovations that would, if permitted, benefit consumers—either immediately or over the longer term—but which may enhance a platform’s “strategic significance,” as defined by the DMCC.[189] Indeed, improving a firm’s products and thereby increasing its sales will often naturally lead to increased size or scale.

The inverse is also true: product improvements, innovation, and efficiencies can result from size or scale.[190] This is especially relevant in the context of digital platforms, where a product’s attractiveness often comes precisely from its size and scope. In two-sided markets like digital platforms, product quality often derives from the direct and indirect network effects that result from adding an additional user to the network. In other words, the more consumers use a product or service, the more valuable that product or service becomes to consumers on both sides of the platform.[191] Capping scale and size thus curtails one of the primary (if not the main) spurs of digital platforms’ growth and competitiveness.

Which, of course, arguably was the intent behind DCRs all along. In this context, some DCRs contain provisions that allow enforcers to impose a moratorium on mergers and acquisitions involving a gatekeeper, even where such concentrations would not ordinarily fall within the scope of merger-control rules.[192]

This degree of animosity may seem puzzling.[193] but one’s priors matter quite a bit here. If one accepts, tout court, the dystopian narrative that casts digital platforms as uniquely powerful, unfair, and abusive (see Section IIA),[194] this punitive approach[195] is understandable and, in a sense, even required.

D. Consumers as an Afterthought

DCRs affect wealth transfers from gatekeepers to other firms (see Section IIIA). But DCRs also affect—or, at least, tacitly accept—wealth transfers from consumers to other firms. First, DCRs generally do not require a finding of consumer harm to intercede. Second, DCRs provide limited scope for efficiency defenses. Generally, only defenses rooted in objective privacy and security concerns are allowed,[196] and even these are subject to a high evidentiary burden.[197]

On the other hand, justifications related to product quality, curation, or that otherwise seek to preserve the consumers’ experience are not typically permitted. For example, the quality-of-life improvements that may come from better curation and selection of apps in a closed platform (e.g., one that does not allow for the sideloading of apps or third-party app stores) are not relevant under the DMA, nor is any other dimension of consumer welfare, including price, quality, aesthetics, or curation. The Turkish DCR goes even further than the DMA, in that does not appear to allow for any exemptions (even on the basis of safety and privacy).[198] The SACC’s proposal likewise does not appear to provide scope for affirmative defenses.

In Australia, the DPI states that exemptions should be put in place to mitigate “unintended consequences.” This could, in principle, include consumer-welfare considerations, but the DPI’s explicit reference to the DMA[199] and various public statements by the ACCC suggest that this is unlikely to be the case. The ACCC said in its Fifth Interim Report that “[t]he drafting of obligations should consider any justifiable reasons for the conduct (such as necessary and proportionate privacy or security justifications).”[200]

The narrow and strict exceptions to the above DCRs confirm the downgraded status of consumer welfare in digital competition regulation (vis-à-vis competition law). German Article 19a, for example, allows for exemptions where there is an “objective justification.”[201] But unlike in every other instance under the German Competition Act, Article 19a reverses the burden of proof and requires the gatekeeper, not the Bundeskartellamt, to prove that the prohibited conduct is objectively justified.

In a similar vein, the AICOA bill in the United States would only require that the plaintiff show “material harm to competition” in provisions related to self-preferencing and service discrimination provisions.[202] The remaining provisions do not contain affirmative-effects requirements, but would not apply if the defendant shows a lack of “material harm to competition.” In other words, the burden of proof is shifted from the plaintiff to the defendant — who must prove a negative.[203]

The UK’s DMCC allows for a “countervailing benefits exception,”[204] which would apply when behavior that breaches a conduct requirement is found to provide sufficient other benefits to consumers without making effective competition impossible, and is “indispensable and proportionate” (s. 29(2)(c)) to the achievement of the benefit.[205] Again, this sets a high bar to clear.[206] For example, a limitation on interoperability might provide a benefit to user security and safety. But the exemption would apply only if the CMA were persuaded that this limitation was the only way to achieve such protection, which could be very hard or impossible to demonstrate.

The marginality of consumer welfare as a relevant policy factor is compounded in the UK by the fact that CMA decisions would only be subject to judicial review. Firms will thus be unable to challenge the authority’s factual assessments on questions such as indispensability and proportionality.[207] Even the chance that such a thing could be shown will be of little value to affected firms since the exemption can apply only once an investigation into a breach of a conduct requirement is underway.[208]

Finally, the Brazilian proposal states that costs, benefits, and proportionality should be observed when establishing an obligation under Art.10, [209] although there is no telling what this would mean in practice, or whether it encompasses consumer welfare (Arts. 10 and 11 of PL 2768 do not mention consumer welfare).[210]

The broader question, however, is whether a pro-consumer approach is even compatible with the overarching goals of digital competition regulation. A corollary of facilitating competitors and levelling down gatekeepers is that successful companies and their products are made worse—often at consumers’ expense. For instance, choice screens may facilitate competitors, but at the expense of the user experience, in terms of the time taken to make such choices. Not integrating products might give a leg up to competing services, but consumers might resent the diminished functionality.[211] Interoperability may similarly reduce the benefits an incumbent enjoys from network effects, but users may prefer the improved safety, privacy, and curation that typically comes with closed or semi-closed “walled-garden” ecosystems, like Apple’s iOS.[212]

In sum, proponents of DCRs appear to see losses in consumer welfare as a valid and potentially even desirable tradeoff for competitors’ increased ability to contest the incumbents’ position, as well as for wealth transfers across the supply chain that are seen as inherently just, equitable, and fair.

E. Partial Conclusion: The Perils of Redistributive and Protectionist Competition Regulation

While competition enforcement can affect the allocation of rents among firms, this is generally not the goal of competition policy. The only rent redistribution that is, in principle, relevant in competition law is the one between companies that misuse their market power and consumers (or, in some cases, trading parties). But the overarching goal is to prevent distortions of competition that result in deadweight loss and transfer consumer surplus to the monopolist, not to allocate resources among a set of hand-picked “big” firms and their smaller rivals in way that legislators or regulators consider “fair.” It is the market, not the government, that determines what is “fair.” Competition laws exist to preserve, not to rewrite, that outcome.

Indeed, even some advocates of incorporating political goals into antitrust law, such as Robert Pitofsky, have opposed using the law to protect small businesses and redistribute income to achieve social goals.[213] This is for good reason. Rent redistribution among firms entails significant risks of judicial error and rent seeking. Regulators may require firms to supply their services at inefficiently low prices that are not mutually advantageous, which may in turn diminish those firms’ incentives to invest and innovate.

DCR backers may retort that rent redistribution is the goal of most natural-monopoly regulations (such as those in the telecommunications and energy-distribution industries), which generally rely on both price regulation and access regimes to favor downstream firms and (ultimately) consumers.[214] But digital markets tend to be very different to those traditionally subject to price regulation and access regimes. And even in those industries, price regulation and access regimes raise many difficulties—such as identifying appropriate price/cost ratios and fleshing out the nonprice aspects of the goods/services or regulated firms.

Those difficulties are compounded in the fast-moving digital space, where innovation cycles are faster and yesterday’s prices and other nonprice factors may no longer be relevant today.[215] In short, rent redistribution is difficult to do well in traditional natural-monopoly settings, and may be impossible to do without judicial error in the digital world.

Assuming that such redistribution was to take place, what would a fair redistribution entail? “Fairness” is subjective and, as such, in the eye of the beholder.[216] Moreover, reasonable people may and often do disagree on what is and is not fair. What is “unfair” for the app distributor who pays a commission to use in-app payments may seem “fair” to the owner of the operating system and the app store that makes significant investments to maintain them.[217] Because fairness is such an inherently elusive concept,[218] DCRs ultimately define “fair” and “unfair” by induction—i.e., from the bottom up, in a “you know it when you see it” approach that is difficult to square with any cogent normative theory or limiting principle.[219]

For example, in response to claims that Apple must allow competing in-app purchases (“IAPs”) on its App Store in order to make its 30% IAP fee more competitive (cheaper), Apple could allow independent payment processors to compete, charge an all-in fee of 30% when Apple’s IAP is chosen and, in order to recoup the costs of developing and running its App Store, charge app developers a reduced, mandatory per-transaction fee (on top of developers’ “competitive” payment to a third-party IAP provider) when Apple’s IAP is not used. Indeed, where such a remedy has already been imposed, that is exactly what Apple has done. In the Netherlands, where Apple is required by the Authority for Consumers and Markets (“ACM”) to uncouple distribution and payments for dating apps, Apple adopted the following policy:

Developers of dating apps who want to continue using Apple’s in-app purchase system may do so and no further action is needed…. Consistent with the ACM’s order, dating apps that . . . use a third-party in-app payment provider will pay Apple a commission on transactions. Apple will charge a 27% commission on the price paid by the user, net of value-added taxes. This is a reduced rate that excludes value related to payment processing and related activities.[220]

The company responded similarly to the DMA.[221] It is not hard to see the fundamental problem with this approach. If a 27% commission plus competitive payment-provider fee permits more “competition,” or is fairer, than complete exclusion of third-party providers, then surely a 26% fee would permit even more competition, or be even fairer. And a 25% fee fairer still. Such a hypothetical exercise logically ends only where a self-interested competitor or customer wants it to end, and is virtually impossible to measure.[222]

Even if it were possible, it would entail precisely the kind of price management that antitrust law has long rejected as being at loggerheads with a free market.[223] Without a measurable market failure, what is the frontier of fairness? When does a complaint stop being a competition or gatekeeper issue and become a private dispute about wanting to pay less—or nothing—for a service?[224]

Another obvious problem with facilitating competitors and levelling down gatekeepers is that it discourages investment, innovation, and competition on the merits. Having been encouraged to bring new, innovative products to market and compete for consumers’ business, successful companies—now branded with the “gatekeeper” epithet—are subject to punitive regulation.[225] The benefits that they have legitimately and arduously acquired are dissipated across the supply chain and their competitors, who lacked the foresight and business acumen to make the same or similar investments, are rewarded for their sluggishness.[226] This stifles the mechanisms that propel competition. As Justice Learned Hand observed almost 80 years ago, “the successful competitor, having been urged to compete, must not be turned upon when he wins.”[227] There is no reason why digital competition regulation should be impervious to that logic.

The abrupt shift from competition law to digital competition regulation also sends investors the wrong message by creating commitment issues.

Commitment issues arise where a government commits itself in one period to behaving in certain ways in the future but, when it comes to a future point in time, reneges on the earlier commitment to reflect its preferences at that later point in time.[228]

For example, today’s gatekeepers have made significant investments in data processing, vertical integration, scaling, and building ecosystems. Many of these investments are sunk, meaning that they can no longer be recouped or can be recouped only partially. With the various DCRs’ entry into force, however, gatekeepers can no longer fully utilize those investments. For instance, they cannot self-preference and thereby reap the full benefits of vertical integration;[229] they cannot use third-party data generated on the platforms they have built and in which they have invested; and they must now allow third-parties and competitors to free ride on those investments in a plethora of ways, ranging from allowing sideloading to mandated data sharing (see Section IIIB).

In dynamic contexts, time-inconsistency can obviously affect firms’ actions and decisions, leading to diminished investments.[230] From a less consequentialist and more deontological perspective, however, it is also questionable how “fair” (to use the mot du jour of digital competition regulation) it is to expropriate a company’s sunk-cost investments by abruptly shifting the regulatory goalposts under a new paradigm of competition regulation that essentially subverts the logic of the previous one, and penalizes what was until recently seen as permissible and even desirable conduct (see Section II).

IV. Conclusion: Beyond Digital Competition Regulation

Aldous Huxley once wrote that several excuses are always less convincing than one.[231] His point was that multiple justifications may often conceal the fact that none of them are entirely convincing in their own right. This maxim aptly captures the doubts that persist surrounding DCRs.

On the surface, DCRs pursue a variety of sometimes overlapping, sometimes contradictory, and sometimes disparate goals and objectives (Section I). Some of these goals and objectives hearken back to familiar antitrust themes, but it would be a mistake to treat DCRs as either an appendix to or extension of competition law (Section II). Unlike mainstream competition laws, DCRs address a moral, rather than an economic failure. DCRs emphasize notions of power that are foreign to competition law, essentially promulgating a new form of competition regulation that subverts the logic, rationale, and goals of the existing paradigm.

This approach to regulating competition may be new, but it is not original. To the contrary, the use of antitrust law to castigate concerns seen as “too big and powerful,” promote visions of social justice, and facilitate laggard competitors (even if it comes at the expense of competition, total, or consumer welfare) have been around since the inception of the Sherman Act.[232] In this sense, those who say that digital competition regulation is not competition law, and should therefore not be judged by competition-law standards, [233] are correct on the form but wrong on the substance.[234] They miss the bigger and more important point, which is that—regardless of its legal classification—digital competition regulation is competition regulation, just not the kind we have known for (at least) the last half a century.

The rationale that underpins digital competition regulation can be explained as follows. (Section III). Competition is no longer about consumer-facing efficiency, but about fairness, equality, and inclusivity. In practice, this means improving the lot of some, while “levelling down” others—regardless of the respective merits or demerits of each group (or their products). In this world, “contestability” is not so much the ability to displace an incumbent through competition on the merits, but very much the reverse. It is about lowering the competitive bar to increase the number of companies on the market—full stop. Whether or not this benefits consumers is largely immaterial, as the normative lodestars of digital competition regulation—fairness and contestability—are seen as having inherent and deontological value and thus removed from any utilitarian calculi of countervailing efficiencies (except, arguably, increases in competitors’ market shares).

Ultimately, however, this “new” approach to competition will have to reckon with the same problems and contradictions as the erstwhile antitrust paradigms from which it draws inspiration. The minefield of redistributive policies is likely to hamstring investment and innovation by targeted digital platforms significantly, while simultaneously encouraging rent-seeking behavior by self-interested third parties. Enabling competitors and purposefully harming incumbents also sends the message that equitable outcomes are preferred to excellence, which could encourage even more free riding and rent seeking and further stifle procompetitive conduct.

Finally, the irony is likely not lost on even the most casual observer that, for regulations so obsessed with “fairness,” it is fundamentally unfair for DCRs to syphon rents away from some companies and into others by fiat; and to force those companies to share their hard-earned competitive advantages with others who have not had the foresight or business acumen to make the same investments in a timely fashion.

It is difficult to overstate how big of a departure from competition law this approach to competition regulation is. But digital competition regulation is potentially more than just a digression from established principles in a relatively niche, technical field like competition law. Under the most expansive version of this interpretation, digital competition regulation heralds a new conception of the role and place of companies, markets, and the state in society.

In this “post-neoliberal” world,[235] the role of the state would not be to address market failures that harm consumers through discrete interventions guided by general, abstract, and reactive rules (such as competition law). Instead, it would be to intercede aggressively to redraw markets, redesign products, pick winners, and redistribute rents; indeed, to act as the ultimate ordering power of the economy.[236] It is not difficult to see how “old” competition-law principles, such as the consumer-welfare standard, effects-based analysis, and the procedural safeguards designed to cabin enforcers’ discretion could disrupt this system.

But for now, this remains just a hypothesis, and some would say—perhaps rightly so—an alarmist one. Yet there are unmistakable signs—as unmistakable as social science will allow—that a new paradigm of political philosophy is in the making: from the rehabilitation of once-maligned industrial policy to the rise of neo-Brandeisianism to recurrent proclamations of the “death of neoliberalism”[237] and its “idols,” including the consumer-welfare standard in antitrust law.[238]

Only time will tell if the digital competition regulation is truly sign of things to come, or merely a small but ultimately insignificant and abrupt dirigiste turn in the zig-zagging of antitrust history.[239] And only time will tell whether the approach to competition regulation promulgated by digital competition regulation will stay confined to the activities of a few large concerns and a handful of core platform services, or whether its logic will, in the end, seep into other spheres of policy and social life.

[1] Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on Contestable and Fair Markets in the Digital Sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828, 2022 O.J. (L 265) 1 (hereinafter “Digital Markets Act” or “DMA”).

[2] “Digital competition regulation” or “DCR” will be used throughout to refer both to rules already in place and to rules currently under consideration. Context on legislative status will be given where available and appropriate.

[3] The terms “competition law” and “antitrust law” will be used interchangeably.

[4] See, e.g., Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, COM (2021) 206 final (Apr. 21, 2021).

[5] Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market for Digital Services and Amending Directive 2000/31/EC, 2022, O.J. (L 277) 1 (hereinafter “Digital Services Act” or “DSA”).

[6] Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC, 2016 O.J. (L 119) 1 (hereinafter “General Data Protection Regulation” or “GDPR”).

[7] See, e.g., DMA, supra note 1.

[8] Press Release, Amendment of the German Act Against Restraints of Competition, Bundeskartellamt (Jan. 19, 2021), https://www.bundeskartellamt.de/SharedDocs/Meldung/EN/Pressemitteilungen/2021/19_01_2021_GWB%20Novelle.html.

[9] Id.

[10] The Act on the Protection of Competition No. 4054, Official Gazette (Dec. 13, 1994) (Turk.).

[11] See, E-Pazaryeri Platformari Sektor Incelemesi Nihai Raporu, Turkish Competition Authority (2022), available at https://www.tpf.com.tr/dosyalar/2022/06/e-pazaryeri-si-raporu-pdf.pdf (Turkish language only).

[12] Arguably, however, there is an increased emphasis on “business rights.”

[13] See, KFTC Proposes Ex-Ante Regulation of Platforms Under the “Platform Competition Promotion Act,” Legal 500 (Jan. 4, 2024), https://www.legal500.com/developments/thought-leadership/kftc-proposes-ex-ante-regulation-of-platforms-under-the-platform-competition-promotion-act.

[14] Park So-Jeong & Lee Jung-Soo, S. Korea Speeds Up to Regulate Platform Giants Such as Google or Apple, The Chosun Daily (Feb. 4, 2024), https://www.chosun.com/english/national-en/2024/02/04/MCCJQZTJ3ZC5JJ7NVDM46D6R2I.

[15] Id.

[16] Monopoly Regulation and Fair Trade Act, Act. No, 3320, Dec. 31, 1980, amended by Act No. 18661, Dec. 28, 2021 (S. Kor.).

[17] Digital Markets Competition and Consumer Bill, 2023-24, H.L. Bill (53) (U.K.)  (hereinafter “DMCC”).

[18] See, e.g., id. at Part 1, S. 2, which defines companies with “strategic market status” as those with “substantial and entrenched market power.” By contrast, Recital 5 of the DMA states: “Although Articles 101 and 102 of the Treaty on the Functioning of the European Union (TFEU) apply to the conduct of gatekeepers, the scope of those provisions is limited to certain instances of market power, for example dominance on specific markets and of anti-competitive behaviour, and enforcement occurs ex post and requires an extensive investigation of often very complex facts on a case by case basis. Moreover, existing Union law does not address, or does not address effectively, the challenges to the effective functioning of the internal market posed by the conduct of gatekeepers that are not necessarily dominant in competition-law terms.”

[19] DMCC, supra note 18, at Part 1, Chapter 4.

[20] Press Release, New Bill to Stamp Out Unfair Practices and Promote Competition in Digital Markets, UK Competition and Markets Authority (Apr. 25, 2023), https://www.gov.uk/government/news/new-bill-to-stamp-out-unfair-practices-and-promote-competition-in-digital-markets.

[21] DMCC, supra note 18, at Part 4.

[22] Competition Act 1998 c.41 (U.K.).

[23] See Press Release, supra note 21.

[24] Id.

[25] Id. (emphasis added).

[26] The DMCC defines “digital activities” as those involving the purchase or sale of goods over the internet, or the provision of digital content. DMCC, Part 1, S.3.

[27] The provisions on digital markets are covered in Part 1 of the DMCC. DMCC, Part 2 covers competition.

[28] Digital Platform Services Inquiry 2020-25, Australian Competition and Consumer Commission, https://www.accc.gov.au/inquiries-and-consultations/digital-platform-services-inquiry-2020-25 (last accessed May 13, 2024).

[29] Digital Platform Services Inquiry, Interim Report 5, Australian Competition and Consumer Commission (2022), at 5 (“The ACCC recommends a new regulatory regime to promote competition in digital platform services. The regime would introduce new competition measures for digital platforms.”). The term “digital regime” has also been used to describe the authority granted to the UK’s newly created Digital Markets Unit. See Moritz Godel, Mayumi Louguet, Paula Ramada, & Rhys Williams, Monitoring and Evaluating the Digital Markets Unit (DMU) and New Pro-Competition Regime for Digital Markets, London Economics (Jan. 2023), available at https://assets.publishing.service.gov.uk/media/64538076c33b460012f5e65d/monitoring_and_evaluating_the_new_pro-competition_regime_for_digital_markets.pdf.

[30] Digital Platform Services Inquiry, Interim Report 5, id. at 5.

[31] American Innovation and Choice Online Act, S. 2992, 117^th Cong. (2022), (hereinafter “AICOA”).

[32] Open App Market Act, S. 2710, 117^th Cong. (2022), (hereinafter “OAMA”).

[33] ACCESS Act of 2021, H.R. 3849, 117^th Cong. (2021).

[34] AICOA, § 3.

[35] OAMA.

[36] Id.

[37] Press Release, Klobuchar, Grassley, Colleagues to Introduce Bipartisan Legislation to Rein in Big Tech, U.S. Sen. Amy Klobuchar (Oct. 14, 2021), https://www.klobuchar.senate.gov/public/index.cfm/2021/10/klobuchar-grassley-colleagues-to-introduce-bipartisan-legislation-to-rein-in-big-tech. The bill’s title is somewhat ambiguous, as it reads: “to provide that certain discriminatory conduct by covered platforms shall be unlawful, and for other purposes.” See AICOA, supra note 36.

[38] See id.

[39] Comments of the American Bar Association Antitrust Law Section Regarding the American Innovation and Choice Online Act (S. 2992), American Bar Association Antitrust Law Section (Apr. 27, 2022) at 5, available at https://appliedantitrust.com/00_basic_materials/pending_legislation/Senate_2021/S2992_aba_comments2022_04_27.pdf (hereinafter “ABA Letter”).

[40] Press Release, Klobuchar Statement on Judiciary Passage of Legislation to Set App Store Rules of the Road, U.S. Senator Amy Klobuchar (Feb. 3, 2022), https://www.klobuchar.senate.gov/public/index.cfm/2022/2/klobuchar-statement-on-judiciary-committee-passage-of-legislation-to-set-app-store-rules-of-the-road.

[41] This is stated in the title of the bill. The ACCESS Act also claims to “encourage entry by reducing or eliminating the network effects that limit competition with the covered platform.” See ACCESS ACT at § 6(c).

[42] Press Release, Lawmakers Reintroduce Bipartisan Legislation to Encourage Competition in Social Media, U.S. Sen. Mark R. Warner (May 25, 2022), https://www.warner.senate.gov/public/index.cfm/2022/5/lawmakers-reintroduce-bipartisan-legislation-to-encourage-competition-in-social-media; see also, The ACCESS Act of 2022, U.S. Senator Mark R. Warner, available at https://www.warner.senate.gov/public/_cache/files/9/f/9f5af2f7-de62-4c05-b1dd-82d5618fb843/BA9F3B16A519F296CAEDE9B7EFAB0B7A.access-act-one-pager.pdf.

[43] Online Intermediation Platforms Market Inquiry, Summary of Final Report and Remedial Actions, South African Competition Commission (2023), 13, available at https://www.compcom.co.za/wp-content/uploads/2023/07/CC_OIPMI-Summary-of-Findings-and-Remedial-action.pdf.

[44] Projeto de Lei PL 2768/2022, https://www.camara.leg.br/proposicoesWeb/fichadetramitacao?idProposicao=2337417 (Braz.) (Portuguese language only).

[45] Id. at Art. 4.

[46] Id. at Art. 5.

[47] DMA, supra note 2 at recitals 2, 31. On the two objectives being intertwined, see Recital 34.

[48] Id., at Recital 10.

[49] Id.

[50] Anti-Competitive Practices by Big Tech Companies, Fifty Third Report, Standing Committee on Finance, 17th Lok Sahba (India), (2022-23), available at https://eparlib.nic.in/bitstream/123456789/1464505/1/17_Finance_53.pdf, at 29.

[51] Report of the Committee on Digital Competition Law (India), Annexure IV: Draft Digital Competition Bill (2024), https://www.mca.gov.in/bin/dms/getdocument?mds=gzGtvSkE3zIVhAuBe2pbow%253D%253D&type=open.

[52] The Competition Act, No. 12 of 2003, INDIA CODE (1993).

[53] CDC Report, at 4, 42.

[54] ICA, preamble. The ICA does not mention “contestability.”

[55] Report of the High-Powered Expert Committee on Competition Law and Policy (India) (1999), available at https://theindiancompetitionlaw.wordpress.com/wp-content/uploads/2013/02/report_of_high_level_committee_on_competition_policy_law_svs_raghavan_committee.pdf.

[56] Raghavan Committee Report, at 1.1.9. “The ultimate raison d’être of competition is the interest of the consumer”; see also at 1.2.0.

[57] Raghavan Committee Report, at 2.4.1.

[58] Raghavan Committee Report, at 3.2.8. “If multiple objectives are allowed to rein in the Competition Policy, conflicts and inconsistent results may surface detriment to the consumers… In addition, such concerns as community breakdown, fairness, equity and pluralism cannot be quantified easily or even defined acceptably… it needs to be underscored that attempts to incorporate such concerns may result in inconsistent application and interpretation of Competition Policy, besides dilution of competition principles. The peril is that the competitive process may be undermined, if too many objectives are built into the Competition Policy and too many exemptions/exceptions are laid down in dilution of competition principles.”

[59] See, e.g., Pelle Beems, The DMA in the Broader Regulatory Landscape of the EU: An Institutional Perspective, 19 Eur. Comp. J. 1, 27 (2023); Pierre Larouche & Alexandre De Streel, The European Digital Market: A Revolution Grounded on Traditions, 12 J. Eur. Comp. L. & Practice 542, 542 (2021) (arguing that the DMA’s conceptual nature is in a “difficult epistemological position”).

[60] See Nicolas Petit, The Proposed Digital Markets Act (DMA): A Legal and Policy Review, 12 J. Eur. Competition L. & Practice 529, 530 (2021) (“The DMA is essentially sector-specific competition law.”). The DMA’s competition-law DNA is also explicitly reflected in Section 1.4.1 of the Legislative Financial Statement, which is annexed to the DMA proposal. See id. (“The general objective of this initiative is to ensure the proper functioning of the internal market by promoting effective competition in digital markets.”). See also Beems, supra note 62, at 27 (“In my view, it could be desirable to qualify the DMA as a specific branch of competition law that applies to gatekeepers.”).

[61] See Giuseppe Colangelo, The European Digital Markets Act and Antitrust Enforcement: A Liaison Dangereuse, 5 Eur. L. Rev., 597, 610 (2022) (“In service of this goal of speedier enforcement, the DMA dispenses with economic analysis and the efficiency-oriented consumer welfare test, substituting lower legal standards and evidentiary burdens.”). See also Pablo Iba?n?ez Colomo, The Draft Digital Markets Act: A Legal and Institutional Analysis, 12 J. Eur. Competition L. & Practice 561, 566 (2021).

[62] It should be underscored that “power” here means something much broader and general than the narrow concept of “market power” under competition law. Unlike “market power,” assertions that so-called “Big Tech” wield “power” are not intended to invoke a state-of-the art term, but rather are general references to companies’ size, resources, and capacity. Neo-Brandeisians like Lina Khan and Tim Wu often refer to the “power” of Big Tech in such terms. See generally Tim Wu, The Curse of Bigness: Antitrust in the Gilded Age (2018). For Wu, like Khan, the harmful “power” of Big Tech refers not just to concentrated economic power or market power, but to a range of other mechanisms by which these firms allegedly hold sway over democracy, elections, and society at-large. See also Zephyr Teachout & Lina Khan, Market Structure and Political Law: A Taxonomy of Power, 9 Duke J. Const. L. & Pub. Pol’y 37,74 (2014).

[63] See, e.g., Joshua Q. Nelson, Joe Concha: “Big Tech is More Powerful than Government” in Terms of Speech, Fox News (Jan. 27 2021), https://www.foxnews.com/media/joe-concha-big-tech-more-powerful-government-speech; How 5 Tech Giants Have Become More like Governments than Companies, Fresh Air (Oct. 26, 2017), https://www.npr.org/2017/10/26/560136311/how-5-tech-giants-have-become-more-like-governments-than-companies (“New York Times tech columnist Farhad Manjoo warns that the ‘frightful five’—Amazon, Google, Apple, Microsoft and Facebook—are collectively more powerful than many governments.”).

[64] See, e.g., Press Release, Klobuchar, Grassley Statements on Judiciary Committee Passage of First Major Technology Bill on Competition to Advance to Senate Floor Since the Dawn of the Internet, U.S. Sen. Amy Klobuchar (Jan. 20, 2022), https://www.klobuchar.senate.gov/public/index.cfm/2022/1/klobuchar-grassley-statements-on-judiciary-committee-passage-of-first-major-technology-bill-on-competition-to-advance-to-senate-floor-since-the-dawn-of-the-internet (“Everyone acknowledges the problems posed by dominant online platforms.”).

[65] See, e.g., DMA recitals 3, 4, 33, and 62.

[66] See, e.g., The Social Dilemma (Exposure Labs, Argent Pictures & The Space Program, 2020); Tech Monopolies: Last Week Tonight with John Oliver (HBO, 2022); Yanis Varoufakis, Technofeudalism: What Killed Capitalism (2023); Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (2019).

[67] See Luca Bertuzzi, EU Commission Launches Connectivity Package with ‘Fair Share‘ Consultation, EurActiv (Feb. 28, 2023), https://www.euractiv.com/section/digital/news/eu-commission-launches-connectivity-package-with-fair-share-consultation; see also Daniele Condorelli, Jorge Padilla, & Zita Vasas, Another Look at the Debate on the “Fair Share” Proposal: An Economic Viewpoint, Compass Lexecon (2023), available at https://www.telefonica.com/en/wp-content/uploads/sites/5/2023/05/Compass-Lexecon-Report-on-the-fair-share-debate.pdf. On the supposed bargaining-power imbalance between large traffic originators and telecommunications companies, see id. at point 1.34(d). “There is a risk that the current unregulated arrangements result in no payments from LTOs due to asymmetries of information between industry participants, free-riding among LTOs, and the large imbalance in bargaining power between LTOs and TELCOs.” See also id. at points 3.77, 3.78 and 3.79-3.84 for the argument that the power imbalances require intervention. For a different view of “fair share,” see Giuseppe Colangelo, Fair Share of Network Costs and Regulatory Myopia: Learning from Net Neutrality Mistakes, Int’l. Ctr. for Law & Econ. (Jul. 18, 2023) (forthcoming in Law, Innovation and Technology), available at https://ssrn.com/abstract=4452280.

[68] See Treasury Laws Amendment (News Media and Digital Platforms Mandatory Bargaining Code) Act 2021 (Austl.); for a defense of legislation forcing digital platforms to compensate media companies, see Zephyr Teachout, The Big Unfriendly Tech Giants, The Nation (Dec. 25, 2023), https://www.thenation.com/article/society/big-tech-nondiscrimination.

[69] News Media Bargaining Code, Australian Competition and Consumer Commission, https://www.accc.gov.au/focus-areas/digital-platforms/news-media-bargaining-code/news-media-bargaining-code (last accessed May 14, 2024).

[70] See, e.g., Journalism and Competition Preservation Act of 2023, S. 1094, 118th Cong. (2023); Online News Act (S.C. 2023, c.23) (Can.).

[71] See, e.g., DMA at recitals 1, 15, 20, 62, and Art.1(b); DMCC at s.6(b); PL 2768 at Art. 2, which defines the regulation’s targets as companies with the “power to control essential access”; Competition Act in the version published on 26 June 2013 (Bundesgesetzblatt (Federal Law Gazette) I, 2013, p. 1750, 3245), as last amended by Article 1 of the Act of 25 October 2023 (Federal Law Gazette I, p. 294), Art.19(a) (1)5 (Ger.) (hereinafter “German Competition Act”).

[72] See, e.g., DMA at Recital 23, Art.3 and Art.3(8)(a); DMCC at s.6(a); German Competition Act, Art.19(a); but see DSA, Section 5, which imposes special obligations on “very large online platforms.”

[73] “From Price to Power”? Reorienting Antitrust for the New Political Economy, panel at Antitrust, Regulation and the Next World Order conference, Youtube.com (Feb. 2, 2024), https://www.youtube.com/watch?v=rWNIhGA8Rx8&ab_channel=Antitrust%2CRegulationandtheNextWorldOrder.

[74] DMA, at recital 4 (emphasis added).

[75] DMA, at recital 33.

[76] See Press Release, Digital Markets Act: Commission Welcomes Political Agreement on Rules to Ensure Fair and Open Digital Markets, European Commission (Mar. 25, 2022), https://ec.europa.eu/commission/presscorner/detail/en/ip_22_1978 (“What we want is simple: Fair markets also in digital. We are now taking a huge step forward to get there—that markets are fair, open and contestable…. This regulation, together with strong competition law enforcement, will bring fairer conditions to consumers and businesses for many digital services across the EU.”) (emphasis added); see also Press Release, Klobuchar, Grassley, Colleagues to Introduce Bipartisan Legislation to Rein in Big Tech, U.S. Sen. Amy Klobuchar (Oct. 14, 2021), https://www.klobuchar.senate.gov/public/index.cfm/2021/10/klobuchar-grassley-colleagues-to-introduce-bipartisan-legislation-to-rein-in-big-tech (joint statement by Sens. Amy Klobuchar and Chuck Grassley with references to “fair competition,” “fair prices,” “unfairly preferencing their own products,” “fairer prices,” “unfairly limiting consumer choices,” “fair rules for the road”).

[77] For example, the DMA mentions the term “fairness,” or some variation thereof, 90 times in 66 pages.

[78] DMA, at Recital 11 (emphasis added).

[79] See DMA, Arts. 5-7.

[80] See DMA, Art. 3.

[81] CDC Report, at 2.

[82] See, e.g., German Competition Act, at Section 19a(1), stating that, in determining the paramount significance for competition across an undertaking’s markets, there shall be particular account taken of its dominant position; financial strength or access to other resources; vertical integration; access to data relevant to competition; and the relevance of its activities for third-party access to supply and sales markets. See also DMCC, at S. 5 and S.6 (substantial and entrenched market power is a cumulative criterion, together with a position of strategic significance); DMA, at Recital 5 and Art. 3 (market power is irrelevant because the criteria for designation are (a) having a significant impact on the internal market; (b) providing a core platform service that is an important gateway for business users to reach end users; and (c) enjoying an entrenched and durable position). PL 2768 does not mention market power, and instead references control of essential access; the U.S. tech bills do not define covered platforms on the basis of market power either.

[83] The DMA explicitly rejects it. See Recital 23.

[84] Examples include online-intermediation services, online search engines, online social-networking services, and video-sharing platform services. See DMA, at Art. 2.

[85] See Elise Dorsey, Geoffrey A. Manne, Jan M. Rybnicek, Kristian Stout, and Joshua D. Wright, Consumer Welfare & the Rule of Law: The Case Against the New Populist Antitrust Movement, 47 Pepp. L. Rev. 861, 916 (2020).

[86] There are some exceptions to this. Some digital competition regulations seem to incorporate consumer-welfare considerations. One example is the KFTC’s recently proposed digital competition regulation, which is putatively aimed at protecting business users and consumers, and would allow for an efficiency defense. See Lee & Ko, supra note 21.

[87] See supra note 76.

[88] See infra Sections II.C and II.D.

[89] On the essential-facilities doctrine in the United States, see Philip K. Areeda, Essential Facilities: An Epithet in Need of Limiting Principles, 58 Antitrust L.J. 841 (1990); ever since the Supreme Court’s ruling in Trinko, no plaintiff has successfully litigated an essential-facilities claim to judgment. See, Verizon Communications, Inc. v. Law Offices of Curtis V. Trinko, LLP, 540 U.S. 398 (2003) (“As a general matter, the Sherman Act “does not restrict the long recognized right of [a] trader or manufacturer engaged in an entirely private business, freely to exercise his own independent discretion as to parties with whom he will deal.’”) (citations omitted).

[90] Communication from the Commission — Guidance on the Commission’s enforcement priorities in applying Article 82 of the EC Treaty to abusive exclusionary conduct by dominant undertakings (2009), at recital 13.

[91] Richard Whish & David Bailey, Competition Law (10^th ed. 2021), at 142-3.

[92] See, e.g., Christopher M. Seelen, The Essential Facilities Doctrine: What does it Mean to be Essential? 80 Marq. L. Rev. 1117, 1123 (1997), discussing free-riding and the moral-hazard considerations implicit in defining essential facilities as essential to a competitor, rather than to competition. (“[A]pplication of the doctrine often focuses unduly on the effect of the denial of access on the plaintiff’s ability to compete-not on the infringement of competition which is the objective of the antitrust law.” (citations omitted), and at 1124 (“There exists a moral hazard when plaintiffs bring an essential facility claim against a single competitor. Indeed, firms might try to use the doctrine to take a ‘free ride’ on the efforts of a competitor.”). See also, Verband Deutscher Wetterdienstleister v. Google, Reference No. 408 HKO 36/13, Court of Hamburg (Apr. 4, 2013), 4, available at http://deutschland.taylorwessing.com/documents/get/150/court-order-googleweatherinbox-english-unofficial-translation.pdf (“[A]pplicant’s members have been participating and will continue to participate in Google Search as ‘free riders.’ They demand favorable positioning without offering compensation.”); Continental T.V., Inc. v. GTE Sylvania Inc., 433 U.S. 36 (1977) (applying the rule of reason to territorial restrictions because they might be imposed by a manufacturer who wishes to prevent dealers from free-riding on point-of-sale services provided by another dealer).

[93] See, e.g., Brown Shoe Co. v. United States, 370 U.S. 294, 344 (1962) (“It is competition, not competitors, which the Act protects.”). See also Donna E. Patterson and Carl Shapiro, Transatlantic Divergence in GE/Honeywell: Causes and Lessons, Antitrust 18 (2001); Maureen K. Ohlhausen & John M. Taladay, Are Competition Officials Abandoning Competition Principles?, 13 J. Comp. L. & Practice 463 (2022).

[94] See, e.g., Trinko at 408; Pac. Bell Tel. Co. v. Linkline Commc’ns, Inc., 555 U.S. 438, 448 (2009); Chavez v. Whirlpool Corp., 113 Cal. Rptr. 2d, 182-83 (Ct. App. 2001); Foremost Pro Color, Inc. v. Eastman Kodak Co., 703 F.2d 534, 545 (9th Cir. 1983) (“The antitrust laws [do] not impose a duty on [firms] . . . to assist [competitors] . . . to ‘survive or expand.’”) (citations omitted).

[95] Mario Monti, Speech at the Third Nordic Competition Policy Conference, Stockholm: Fighting Cartels Why and How? Why Should We be Concerned with Cartels and Collusive Behaviour? (Sept. 11, 2000); see also Trinko at 408 (characterizing cartels as “the supreme evil of antitrust”).

[96] Although there is a rebuttable presumption to the contrary, undertakings can argue that agreements containing hardcore restrictions should benefit from an individual exemption under Article 101(3) TFEU. See Judgment of 13 October 2011, Pierre Fabre, C?439/09, ECLI:EU:C:2011:649. Moreover, “hardcore restrictions,” like cartels, need to be restrictions of competition “by object,” within the meaning of Art. 101(1) TFEU. Undertakings can hence try to demonstrate that a given hardcore restriction, examined in its economic and legal context, is objectively justified and does not fall within the prohibition laid down in Article 101(1) TFEU. See Opinion of Advocate General Wahl delivered on 16 July 2017, Coty, C-230/16, ECLI:EU:C:2017:603.

[97] For an extensive set of views opposing those endorsed by proponents of digital competition regulations, see, e.g., The Global Antitrust Institute Report on the Digital Economy (Joshua D. Wright & Douglas H. Ginsburg, eds., Nov. 11, 2020), https://gaidigitalreport.com.

[98] See ABA letter, supra note 41.

[99] Law No. 12.529 of 30 November, 2011 (Braz.), available at https://www.icao.int/sustainability/Documents/Compendium_FairCompetition/LACAC/LAW_12529-2011_en.pdf.

[100] PL 2768, art. 4.

[101] See Section I.

[102] See Section I.

[103] See, e.g., Rambus v. Fed. Trade Comm’n, 522 F.3d 456, 459 (D.C. Cir. 2008) (“[D]eceit merely enabling a monopolist to charge higher prices than it otherwise could have charged—would not in itself constitute monopolization.”). See also Judgment of 4 August 2023, Meta Platforms v. Bundeskartellamt, Case C 252-21, ECLI:EU:C:2023:537.

[104] For example, where a small company increases prices or downgrades its product, this can generally be corrected through competition, as the company will lose market share and be forced out of the market unless it changes its behavior. But when the same outcome is achieved through restrictions of competition or the misuse of market power, the market may be unable to respond effectively, and intervention may become necessary.

[105] We question whether this was ever the true intent behind digital competition regulation, see Section IIII.C.

[106] See also Section IIB.

[107] See, e.g., Svend Albaek, Consumer Welfare in EU Competition Policy, in Aims and Values in Competition Law, 67, 75 (Caroline Heide-Jørgensen, Ulla Neergaard, Christian Bergqvist, & Sune T. Poulsen eds., 2013) (“In practice it turns out that we should understand ‘consumers’ as customers rather than ‘real’ or ‘final’ consumers. Paragraph 84 of the General Guidelines takes a first step towards clarifying this: ‘[C]onsumers within the meaning of Article 81(3) are the customers of the parties to the agreement and subsequent purchasers.”); see also Article 102 (c) TFEU, which prohibits dominant companies from “applying dissimilar conditions to equivalent transactions with other trading parties, thereby placing them at a competitive disadvantage” (emphasis added). For a U.S. perspective, see, e.g., Kenneth Heyer, Welfare Standards and Merger Analysis: Why Not the Best? 2 Comp. Pol’y Int’l 29 (2006).

[108] In the United States, the clearest exponent of these ideas was Justice Louis D. Brandeis, who coined the term “curse of bigness” to refer to the material, social, and political ills that accompanied large corporations. See, e.g., Louis D. Brandeis, The Curse of Bigness: Miscellaneous Papers of Louis D. Brandeis (Osmond K. Fraenkel ed., 1934); in Europe, the notion is associated with the ordoliberal school. See, e.g., Wilhelm Roepke, A Humane Economy: The Social Framework of the Free Market (2014) at 32 (“If we want to name a common denominator for the social disease of our times, then it is concentration”).

[109] See, e.g. Wu, 2018 supra note 65; Sally Lee, Tim Wu Explains How Big Tech is Crippling Democracy, Columbia Magazine (Spring 2019) https://magazine.columbia.edu/article/how-mega-corporations-are-crippling-democracy. Asked whether bigness must be bad by its very nature, Tim Wu replies: “well, it’s designed to put its own interests over human interests, to grow like a cancer, and to never die. I once heard someone say that if a corporation were a person, it would be a sociopath. Which brings us to the real question: who is this country for? For humans or these artificial entities?”; See also Khan & Teachout, 2014, supra note 65, at 37. “Ever-increasing corporate size and concentration undercut democratic self-governance by disproportionately influencing governmental actors, as recognized by campaign finance reformers.”; and at 40-1. “Antitrust means, for us, government power to limit company size and concentration; this incarnation is an ethos, not a legal term.”

[110] See, e.g., Amanda Lotz, “Big Tech” Isn’t One Big Monopoly — It’s 5 Companies All in Different Businesses, The Conversation (Mar. 23, 2018), https://theconversation.com/big-tech-isnt-one-big-monopoly-its-5-companies-all-in-different-businesses-92791; Isobel A. Hamilton, Tim Cook Says He‘s Tired of Big Tech Being Painted as a Monolithic Force That Needs Tearing Apart, Business Insider (May 7, 2019), https://www.businessinsider.com/apple-ceo-tim-cook-tired-of-big-tech-being-viewed-as-monolithic-2019-5. (“Tech is not monolithic. That would be like saying ‘all restaurants are the same,’ or ‘all TV networks are the same.’”) See also Nicolas Petit, Big tech and the Digital Economy: The Moligopoly Scenario (2022); Frank H. Easterbrook, Cyberspace and the Law of the Horse, 1996 U. Chi. Leg. Forum 207 (1996).

[111] See Friso Bostoen, Understanding the Digital Markets Act, 68 Antitrust Bull. 263, 282 (2023) (“It is difficult to find a common thread here. For starters, NIICS and cloud services are one-sided rather than multisided, so they can hardly be core platform services”).

[112] See Lazar Radic, Gatekeeping, the DMA, and the Future of Competition Regulation, Truth on the Market (Nov. 8, 2023), https://truthonthemarket.com/2023/11/08/gatekeeping-the-dma-and-the-future-of-competition-regulation. On tech disruption of traditional industries, see Adam Hayes, 20 Industries Threatened by Tech Disruption, Investopedia (Jan. 23, 2022), https://www.investopedia.com/articles/investing/020615/20-industries-threatened-tech-disruption.asp; on the bipartisan hostility toward “Big Tech” in the United States, see Nitasha Tiku, How Big Tech Became a Bipartisan Whipping Boy, Wired (Oct. 23, 2017), https://www.wired.com/story/how-big-tech-became-a-bipartisan-whipping-boy.

[113] See, e.g., Lina Khan, Amazon’s Antitrust Paradox, 126 Yale L.J. 710 (2017); Zephyr Teachout & Lina Khan, Market Power and Political Law: A Taxonomy of Power, 9 Duke J. Const. L. & Pub. Pol’y 37 (2014); Kirk Ott, Event Notes: The Consumer Welfare Standard is Dead, Long Live the Standard, ProMarket (Nov.1, 2022), https://www.promarket.org/2022/11/01/event-notes-the-consumer-welfare-standard-is-dead-long-live-the-standard; Zephyr Teachout, The Death of the Consumer Welfare Standard, ProMarket (Nov. 7, 2023), https://www.promarket.org/2023/11/07/zephyr-teachout-the-death-of-the-consumer-welfare-standard.

[114] See, e.g., Rana Foohar, The Great US-Europe Antitrust Divide, Financial Times (Feb. 5, 2024), https://www.ft.com/content/065a2f93-dc1e-410c-ba9d-73c930cedc14.

[115] Neo-Brandeisians often argue that antitrust law should strive to uphold a dispersed market structure and protect small business. See, e.g., Lina Khan & Sandeep Vaheesan. Market Power and Inequality: The Antitrust Counterrevolution and its discontents, 11 Harv. L. & Pol’y Rev. 235 (2017), at 237. “Antitrust laws must be reoriented away from the current efficiency focus toward a broader understanding that aims to protect consumers and small suppliers from the market power of large sellers and buyers, maintain the openness of markets, and disperse economic and political power.”

[116] See Khan & Vaheesan, supra note 122 at 236-7 (2017). “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”; and, at 294, “It is important to trace contemporary antitrust enforcement and the philosophy underpinning it to the Chicago School intellectual revolution of the 1970s and 1980s, codified into policy by President Reagan. By collapsing a multitude of goals into the pursuit of narrow ‘economic efficiency,’ both scholars and practitioners ushered in standards and analyses that have heavily tilted the field in favor of defendants.”

[117] See, e.g., Nicolas Petit, Understanding Market Power (Robert Schuman Centre for Advanced Studies Working Paper No. RSC 14, 1, 2022) (“Antitrust laws are concerned with undue market power. In an economic conception of the law, antitrust rules of liability strike down anticompetitive business conduct or mergers that represent illegitimate market power strategies.”).

[118] On inefficient and efficient market exit, see Dirk Auer & Lazar Radic, The Growing Legacy of Intel, 14 J. Comp. L. & Prac. 15 (2023).

[119] According to some, the interpretation of market power as synonymous with size and concentration is the European reading of the concept. See Petit, supra note 124, at 1 (“When European antitrust lawyers think about market power, they do not direct their attention to consumer prices. They think about corporate size and industrial concentration, see giant American firms, and deduce that they have a domestic market power problem.”).

[120] See, e.g., Or Brook, Non-Competition Interests in EU Antitrust Law: An Empirical Study of Article 101 TFEU (1^st ed. 2023), discussing the different goals and values of EU competition law throughout the years; Konstantinos Stylianou & Marcos Iacovides, The Goals of EU Competition Law: A Comprehensive Empirical Investigation, 42 Legal Studies 1, 17-8 (2020). “EU competition law is not monothematic but pursues a multitude of goals historically and today;” In the United States, see Robert H. Bork, The Antitrust Paradox: A Policy at War with Itself, 7 (1978) (finding the collection of socio-political goals at the time to be “mutually incompatible”); Joshua D. Wright & Douglas H. Ginsburg, The Goals of Antitrust: Welfare Trumps Choice, 81 Fordham L Rev 2405, 2405 (2013). “The Court interpreted the Sherman and Clayton Acts to reflect a hodgepodge of social and political goals…”; Thomas A. Lambert & Tate Cooper, Neo-Brandeisianism’s Democracy Paradox, University, 49 Journal of Corporation Law, 18 (2023).“In the mid-Twentieth Century, U.S. courts embraced the sort of multi-goaled deconcentration agenda Neo-Brandeisians advocate;” and Joshua D. Wright, Elyse Dorsey, Jonathan Klick, & Jan M. Rybnicek, Requiem for a Paradox: The Dubious Rise and Inevitable Fall of Hipster Antitrust, 51 Ariz. St. L. J. 293, 300-1 (2019) (discussing multi-goaled approach of mid-20^th-century antitrust).

[121] See, e.g., Ioannis Lianos, Polycentric Competition Law, 71 Current Legal Problems 161 (2019); Maurice E. Stucke, Reconsidering Antitrust’s Goals, 53 B.C.L. Rev. 551, 551 (2012), “[t]he quest for a single economic goal has failed…this article proposes how to integrate antitrust’s multiple policy objectives into the legal framework.”; The Consumer Welfare Standard in Antitrust: Outdated or a Harbor in a Sea of Doubt?: Hearing Before the Subcomm. on Antitrust, Competition and Consumer Rights of the S. Comm. on the Judiciary, 115th Cong. (2017) (statement of Barry Lynn), arguing for the return to a “political antitrust”; Dina I. Walked, Antitrust as Public Interest Law: Redistribution, Equity, and Social Justice, 65 Antitrust Bull 87, 87 (2020), “[o]nce we frame antitrust as public interest law, in its broadest sense, we are empowered to use it to address inequality;” Saksham Malik, Social Justice as a Goal of Competition Policy, Kluwer Competition Law Blog (Feb. 23, 2024), https://competitionlawblog.kluwercompetitionlaw.com/2024/02/23/social-justice-as-a-goal-of-competition-policy.

[122] It is no coincidence that critics of the “status quo” consistently attempt to cast economic analysis and (certain) antitrust case-law as a mistake brought about by judges adhering to the ideology of “neoliberalism,” rather than as the result of organic, piecemeal progression. See Khan & Vaheesan, supra note 122.

[123] Magrethe Vestager, Keynote of EVP Vestager at the European Competition Law Tuesdays: A Principles Based Approach to Competition Policy (Oct. 25, 2022), https://ec.europa.eu/commission/presscorner/detail/en/SPEECH_22_6393; See also Ohlhausen & Taladay, supra note 70 at 465.

[124] See, supra note 8.

[125] See also Press Release, Antitrust: Commission Accepts Commitments by Amazon Barring It from Using Marketplace Seller Data and Ensuring Equal Access to BuyBox and Prime, European Commission (Dec. 20, 2022), https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7777.

[126] For commentary, see Lazar Radic, Apple Fined at the 11^th Hour Before DMA Enters into Force, Truth on the Market (Mar. 5 2024), https://truthonthemarket.com/2024/03/05/apple-fined-at-the-11th-hour-before-the-dma-enters-into-force.

[127] Giuseppe Colangelo (@GiuColangelo), Twitter (Oct. 5, 2023, 2:37 PM), https://x.com/GiuColangelo/status/1709910565496172793?s=20.

[128] Digital Platform Services Inquiry, supra note 18 at 14.

[129] Standing Committee on Finance, supra note 22, at 28, 38-39.

[130] Id. at 30.

[131] Shivi Gupta & Mansi Raghav, Digital Competition Law Committee to Finalise Report by August 2023, Lexology (Jul. 31, 2023), https://www.lexology.com/library/detail.aspx?g=70b95f94-1ee2-4b11-bfc2-96155a8c333d; Whole Government Approach to be Adopted for Digital Competition Laws, The Economic Times (Jul. 4 2023), https://economictimes.indiatimes.com/tech/technology/whole-government-approach-to-be-adopted-for-digital-competition-laws/articleshow/101495358.cms.

[132] The Competition Act, 2002, No.12 of 2003 (India), available at https://www.cci.gov.in/images/legalframeworkact/en/the-competition-act-20021652103427.pdf.

[133] Antitrust, Regulation, and the Next World Order, supra note 53.

[134] See, e.g., the DMA’s definition of “fairness.” DMA, Recital 4.

[135] Ex Ante Regulation in Digital Markets – Background Note, DAF/COMP(2021)15, 16, OECD (Dec. 1, 2021) (“Framing regulations in terms of fairness may therefore also refer to redistribution, better treatment of users, or a host of other goals”). See also id. at 19.

[136] Pablo Ibanez Colomo, The Draft Digital Markets Act: A Legal and Institutional Analysis, 12 Journal of Competition Law & Practice 561, 562 (2021). See also id. at 565(“The driver of many disputes that may superficially be seen as relating to leveraging can be more rationalised, more convincingly, as attempts to re-allocate rents away from vertically-integrated incumbents to rivals”).

[137] See, e.g., Fiona Scott Morton & Cristina Caffarra, The European Commission Digital Markets Act: A Translation, VoxEU (Jan. 5, 021), https://cepr.org/voxeu/columns/european-commission-digital-markets-act-translation. We contest the assertion that the DMA and other digital competition regulations aim to create competition, rather than aid competitors, in Section IIIB.

[138] On the relationship between rent seeking and ex-ante regulation, see generally Thom Lambert, Rent-Seeking and Public Choice in Digital Markets, in The Global Antitrust Institute Report on the Digital Economy (Joshua D. Wright & Douglas H. Ginsburg, eds., Nov. 11, 2020). https://gaidigitalreport.com/2020/08/25/rent-seeking-and-public-choice-in-digital-markets.

[139] See, e.g., Making the Digital Market Easier to Use: The Act on Improving Transparency and Fairness of Digital Platforms (TFDPA), Japanese Ministry of Economy, Trade, and Industry (Apr. 23, 2021), https://www.meti.go.jp/english/mobile/2021/20210423001en.html. The Ministry of Economy, Trade, and Industry specifically links the TFDPA to benefits for SMEs; see also Ebru Gokce Dessemond, Restoring Competition in ”Winner-Took-All” Digital Platform Markets, UNCTAD (Feb. 4, 2020), https://unctad.org/news/restoring-competition-winner-took-all-digital-platform-markets (“Competition law provisions on unfair trade practices and abuse of superior bargaining position, as found in competition laws of Japan and the Republic of Korea, would empower competition authorities in protecting the interests of smaller firms vis-à-vis big platforms”).

[140] See DMA, Recital 2, referring to a significant degree of dependence of both consumers and business users. See, in a similar vein, DMA Recitals 20, 43, 75. On self-inflicted dependence, see Geoffrey A. Manne, The Real Reason Foundem Foundered, Int’l. Ctr. for Law & Econ. (2018), available at https://laweconcenter.org/wp-content/uploads/2018/05/manne-the_real_reaon_foundem_foundered_2018-05-02-1.pdf.

[141] For commentary on how bans on self-preferencing benefit large, but less-efficient competitors, see Lazar Radic & Geoffrey A. Manne, Amazon Italy’s Efficiency Offense, Truth on the Market (Jan. 11, 2022), https://truthonthemarket.com/2022/01/11/amazon-italys-efficiency-offense.

[142] Adam Kovacevich, The Digital Markets Act’s “Statler & Waldorf” Problem, Medium (Mar. 7 2024), https://medium.com/chamber-of-progress/the-digital-markets-acts-statler-waldorf-problem-2c9b6786bb55 (arguing that the companies who lobbied for the DMA are content aggregators like Yelp, Tripadvisor, and Booking.com; big app makers like Spotify, Epic Games, and Match.com; and rival search engines like Ecosia, Yandex, and DuckDuckGo).

[143] For example, Epic Games’ revenue in 2023 was roughly $5.6 billion. In 2023, Epic Games employed about 4,300 workers. See, respectively, https://www.statista.com/statistics/1234106/epic-games-annual-revenue and https://www.statista.com/statistics/1234218/epic-games-employees. According to the OECD, a small and medium-sized enterprise is one that employs fewer than 250 people. Enterprises by Business Size (Indicator), OECD https://data.oecd.org/entrepreneur/enterprises-by-business-size.htm#:~:text=In%20small%20and%20medium%2Dsized,More, (last accessed May 14, 2024).

[144] Mathieu Pollet, France to Prioritise Digital Regulation, Tech Sovereignty During EU Council Presidency, EurActiv (Dec. 14, 2021), https://www.euractiv.com/section/digital/news/france-to-prioritise-digital-regulation-tech-sovereignty-during-eu-council-presidency.

[145] See, e.g., Matthias Bauer & Fredrik Erixon, Europe’s Quest for Technology Sovereignty: Opportunities and Pitfalls, ECIPE (2020), https://ecipe.org/publications/europes-technology-sovereignty; see also Dennis Csernatoni et al., Digital Sovereignty: From Narrative to Policy?, EU Cyber Direct (2022), https://eucyberdirect.eu/research/digital-sovereignty-narrative-policy.

[146] Digital Sovereignty for Europe, European Parliament (2020), available at https://www.europarl.europa.eu/RegData/etudes/BRIE/2020/651992/EPRS_BRI(2020)651992_EN.pdf. For further discussion, see Lazar Radic, Gatekeeping, the DMA, and the Future of Competition Regulation, Truth on the Market (Nov. 8, 2023), https://truthonthemarket.com/2023/11/08/gatekeeping-the-dma-and-the-future-of-competition-regulation.

[147] Press Release, Digital Markets Act: Commission Designates Six Gatekeepers, European Commission (Sep. 6, 2023), https://ec.europa.eu/commission/presscorner/detail/en/ip_23_4328.

[148] Online Intermediation Platforms Market Inquiry, Summary of Final Report, South African Competition Commission (2023), https://www.compcom.co.za/online-intermediation-platforms-market-inquiry.

[149] Note that the unfairness here stems from having the resources to invest in search-engine optimization.

[150] Id. at 3.

[151] Id.

[152] Id. at 10.

[153] Id, at 6, 9, 23, 32, and 67.

[154] It is becoming clearer and clearer that the test for compliance with DMA’s rules will be whether competitors and complementors enjoy an increase in market share. See Foo Yun Chee & Martin Coulter, EU’s Digital Markets Act Hands Boost to Big Tech’s Smaller Rivals, Reuters (Mar. 11 2024) https://www.reuters.com/technology/eus-digital-markets-act-hands-boost-big-techs-smaller-rivals-2024-03-08. The public-policy chief of Ecosia, one of Google’s competitors in search, had this to say about the implementation of the DMA: “the implementation of these new rules is a step in the right direction, but the proof of the pudding is always in the eating, and whether we see any meaningful changes in market share.”

[155] Even the DMA’s supporters accept that the regulation is not grounded in economics. Cristina Caffarra, Europe’s Tech Regulation is Not Economic Policy, Project Syndicate (Oct. 11, 2023), https://www.project-syndicate.org/commentary/european-union-digital-markets-act-will-not-tame-big-tech-by-cristina-caffarra-2023-10?barrier=accesspaylog.

[156] Press Release, Apple Announces Changes to iOS, Safari, and the App Store in the European Union, Apple Inc., (Jan. 25, 2024), https://www.apple.com/newsroom/2024/01/apple-announces-changes-to-ios-safari-and-the-app-store-in-the-european-union.

[157] Andy Yen, Apple’s DMA Compliance Plan Is a Trap and a Slap in the Face for the European Commission, Proton (2024), https://proton.me/blog/apple-dma-compliance-plan-trap; Press Release, Apple’s Proposed Changes Reject the Goals of the DMA, Spotify (Jan. 26, 2024), https://newsroom.spotify.com/2024-01-26/apples-proposed-changes-reject-the-goals-of-the-dma; Morgan Meaker, Apple Isn’t Ready to Release Its Grip on the App Store (Jan. 26, 2024), https://www.wired.com/story/apple-app-store-sideloading-europe-dma.

[158] See, supra note 125 (discussing who lobbied for the DMA).

[159] DMCC, S. 38-45.

[160] See, A New Pro-competition Regime for Digital Markets: Policy Summary Briefing, UK Department for Business & Trade & Department for Science Innovation & Technology (2023), https://www.gov.uk/government/publications/digital-markets-competition-and-consumers-bill-supporting-documentation/a-new-pro-competition-regime-for-digital-markets-policy-summary-briefing; see also, A New Pro-Competition Regime for Digital Markets. Consultation Document, UK Department for Culture, M. S. and Department for Business Energy & Industrial Strategy (2022), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1003913/Digital_Competition_Consultation_v2.pdf.

[161] Dirk Auer, Matthew Lesh, & Lazar Radic, Digital Overload: How the Digital Markets, Competition and Consumers Bill’s Sweeping New Powers Threaten Britain’s Economy, Institute of Economic Affairs (Sep. 18, 2023), https://iea.org.uk/publications/digital-overload-how-the-digital-markets-competition-and-consumers-bills-sweeping-new-powers-threaten-britains-economy.

[162] See also Alfonso Lamadrid & Pablo Ibáñez Colomo, The DMA – Procedural Afterthoughts, Chillin’ Competition (Sep. 5, 2022), https://chillingcompetition.com/2022/09/05/the-dma-procedural-afterthoughts (“Unlike competition law, the DMA is not so much about protecting consumers, but competitors/ third parties”); Chee & Coulter, supra note 137. “As the world’s biggest tech companies revamp their core online services to comply with the European Union’s landmark Digital Markets Act, the changes could give some smaller rivals and even peers a competitive edge.”

[163] See, e.g., Judgment of 6 September 2016, Intel v. Commission, Case C?413/14 P, EU:C:2017:632, para. 134 (“Thus, not every exclusionary effect is necessarily detrimental to competition. Competition on the merits may, by definition, lead to the departure from the market or the marginalisation of competitors that are less attractive to consumers from the point of view of, among other things, price, choice, quality or innovation”) (emphasis added).

[164] See, Auer & Radic, supra note 91.

[165] See, e.g., Competition on the Merits, DAF/COMP(2005)27, 9, OECD (2005), available at https://www.oecd.org/competition/abuse/35911017.pdf (“It is widely agreed that the purpose of competition policy is to protect competition, not competitors”).

[166] Ohlhausen & Taladay, supra note 70 at 465.

[167] See e.g., Questions and Answers, Digital Markets Act: Ensuring Fair and Open Digital Markets, European Commission (Sep. 6, 2023), https://ec.europa.eu/commission/presscorner/detail/en/qanda_20_2349 (“[Gatekeepers] will therefore have to proactively implement certain behaviours that make the markets more open and contestable”).

[168] Jan Krämer & Daniel Schnurr, Big Data and Digital Markets Contestability: Theory of Harm and Data Access Remedies, 18 Journal of Competition Law & Economics 255 (2021).

[169] On self-preferencing in the context of antitrust, see Radic & Manne, supra note 113.

[170] On data portability and free-riding, see Sam Bowman, Data Portability: The Costs of Imposed Openness, Int’l. Ctr. for Law & Econ. (2020), available at https://laweconcenter.org/wp-content/uploads/2020/09/ICLE-tldr-Data-Portability.pdf.

[171] H.R. 3849, supra note 29.

[172] Id. at § 7.

[173] Id. at § 7(b)(4).

[174] Id. at § 4(e)(1).

[175] Remarks by Executive-Vice President Vestager and Commissioner Breton on the Opening of Non-Compliance Investigations under the Digital Markets Act, European Commission (Mar. 25 2024), https://ec.europa.eu/commission/presscorner/detail/es/speech_24_1702. “Stakeholders provided feedback on the compliance solutions offered. Their feedback tells us that certain compliance measures fail to achieve their objectives and fall short of expectations.”

[176] The terms “levelling down” and “levelling up” are, to our knowledge, not normally deployed in the fields of antitrust law and digital competition regulation. They are, however, used frequently in areas of constitutional law, such as equality and free speech. In the context of equality law, see generally Deborah L. Brake, When Equality Leaves Everyone Worse Off: The Problem of Levelling Down in Equality Law, 46 Wm. & Mary L. Rev. 513 (2004). Examples include achieving equality between men and women by levelling down men’s opportunities until they reach parity with women’s, or levelling down public spending in wealthier school districts to reach equality with poorer districts.

[177] See, e.g., DMA Art. 6(7), establishing a duty to provide interoperability with the gatekeepers’ services, free of charge; see also arts.5(4), 5(10), 6(8), 6(9), and 7(1).

[178] See, e.g., Dirk Auer & Geoffrey A. Manne, TL;DR: Apple v Epic: The Value of Closed Systems, Int’l. Ctr. for Law & Econ. (Apr. 20, 2021), available at https://laweconcenter.org/wp-content/uploads/2021/04/tldr-Apple-v-Epic.pdf.

[179] This argument was accepted in the context of in-app payment systems by the U.S. District Court in Epic Games, Inc. v. Apple Inc., 4:20-cv-05640-YGR (N.D. Cal. Nov. 9, 2021). On the security and privacy risks posed by sideloading and interoperability, see, e.g., Mikolaj Barczentewicz, Privacy and Security Implications of Regulation of Digital Services in the EU and in the U.S., Stanford-Vienna Transatlantic Technology Law Forum TTLF Working Papers No. 84 (2022); Bjorn Lundqvist, Injecting Security into European Tech Policy, CEPA (2023), https://cepa.org/comprehensive-reports/reining-in-the-gatekeepers-and-opening-the-door-to-security-risks.

[180] “Open” and “closed” platforms are not synonymous with “good” and “bad” platforms. These are legitimate differences in product design and business philosophy, and neither is inherently more restrictive than the other. Andrei Haigu, Proprietary vs. Open Two-Sided Platforms and Social Efficiency, Harvard Business School Strategy Unit Working Paper No. 09-113 (2007), 2-3 (explaining that there is a “fundamental welfare tradeoff between two-sided proprietary . . . platforms and two-sided open platforms, which allow ‘free entry’ on both sides of the market” and thus “it is by no means obvious which type of platform will create higher product variety, consumer adoption and total social welfare”); see also Jonathan M. Barnett, The Host’s Dilemma: Strategic Forfeiture in Platform Markets for Informational Goods, 124 Harv. L. Rev. 1861, 1927 (2011).

[181] See, e.g., Bus. Elecs. Corp. v. Sharp Elecs. Corp., 485 U.S. 717, 748– 49 (1988) (Stevens, J., dissenting) (“A demonstrable benefit to interbrand competition will outweigh the harm to intrabrand competition that is caused by the imposition of vertical nonprice restrictions on dealers”); Leegin Creative Leather Products, Inc. v. PSKS, Inc., 551 U.S. 877 (2007) (“For, as has been indicated already, the antitrust laws are designed primarily to protect interbrand competition, from which lower prices can later result”).

[182] Issue Spotlight: Self-Preferencing, Int’l. Ctr. for Law & Econ. (last updated Nov. 10, 2022), https://laweconcenter.org/spotlights/self-preferencing.

[183] Sam Bowman & Geoffrey A. Manne, Platform Self-Preferencing Can be Good for Consumers and Even Competitors, Truth on the Market (Mar. 4, 2021), https://laweconcenter.wpengine.com/2021/03/04/platform-self-preferencing-can-be-good-for-consumers-and-even-competitors.

[184] Kadir Bas & Kerem Cem Sanli, Amendments to E-Commerce Law: Protecting or Preventing Competition?, Marmara University Law School Journal (2024) (forthcoming).

[185] Id. at 10.

[186] Id. at 21.

[187] Id. at 5 (emphasis added).

[188] DMCC, S. 20(3)(c).

[189] Auer, Lesh, & Radic, supra note 128.

• [190] Joseph A. Schumpeter, Capitalism, Socialism, and Democracy, 100-1 (Harper and Row, New York 1942), 100-1 (“[t]here cannot be any reasonable doubt that under the conditions of our epoch such [technological] superiority is as a matter of fact the outstanding feature of the typical large-scale unit of control”); Hadi Houlla & Aurelien Portuese, The Great Revealing: Taking Competition in America and Europe Seriously, ITIF 23 (2023). (“In highly innovative industries, greater firm size and concentration lower industry-wide costs. A European study shows that larger high-tech firms could increase technological knowledge better than smaller ones…When economies of scale or network effects are large, firms must be sufficiently large to be efficient”); William Baumol, The Free Market Innovation Machine (2002), 196 (“Oligopolistic competition among large, high-tech, business firms, with innovation as a prime competitive weapon, ensures continued innovative activities and, very plausibly, their growth. In this market form, in which a few giant firms dominate a particular market, innovation has replaced price as the name of the game in a number of important industries.”).

[191] Two-sided markets connect distinct sets of users whose demands for the platform are interdependent—i.e., consumers’ demand for a platform increases as more products are available and, conversely, sellers’ demand for a platform increases as additional consumers use the platform, increasing the overall potential for transactions. These network effects can be direct (more consumers on one side attract more consumers on the same side), or indirect (more consumers on one side attract more consumers on the other side). See Bruno Jullien, Alessandro Pavan, & Marc Rysman, Two-Sided Markets, Pricing and Network Effects, 4 Handbook of Industrial Organization 485, 487 (2021)(“A central aspect of platform economics is the role of network effects, which apply when a product is valued based on the extent to which other market participants adopt or use the same product”); OECD Policy Roundtables, Two-Sided Markets 11 (Dec. 17, 2009), available at https://www.oecd.org/daf/competition/44445730.pdf.

[192] Art. 14 DMA establishes a duty to report mergers that would ordinarily fall under the relevant EU merger-control rules threshold. Art. 18(2) also empowers the Commission to prohibit gatekeepers from entering into future concentrations concerning core platform services or any digital products or services, in cases where gatekeepers have engaged in “systematic non-compliance.” Systemic noncompliance occurs when a gatekeeper receives as few as three noncompliance decisions within eight years (Art. 18(3)); S. 55 of the DMCC mandates companies with SMS to notify certain mergers, even though the UK does not have a compulsory notification regime.

[193] For a tongue-in-cheek remark, see Herbert Hovenkamp (@Sherman1890), Twitter (Jan. 15, 2024, 7:22 AM), https://x.com/Sherman1890/status/1746870481393762534?s=20; see also Robert Armstrong & Ethan Wu, What Big Tech Antitrust Gets Wrong, An Interview with Herbert Hovenkamp, Financial Times (Jan. 19, 2024), https://www.ft.com/content/4eec8bc3-c892-4704-ae66-a4432c6d4fd7 (“With Big Tech, we’re looking at probably the most productive part of the economy. The rate of innovation is high. They spend a lot of money on R&D. They are among the largest patent holders. There’s very little evidence of collusion. They seem to be competing with each other quite strongly. They pay their workers relatively well and have fairly educated workforces. None of this is a sign that these are industries we should be pursuing. That doesn’t mean they don’t do some anti-competitive things. But the whole idea that we should be targeting Big Tech strikes me as fundamentally wrong-headed”). It should be noted that Hovenkamp’s comment is made within the context of antitrust law. But the general sentiment about the unique hostility of certain regulators and legislatures toward certain tech companies could be extrapolated, mutatis mutandis, to digital competition regulation, especially with respect to the competition-oriented elements of DCRs (see Section II).

[194] See also Dirk Auer & Geoffrey Manne, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and their Origins, 28(4) Geo. Mason L. Rev. 1279 (2023), https://lawreview.gmu.edu/print__issues/antitrust-dystopia-and-antitrust-nostalgia-alarmist-theories-of-harm-in-digital-markets-and-their-origins.

[195] Oles Andriychuk, Do DMA Obligations for Gatekeepers Create Entitlements for Business Users?, 11 Journal of Antitrust Enforcement 123 (2022) (Referring to the DMA as “punitive” and “interventionist,” and suggesting that exceptionally demanding obligations are put in place to slow down gatekeepers). See also at 127 (“the means for allowing the second-tier ersatz-Big Tech to scale up is punitive: to slow down the current gatekeepers by imposing upon them a catalogue of exceptionally demanding obligations”) and at 131 (“This punitive nature of the DMA also means that the obligations can be blatantly arduous and interventionist”) (emphasis added).

[196] DMA, Art. 7(9). There is also a limited exemption in which the gatekeeper can show that, due to exceptional circumstances beyond its control, complying with the obligations of the DMA endangers the economic viability of its operation in the EU. DMA, Art. 9(1).

[197] Id. (“…provided that such measures are strictly necessary and proportionate and are duly justified by the gatekeeper”) (emphasis added).

[198] Digital Markets Regulation Handbook, Cleary Gottlieb (Thomas Graf, et al., eds. 2022), 59, https://www.clearygottlieb.com/-/media/files/rostrum/22092308%20digital%20markets%20regulation%20handbookr16.

[199] See Digital Platform Services Inquiry, supra note 18 at § 7.2.4.

[200] Id. at 123 (emphasis added).

[201] German Competition Act, supra note 50 at Art. 19a(7).

[202] As discussed in Section II, “material harm to competition” already establishes a lower (but also fundamentally different) threshold for the plaintiff than the standard typically applied in antitrust law, as it implies a showing of harm to competitors, rather than to competition.

[203] Cleary Gottlieb, supra note 162 at 76.

[204] DMCC at S. 29; see also, A New Pro-Competition Regime for Digital Markets: Advice for the Digital Markets Taskforce section 4.40, CMA (2020), available at https://assets.publishing.service.gov.uk/media/5fce7567e90e07562f98286c/Digital_Taskforce_-_Advice.pdf (“Conduct which may in some circumstances be harmful, in others may be permissible or desirable as it produces sufficient countervailing benefits”).

[205] Auer, Lesh, & Radic, supra note 128.

[206] Id.

[207] Id.

[208] This is implied by the fact such an exemption arises only in S. 29, which concerns investigations into breaches of conduct requirements.

[209] PL 2768, supra note 32 at Art. 11.

[210] As discussed in Section I, PL 2768 pursues a multiplicity of goals, and there is no telling how much weight would be afforded to consumer protection under Art. 10.

[211] There is some evidence that this has already happened with Google and Google Maps. See Edith Hancock, “Severe Pain in the Butt”: EU’s Digital Competition Rules Make New Enemies on the Internet, Politico (Mar. 25 2024), https://www.politico.eu/article/european-union-digital-markets-act-google-search-malicious-compliance (“Before [the DMA], users could search for a location on Google by simply clicking on the Google Map link to expand it and navigate it easily. That feature doesn’t work in the same way in Europe anymore and users are irritated.”).

[212] For the importance of interbrand competition between closed and open platforms, see ICLE Brief for the 9^th Circuit in Epic Games v. Apple, No. 21-16695 (9th Cir.), ID: 12409936, Dkt Entry: 98, Int’l. Ctr. for Law & Econ. (Mar. 31, 2022), https://laweconcenter.org/resources/icle-brief-for-9th-circuit-for-epic-games-v-apple. See also id. at 26 (“Even if an open platform led to more apps and IAP options for all consumers, some consumers may be better off as a result and others may be worse off. More vigilant users may avoid downloading apps and using IAP systems that are unreliable or which impose invasive data-sharing obligations, but less vigilant users will fall prey to malware, spyware, and other harmful content invited by an open system. The upshot is, “a more competitive market may be better at delivering to vigilant consumers what they want, but may end up exploiting more vulnerable consumers”). See also Mark Armstrong, Interactions Between Competition and Consumer Policy, Competition Policy International (2008), https://ora.ox.ac.uk/objects/uuid:ff166fcf-c3c1-4057-9cf5-10e295b66468/files/m4cc2cf988db14b5da92bb20f1f1a838b.

[213] Robert Pitofsky, The Political Content of Antitrust, 127 Penn. L. Rev. 1051, 1058 (1979).

[214] See, generally, Christopher Decker, Modern Economic Regulation (2014).

[215] In the context of the DMCC, see Auer, Lesh, & Radic, supra note 128.

[216] Pinar Akman, Regulating Competition in Digital Platform Markets: A Critical Assessment of the Framework and Approach of the EU Digital Markets Act, 47(1) European Law Review 85, 110 (2023) ( “The description of “(un)fairness” as provided for in the DMA cannot be said to improve upon the position of the concept in competition law, as it, too, relies on an assessment that is ultimately subjective and involves a value judgement”). See also id. at n. 134 (“This is because it involves establishing what counts as an ‘imbalance of rights and obligations’ on the business users of a gatekeeper and what counts as an ‘advantage’ obtained by the gatekeeper from its business users that is ‘disproportionate’ to the service provided by the gatekeeper to its business users’; see DMA (n 2) Article 10(2)(a) DMA. On the vagueness of the ‘fairness’ concept embodied in the DMA from an economics perspective, see also Monopolkommission (n 38) [23].”). The report Akman references is: Recommendations for an Effective and Efficient Digital Markets Act, Special Report 82, Monopolkommission (2021), https://www.monopolkommission.de/en/reports/special-reports/specialreports-on-own-initiative/372-sr-82-dma.html.

[217] On the in-app payment commission being a legitimate way to recoup investments, see ICLE Brief in Epic Games v. Apple, supra note 177.

[218] Giuseppe Colangelo, In Fairness we (Should Not) Trust. The Duplicity of the EU Competition Policy Mantra in Digital Markets, 68 Antitrust Bulletin 618, 622 (2023) (“Despite its appealing features, fairness appears a subjective and vague moral concept, hence useless as a tool in decisionmaking”).

[219] As an example, Chapter III of the DMA is appropriately entitled: “Practices of Gatekeepers that Limit Contestability or are Unfair.” The chapter sets out practices that are, by definition, unfair.

[220] Distributing Dating Apps in the Netherlands, Apple Developer Support, https://developer.apple.com/support/storekit-external-entitlement (last visited Mar. 10, 2024),

[221] Press Release, Apple Announces Changes to IOS, Safari, and the App Store in the European Union, Apple Inc. (Jan. 25, 2024), https://www.apple.com/newsroom/2024/01/apple-announces-changes-to-ios-safari-and-the-app-store-in-the-european-union.

[222] Adam Kovacevich has referred to this as the “Stalter and Waldorf” problem. See, supra note 125.

[223] Trinko at 407 (2003) (“The mere possession of monopoly power, and the concomitant charging of monopoly prices, is not only not unlawful; it is an important element of the free-market system […] Firms may acquire monopoly power by establishing an infrastructure that renders them uniquely suited to serve their customers […] Enforced sharing also requires antitrust courts to act as central planners, identifying the proper price, quantity, and other terms of dealing—a role for which they are ill-suited”); see also Brian Albrecht, Imposed Final Offer Arbitration: Price Regulation by Any Other Name, Truth on the Market (Dec. 7, 2022), https://truthonthemarket.com/2022/12/07/imposed-final-offer-arbitration-price-regulation-by-any-other-name.

[224] See, ICLE Brief in Epic Games v. Apple, supra note 174 (“In essence, Epic is trying to recast its objection to Apple’s 30% commission for use of Apple’s optional IAP system as a harm to consumers and competition more broadly”); on a similar trend in antitrust that we believe is even more relevant in the context of DCRs, see Jonathan Barnett, Antitrustifying Contract: Thoughts on Epic Games v. Apple and Apple v. Qualcomm, Truth on the Market (Oct. 26 2020) https://truthonthemarket.com/2020/10/26/antitrustifying-contract-thoughts-on-epic-games-v-apple-and-apple-v-qualcomm.

[225] Andriychuk, supra note 159.

[226] See also Ohlhausen & Taladay supra note 69.

[227] United States v. Aluminum Co. of America, 148 F.2d 416, 430 (2d Cir. 1945).

[228] Decker, supra note 176.

[229] Many companies vertically integrate to have the ability to preference their own downstream or upstream products or services. See generally Eric Fruits, Geoffrey Manne, & Kristian Stout, The Fatal Economic Flaws of the Contemporary Campaign Against Vertical Integration, 68 Kan. L. Rev. 5 (2020), https://kuscholarworks.ku.edu/handle/1808/30526; Sam Bowman & Geoffrey Manne, Tl;DR: Self-Preferencing: Building an Ecosystem, Int’l. Ctr. for Law & Econ. (Jul. 21, 2020).

[230]Decker, supra note 176 at 190-1.

[231] Aldous Huxley, Point Counter Point (1928).

[232] As discussed, these ideas are, at least to some extent, redolent of the neo-Brandeisian school of thought in the United States and ordoliberalism in Europe. See e.g., Joseph Coniglio, Why the “New Brandeis Movement Gets Antitrust Wrong, Law360 (Apr. 24, 2018), https://www.law360.com/articles/1036456/why-the-new-brandeis-movement-gets-antitrust-wrong (“The [neo-Brandeisian movement] is not a new entrant in the marketplace of ideas”); see also Daniel Crane, How Much Brandeis Do the Neo-Brandeisians Want?, 64 Antitrust Bulletin 4 (2019).

[233] See, e.g., Rupprecht Podszun, Philipp Bongartz, & Sarah Langenstein, Proposals on How to Improve the Digital Markets Act, 3, (Feb. 18, 2021), https://ssrn.com/abstract=3788571 (“Critics who wish to place the tool into the realm of competition law miss the point that this is a fundamentally different approach”).

[234] In the EU, for example, the DMA was proposed on the basis of Article 114 TFEU, rather than Article 352 TFEU. The consequence is that, for the purpose of EU law, the DMA is considered internal market regulation, rather than competition legislation. It has been argued that Article 352 TFEU, or Article 114 TFEU in conjunction with Article 103 TFEU, would have been the more appropriate legal mechanism. See, e.g., Alfonso Lamadrid de Pablo & Nieves Bayón Fernández, Why the Proposed DMA Might be Illegal Under Article 114 TFEU, and How to Fix It, 12 J. Competition L. & Prac. 7, (2021). One reason why the Commission might have preferred to use Art.114 TFEU over Art.352 TFEU is that the process under Art.114 TFEU is less cumbersome. Unlike Art. 114 TFEU, Article 352 TFEU requires unanimity among EU member states and would not enable the European Parliament to function as co-legislator. Alfonso Lamadrid de Pablo, The Key to Understand the Digital Markets Act: It’s the Legal Basis, Chilling Competition (Dec. 03, 2020), https://chillingcompetition.com/2020/12/03/the-key-to-understand-the-digital-markets-act-its-the-legal-basis.

[235] The term is used often in the literature and media. For an example of the former, see William Davies & Nicholas Gane, Post-Neoliberalism? An Introduction, 38 Theory, Culture & Soc’y 3 (2021); for an example of the latter, see Rana Fohar, The New Rules for Business in a Post-Neoliberal World, Financial Times (Oct. 9, 2022), https://www.ft.com/content/e04bc664-04b2-4ef6-90f9-64e9c4c126aa.

[236] Thomas Biebricher and Frieder Vogelmann have used the term in describing the views of ordoliberals on the role of the market and the state. Thomas Biebricher and Frieder Vogelmann, The Birth of Austerity: German Ordoliberalism and Contemporary Neoliberalism, 138-139 (2017).

[237] Davies and Gane, supra note 198 at 1 (“While events of 2020–21 have facilitated new forms of privatization of many public services and goods, they also signal, potentially, a break from the neoliberal orthodoxies of the previous four decades, and, in particular, from their overriding concern for the market”); see also Edward Luce, It’s the End of Globalism As We Know It, Financial Times (May 8, 2020), https://www.ft.com/content/3b64a08a-7d91-4f09-9a31-0157fa9192cf (“The past 40 years have been predicated on a complex system of neoliberalism that is slowly but surely coming undone, but as of yet, we don’t have any global replacement”); Paolo Gerbaudo, A Post-Neoliberal Paradigm is Emerging: Conversation with Felicia Wong, El Pais (Nov. 4, 2022), https://agendapublica.elpais.com/noticia/18303/post-neoliberal-paradigm-is-emerging-conversation-with-felicia-wong.

[238] Zephyr Teachout, The Death of the Consumer Welfare Standard, ProMarket (Nov. 07, 2023), https://www.promarket.org/2023/11/07/zephyr-teachout-the-death-of-the-consumer-welfare-standard.

[239] After Hillary Clinton lost the 2016 U.S. presidential election to Donald Trump, Barack Obama referred to history and progress in the United States as zig-zagging, rather than moving in a straight line. See, Statement by the President, White House Office of the Press Secretary (Nov. 09, 2016), https://obamawhitehouse.archives.gov/the-press-office/2016/11/09/statement-president.

Abstract

Sherlocking refers to an online platform’s use of nonpublic third-party business data to improve its own business decisions—for instance, by mimicking the successful products and services of edge providers. Such a strategy emerges as a form of self-preferencing and, as with other theories about preferential access to data, it has been targeted by some policymakers and competition authorities due to the perceived competitive risks originating from the dual role played by hybrid platforms (acting as both referees governing their platforms, and players competing with the business they host). This paper investigates the competitive implications of sherlocking, maintaining that an outright ban is unjustified. First, the paper shows that, by aiming to ensure platform neutrality, such a prohibition would cover scenarios (i.e., the use of nonpublic third-party business data to calibrate business decisions in general, rather than to adopt a pure copycat strategy) that should be analyzed separately. Indeed, in these scenarios, sherlocking may affect different forms of competition (inter-platform v. intra-platform competition). Second, the paper argues that, in either case, the practice’s anticompetitive effects are questionable and that the ban is fundamentally driven by a bias against hybrid and vertically integrated players.

I. Introduction

The dual role some large digital platforms play (as both intermediary and trader) has gained prominence among the economic arguments used to justify the recent wave of regulation hitting digital markets around the world. Many policymakers have expressed concern about potential conflicts of interest among companies that have adopted this hybrid model and that also control important gateways for business users. In other words, the argument goes, some online firms act not only as regulators who set their platforms’ rules and as referees who enforce those rules, but also as market players who compete with their business users. This raises the fear that large platforms could reserve preferential treatment for their own services and products, to the detriment of downstream rivals and consumers. That, in turn, has led to calls for platform-neutrality rules.

Toward this aim, essentially all of the legislative initiatives undertaken around the world in recent years to enhance competition in digital markets have included anti-discrimination provisions that target various forms of self-preferencing. Self-preferencing, it has been said, serves as the symbol of the current competition-policy zeitgeist in digital markets.[1] Indeed, this conduct is considered functional to leveraging strategies that would grant gatekeepers the chance to entrench their power in core markets and extend it into associated markets.[2]

Against this background, so-called “sherlocking” has emerged as one form of self-preferencing. The term was coined roughly 20 years ago, after Apple updated its own app Sherlock (a search tool on its desktop-operating system) to mimic a third-party application called Watson, which was created by Karelia Software to complement the Apple tool’s earlier version.[3] According to critics of self-preferencing generally and sherlocking in particular, biased intermediation and related conflicts of interest allow gatekeepers to exploit their preferential access to business users’ data to compete against them by replicating successful products and services. The implied assumption is that this strategy is relevant to competition policy, even where no potential intellectual-property rights (IPRs) are infringed and no slavish imitation sanctionable under unfair-competition laws is detected. Indeed, under such theories, sherlocking would already be prevented by the enforcement of these rules.

To tackle perceived misuse of gatekeepers’ market position, the European Union’s Digital Markets Act (DMA) introduced a ban on sherlocking.[4] Similar concerns have also motivated requests for intervention in the United States,[5] Australia,[6] and Japan.[7] In seeking to address at least two different theories of gatekeepers’ alleged conflicts of interest, these proposed bans on exploiting access to business users’ data are not necessarily limited to the risk of product imitation, but may include any business decision whatsoever that a platform may make while relying on that data.

In parallel with the regulatory initiatives, the conduct at-issue has also been investigated in some antitrust proceedings, which appear to seek the very same twofold goal. In particular, in November 2020, the European Commission sent a statement of objections to Amazon that argued the company had infringed antitrust rules through the systematic use of nonpublic business data from independent retailers who sell on the Amazon online marketplace in order to benefit Amazon’s own retail business, which directly competes with those retailers.[8] A similar investigation was opened by the UK Competition and Markets Authority (CMA) in July 2022.[9]

Further, as part of the investigation opened into Apple’s App Store rule requiring developers to use Apple’s in-app purchase mechanism to distribute paid apps and/or paid digital content, the European Commission also showed interest in evaluating whether Apple’s conduct might disintermediate competing developers from relevant customer data, while Apple obtained valuable data about those activities and its competitors’ offers.[10] The European Commission and UK CMA likewise launched an investigation into Facebook Marketplace, with accusations that Meta used data gathered from advertisers in order to compete with them in markets where the company is active, such as classified ads.[11]

There are two primary reasons these antitrust proceedings are relevant. First, many of the prohibitions envisaged in regulatory interventions (e.g., DMA) clearly took inspiration from the antitrust investigations, thus making it important to explore the insights that competition authorities may provide to support an outright ban. Second, given that regulatory intervention will be implemented alongside competition rules (especially in Europe) rather than displace them,[12] sherlocking can be assessed at both the EU and national level against dominant players that are not eligible for “gatekeeper” designation under the DMA. For those non-gatekeeper firms, the practice may still be investigated by antitrust authorities and assessed before courts, aside from the DMA’s per se prohibition. And, of course, investigations and assessments of sherlocking could also be made even in those jurisdictions where there isn’t an outright ban.

The former sis well-illustrated by the German legislature’s decision to empower its national competition authority with a new tool to tackle abusive practices that are similar and functionally equivalent to the DMA.[13] Indeed, as of January 2021, the Bundeskartellamt may identify positions of particular market relevance (undertakings of “paramount significance for competition across markets”) and assess their possible anticompetitive effects on competition in those areas of digital ecosystems in which individual companies may have a gatekeeper function. Both the initiative’s aims and its list of practices are similar to the DMA. They are distinguished primarily by the fact that the German list is exhaustive, and the practices at-issue are not prohibited per se, but are subject to a reversal of the burden of proof, allowing firms to provide objective justifications. For the sake of this analysis, within the German list, one provision prohibits designated undertakings from “demanding terms and conditions that permit … processing data relevant for competition received from other undertakings for purposes other than those necessary for the provision of its own services to these undertakings without giving these undertakings sufficient choice as to whether, how and for what purpose such data are processed.”[14]

Unfortunately, none of the above-mentioned EU antitrust proceedings have concluded with a final decision that addresses the merits of sherlocking. This precludes evaluating whether the practice would have survived before the courts. Regarding the Apple investigation, the European Commission dropped the case over App Store rules and issued a new statement of objections that no longer mentions sherlocking.[15] Further, the European Commission and the UK CMA accepted the commitments offered by Amazon to close those investigations.[16] The CMA likewise accepted the commitments offered by Meta.[17]

Those outcomes can be explained by the DMA’s recent entry into force. Indeed, because of the need to comply with the new regulation, players designated as gatekeepers likely have lost interest in challenging antitrust investigations that target the very same conduct prohibited by the DMA.[18] After all, given that the DMA does not allow any efficiency defense against the listed prohibitions, even a successful appeal against an antitrust decision would be a pyrrhic victory. From the opposite perspective, the same applies to the European Commission, which may decide to save time, costs, and risks by dropping an ongoing case against a company designated as a gatekeeper under the DMA, knowing that the conduct under investigation will be prohibited in any case.

Nonetheless, despite the lack of any final decision on sherlocking, these antitrust assessments remain relevant. As already mentioned, the DMA does not displace competition law and, in any case, dominant platforms not designated as gatekeepers under the DMA still may face antitrust investigations over sherlocking. This applies even more for jurisdictions, such as the United States, that are evaluating DMA-like legislative initiatives (e.g., the American Innovation and Choice Online Act, or “AICOA”).

Against this background, drawing on recent EU cases, this paper questions the alleged anticompetitive implications of sherlocking, as well as claims that the practice fails to comply with existing antitrust rules.

First, the paper illustrates that prohibitions on the use of nonpublic third-party business data would cover two different theories that should be analyzed separately. Whereas a broader case involves all the business decisions adopted by a dominant platform because of such preferential access (e.g., the launch of new products or services, the development or cessation of existing products or services, the calibration of pricing and management systems), a more specific case deals solely with the adoption of a copycat strategy. By conflating these theories in support of a blanket ban that condemns any use of nonpublic third-party business data, EU antitrust authorities are fundamentally motivated by the same policy goal pursued by the DMA—i.e., to impose a neutrality regime on large online platforms. The competitive implications differ significantly, however, as adopting copycat strategies may only affect intra-brand competition, while using said data to improve other business decisions could also affect inter-platform competition.

Second, the paper shows that, in both of these scenarios, the welfare effects of sherlocking are unclear. Notably, exploiting certain data to better understand the market could help a platform to develop new products and services, to improve existing products and services, or more generally to be more competitive with respect to both business users and other platforms. As such outcomes would benefit consumers in terms of price and quality, any competitive advantage achieved by the hybrid platform could be considered unlawful only if it is not achieved on the merits. In a similar vein, if sherlocking is used by a hybrid platform to deliver replicas of its business users’ products and services, that would likely provide short-term procompetitive effects benefitting consumers with more choice and lower prices. In this case, the only competitive harm that would justify an antitrust intervention resides in (uncertain) negative long-term effects on innovation.

As a result, in any case, an outright ban of sherlocking, such as is enshrined in the DMA, is economically unsound since it would clearly harm consumers.

The paper is structured as follows. Section II describes the recent antitrust investigations of sherlocking, illustrating the various scenarios that might include the use of third-party business data. Section III investigates whether sherlocking may be considered outside the scope of competition on the merits for bringing competitive advantages to platforms solely because of their hybrid business model. Section IV analyzes sherlocking as a copycat strategy by investigating the ambiguous welfare effects of copying in digital markets and providing an antitrust assessment of the practice at issue. Section V concludes.

II. Antitrust Proceedings on Sherlocking: Platform Neutrality and Copycat Competition

Policymakers’ interest in sherlocking is part of a larger debate over potentially unfair strategies that large online platforms may deploy because of their dual role as an unavoidable trading partner for business users and a rival in complementary markets.

In this scenario, as summarized in Table 1, the DMA outlaws sherlocking, establishing that to “prevent gatekeepers from unfairly benefitting from their dual role,”[19] they are restrained from using, in competition with business users, “any data that is not publicly available that is generated or provided by those business users in the context of their use of the relevant core platform services or of the services provided together with, or in support of, the relevant core platform services, including data generated or provided by the customers of those business users.”[20] Recital 46 further clarifies that the “obligation should apply to the gatekeeper as a whole, including but not limited to its business unit that competes with the business users of a core platform service.”

A similar provision was included in the American Innovation and Choice Online Act (AICOA), which was considered, but not ultimately adopted, in the 117^th U.S. Congress. AICOA, however, would limit the scope of the ban to the offer of products or services that would compete with those offered by business users.[21] Concerns about copycat strategies were also reported in the U.S. House of Representatives’ investigation of the state of competition in digital markets as supporting the request for structural-separation remedies and line-of-business restrictions to eliminate conflicts of interest where a dominant intermediary enters markets that place it in competition with dependent businesses.[22] Interestingly, however, in the recent complaint filed by the U.S. Federal Trade Commission (FTC) and 17 state attorneys general against Amazon that accuses the company of having deployed an interconnected strategy to block off every major avenue of competition (including price, product selection, quality, and innovation), there is no mention of sherlocking among the numerous unfair practices under investigation.[23]

Evaluating regulatory-reform proposals for digital markets, the Australian Competition and Consumer Commission (ACCC) also highlighted the risk of sherlocking, arguing that it could have an adverse effect on competition, notably on rivals’ ability to compete, when digital platforms exercise their strong market position to utilize nonpublic data to free ride on the innovation efforts of their rivals.[24] Therefore, the ACCC suggested adopting service-specific codes to address self-preferencing by, for instance, imposing data-separation requirements to restrain dominant app-store providers from using commercially sensitive data collected from the app-review process to develop their own apps.[25]

Finally, on a comparative note, it is also useful to mention the proposals advanced by the Japanese Fair Trade Commission (JFTC) in its recent market-study report on mobile ecosystems.[26] In order to ensure equal footing among competitors, the JFTC specified that its suggestion to prevent Google and Apple from using nonpublic data generated by other developers’ apps aims at pursuing two purposes. Such a ban would, indeed, concern not only use of the data for the purpose of developing competing apps, products, and services, but also its use for developing their own apps, products, and services.

TABLE 1: Legislative Initiatives and Proposals to Ban Sherlocking

As previously anticipated, sherlocking recently emerged as an antitrust offense in three investigations launched by the European Commission and the UK CMA.

In the first case, Amazon’s alleged reliance on marketplace sellers’ nonpublic business data has been claimed to distort fair competition on its platform and prevent effective competition. In its preliminary findings, the Commission argued that Amazon takes advantage of its hybrid business model, leveraging its access to nonpublic third-party sellers’ data (e.g., the number of ordered and shipped units of products; sellers’ revenues on the marketplace; the number of visits to sellers’ offers; data relating to shipping, to sellers’ past performance, and to other consumer claims on products, including the activated guarantees) to adjust its retail offers and strategic business decisions to the detriment of third-party sellers, which are direct competitors on the marketplace.[27] In particular, the Commission was concerned that Amazon uses such data for its decision to start and end sales of a product, for its pricing system, for its inventory-planning and management system, and to identify third-party sellers that Amazon’s vendor-recruitment teams should approach to invite them to become direct suppliers to Amazon Retail. To address the data-use concern, Amazon committed not to use nonpublic data relating to, or derived from, independent sellers’ activities on its marketplace for its retail business and not to use such data for the purposes of selling branded goods, as well as its private-label products.[28]

A parallel investigation ended with similar commitments in the UK.[29] According to the UK CMA, Amazon’s access to and use of nonpublic seller data could result in a competitive advantage for Amazon Retail arising from its operation of the marketplace, rather than from competition on the merits, and may lead to relevant adverse effects on competition. Notably, it was alleged this could result in a reduction in the scale and competitiveness of third-party sellers on the Amazon Marketplace; a reduction in the number and range of product offers from third-party sellers on the Amazon Marketplace; and/or less choice for consumers, due to them being offered lower quality goods and/or paying higher prices than would otherwise be the case.

It is also worth mentioning that, by determining that Amazon is an undertaking of paramount significance for competition across markets, the Bundeskartellamt emphasized the competitive advantage deriving from Amazon’s access to nonpublic data, such as Glance Views, sales figures, sale quantities, cost components of products, and reorder status.[30] Among other things, with particular regard to Amazon’s hybrid role, the Bundeskartellamt noted that the preferential access to competitively sensitive data “opens up the possibility for Amazon to optimize its own-brand assortment.”[31]

A second investigation involved Apple and its App Store rule.[32] According to the European Commission, the mandatory use of Apple’s own proprietary in-app purchase system (IAP) would, among other things, grant Apple full control over the relationship its competitors have with customers, thus disintermediating those competitors from customer data and allowing Apple to obtain valuable data about the activities and offers of its competitors.

Finally, Meta faced antitrust proceedings in both the EU and the UK.[33] The focus was on Facebook Marketplace—i.e., an online classified-ads service that allows users to advertise goods for sale. According to the European Commission and the CMA, Meta unilaterally imposes unfair trading conditions on competing online-classified ads services that advertise on Facebook or Instagram. These terms and conditions, which authorize Meta to use ads-related data derived from competitors for the benefit of Facebook Marketplace, are considered unjustified, as they impose an unnecessary burden on competitors and only benefit Facebook Marketplace. The suspicion is that Meta has used advertising data from Facebook Marketplace competitors for the strategic planning, product development, and launch of Facebook Marketplace, as well as for Marketplace’s operation and improvement.

Overall, these investigations share many features. The concerns about third-party business-data use, as well as about other forms of self-preferencing, revolve around the competitive advantages that accrue to a dominant platform because of its dual role. Such advantages are considered unfair, as they are not the result of the merits of a player, but derived purely and simply from its role as an important gateway to reach end users. Moreover, this access to valuable business data is not reciprocal. The feared risk is the marginalization of business users competing with gatekeepers on the gatekeepers’ platforms and, hence, the alleged harm to competition is the foreclosure of rivals in complementary markets (horizontal foreclosure).

The focus of these investigations was well-illustrated by the European Commission’s decision on Amazon’s practice.[34] The Commission’s concern was about the “data delta” that Amazon may exploit, namely the additional data related to third-party sellers’ listings and transactions that are not available to, and cannot be replicated by, the third-party sellers themselves, but are available to and used by Amazon Retail for its own retail operations.[35] Contrary to Amazon Retail—which, according to Commission’s allegations, would have full access to and would use such individual, real-time data of all its third-party sellers to calibrate its own retail decisions—sellers would have access only to their own individual listings and sales data. As a result, the Commission came to the (preliminary) conclusion that real-time access to and use of such volume, variety, and granularity of non-publicly available data from its retail competitors generates a significant competitive advantage for Amazon Retail in each of the different decisional processes that drive its retail operations.[36]

On a closer look, however, while antitrust authorities seem to target the use of nonpublic third-party business data as a single theory of harm, their allegations cover two different scenarios along the lines of what has already been examined with reference to the international legislative initiatives and proposals. Indeed, the Facebook Marketplace case does not involve an allegation of copying, as Meta is accused of gathering data from its business users to launch and improve its ads service, instead of reselling goods and services.

FIGURE 1: Sherlocking in Digital Markets

As illustrated above in Figure 1, while the claim in the latter scenario is that the preferential data use would help dominant players calibrate business decisions in general, the former scenario instead involves the use of such data for a pure copycat strategy of an entire product or service, or some of its specific features.

In both scenarios the aim of the investigations is to ensure platform neutrality. Accordingly, as shown by the accepted commitments, the envisaged solution for antitrust authorities is to impose  data-separation requirements to restrain dominant platforms from using third-party commercially sensitive data. Putting aside that these investigations concluded with commitments from the firms, however, their chances of success before a court differ significantly depending on whether they challenge a product-imitation strategy, or any business decision adopted because of the “data delta.”

A. Sherlocking and Unconventional Theories of Harm for Digital Markets

Before analyzing how existing competition-law rules could be applied to the various scenarios involving the use of third-party business data, it is worth providing a brief overview of the framework in which the assessment of sherlocking is conducted. As competition in the digital economy is increasingly a competition among ecosystems,[37] a lively debate has emerged on the capacity of traditional antitrust analysis to adequately capture the peculiar features of digital markets. Indeed, the combination of strong economies of scale and scope; indirect network effects; data advantages and synergies across markets; and portfolio effects all facilitate ecosystem development all contribute to making digital markets highly concentrated, prone to tipping, and not easily contestable.[38] As a consequence, it’s been suggested that addressing these distinctive features of digital markets requires an overhaul of the antitrust regime.

Such discussions require the antitrust toolkit and theories of harm to illustrate whether and how a particular practice, agreement, or merger is anticompetitive. Notably, at issue is whether traditional antitrust theories of harm are fit for purpose or whether novel theories of harm should be developed in response to the emerging digital ecosystems. The latter requires looking at the competitive impact of expanding, protecting, or strengthening an ecosystem’s position, and particularly whether such expansion serves to exploit a network of capabilities and to control access to key inputs and components.[39]

A significant portion of recent discussions around developing novel theories of harm to better address the characteristics of digital-business models and markets has been devoted to the topic of merger control—in part a result of the impressive number of acquisitions observed in recent years.[40] In particular, the focus has been on analyzing conglomerate mergers that involve acquiring a complementary or unrelated asset, which have traditionally been assumed to raise less-significant competition concerns.

In this regard, an ecosystem-based theory seems to have guided the Bundeskartellamt in its assessment of Meta’s acquisition of Kustomer[41] and by the CMA in Microsoft/Activision.[42] A more recent example is the European Commission’s decision to prohibit the proposed Booking/eTraveli merger, where the Commission explicitly noted that the transaction would have allowed Booking to expand its travel-services ecosystem.[43] The Commission’s concerns were related primarily to the so-called “envelopment” strategy, in which a prominent platform within a specific market broadens its range of services into other markets where there is a significant overlap of customer groups already served by the platform.[44]

Against this background, putative self-preferencing harms represent one of the European Commission’s primary (albeit contentious)[45] attempts to develop new theories of harm built on conglomerate platforms’ ability to bundle services or use data from one market segment to inform product development in another.[46] Originally formulated in the Google Shopping decision,[47] the theory of harm of (leveraging through) self-preferencing has subsequently inspired the DMA, which targets different forms of preferential treatment, including sherlocking.

In particular, it is asserting that platform may use self-preferencing to adopt a leveraging strategy with a twofold anticompetitive effect—that is, excluding or impeding rivals from competing with the platform (defensive leveraging) and extending the platform’s market power into associated markets (offensive leveraging). These goals can be pursued because of the unique role that some large digital platforms play. That is, they not only enjoy strategic market status by controlling ecosystems of integrated complementary products and services, which are crucial gateways for business users to reach end users, but they also perform a dual role as both a critical intermediary and a player active in complementors’ markets. Therefore, conflicts of interests may provide incentives for large vertically integrated platforms to favor their own products and services over those of their competitors.[48]

The Google Shopping theory of harm, while not yet validated by the Court of Justice of the European Union (CJEU),[49] has also found its way into merger analysis, as demonstrated by the European Commission’s recent assessment of iRobot/Amazon.[50] In its statement of objections, the Commission argued that the proposed acquisition of iRobot may give Amazon the ability and incentive to foreclose iRobot’s rivals by engaging in several foreclosing strategies to prevent them from selling robot vacuum cleaners (RVCs) on Amazon’s online marketplace and/or at degrading such rivals’ access to that marketplace. In particular, the Commission found that Amazon could deploy such self-preferencing strategies as delisting rival RVCs; reducing rival RVCs’ visibility in both organic and paid results displayed in Amazon’s marketplace; limiting access to certain widgets or commercially attractive labels; and/or raising the costs of iRobot’s rivals to advertise and sell their RVCs on Amazon’s marketplace.[51]

Sherlocking belongs to this framework of analysis and can be considered a form of self-preferencing, specifically because of the lack of reciprocity in accessing sensitive data.[52] Indeed, while gatekeeper platforms have access to relevant nonpublic third-party business data as a result of their role as unavoidable trading partners, they leverage this information exclusively, without sharing it with third-party sellers, thus further exacerbating an already uneven playing field.[53]

III. Sherlocking for Competitive Advantage: Hybrid Business Model, Neutrality Regimes, and Competition on the Merits

Insofar as prohibitions of sherlocking center on the competitive advantages that platforms enjoy because of their dual role—thereby allowing some players to better calibrate their business decisions due to their preferential access to business users’ data—it should be noted that competition law does not impose a general duty to ensure a level playing field.[54] Further, a competitive advantage does not, in itself, amount to anticompetitive foreclosure under antitrust rules. Rather, foreclosure must not only be proved (in terms of actual or potential effects) but also assessed against potential benefits for consumers in terms of price, quality, and choice of new goods and services.[55]

Indeed, not every exclusionary effect is necessarily detrimental to competition.[56] Competition on the merits may, by definition, lead to the departure from the market or the marginalization of competitors that are less efficient and therefore less attractive to consumers from the point of view of, among other things, price, choice, quality or innovation.[57] Automatically classifying any conduct with exclusionary effects were as anticompetitive could well become a means to protect less-capable, less-efficient undertakings and would in no way protect more meritorious undertakings—thereby potentially hindering a market’s competitiveness.[58]

As recently clarified by the CJEU regarding the meaning of “competition on the merits,” any practice that, in its implementation, holds no economic interest for a dominant undertaking except that of eliminating competitors must be regarded as outside the scope of competition on the merits.[59] Referring to the cases of margin squeezes and essential facilities, the CJEU added that the same applies to practices that a hypothetical equally efficient competitor is unable to adopt because that practice relies on using resources or means inherent to the holding of such a dominant position.[60]

Therefore, while antitrust cases on sherlocking set out to ensure a level playing field and platform neutrality, and therefore center on the competitive advantages that a platform enjoys because of its dual role, mere implementing a hybrid business model does not automatically put such practices outside the scope of competition on the merits. The only exception, according to the interpretation provided in Bronner, is the presence of an essential facility—i.e., an input whose access should be considered indispensable, as there are no technical, legal, or economic obstacles capable of making it impossible, or even unreasonably difficult, to duplicate it.[61]

As a result, unless it is proved that the hybrid platform is an essential facility, sherlocking and other forms of self-preferencing cannot be considered prima facie outside the scope of competition on the merits, or otherwise unlawful. Rather, any assessment of sherlocking demands the demonstration of anticompetitive effects, which in turn requires finding an impact on efficient firms’ ability and incentive to compete. In the scenario at-issue, for instance, the access to certain data may allow a platform to deliver new products or services; to improve existing products or services; or more generally to compete more efficiently not only with respect to the platform’s business users, but also against other platforms. Such an increase in both intra-platform and inter-platform competition would benefit consumers in terms of lower prices, better quality, and a wider choice of new or improved goods and services—i.e., competition on the merits.[62]

In Facebook Marketplace, the European Commission and UK CMA challenged the terms and conditions governing the provision of display-advertising and business-tool services to which Meta required its business customers to sign up.[63] In their view, Meta abused its dominant position by imposing unfair trading conditions on its advertising customers, which authorized Meta to use ads-related data derived from the latter in a way that could afford Meta a competitive advantage on Facebook Marketplace that would not have arisen from competition on the merits. Notably, antitrust authorities argued that Meta’s terms and conditions were unjustified, disproportionate, and unnecessary to provide online display-advertising services on Meta’s platforms.

Therefore, rather than directly questioning the platform’s dual role or hybrid business model, the European Commission and UK CMA decided to rely on traditional case law which considers unfair those clauses that are unjustifiably unrelated to the purpose of the contract, unnecessarily limit the parties’ freedom, are disproportionate, or are unilaterally imposed or seriously opaque.[64] This demonstrates that, outside the harm theory of the unfairness of terms and conditions, a hybrid platform’s use of nonpublic third-party business data to improve its own business decisions is generally consistent with antitrust provisions. Hence, an outright ban would be unjustified.

IV. Sherlocking to Mimic Business Users’ Products or Services

The second, and more intriguing, sherlocking scenario is illustrated by the Amazon Marketplace investigations and regards the original meaning of sherlocking—i.e., where a data advantage is used by a hybrid platform to mimic its business users’ products or services.

Where sherlocking charges assert that the practice allows some platforms to use business users’ data to compete against them by replicating their products or services, it should not be overlooked that the welfare effects of such a copying strategy are ambiguous. While the practice could benefit consumers in the short term by lowering prices and increasing choice, it may discourage innovation over the longer term if third parties anticipate being copied whenever they deliver successful products or services. Therefore, the success of an antitrust investigation essentially relies on demonstrating a harm to innovation that would induce business users to leave the market or stop developing their products and services. In other words, antitrust authorities should be able to demonstrate that, by allowing dominant platforms to free ride on their business guests’ innovation efforts, sherlocking would negatively affect rivals’ ability to compete.

A. The Welfare Effects of Copying

The tradeoff between the short- and long-term welfare effects of copying has traditionally been analyzed in the context of the benefits and costs generated by intellectual-property protection.[65] In particular, the economic literature investigating the optimal life of patents[66] and copyrights[67] focuses on the efficient balance between dynamic benefits associated with innovation and the static costs of monopoly power granted by IPRs.

More recently, product imitation has instead been investigated in the different scenario of digital markets, where dominant platforms adopting a hybrid business model may use third-party sellers’ market data to design and promote their own products over their rivals’ offerings. Indeed, some studies report that large online platforms may attempt to protect their market position by creating “kill zones” around themselves—i.e., by acquiring, copying, or eliminating their rivals.[68] In such a novel setting, the welfare effects of copying are assessed regardless of the presence and the potential enforcement of IPRs, but within a strategy aimed at excluding rivals by exploiting the dual role of both umpire and player to get preferential access to sensitive data and free ride on their innovative efforts.[69]

Even in this context, however, a challenging tradeoff should be considered. Indeed, while in the short term, consumers may benefit from the platform’s imitation strategy in terms of lower prices and higher quality, they may be harmed in the longer term if third parties are discouraged from delivering new products and services. As a result, while there is empirical evidence on hybrid platforms successfully entering into third parties’ adjacent market segments, [70] the extant academic literature finds the welfare implications of such moves to be ambiguous.

A first strand of literature attempts to estimate the welfare impact of the hybrid business model. Notably, Andre Hagiu, Tat-How Teh, and Julian Wright elaborated a model to address the potential implications of an outright ban on platforms’ dual mode, finding that such a structural remedy may harm consumer surplus and welfare even where the platform would otherwise engage in product imitation and self-preferencing.[71] According to the authors, banning the dual mode does not restore the third-party seller’s innovation incentives or the effective price competition between products, which are the putative harms caused by imitation and self-preferencing. Therefore, the authors’ evaluation was that interventions specifically targeting product imitation and self-preferencing were preferable.

Germa?n Gutie?rrez suggested that banning the dual model would generate hardly any benefits for consumers, showing that, in the Amazon case, interventions that eliminate either the Prime program or product variety are likely to decrease welfare.[72]

Further, analyzing Amazon’s business model, Federico Etro found that the platform and consumers’ incentives are correctly aligned, and that Amazon’s business model of hosting sellers and charging commissions prevents the company from gaining through systematic self?preferencing for its private-label and first-party products.[73] In the same vein, on looking at its business model and monetization strategy, Patrick Andreoli-Versbach and Joshua Gans argued that Amazon does not have an obvious incentive to self-preference.[74] Indeed, Amazon’s profitability data show that, on average, the company’s operating margin is higher on third-party sales than on first-party retail sales.

Looking at how modeling details may yield different results with regard to the benefits and harms of the hybrid business model, Simon Anderson and O?zlem Bedre-Defoile maintain that the platform’s choice to sell its own products benefits consumers by lowering prices when a monopoly platform hosts competitive fringe sellers, regardless of the platform’s position as a gatekeeper, whether sellers have an alternate channel to reach consumers, or whether alternate channels are perfect or imperfect substitutes for the platform channel.[75] On the other hand, the authors argued that platform product entry might harm consumers when a big seller with market power sells on its own channel and also on the platform. Indeed, in that case, the platform setting a seller fee before the big seller prices its differentiated products introduces double markups on the big seller’s platform-channel price and leaves some revenue to the big seller.

Studying whether Amazon engages in self-preferencing on its marketplace by favoring its own brands in search results, Chiara Farronato, Andrey Fradkin, and Alexander MacKay demonstrate empirically that Amazon brands remain about 30% cheaper and have 68% more reviews than other similar products.[76] The authors acknowledge, however, that their findings do not imply that consumers are hurt by Amazon brands’ position in search results.

Another strand of literature specifically tackles the welfare effects of sherlocking. In particular, Erik Madsen and Nikhil Vellodi developed a theoretical framework to demonstrate that a ban on insider imitation can either stifle or stimulate innovation, depending on the nature of innovation.[77] Specifically, the ban could stimulate innovation for experimental product categories, while reducing innovation in incremental product markets, since the former feature products with a large chance of superstar demand and the latter generate mostly products with middling demand.

Federico Etro maintains that the tradeoffs at-issue are too complex to be solved with simple interventions, such as bans on dual mode, self-preferencing, or copycatting.[78] Indeed, it is difficult to conclude that Amazon entry is biased to expropriate third-party sellers or that bans on dual mode, self-preferencing, or copycatting would benefit consumers, because they either degrade services and product variety or induce higher prices or commissions.

Similar results are provided by Jay Pil Choi, Kyungmin Kim, and Arijit Mukherjee, who developed a tractable model of a platform-run marketplace where the platform charges a referral fee to the sellers for access to the marketplace, and may also subsequently launch its own private-label product by copying a seller.[79] The authors found that a policy to either ban hybrid mode or only prohibit information use for the launch of private-label products may produce negative welfare implications.

Further, Radostina Shopova argues that, when introducing a private label, the marketplace operator does not have incentive to distort competition and foreclose the outside seller, but does have an incentive to lower fees charged to the outside seller and to vertically differentiate its own product in order to protect the seller’s channel.[80] Even when the intermediary is able to perfectly mimic the quality of the outside seller and monopolize its product space, the intermediary prefers to differentiate its offer and chooses a lower quality for the private-label product. Accordingly, as the purpose of private labels is to offer a lower-quality version of products aimed at consumers with a lower willingness to pay, a marketplace operator does not have an incentive to distort competition in favor of its own product and foreclose the seller of the original higher-quality product.

In addition, according to Jean-Pierre Dubé, curbing development of private-label programs would harm consumers and Amazon’s practices amount to textbook retailing, as they follow an off-the-shelf approach to managing private-label products that is standard for many retail chains in the West.[81] As a result, singling out Amazon’s practices would set a double standard.

Interestingly, such findings about predictors and effects of Amazon’s entry in competition with third-party merchants on its own marketplace are confirmed by the only empirical study developed so far. In particular, analyzing the Home & Kitchen department of Germany’s version of Amazon Marketplace between 2016 and 2021, Gregory S. Crawford, Matteo Courthoud, Regina Seibel, and Simon Zuzek’s results suggest that Amazon’s entry strategy was more consistent with making Marketplace more attractive to consumers than expropriating third-party merchants.[82] Notably, the study showed that, comparing Amazon’s entry decisions with those of the largest third-party merchants, Amazon tends to enter low-growth and low-quality products, which is consistent with a strategy that seeks to make Marketplace more attractive by expanding variety, lessening third-party market power, and/or enhancing product availability. The authors therefore found that Amazon’s entry on Amazon Marketplace demonstrated no systematic adverse effects and caused a mild market expansion.

Massimo Motta and Sandro Shelegia explored interactions between copying and acquisitions, finding that the former (or the threat of copying) can modify the outcome of an acquisition negotiation.[83] According to their model, there could be both static and dynamic incentives for an incumbent to introduce a copycat version of a complementary product. The static rationale consists of lowering the price of the complementary product in order to capture more rents from it, while the dynamic incentive consists of harming a potential rival’s prospects of developing a substitute. The latter may, in turn, affect the direction the entrant takes toward innovation. Anticipating the incumbent’s copying strategy, the entrant may shift resources from improvements to compete with the incumbent’s primary product to developing complementary products.

Jingcun Cao, Avery Haviv, and Nan Li analyzed the opposite scenario—i.e., copycats that seek to mimic the design and user experience of incumbents’ successful products.[84] The authors find empirically that, on average, copycat apps do not have a significant effect on the demand for incumbent apps and that, as with traditional counterfeit products, they may generate a positive demand spillover toward authentic apps.

Massimo Motta also investigated the potential foreclosure effects of platforms adopting a copycat strategy committed to non-discriminatory terms of access for third parties (e.g., Apple App Store, Google Play, and Amazon Marketplace).[85] Notably, according to Motta, when a third-party seller is particularly successful and the platform is unable to raise fees and commissions paid by that seller, the platform may prefer to copy its product or service to extract more profits from users, rather than rely solely on third-party sales. The author acknowledged, however, that even though this practice may create an incentive for self-preferencing, it does not necessarily have anticompetitive effects. Indeed, the welfare effects of the copying strategy are a priori ambiguous.[86] While, on the one hand, the platform’s copying of a third-party product benefits consumers by increasing variety and competition among products, on the other hand, copying might be wasteful for society, in that it entails a fixed cost and may discourage innovation if rivals anticipate that they will be systematically copied whenever they have a successful product.[87] Therefore, introducing a copycat version of a product offered by a firm in an adjacent market might be procompetitive.

B. Antitrust Assessment: Competition, Innovation, and Double Standards

The economic literature has demonstrated that the rationale and welfare effects of sherlocking by hybrid platforms are definitively ambiguous. Against concerns about rivals’ foreclosure, some studies provide a different narrative, illustrating that such a strategy is more consistent with making the platform more attractive to consumers (by differentiating the quality and pricing of the offer) than expropriating business users.[88] Furthermore, copies, imitations, and replicas undoubtedly benefit consumers with more choice and lower prices.

Therefore, the only way to consider sherlocking anticompetitive is by demonstrating long-term deterrent effects on innovation (i.e., reducing rivals’ incentives to invest in new products and services) outweigh consumers’ short-term advantages.[89] Moreover, deterrent effects must not be merely hypothetical, as a finding of abuse cannot be based on a mere possibility of harm.[90] In any case, such complex tradeoffs are at odds with a blanket ban.[91]

Moreover, assessments of the potential impact of sherlocking on innovation cannot disregard the role of IPRs—which are, by definition, the main primary to promote innovation. From this perspective, intellectual-property protection is best characterized as another form of tradeoff. Indeed, the economic rationale of IPRs (in particular, of patents and copyrights) involves, among other things, a tradeoff between access and incentives—i.e., between short-term competitive restrictions and long-term innovative benefits.[92]

According to the traditional incentive-based theory of intellectual property, free riding would represent a dangerous threat that justifies the exclusive rights granted by intellectual-property protection. As a consequence, so long as copycat expropriation does not infringe IPRs, it should be presumed legitimate and procompetitive. Indeed, such free riding is more of an intellectual-property issue than a competitive concern.

In addition, to strike a fair balance between restricting competition and providing incentives to innovation, the exclusive rights granted by IPRs are not unlimited in terms of duration, nor in terms of lawful (although not authorized) uses of the protected subject matter. Under the doctrine of fair use, for instance, reverse engineering represents a legitimate way to obtain information about a firm’s product, even if the intended result is to produce a directly competing product that may steer customers away from the initial product and the patented invention.

Outside of reverse engineering, copying is legitimately exercised once IPRs expire, when copycat competitors can reproduce previously protected elements. As a result of the competitive pressure exerted by new rivals, holders of expired IPRs may react by seeking solutions designed to block or at least limit the circulation of rival products. They could, for example, request other IPRs to cover aspects or functionalities different from those previously protected. They could also bring (sometimes specious) legal action for infringement of the new IPR or for unfair competition by slavish imitation. For these reasons, there have been occasions where copycat competitors have received protection from antitrust authorities against sham litigation brought by IPR holders concerned about losing margins due to pricing pressure from copycats.[93]

Finally, within the longstanding debate on the intersection of intellectual-property protection and competition, EU antitrust authorities have traditionally been unsympathetic toward restrictions imposed by IPRs. The success of the essential-facility doctrine (EFD) is the most telling example of this attitude, as its application in the EU has been extended to IPRs. As a matter of fact, the EFD represents the main antitrust tool for overseeing intellectual property in the EU.[94]

After Microsoft, EU courts have substantially dismantled one of the “exceptional circumstances” previously elaborated in Magill and specifically introduced for cases involving IPRs, with the aim of safeguarding a balance between restrictions to access and incentives to innovate. Whereas the CJEU established in Magill that refusal to grant an IP license should be considered anticompetitive if it prevents the emergence of a new product for which there is potential consumer demand, in Microsoft, the General Court considered such a requirement met even when access to an IPR is necessary for rivals to merely develop improved products with added value.

Given this background, recent competition-policy concerns about sherlocking are surprising. To briefly recap, the practice at-issue increases competition in the short term, but may affect incentives to innovate in the long-term. With regard to the latter, however, the practice neither involves products protected by IPRs nor constitutes a slavish imitation that may be caught under unfair-competition laws.

The case of Amazon, which has received considerable media coverage, is illustrative of the relevance of IP protection. Amazon has been accused of cloning batteries, power strips, wool runner shoes, everyday sling bags, camera tripods, and furniture.[95] One may wonder what kind of innovation should be safeguarded in these cases against potential copies. Admittedly, such examples appear consistent with the findings of the already-illustrated empirical study conducted by Crawford et al. indicating that Amazon tends to enter low-quality products in order to expand variety on the Marketplace and to make it more attractive to consumers.

Nonetheless, if an IPR is involved, right holders are provided with proper means to protect their products against infringement. Indeed, one of the alleged targeted companies (Williams-Sonoma) did file a complaint for design and trademark infringement, claiming that Amazon had copied a chair (Orb Dining Chair) sold by its West Elm brand. According to Williams-Sonoma, the Upholstered Orb Office Chair—which Amazon began selling under its Rivet brand in 2018—was so similar that the ordinary observer would be confused by the imitation.[96] If, instead, the copycat strategy does not infringe any IPR, the potential impact on innovation might not be considered particularly worrisome—at least at first glance.

Further, neither the degree to which third-party business data is unavailable nor the degree to which they are relevant in facilitating copying are clear cut. For instance, in the case of Amazon, public product reviews supply a great deal of information[97] and, regardless of the fact that a third party is selling a product on the Marketplace, anyone can obtain an item for the purposes of reverse engineering.[98]

In addition, antitrust authorities are used to intervening against opportunistic behavior by IPR holders. European competition authorities, in particular, have never before seemed particularly responsive to the motives of inventors and creators versus the need to encourage maximum market openness.

It should also be noted that cloning is a common strategy in traditional markets (e.g., food products)[99] and has been the subject of longstanding controversies between high-end fashion brands and fast-fashion brands (e.g., Zara, H&M).[100] Furthermore, brick-and-mortar retailers also introduce private labels and use other brands’ sales records in deciding what to produce.[101]

So, what makes sherlocking so different and dangerous when deployed in digital markets as to push competition authorities to contradict themselves?[102]

The double standard against sherlocking reflects the same concern and pursues the same goal of the various other attempts to forbid any form of self-preferencing in digital markets. Namely, antitrust investigations of sherlocking are fundamentally driven by the bias against hybrid and vertically integrated players. The investigations rely on the assumption that conflicts of interest have anticompetitive implications and that, therefore, platform neutrality should be promoted to ensure the neutrality of the competitive process.[103] Accordingly, hostility toward sherlocking may involve both of the illustrated scenarios—i.e., the use of nonpublic third-party business data either in adopting any business decision, or just copycat strategies, in particular.

As a result, however, competition authorities end up challenging a specific business model, rather than the specific practice at-issue, which brings undisputed competitive benefits in terms of lower prices and wider consumer choice, and which should therefore be balanced against potential exclusionary risks. As the CJEU has pointed out, the concept of competition on the merits:

…covers, in principle, a competitive situation in which consumers benefit from lower prices, better quality and a wider choice of new or improved goods and services. Thus, … conduct which has the effect of broadening consumer choice by putting new goods on the market or by increasing the quantity or quality of the goods already on offer must, inter alia, be considered to come within the scope of competition on the merits.[104]

Further, in light of the “as-efficient competitor” principle, competition on the merits may lead to “the departure from the market, or the marginalization of, competitors that are less efficient and so less attractive to consumers from the point of view of, among other things, price, choice, quality or innovation.”[105]

It has been correctly noted that the “as-efficient competitor” principle is a reminder of what competition law is about and how it differs from regulation.[106] Competition law aims to protect a process, rather than engineering market structures to fulfill a particular vision of how an industry is to operate.[107] In other words, competition law does not target firms on the basis of size or status and does not infer harm from (market or bargaining) power or business model. Therefore, neither the dual role played by some large online platforms nor their preferential access to sensitive business data or their vertical integration, by themselves, create a competition problem. Competitive advantages deriving from size, status, power, or business model cannot be considered per se outside the scope of competition on the merits.

Some policymakers have sought to resolve these tensions in how competition law regards sherlocking by introducing or envisaging an outright ban. These initiatives and proposals have clearly been inspired by antitrust investigations, but they did so for the wrong reasons. Instead of taking stock of the challenging tradeoffs between short-term benefits and long-term risks that an antitrust assessment of sherlocking requires, they blamed competition law for not providing effective tools to achieve the policy goal of platform neutrality.[108] Therefore, the regulatory solution is merely functional to bypass the traditional burden of proof of antitrust analysis and achieve what competition-law enforcement cannot provide.

V. Conclusion

The bias against self-preferencing strikes again. Concerns about hybrid platforms’ potential conflicts of interest have led policymakers to seek prohibitions to curb different forms of self-preferencing, making the latter the symbol of the competition-policy zeitgeist in digital markets. Sherlocking shares this fate. Indeed, the DMA outlaws any use of business users’ nonpublic data and similar proposals have been advanced in the United States, Australia, and Japan. Further, like other forms of self-preferencing, such regulatory initiatives against sherlocking have been inspired by previous antitrust proceedings.

Drawing on these antitrust investigations, the present research shows the extent to which an outright ban on sherlocking is unjustified. Notably, the practice at-issue includes two different scenarios: the broad case in which a gatekeeper exploits its preferential access to business users’ data to better calibrate all of its business decisions and the narrow case in which such data is used to adopt a copycat strategy. In either scenario, the welfare effects and competitive implications of sherlocking are unclear.

Indeed, the use of certain data by a hybrid platform to improve business decisions generally should be classified as competition on the merits, and may yield an increase in both intra-platform (with respect to business users) and inter-platform (with respect to other platforms) competition. This would benefit consumers in terms of lower prices, better quality, and a wider choice of new or improved goods and services. In a similar vein, if sherlocking is used to deliver replicas of business users’ products or services, the anti-competitiveness of such a strategy may only result from a cumbersome tradeoff between short-term benefits (i.e., lower prices and wider choice) and negative long-term effects on innovation.

An implicit confirmation of the difficulties encountered in demonstrating the anti-competitiveness of sherlocking comes from the recent complaint issued by the FTC against Amazon.[109] Current FTC Chairwoman Lina Khan devoted a significant portion of her previous academic career to questioning Amazon’s practices (including the decision to introduce its own private labels inspired by third-party products)[110] and to supporting the adoption of structural-separation remedies to tackle platforms’ conflicts of interest that induce them to exploit their “systemic informational advantage (gleaned from competitors)” to thwart rivals and strengthen their own position by introducing replica products.[111] Despite these premises and although the FTC’s complaint targets numerous practices belonging to what has been described as an interconnected strategy to block off every major avenue of competition, however, sherlocking is surprisingly off the radar.

Regulatory initiatives to ban sherlocking in order to ensure platform neutrality with respect to business users and a level playing field among rivals would sacrifice undisputed procompetitive benefits on the altar of policy goals that competition rules are not meant to pursue. Sherlocking therefore appears to be a perfect case study of the side effects of unwarranted interventions in digital markets.

[1] Giuseppe Colangelo, Antitrust Unchained: The EU’s Case Against Self-Preferencing, 72 GRUR International 538 (2023).

[2] Jacques Cre?mer, Yves-Alexandre de Montjoye, & Heike Schweitzer, Competition Policy for the Digital Era (2019), 7, https://op.europa.eu/en/publication-detail/-/publication/21dc175c-7b76-11e9-9f05-01aa75ed71a1/language-en (all links last accessed 3 Jan. 2024); UK Digital Competition Expert Panel, Unlocking Digital Competition, (2019) 58, available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_review_web.pdf.

[3] You’ve Been Sherlocked, The Economist (2012), https://www.economist.com/babbage/2012/07/13/youve-been-sherlocked.

[4] Regulation (EU) 2022/1925 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act) (2022), OJ L 265/1, Article 6(2).

[5] U.S. S. 2992, American Innovation and Choice Online Act (AICOA) (2022), Section 3(a)(6), available at https://www.klobuchar.senate.gov/public/_cache/files/b/9/b90b9806-cecf-4796-89fb-561e5322531c/B1F51354E81BEFF3EB96956A7A5E1D6A.sil22713.pdf. See also U.S. House of Representatives, Subcommittee on Antitrust, Commercial, and Administrative Law, Investigation of Competition in Digital Markets, Majority Staff Reports and Recommendations (2020), 164, 362-364, 378, available at https://democrats-judiciary.house.gov/uploadedfiles/competition_in_digital_markets.pdf.

[6] Australian Competition and Consumer Commission, Digital Platform Services Inquiry Report on Regulatory Reform (2022), 125, https://www.accc.gov.au/about-us/publications/serial-publications/digital-platform-services-inquiry-2020-2025/digital-platform-services-inquiry-september-2022-interim-report-regulatory-reform.

[7] Japan Fair Trade Commission, Market Study Report on Mobile OS and Mobile App Distribution (2023), https://www.jftc.go.jp/en/pressreleases/yearly-2023/February/230209.html.

[8] European Commission, 10 Nov. 2020, Case AT.40462, Amazon Marketplace; see Press Release, Commission Sends Statement of Objections to Amazon for the Use of Non-Public Independent Seller Data and Opens Second Investigation into Its E-Commerce Business Practices, European Commission (2020), https://ec.europa.eu/commission/presscorner/detail/en/ip_20_2077.

[9] Press Release, CMA Investigates Amazon Over Suspected Anti-Competitive Practices, UK Competition and Markets Authority (2022), https://www.gov.uk/government/news/cma-investigates-amazon-over-suspected-anti-competitive-practices.

[10] European Commission, 16 Jun. 2020, Case AT.40716, Apple – App Store Practices.

[11] Press Release, Commission Sends Statement of Objections to Meta over Abusive Practices Benefiting Facebook Marketplace, European Commission (2022), https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7728; Press Release, CMA Investigates Facebook’s Use of Ad Data, UK Competition and Markets Authority (2021), https://www.gov.uk/government/news/cma-investigates-facebook-s-use-of-ad-data.

[12] DMA, supra note 4, Recital 10 and Article 1(6).

[13] GWB Digitalization Act, 18 Jan. 2021, Section 19a. On risks of overlaps between the DMA and the competition law enforcement, see Giuseppe Colangelo, The European Digital Markets Act and Antitrust Enforcement: A Liaison Dangereuse, 47 European Law Review 597.

[14] GWB, supra note 13, Section 19a (2)(4)(b).

[15] Press Release, Commission Sends Statement of Objections to Apple Clarifying Concerns over App Store Rules for Music Streaming Providers, European Commission (2023), https://ec.europa.eu/commission/presscorner/detail/en/ip_23_1217.

[16] European Commission, 20 Dec. 2022, Case AT.40462; Press Release, Commission Accepts Commitments by Amazon Barring It from Using Marketplace Seller Data, and Ensuring Equal Access to Buy Box and Prime, European Commission (2022), https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7777; UK Competition and Markets Authority, 3 Nov. 2023, Case No. 51184, https://www.gov.uk/cma-cases/investigation-into-amazons-marketplace.

[17] UK Competition and Markets Authority, 3 Nov. 2023, Case AT.51013, https://www.gov.uk/cma-cases/investigation-into-facebooks-use-of-data.

[18] See, e.g., Gil Tono & Lewis Crofts (2022), Amazon Data Commitments Match DMA Obligations, EU’s Vestager Say, mLex (2022), https://mlexmarketinsight.com/news/insight/amazon-data-commitments-match-dma-obligation-eu-s-vestager-says (reporting that Commissioner Vestager stated that Amazon’s data commitments definitively appear to match what would be asked within the DMA).

[19] DMA, supra note 4, Recital 46.

[20] Id., Article 6(2) (also stating that, for the purposes of the prohibition, non-publicly available data shall include any aggregated and non-aggregated data generated by business users that can be inferred from, or collected through, the commercial activities of business users or their customers, including click, search, view, and voice data, on the relevant core platform services or on services provided together with, or in support of, the relevant core platform services of the gatekeeper).

[21] AICOA, supra note 5.

[22] U.S. House of Representatives, supra note 5; see also Lina M. Khan, The Separation of Platforms and Commerce, 119 Columbia Law Review 973 (2019).

[23] U.S. Federal Trade Commission, et al. v. Amazon.com, Inc., Case No. 2:23-cv-01495 (W.D. Wash., 2023).

[24] Australian Competition and Consumer Commission, supra note 6, 125.

[25] Id., 124.

[26] Japan Fair Trade Commission, supra note 7, 144.

[27] European Commission, supra note 8. But see also Amazon, Supporting Sellers with Tools, Insights, and Data (2021), https://www.aboutamazon.eu/news/policy/supporting-sellers-with-tools-insights-and-data (claiming that the company is just using aggregate (rather than individual) data: “Just like our third-party sellers and other retailers across the world, Amazon also uses data to run our business. We use aggregated data about customers’ experience across the store to continuously improve it for everyone, such as by ensuring that the store has popular items in stock, customers are finding the products they want to purchase, or connecting customers to great new products through automated merchandising.”)

[28] European Commission, supra note 16.

[29] UK Competition and Markets Authority, supra notes 9 and 16.

[30] Bundeskartellamt, 5 Jul. 2022, Case B2-55/21, paras. 493, 504, and 518.

[31] Id., para. 536.

[32] European Commission, supra note 10.

[33] European Commission, supra note 11; UK Competition and Markets Authority, supra note 11.

[34] European Commission, supra note 16. In a similar vein, see also UK Competition and Markets Authority, supra note 16, paras. 4.2-4.7.

[35] European Commission, supra note 16, para. 111.

[36] Id., para. 123.

[37] Cre?mer, de Montjoye, & Schweitzer, supra note 2, 33-34.

[38] See, e.g., Marc Bourreau, Some Economics of Digital Ecosystems, OECD Hearing on Competition Economics of Digital Ecosystems (2020), https://www.oecd.org/daf/competition/competition-economics-of-digital-ecosystems.htm; Amelia Fletcher, Digital Competition Policy: Are Ecosystems Different?, OECD Hearing on Competition Economics of Digital Ecosystems (2020).

[39] See, e.g., Cristina Caffarra, Matthew Elliott, & Andrea Galeotti, ‘Ecosystem’ Theories of Harm in Digital Mergers: New Insights from Network Economics, VoxEU (2023), https://cepr.org/voxeu/columns/ecosystem-theories-harm-digital-mergers-new-insights-network-economics-part-1 (arguing that, in merger control, the implementation of an ecosystem theory of harm would require assessing how a conglomerate acquisition can change the network of capabilities (e.g., proprietary software, brand, customer-base, data) in order to evaluate how easily competitors can obtain alternative assets to those being acquired); for a different view, see Geoffrey A. Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 George Mason Law Review 1281(2021).

[40] See, e.g., Viktoria H.S.E. Robertson, Digital merger control: adapting theories of harm, (forthcoming) European Competition Journal; Caffarra, Elliott, & Galeotti, supra note 39; OECD, Theories of Harm for Digital Mergers (2023), available at www.oecd.org/daf/competition/theories-of-harm-for-digital-mergers-2023.pdf; Bundeskartellamt, Merger Control in the Digital Age – Challenges and Development Perspectives (2022), available at https://www.bundeskartellamt.de/SharedDocs/Publikation/EN/Diskussions_Hintergrundpapiere/2022/Working_Group_on_Competition_Law_2022.pdf?__blob=publicationFile&v=2; Elena Argentesi, Paolo Buccirossi, Emilio Calvano, Tomaso Duso, Alessia Marrazzo, & Salvatore Nava, Merger Policy in Digital Markets: An Ex Post Assessment, 17 Journal of Competition Law & Economics 95 (2021); Marc Bourreau & Alexandre de Streel, Digital Conglomerates and EU Competition Policy (2019), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350512.

[41] Bundeskartellamt, 11 Feb. 2022, Case B6-21/22, https://www.bundeskartellamt.de/SharedDocs/Entscheidung/EN/Fallberichte/Fusionskontrolle/2022/B6-21-22.html;jsessionid=C0837BD430A8C9C8E04D133B0441EB95.1_cid362?nn=4136442.

[42] UK Competition and Markets Authority, Microsoft / Activision Blizzard Merger Inquiry (2023), https://www.gov.uk/cma-cases/microsoft-slash-activision-blizzard-merger-inquiry.

[43] See European Commission, Commission Prohibits Proposed Acquisition of eTraveli by Booking (2023), https://ec.europa.eu/commission/presscorner/detail/en/ip_23_4573 (finding that a flight product is a crucial growth avenue in Booking’s ecosystem, which revolves around its hotel online-travel-agency (OTA) business, as it would generate significant additional traffic to the platform, thus allowing Booking to benefit from existing customer inertia and making it more difficult for competitors to contest Booking’s position in the hotel OTA market).

[44] Thomas Eisenmann, Geoffrey Parker, & Marshall Van Alstyne, Platform Envelopment, 32 Strategic Management Journal 1270 (2011).

[45] See, e.g., Colangelo, supra note 1, and Pablo Iba?n?ez Colomo, Self-Preferencing: Yet Another Epithet in Need of Limiting Principles, 43 World Competition 417 (2020) (investigating whether and to what extent self-preferencing could be considered a new standalone offense in EU competition law); see also European Commission, Digital Markets Act – Impact Assessment Support Study (2020), 294, https://op.europa.eu/en/publication-detail/-/publication/0a9a636a-3e83-11eb-b27b-01aa75ed71a1/language-en (raising doubts about the novelty of this new theory of harm, which seems similar to the well-established leveraging theories of harm of tying and bundling, and margin squeeze).

[46] European Commission, supra note 45, 16.

[47] European Commission, 27 Jun. 2017, Case AT.39740, Google Search (Shopping).

[48] See General Court, 10 Nov. 2021, Case T-612/17, Google LLC and Alphabet Inc. v. European Commission, ECLI:EU:T:2021:763, para. 155 (stating that the general principle of equal treatment obligates vertically integrated platforms to refrain from favoring their own services as opposed to rival ones; nonetheless, the ruling framed self-preferencing as discriminatory abuse).

[49] In the meantime, however, see Opinion of the Advocate General Kokott, 11 Jan. 2024, Case C-48/22 P, Google v. European Commission, ECLI:EU:C:2024:14, paras. 90 and 95 (arguing that the self-preferencing of which Google is accused constitutes an independent form of abuse, albeit one that exhibits some proximity to cases involving margin squeezing).

[50] European Commission, Commission Sends Amazon Statement of Objections over Proposed Acquisition of iRobot (2023), https://ec.europa.eu/commission/presscorner/detail/en/IP_23_5990.

[51] The same concerns and approach have been shared by the CMA, although it reached a different conclusion, finding that the new merged entity would not have incentive to self-preference its own branded RVCs: see UK Competition and Markets Authority, Amazon / iRobot Merger Inquiry – Clearance Decision (2023), paras. 160, 188, and 231, https://www.gov.uk/cma-cases/amazon-slash-irobot-merger-inquiry.

[52] See European Commission, supra note 45, 304.

[53] Id., 313-314 (envisaging, among potential remedies, the imposition of a duty to make all data used by the platform for strategic decisions available to third parties); see also Désirée Klinger, Jonathan Bokemeyer, Benjamin Della Rocca, & Rafael Bezerra Nunes, Amazon’s Theory of Harm, Yale University Thurman Arnold Project (2020), 19, available at https://som.yale.edu/sites/default/files/2022-01/DTH-Amazon.pdf.

[54] Colangelo, supra note 1; see also Oscar Borgogno & Giuseppe Colangelo, Platform and Device Neutrality Regime: The New Competition Rulebook for App Stores?, 67 Antitrust Bulletin 451 (2022).

[55] See Court of Justice of the European Union (CJEU), 12 May 2022, Case C-377/20, Servizio Elettrico Nazionale SpA v. Autorità Garante della Concorrenza e del Mercato, ECLI:EU:C:2022:379; 19 Apr. 2018, Case C-525/16, MEO v. Autoridade da Concorrência, ECLI:EU:C:2018:270; 6 Sep. 2017, Case C-413/14 P, Intel v. Commission, ECLI:EU:C:2017:632; 6 Oct. 2015, Case C-23/14, Post Danmark A/S v. Konkurrencerådet (Post Danmark II), ECLI:EU:C:2015:651; 27 Mar. 2012, Case C-209/10, Post Danmark A/S v Konkurrencera?det (Post Danmark I), ECLI: EU:C:2012:172; for a recent overview of the EU case law, see also Pablo Iba?n?ez Colomo, The (Second) Modernisation of Article 102 TFEU: Reconciling Effective Enforcement, Legal Certainty and Meaningful Judicial Review, SSRN (2023), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4598161.

[56] CJEU, Intel, supra note 55, paras. 133-134.

[57] CJEU, Servizio Elettrico Nazionale, supra note 55, para. 73.

[58] Opinion of Advocate General Rantos, 9 Dec. 2021, Case C?377/20, Servizio Elettrico Nazionale SpA v. Autorità Garante della Concorrenza e del Mercato, ECLI:EU:C:2021:998, para. 45.

[59] CJEU, Servizio Elettrico Nazionale, supra note 55, para. 77.

[60] Id., paras. 77, 80, and 83.

[61] CJEU, 26 Nov.1998, Case C-7/97, Oscar Bronner GmbH & Co. KG v. Mediaprint Zeitungs- und Zeitschriftenverlag GmbH & Co. KG, Mediaprint Zeitungsvertriebsgesellschaft mbH & Co. KG and Mediaprint Anzeigengesellschaft mbH & Co. KG, ECLI:EU:C:1998:569.

[62] CJEU, Servizio Elettrico Nazionale, supra note 55, para. 85.

[63] European Commission, supra note 11; UK Competition and Markets Authority, supra note 17, paras. 2.6, 4.3, and 4.7.

[64] See, e.g., European Commission, Case COMP D3/34493, DSD, para. 112 (2001) OJ L166/1; affirmed in GC, 24 May 2007, Case T-151/01, DerGru?nePunkt – Duales System DeutschlandGmbH v. European Commission, ECLI:EU:T:2007:154 and CJEU, 16 Jul. 2009, Case C-385/07 P, ECLI:EU:C:2009:456; European Commission, Case IV/31.043, Tetra Pak II, paras. 105–08, (1992) OJ L72/1; European Commission, Case IV/29.971, GEMA III, (1982) OJ L94/12; CJUE, 27 Mar. 1974, Case 127/73, Belgische Radio en Televisie e socie?te? belge des auteurs, compositeurs et e?diteurs v. SV SABAM and NV Fonior, ECLI:EU:C:1974:25, para. 15; European Commission, Case IV/26.760, GEMA II, (1972) OJ L166/22; European Commission, Case IV/26.760, GEMA I, (1971) OJ L134/15.

[65] See, e.g., Richard A. Posner, Intellectual Property: The Law and Economics Approach, 19 The Journal of Economic Perspectives 57 (2005).

[66] See, e.g., Richard Gilbert & Carl Shapiro, Optimal Patent Length and Breadth, 21 The RAND Journal of Economics 106 (1990); Pankaj Tandon, Optimal Patents with Compulsory Licensing, 90 Journal of Political Economy 470 (1982); Frederic M. Scherer, Nordhaus’ Theory of Optimal Patent Life: A Geometric Reinterpretation, 62 American Economic Review 422 (1972); William D. Nordhaus, Invention, Growth, and Welfare: A Theoretical Treatment of Technological Change, Cambridge, MIT Press (1969).

[67] See, e.g., Hal R. Varian, Copying and Copyright, 19 The Journal of Economic Perspectives 121 (2005); William R. Johnson, The Economics of Copying, 93 Journal of Political Economy 158 (1985); Stephen Breyer, The Uneasy Case for Copyright: A Study of Copyright in Books, Photocopies, and Computer Programs, 84 Harvard Law Review 281 (1970).

[68] Sai Krishna Kamepalli, Raghuram Rajan, & Luigi Zingales, Kill Zone, NBER Working Paper No. 27146 (2022), http://www.nber.org/papers/w27146; Massimo Motta & Sandro Shelegia, The “Kill Zone”: Copying, Acquisition and Start-Ups’ Direction of Innovation, Barcelona GSE Working Paper Series Working Paper No. 1253 (2021), https://bse.eu/research/working-papers/kill-zone-copying-acquisition-and-start-ups-direction-innovation; U.S. House of Representatives, Subcommittee on Antitrust, Commercial, and Administrative Law, supra note 8, 164; Stigler Committee for the Study of Digital Platforms, Market Structure and Antitrust Subcommittee (2019) 54, https://research.chicagobooth.edu/stigler/events/single-events/antitrust-competition-conference/digital-platforms-committee; contra, see Geoffrey A. Manne, Samuel Bowman, & Dirk Auer, Technology Mergers and the Market for Corporate Control, 86 Missouri Law Review 1047 (2022).

[69] See also Howard A. Shelanski, Information, Innovation, and Competition Policy for the Internet, 161 University of Pennsylvania Law Review 1663 (2013), 1999 (describing as “forced free riding” the situation occurring when a platform appropriates innovation by other firms that depend on the platform for access to consumers).

[70] See Feng Zhu & Qihong Liu, Competing with Complementors: An Empirical Look at Amazon.com, 39 Strategic Management Journal 2618 (2018).

[71] Andrei Hagiu, Tat-How Teh, and Julian Wright, Should Platforms Be Allowed to Sell on Their Own Marketplaces?, 53 RAND Journal of Economics 297 (2022), (the model assumes that there is a platform that can function as a seller and/or a marketplace, a fringe of small third-party sellers that all sell an identical product, and an innovative seller that has a better product in the same category as the fringe sellers and can invest more in making its product even better; further, the model allows the different channels (on-platform or direct) and the different sellers to offer different values to consumers; therefore, third-party sellers (including the innovative seller) can choose whether to participate on the platform’s marketplace, and whenever they do, can price discriminate between consumers that come to it through the marketplace and consumers that come to it through the direct channel).

[72] See Germa?n Gutie?rrez, The Welfare Consequences of Regulating Amazon (2022), available at http://germangutierrezg.com/Gutierrez2021_AMZ_welfare.pdf (building an equilibrium model where consumers choose products on the Amazon platform, while third-party sellers and Amazon endogenously set prices of products and platform fees).

[73] See Federico Etro, Product Selection in Online Marketplaces, 30 Journal of Economics & Management Strategy 614 (2021), (relying on a model where a marketplace such as Amazon provides a variety of products and can decide, for each product, whether to monetize sales by third-party sellers through a commission or become a seller on its platform, either by commercializing a private label version or by purchasing from a vendor and resell as a first party retailer; as acknowledged by the author, a limitation of the model is that it assumes that the marketplace can set the profit?maximizing commission on each product; if this is not the case, third-party sales would be imperfectly monetized, which would increase the relative profitability of entry).

[74] Patrick Andreoli-Versbach & Joshua Gans, Interplay Between Amazon Store and Logistics, SSRN (2023) https://ssrn.com/abstract=4568024.

[75] Simon Anderson & O?zlem Bedre-Defolie, Online Trade Platforms: Hosting, Selling, or Both?, 84 International Journal of Industrial Organization 102861 (2022).

[76] Chiara Farronato, Andrey Fradkin, & Alexander MacKay, Self-Preferencing at Amazon: Evidence From Search Rankings, NBER Working Paper No. 30894 (2023), http://www.nber.org/papers/w30894.

[77] See Erik Madsen & Nikhil Vellodi, Insider Imitation, SSRN (2023) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3832712 (introducing a two-stage model where the platform publicly commits to an imitation policy and the entrepreneur observes this policy and chooses whether to innovate: if she chooses not to, the game ends and both players earn profits normalized to zero; otherwise, the entrepreneur pays a fixed innovation cost to develop the product, which she then sells on a marketplace owned by the platform).

[78] Federico Etro, The Economics of Amazon, SSRN (2022), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4307213.

[79] Jay Pil Choi, Kyungmin Kim, & Arijit Mukherjee, “Sherlocking” and Information Design by Hybrid Platforms, SSRN (2023), https://ssrn.com/abstract=4332558 (the model assumes that the platform chooses its referral fee at the beginning of the game and that the cost of entry is the same for both the seller and the platform).

[80] Radostina Shopova, Private Labels in Marketplaces, 89 International Journal of Industrial Organization 102949 (2023), (the model assumes that the market structure is given exogenously and that the quality of the seller’s product is also exogenous; therefore, the paper does not investigate how entry by a platform affects the innovation incentives of third-party sellers).

[81] Jean-Pierre Dube?, Amazon Private Brands: Self-Preferencing vs Traditional Retailing, SSRN (2022) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4205988.

[82] Gregory S. Crawford, Matteo Courthoud, Regina Seibel, & Simon Zuzek, Amazon Entry on Amazon Marketplace, CEPR Discussion Paper No. 17531 (2022), https://cepr.org/publications/dp17531.

[83] Motta & Shelegia, supra note 68.

[84] Jingcun Cao, Avery Haviv, & Nan Li, The Spillover Effects of Copycat Apps and App Platform Governance, SSRN (2023), https://ssrn.com/abstract=4250292.

[85] Massimo Motta, Self-Preferencing and Foreclosure in Digital Markets: Theories of Harm for Abuse Cases, 90 International Journal of Industrial Organization 102974 (2023).

[86] Id.

[87] Id.

[88] See, e.g., Crawford, Courthoud, Seibel, & Zuzek, supra note 82; Etro, supra note 78; Shopova, supra note 80.

[89] Motta, supra note 85.

[90] Servizio Elettrico Nazionale, supra note 55, paras. 53-54; Post Danmark II, supra note 55, para. 65.

[91] Etro, supra note 78; see also Herbert Hovenkamp, The Looming Crisis in Antitrust Economics, 101 Boston University Law Review 489 (2021), 543, (arguing that: “Amazon’s practice of selling both its own products and those of rivals in close juxtaposition almost certainly benefits consumers by permitting close price comparisons. When Amazon introduces a product such as AmazonBasics AAA batteries in competition with Duracell, prices will go down. There is no evidence to suggest that the practice is so prone to abuse or so likely to harm consumers in other ways that it should be categorically condemned. Rather, it is an act of partial vertical integration similar to other practices that the antitrust laws have confronted and allowed in the past.”)

[92] On the more complex economic rationale of intellectual property, see, e.g., William M. Landes & Richard A. Posner, The Economic Structure of Intellectual Property Law, Cambridge, Harvard University Press (2003).

[93] See, e.g., Italian Competition Authority, 18 Jul. 2023 No. 30737, Case A538 – Sistemi di sigillatura multidiametro per cavi e tubi, (2023) Bulletin No. 31.

[94] See CJEU, 6 Apr. 1995, Joined Cases C-241/91 P and 242/91 P, RTE and ITP v. Commission, ECLI:EU:C:1995:98; 29 Apr. 2004, Case C-418/01, IMS Health GmbH & Co. OHG v. NDC Health GmbH & Co. GH, ECLI:EU:C:2004:257; General Court, 17 Sep. 2007, Case T-201/04, Microsoft v. Commission, ECLI:EU:T:2007:289; CJEU, 16 Jul. 2015, Case C-170/13, Huawei Technologies Co. Ltd v. ZTE Corp., ECLI:EU:C:2015:477.

[95] See, e.g., Dana Mattioli, How Amazon Wins: By Steamrolling Rivals and Partners, Wall Street Journal (2022), https://www.wsj.com/articles/amazon-competition-shopify-wayfair-allbirds-antitrust-11608235127; Aditya Kalra & Steve Stecklow, Amazon Copied Products and Rigged Search Results to Promote Its Own Brands, Documents Show, Reuters (2021), https://www.reuters.com/investigates/special-report/amazon-india-rigging.

[96] Williams-Sonoma, Inc. v. Amazon.Com, Inc., Case No. 18-cv-07548 (N.D. Cal., 2018). The suit was eventually dismissed, as the parties entered into a settlement agreement: Williams-Sonoma, Inc. v. Amazon.Com, Inc., Case No. 18-cv-07548-AGT (N.D. Cal., 2020).

[97] Amazon Best Sellers, https://www.amazon.com/Best-Sellers/zgbs.

[98] Hovenkamp, supra note 91, 2015-2016.

[99] Nicolas Petit, Big Tech and the Digital Economy, Oxford, Oxford University Press (2020), 224-225.

[100] For a recent analysis, see Zijun (June) Shi, Xiao Liu, Dokyun Lee, & Kannan Srinivasan, How Do Fast-Fashion Copycats Affect the Popularity of Premium Brands? Evidence from Social Media, 60 Journal of Marketing Research 1027 (2023).

[101] Lina M. Khan, Amazon’s Antitrust Paradox, 126 Yale Law Journal 710 (2017), 782.

[102] See Massimo Motta &Martin Peitz, Intervention Triggers and Underlying Theories of Harm, in Market Investigations. A New Competition Tool for Europe? (M. Motta, M. Peitz, & H. Schweitzer, eds.), Cambridge, Cambridge University Press (2022), 16, 59 (arguing that, while it is unclear to what extent products or ideas are worth protecting and/or can be protected from sherlocking and whether such cloning is really harmful to consumers, this is clearly an area where an antitrust investigation for abuse of dominant position would not help).

[103] Khan, supra note 101, 780 and 783 (arguing that Amazon’s conflicts of interest tarnish the neutrality of the competitive process and that the competitive implications are clear, as Amazon is exploiting the fact that some of its customers are also its rivals).

[104] Servizio Elettrico Nazionale, supra note 55, para. 85.

[105] Post Danmark I, supra note 55, para. 22.

[106] Iba?n?ez Colomo, supra note 55, 21-22.

[107] Id.

[108] See, e.g., DMA, supra note 4, Recital 5 (complaining that the scope of antitrust provisions is “limited to certain instances of market power, for example dominance on specific markets and of anti-competitive behaviour, and enforcement occurs ex post and requires an extensive investigation of often very complex facts on a case by case basis.”).

[109] U.S. Federal Trade Commission, et al. v. Amazon.com, Inc., supra note 23.

[110] Khan, supra note 101.

[111] Khan, supra note 22, 1003, referring to Amazon, Google, and Meta.

Mexico’s Federal Economic Competition Commission (COFECE, after its Spanish acronym) has published the preliminary report it prepared following its investigation of competition in the retail electronic-commerce market (e.g., Amazon). The report finds that: 

Read the full piece here.

tl;dr

Background: In the U.S. Justice Department’s (DOJ) recent suit against Google and the Federal Trade Commission’s (FTC) latest complaint against Amazon, both antitrust agencies allege these large technology firms behave anti-competitively by preventing their rivals from reaching the “scale” needed to compete effectively.

But… achieving scale or a large customer base does not, in itself, violate antitrust law. Private companies also owe no duty to allow their competitors to reach scale. For example, Google is not required to allow Bing to gain more users so that Bing’s quality can improve. Google and Amazon’s competition for users at the expense of competitors is central to the competitive process. To make an effective antitrust case, the agencies must delineate how Amazon and Google allegedly abuse their size in ways that harm competition and consumers.

KEY TAKEAWAYS

‘SCALE’ LACKS PRECISION IN ANTITRUST

Antitrust regulators often cite “scale” in recent complaints against large tech companies. Instead of throwing that particular term around loosely, the enforcement agencies should detail precisely how firms allegedly abuse scale to harm rivals. 

Does scale unfairly raise barriers to entry? Does it impose costs on competitors? In both of the cases cited above, the alleged harm is the direct costs imposed on competitors, not the firm’s scale. After all, scale can be just another way of describing the firm that produces the highest-quality product at the lowest price. Without greater clarity, enforcement agencies would be unable to substantiate antitrust claims centered on “scale.”

To prevail in court, the agencies must articulate precise mechanisms of competitive injury from scale. Broad assertions about nebulous “scale advantages” are unlikely to demonstrate concrete anticompetitive effects. 

SCALE ALONE IS NOT AN ANTITRUST HARM

It has long been recognized that simply “achieving scale” and becoming a large firm with significant market share or production capacity does not constitute an antitrust violation. No law prohibits a company from growing large through legal competitive means. The agencies know this. The FTC argues that its complaint against Amazon is “not for being big.”

While scale can potentially be abused, it also confers significant consumer advantages. Basic economic principles demonstrate the benefits of size or scale, which may allow larger firms to reduce average costs and become more efficient. These cost savings can then be passed on to consumers through lower prices. Larger firms may also be able to make more substantial investments in innovation and product development. And network effects in technology platforms show how scale can improve service quality by attracting more users. 

Scale only becomes an issue if it is leveraged to restrain trade unfairly or in ways that harm consumers. The restraint is the harm, not the scale.

PREVENTING SCALE IS NOT AN ANTITRUST HARM 

Preventing a competitor from achieving greater size and scale is not inherently an antitrust violation either. Companies routinely take business from one another through price competition, product improvements, or other means that may limit rivals’ growth. This is a normal part of market competition. 

For example, if Amazon achieves sufficient scale that allows it to offer better prices or selection than smaller e-commerce websites, that may necessarily limit those competitors’ scale. But this does not constitute an antitrust harm; it is, instead, simply vigorous competition. An antitrust violation requires the firm to take specific actions to restrain trade or artificially raise rivals’ costs. Similar arguments hold for the DOJ’s case against Google over the company paying to be the default search engine on various mobile devices. 

Unless the agencies can demonstrate precisely how a company has abused its position to undermine rivals’ scale unfairly—rather than winning business through competition on the merits—their complaints will struggle to establish antitrust liability.

COMPETITION INCREASES CONCENTRATION, WHICH MAY LOOK LIKE SCALE

Regulators often assume that large scale enables anticompetitive behavior to harm smaller rivals. Economic analysis, however, demonstrates that scale can benefit consumers and simultaneously increase concentration through competition.

Firms that achieve significant scale can leverage resulting efficiencies to reduce costs and prices. Scale enables investments in R&D, specialized assets, advertising, and other drivers of innovation and productive efficiency. By passing cost savings on to consumers, scaled firms often gain share at the expense of higher-cost producers.

As search and switching costs fall, consumers flock to the lowest-cost and highest-quality offerings. Competition redirects purchases toward scaled companies with superior productivity and lower prices stemming from economies of scale. This reallocates market share to efficient large firms, raising concentration.

Greater competition and the competitive advantages of scale are thus entirely consistent with increased concentration. Size alone does not imply anticompetitive behavior. Regulators should evaluate specific evidence of abuse, rather than assume that scale harms competition simply because it leads to concentration.

For more on this issue, see Brian Albrecht’s posts “Is Amazon’s Scale a Harm?” and “Competition Increases Concentration,” both at Truth on the Market. 

This is the second post about the U.S. drug-approval process; the first post is here. It will explore how the Food and Drug Administration (FDA) arose, how disasters drove its expansion and regulatory oversight, and how the epidemic of the human immunodeficiency virus (HIV) changed the approval processes.

Read the full piece here.

The European Commission late last month published the full list of its “gatekeeper” designations under the Digital Markets Act (DMA). Alphabet, Amazon, Apple, ByteDance, Meta, and Microsoft—the six designated gatekeepers—now have six months to comply with the DMA’s list of obligations and restrictions with respect to their core platform services (CPS), or they stand to face hefty fines and onerous remedies (see here and here for our initial reactions).

Read the full piece here.

Under the leadership of its professional anti-Amazoner Chair Lina Khan, the Federal Trade Commission (FTC) has finally filed its antitrust complaint against Amazon. No, not the complaint about how it’s unfair to take six clicks to cancel your Prime membership. This is the big one. It mostly revolves around sellers needing to use Amazon’s fulfillment services to be part of Amazon Prime and lowering reach rankings if products are priced lower on other sites.

Instead of covering the arguments in the complaint, I want to use the complaint as an example of how I use the basics of supply and demand to sort through one of the arguments made by the FTC. Nothing about the use of price theory implies certain policy conclusions about the case. I’m just trying to be transparent, as I’ve done in the past, about how I use economics to reason about these important questions. Besides self-indulgence, the hope is that the examples help readers do the same.

Read the full piece here.

Why do monopolies exist? Many textbooks point to barriers to entry as a cause of monopolies.

Tyler Cowen and Alex Tabarrok’s textbook says: “In addition to patents, government regulation and economies of scale, monopolies may be created whenever there is a significant barrier to entry, something that raises the cost to new firms of entering the industry.” Greg Mankiw’s textbook goes as far as to say: “The fundamental cause of monopoly is barriers to entry.”

Read the full piece here.