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Bill C-59 and the Use of Structural Merger Presumptions in Canada

Regulatory Comments We, the undersigned, are scholars from the International Center for Law & Economics (ICLE) with experience in the academy, enforcement agencies, and private practice in . . .

We, the undersigned, are scholars from the International Center for Law & Economics (ICLE) with experience in the academy, enforcement agencies, and private practice in competition law. We write to address a key aspect of proposed amendments to Canadian competition law. Specifically, we focus on clauses in Bill C-59 pertinent to mergers and acquisitions and, in particular, the Bureau of Competition’s recommendation that the Bill should:

Amend Clauses 249-250 to enact rebuttable presumptions for mergers consistent with those set out in the U.S. Merger Guidelines.[1]

The Bureau’s recommendation seeks to codify in Canadian competition law the structural presumptions outlined in the 2023 U.S. Federal Trade Commission (FTC) and U.S. Justice Department (DOJ) Merger Guidelines.  On balance, however, adoption of that recommendation would impede, rather than promote, fair competition and the welfare of Canadian consumers.

The cornerstone of the proposed change lies in the introduction of rebuttable presumptions of illegality for mergers that exceed specified market-share or concentration thresholds. While this approach may seem intuitive, the economic literature and U.S. enforcement experience militate against its adoption in Canadian law.

The goal of enhancing—indeed, strengthening—Canadian competition law should not be conflated with the adoption of foreign regulatory guidelines. The most recent U.S. Merger Guidelines establish new structural thresholds, based primarily on the Herfindahl-Hirschman Index (HHI) and market share, to establish presumptions of anticompetitive effects and illegality. Those structural presumptions, adopted a few short months ago, are inconsistent with established economic literature and are untested in U.S. courts. Those U.S. guidelines should not be codified in Canadian law without robust deliberation to ensure alignment with Canadian legal principles, on the one hand, and with economic realities and evidence, on the other.

Three points are especially important. First, concentration measures are widely considered to be a poor proxy for the level of competition that prevails in a given market. Second, lower merger thresholds may lead to enforcement errors that discourage investment and entrepreneurial activity and allocate enforcement resources to the wrong cases. Finally, these risks are particularly acute when concentration thresholds are used not as useful indicators but, instead, as actual legal presumptions (albeit rebuttable ones). We discuss each of these points in more detail below.

What Concentration Measures Can and Cannot Tell Us About Competition

While the use of concentration measures and thresholds can provide a useful preliminary-screening mechanism to identify potentially problematic mergers, substantially lowering the thresholds to establish a presumption of illegality is inadvisable for several reasons.

First, too strong a reliance on concentration measures lacks economic foundation and is likely prone to frequent error. Economists have been studying the relationship between concentration and various potential indicia of anticompetitive effects—price, markup, profits, rate of return, etc.—for decades.[2] There are hundreds of empirical studies addressing this topic.[3]

The assumption that “too much” concentration is harmful assumes both that the structure of a market is what determines economic outcomes and that anyone could know what the “right” amount of concentration is. But as economists have understood since at least the 1970s (and despite an extremely vigorous, but futile, effort to show otherwise), market structure does not determine outcomes.[4]

This skepticism toward concentration measures as a guide for policy is well-supported, and is held by scholars across the political spectrum.  To take one prominent, recent example, professors Fiona Scott Morton (deputy assistant U.S. attorney general for economics in the DOJ Antitrust Division under President Barack Obama, now at Yale University); Martin Gaynor (former director of the FTC Bureau of Economics under President Obama, now serving as special advisor to Assistant U.S. Attorney General Jonathan Kanter, on leave from Carnegie Mellon University), and Steven Berry (an industrial-organization economist at Yale University) surveyed the industrial-organization literature and found that presumptions based on measures of concentration are unlikely to provide sound guidance for public policy:

In short, there is no well-defined “causal effect of concentration on price,” but rather a set of hypotheses that can explain observed correlations of the joint outcomes of price, measured markups, market share, and concentration.…

Our own view, based on the well-established mainstream wisdom in the field of industrial organization for several decades, is that regressions of market outcomes on measures of industry structure like the Herfindahl-Hirschman Index should be given little weight in policy debates.[5]

As Chad Syverson recently summarized:

Perhaps the deepest conceptual problem with concentration as a measure of market power is that it is an outcome, not an immutable core determinant of how competitive an industry or market is… As a result, concentration is worse than just a noisy barometer of market power. Instead, we cannot even generally know which way the barometer is oriented.[6]

This does not mean that concentration measures have no use in merger screening. Rather, market concentration is often unrelated to antitrust-enforcement goals because it is driven by factors that are endogenous to each industry. Enforcers should not rely too heavily on structural presumptions based on concentration measures, as these may be poor indicators of the instances in which antitrust enforcement is most beneficial to competition and consumers.

At What Level Should Thresholds Be Set?

Second, if concentration measures are to be used in some fashion, at what level or levels should they be set?

The U.S. 2010 Horizontal Merger Guidelines were “b?ased on updated HHI thresholds that more accurately reflect actual enforcement practice.”[7] These numbers were updated in 2023, but without clear justification. While the U.S. enforcement authorities cite several old cases (cases that implicated considerably higher levels of concentration than those in their 2023 guidelines), we agree with comments submitted in 2022 by now-FTC Bureau of Economics Director Aviv Nevo and colleagues, who argued against such a change. They wrote:

Our view is that this would not be the most productive route for the agencies to pursue to successfully prevent harmful mergers, and could backfire by putting even further emphasis on market definition and structural presumptions.

If the agencies were to substantially change the presumption thresholds, they would also need to persuade courts that the new thresholds were at the right level. Is the evidence there to do so? The existing body of research on this question is, today, thin and mostly based on individual case studies in a handful of industries. Our reading of the literature is that it is not clear and persuasive enough, at this point in time, to support a substantially different threshold that will be applied across the board to all industries and market conditions. (emphasis added) [8]

Lower merger thresholds create several risks. One is that such thresholds will lead to excessive “false positives”; that is, too many presumptions against mergers that are likely to be procompetitive or benign. This is particularly likely to occur if enforcers make it harder for parties to rebut the presumptions, e.g., by requiring stronger evidence the higher the parties are above the (now-lowered) threshold. Raising barriers to establishing efficiencies and other countervailing factors makes it more likely that procompetitive mergers will be blocked. This not only risks depriving consumers of lower prices and greater innovation in specific cases, but chills beneficial merger-and-acquisition activity more broadly. The prospect of an overly stringent enforcement regime discourages investment and entrepreneurial activity. It also allocates scarce enforcement resources to the wrong cases.

Changing the Character of Structural Presumptions

Finally, the risks described above are particularly acute, given the change in the character of structural presumptions described in the U.S. Merger Guidelines. The 2023 Merger Guidelines—and only the 2023 Merger Guidelines—state that certain structural features of mergers will raise a “presumption of illegality.”[9]

U.S. merger guidelines published in 1982,[10] 1992 (revised in 1997),[11] and 2010[12] all describe structural thresholds seen by the agencies as pertinent to merger screening. None of them mention a “presumption of illegality.” In fact, as the U.S. agencies put it in the 2010 Horizontal Merger Guidelines:

The purpose of these thresholds is not to provide a rigid screen to separate competitively benign mergers from anticompetitive ones, although high levels of concentration do raise concerns. Rather, they provide one way to identify some mergers unlikely to raise competitive concerns and some others for which it is particularly important to examine whether other competitive factors confirm, reinforce, or counteract the potentially harmful effects of increased concentration.[13]

The most worrisome category of mergers identified in the 1992 U.S. merger guidelines were said to be presumed “likely to create or enhance market power or facilitate its exercise.” The 1982 guidelines did not describe “presumptions” so much as that certain mergers that may be matters of “significant competitive concern” and “likely” to be subject to challenge.

Hence, earlier editions of the U.S. merger guidelines describe the ways that structural features of mergers might inform, but not determine, internal agency analysis of those mergers. That was useful information for industry, the bar, and the courts. Equally useful were descriptions of mergers that were “unlikely to have adverse competitive effects and ordinarily require no further analysis,”[14] as well as intermediate types of mergers that “potentially raise significant competitive concerns and often warrant scrutiny.”[15]

Similarly, the 1992 U.S. merger guidelines identified a tier of mergers deemed “unlikely to have adverse competitive effects and ordinarily require no further analysis,” as well as intermediate categories of mergers either unlikely to have anticompetitive effects or, in the alternative, potentially raising significant competitive concerns, depending on various factors described elsewhere in the guidelines.[16]

By way of contrast, the new U.S. guidelines include no description of any mergers that are unlikely to have adverse competitive effects. And while the new merger guidelines do stipulate that the “presumption of illegality can be rebutted or disproved,” they offer very limited means of rebuttal.

This is at odds with prior U.S. agency practice and established U.S. law. Until very recently, U.S. agency staff sought to understand proposed mergers under the totality of their circumstances, much as U.S. courts came to do. Structural features of mergers (among many others) might raise concerns of greater or lesser degrees. These might lead to additional questions in some instances; more substantial inquiries under a “second request” in a minority of instances; or, eventually, a complaint against a very small minority of proposed mergers. In the alternative, they might help staff avoid wasting scarce resources on mergers “unlikely to have anticompetitive effects.”

Prior to a hearing or a trial on the merits, there might be strong, weak, or no appreciable assessments of likely liability, but there was no prima facie determination of illegality.

And while U.S. merger trials did tend to follow a burden-shifting framework for plaintiff and defendant production, they too looked to the “totality of the circumstances”[17] and a transaction’s “probable effect on future competition”[18] to determine liability, and they looked away from strong structural presumptions. As then-U.S. Circuit Judge Clarence Thomas observed in the Baker-Hughes case:

General Dynamics began a line of decisions differing markedly in emphasis from the Court’s antitrust cases of the 1960s. Instead of accepting a firm’s market share as virtually conclusive proof of its market power, the Court carefully analyzed defendants’ rebuttal evidence.[19]

Central to the holding in Baker Hughes—and contra the 2023 U.S. merger guidelines—was that, because the government’s prima facie burden of production was low, the defendant’s rebuttal burden should not be unduly onerous.[20] As the U.S. Supreme Court had put it, defendants would not be required to clearly disprove anticompetitive effects, but rather, simply to “show that the concentration ratios, which can be unreliable indicators of actual market behavior . . . did not accurately depict the economic characteristics of the [relevant] market.”[21]

Doing so would not end the matter. Rather, “the burden of producing additional evidence of anticompetitive effects shifts to the government, and merges with the ultimate burden of persuasion, which remains with the government at all times.”[22]

As the U.S. Supreme Court decision in Marine Bancorporation underscores, even by 1974, it was well understood that concentration ratios “can be unreliable indicators” of market behavior and competitive effects.

As explained above, research and enforcement over the ensuing decades have undermined reliance on structural presumptions even further. As a consequence, the structure/conduct/performance paradigm has been largely abandoned, because it’s widely recognized that market structure is not outcome–determinative.

That is not to say that high concentration cannot have any signaling value in preliminary agency screening of merger matters. But concentration metrics that have proven to be unreliable indicators of firm behavior and competitive effects should not be enshrined in Canadian statutory law. That would be a step back, not a step forward, for merger enforcement.

 

[1] Matthew Boswell, Letter to the Chair and Members of the House of Commons Standing Committee on Finance, Competition Bureau Canada (Mar. 1, 2024), available at https://sencanada.ca/Content/Sen/Committee/441/NFFN/briefs/SM-C-59_CompetitionBureauofCND_e.pdf.

[2] For a few examples from a very large body of literature, see, e.g., Steven Berry, Martin Gaynor, & Fiona Scott Morton, Do Increasing Markups Matter? Lessons from Empirical Industrial Organization, 33J. Econ. Perspectives 44 (2019); Richard Schmalensee, Inter-Industry Studies of Structure and Performance, in 2 Handbook of Industrial Organization 951-1009 (Richard Schmalensee & Robert Willig, eds., 1989); William N. Evans, Luke M. Froeb, & Gregory J. Werden, Endogeneity in the Concentration-Price Relationship: Causes, Consequences, and Cures, 41 J. Indus. Econ. 431 (1993); Steven Berry, Market Structure and Competition, Redux, FTC Micro Conference (Nov. 2017), available at https://www.ftc.gov/system/files/documents/public_events/1208143/22_-_steven_berry_keynote.pdf; Nathan Miller, et al., On the Misuse of Regressions of Price on the HHI in Merger Review, 10 J. Antitrust Enforcement 248 (2022).

[3] Id.

[4] See Harold Demsetz, Industry Structure, Market Rivalry, and Public Policy, 16 J. L. & Econ. 1 (1973).

[5] Berry, Gaynor, & Scott Morton, supra note 2.

[6] Chad Syverson, Macroeconomics and Market Power: Context, Implications, and Open Questions 33 J. Econ. Persp. 23, (2019) at 26.

[7] Joseph Farrell & Carl Shapiro, The 2010 Horizontal Merger Guidelines After 10 Years, 58 REV. IND. ORG. 58, (2021). https://link.springer.com/article/10.1007/s11151-020-09807-6.

[8] John Asker et al, Comments on the January 2022 DOJ and FTC RFI on Merger Enforcement (Apr. 20, 2022), available at https://www.regulations.gov/comment/FTC-2022-0003-1847 at 15-6.

[9] U.S. Dep’t Justice & Fed. Trade Comm’n, Merger Guidelines (Guideline One) (Dec. 18, 2023), available at https://www.ftc.gov/system/files/ftc_gov/pdf/2023_merger_guidelines_final_12.18.2023.pdf.

[10] U.S. Dep’t Justice, 1982 Merger Guidelines (1982), https://www.justice.gov/archives/atr/1982-merger-guidelines.

[11] U.S. Dep’t Justice & Fed. Trade Comm’n, 1992 Merger Guidelines (1992), https://www.justice.gov/archives/atr/1992-merger-guidelines; U.S. Dep’t Justice & Fed. Trade Comm’n, 1997 Merger Guidelines (1997), https://www.justice.gov/archives/atr/1997-merger-guidelines.

[12] U.S. Dep’t Justice & Fed. Trade Comm’n, Horizontal Merger Guidelines (Aug. 19, 2010), https://www.justice.gov/atr/horizontal-merger-guidelines-08192010; The U.S. antitrust agencies also issued Vertical Merger Guidelines in 2020. Although these were formally withdrawn in 2021 by the FTC, but not DOJ, they too are supplanted by the 2023 Merger Guidelines. See U.S. Dep’t Justice & Fed. Trade Comm’n, Vertical Merger Guidelines (Jun. 30, 2020), available at https://www.ftc.gov/system/files/documents/public_statements/1580003/vertical_merger_guidelines_6-30-20.pdf.

[13] 2010 Horizontal Merger Guidelines.

[14] Id.

[15] Id.

[16] 1992 Merger Guidelines.

[17]  United States v. Baker-Hughes Inc., 908 F.2d 981, 984 (D.C. Cir. 1990).

[18] Id. at 991.

[19] Id. at 990 (citing Hospital Corp. of Am. v. FTC, 807 F.2d 1381, 1386 (7th Cir.1986), cert. denied, 481 U.S. 1038, 107 S.Ct. 1975, 95 L.Ed.2d 815 (1987).

[20]  Id. at 987, 992.

[21]  United States v. Marine Bancorporation Inc., 418 U.S. 602, 631 (1974) (internal citations omitted).

[22]  Baker-Hughes, 908 F.2d at 983.

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Antitrust & Consumer Protection

Mi Mercado Es Su Mercado: The Flawed Competition Analysis of Mexico’s COFECE

TOTM 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 . . .

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.

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Antitrust & Consumer Protection

The Broken Promises of Europe’s Digital Regulation

TOTM If you live in Europe, you may have noticed issues with some familiar online services. From consent forms to reduced functionality and new fees, there . . .

If you live in Europe, you may have noticed issues with some familiar online services. From consent forms to reduced functionality and new fees, there is a sense that platforms like Amazon, Google, Meta, and Apple are changing the way they do business. 

Many of these changes are the result of a new European regulation called the Digital Markets Act (DMA), which seeks to increase competition in online markets. Under the DMA, so-called “gatekeepers” must allow rivals to access their platforms. Having taken effect March 7, firms now must comply with the regulation, which explains why we are seeing these changes unfold today.

Read the full piece here.

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Antitrust & Consumer Protection

Test SLC (merger)

Popular Media DEFINITION The substantial lessening of competition or “SLC” test is a standard that regulatory authorities use to assess the legality of proposed mergers and acquisitions. . . .

DEFINITION

The substantial lessening of competition or “SLC” test is a standard that regulatory authorities use to assess the legality of proposed mergers and acquisitions. The SLC test examines whether a prospective merger is likely to substantially lessen competition in a given market. Its purpose is to prevent mergers that increase prices, reduce output, limit consumer choice, or stifle innovation as a result of a decrease in competition. Mergers that substantially lessen competition are prohibited under the laws of the jurisdictions that utilize this test, such as the USA, EU, Canada, the United Kingdom, Australia and Nigeria, amongst others.

Read the full piece here.

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Antitrust & Consumer Protection

A Competition Perspective on Physician Non-Compete Agreements

Scholarship Abstract Physician non-compete agreements may have significant competitive implications, and effects on both providers and patients, but they are treated variously under the law on . . .

Abstract

Physician non-compete agreements may have significant competitive implications, and effects on both providers and patients, but they are treated variously under the law on a state-by-state basis. Reviewing the relevant law and the economic literature cannot identify with confidence the net effects of such agreements on either physicians or health care delivery with any generality. In addition to identifying future research projects to inform policy, it is argued that the antitrust “rule of reason” provides a useful and established framework with which to evaluate such agreements in specific health care markets and, potentially, to address those agreements most likely to do significant damage to health care competition and consumers.

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Antitrust & Consumer Protection

ICLE Comments to European Commission on Competition in Virtual Worlds

Regulatory Comments Executive Summary We welcome the opportunity to comment on the European Commission’s call for contributions on competition in “Virtual Worlds”.[1] The International Center for Law . . .

Executive Summary

We welcome the opportunity to comment on the European Commission’s call for contributions on competition in “Virtual Worlds”.[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.

The metaverse is an exciting and rapidly evolving set of virtual worlds. As with any new technology, concerns about the potential risks and negative consequences that the metaverse may bring have moved policymakers to explore how best to regulate this new space.

From the outset, it is important to recognize that simply because the metaverse is new does not mean that competition in this space is unregulated or somehow ineffective. Existing regulations may not explicitly or exclusively target metaverse ecosystems, but a vast regulatory apparatus already covers most aspects of business in virtual worlds. This includes European competition law, the Digital Markets Act (“DMA”), the General Data Protection Act (“GDPR), the Digital Services Act (“DSA”), and many more. Before it intervenes in this space, the commission should carefully consider whether there are any metaverse-specific problems not already addressed by these legal provisions.

This sense that competition intervention would be premature is reinforced by three important factors.

The first is that competition appears particularly intense in this space (Section I). There are currently multiple firms vying to offer compelling virtual worlds. At the time of writing, however, none appears close to dominating the market. In turn, this intense competition will encourage platforms to design services that meet consumers’ demands, notably in terms of safety and privacy. Nor does the market appear likely to fall into the hands of one of the big tech firms that command a sizeable share of more traditional internet services. Meta notoriously has poured more than $3.99 billion into its metaverse offerings during the first quarter of 2023, in addition to $13.72 billion the previous calendar year.[2] Despite these vast investments and a strategic focus on metaverse services, the company has, thus far, struggled to achieve meaningful traction in the space.[3]

Second, the commission’s primary concern appears to be that metaverses will become insufficiently “open and interoperable”.[4] But to the extent that these ecosystems do, indeed, become closed and proprietary, there is no reason to believe this to be a problem. Closed and proprietary ecosystems have several features that may be attractive to consumers and developers (Section II). These include improved product safety, performance, and ease of development. This is certainly not to say that closed ecosystems are always better than more open ones, but rather that it would be wrong to assume that one model or the other is optimal. Instead, the proper balance depends on tradeoffs that markets are better placed to decide.

Finally, timing is of the essence (Section III). Intervening so early in a fledgling industry’s life cycle is like shooting a moving target from a mile away. New rules or competition interventions might end up being irrelevant. Worse, by signaling that metaverses will be subject to heightened regulatory scrutiny for the foreseeable future, the commission may chill investment from the very firms is purports to support. In short, the commission should resist the urge to intervene so long as the industry is not fully mature.

I. Competing for Consumer Trust

The Commission is right to assume, in its call for contributions, that the extent to which metaverse services compete with each other (and continue to do so in the future) will largely determine whether they fulfil consumers’ expectations and meet the safety and trustworthiness requirements to which the commission aspires. As even the left-leaning Lessig put it:

Markets regulate behavior in cyberspace too. Prices structures often constrain access, and if they do not, then busy signals do. (America Online (AOL) learned this lesson when it shifted from an hourly to a flat-rate pricing plan.) Some sites on the web charge for access, as on-line services like AOL have for some time. Advertisers reward popular sites; online services drop unpopular forums. These behaviors are all a function of market constraints and market opportunity, and they all reflect the regulatory role of the market.[5]

Indeed, in a previous call for contributions, the Commission implicitly recognized the important role that competition plays, although it frames the subject primarily in terms of the problems that would arise if competition ceased to operate:

There is a risk of having a small number of big players becoming future gatekeepers of virtual worlds, creating market entry barriers and shutting out EU start-ups and SMEs from this emerging market. Such a closed ecosystem with the prevalence of proprietary systems can negatively affect the protection of personal information and data, the cybersecurity and the freedom and openness of virtual worlds at the same time.[6]

It is thus necessary to ask whether there is robust competition in the market for metaverse services. The short answer is a resounding yes.

A. Competition Without Tipping

While there is no precise definition of what constitutes a metaverse—much less a precise definition of the relevant market—available data suggests the space is highly competitive. This is evident in the fact that even a major global firm like Meta—having invested billions of dollars in its metaverse branch (and having rebranded the company accordingly)—has struggled to gain traction.[7]

Other major players in the space include the likes of Roblox, Fortnite, and Minecraft, which all have somewhere between 70 and 200 million active users.[8] This likely explains why Meta’s much-anticipated virtual world struggled to gain meaningful traction with consumers, stalling at around 300,000 active users.[9] Alongside these traditional players, there are also several decentralized platforms that are underpinned by blockchain technology. While these platforms have attracted massive investments, they are largely peripheral in terms of active users, with numbers often only in the low thousands.[10]

There are several inferences that can be drawn from these limited datasets. For one, it is clear that the metaverse industry is not yet fully mature. There are still multiple paradigms competing for consumer attention: game-based platforms versus social-network platforms; traditional platforms versus blockchain platforms, etc. In the terminology developed by David Teece, the metaverse industry has not yet reached a “paradigmatic” stage. It is fair to assume there is still significant scope for the entry of differentiated firms.[11]

It is also worth noting that metaverse competition does not appear to exhibit the same sort of network effects and tipping that is sometimes associated with more traditional social networks.[12] Despite competing for nearly a decade, no single metaverse project appears to be running away with the market.[13] This lack of tipping might be because these projects are highly differentiated.[14] It may also be due to the ease of multi-homing among them.[15]

More broadly, it is far from clear that competition will lead to a single metaverse for all uses. Different types of metaverse services may benefit from different user interfaces, graphics, and physics engines. This cuts in favor of multiple metaverses coexisting, rather than all services coordinating within a single ecosystem. Competition therefore appears likely lead to the emergence of multiple differentiated metaverses, rather than a single winner.

Ultimately, competition in the metaverse industry is strong and there is little sense these markets are about to tip towards a single firm in the year future.

B. Competing for Consumer Trust

As alluded to in the previous subsection, the world’s largest and most successful metaverse entrants to date are traditional videogaming platforms that have various marketplaces and currencies attached.[16] In other words, decentralized virtual worlds built upon blockchain technology remain marginal.

This has important policy implications. The primary legal issues raised by metaverses are the same as those encountered on other digital marketplaces. This includes issues like minor fraud, scams, and children buying content without their parents’ authorization.[17] To the extent these harms are not adequately deterred by existing laws, metaverse platforms themselves have important incentives to police them. In turn, these incentives may be compounded by strong competition among platforms.

Metaverses are generally multi-sided platforms that bring together distinct groups of users, including consumers and content creators. In order to maximize the value of their ecosystems, platforms have an incentive to balance the interests of these distinct groups.[18] In practice, this will often mean offering consumers various forms of protection against fraud and scams and actively policing platforms’ marketplaces. As David Evans puts it:

But as with any community, there are numerous opportunities for people and businesses to create negative externalities, or engage in other bad behavior, that can reduce economic efficiency and, in the extreme, lead to the tragedy of the commons. Multi-sided platforms, acting selfishly to maximize their own profits, often develop governance mechanisms to reduce harmful behavior. They also develop rules to manage many of the same kinds of problems that beset communities subject to public laws and regulations. They enforce these rules through the exercise of property rights and, most importantly, through the “Bouncer’s Right” to exclude agents from some quantum of the platform, including prohibiting some agents from the platform entirely…[19]

While there is little economic research to suggest that competition directly increases hosts’ incentive to policy their platforms, it stands to reason that doing so effectively can help platforms to expand the appeal of their ecosystems. This is particularly important for metaverse services whose userbases remain just a fraction of the size they could ultimately reach. While 100 or 200 million users already comprises a vast ecosystem, it pales in comparison to the sometimes billions of users that “traditional” online platforms attract.

The bottom line is that the market for metaverses is growing. This likely compounds platforms’ incentives to weed out undesirable behavior, thereby complementing government efforts to achieve the same goal.

II. Opening Platforms or Opening Pandora’s Box?

In its call for contributions, the commission seems concerned that the metaverse competition may lead to closed ecosystems that may be less beneficial to consumers than more open ones. But if this is indeed the commission’s fear, it is largely unfounded.

There are many benefits to closed ecosystems. Choosing the optimal degree of openness entails tradeoffs. At the very least, this suggests that policymakers should be careful not to assume that opening platforms up will systematically provide net benefits to consumers.

A. Antitrust Enforcement and Regulatory Initiatives

To understand why open (and weakly propertized) platforms are not always better for consumers, it is worth looking at past competition enforcement in the online space. Recent interventions by competition authorities have generally attempted (or are attempting) to move platforms toward more openness and less propertization. For their part, these platforms are already tremendously open (as the “platform” terminology implies) and attempt to achieve a delicate balance between centralization and decentralization.

Figure I: Directional Movement of Antitrust Intervention

The Microsoft cases and the Apple investigation both sought or seek to bring more openness and less propertization to those respective platforms. Microsoft was made to share proprietary data with third parties (less propertization) and to open its platform to rival media players and web browsers (more openness).[20] The same applies to Apple. Plaintiffs in private antitrust litigation brought in the United States[21] and government enforcement actions in Europe[22] are seeking to limit the fees that Apple can extract from downstream rivals (less propertization), as well as to ensure that it cannot exclude rival mobile-payments solutions from its platform (more openness).

The various cases that were brought by EU and U.S. authorities against Qualcomm broadly sought to limit the extent to which it was monetizing its intellectual property.[23] The European Union’s Amazon investigation centers on the ways in which the company uses data from third-party sellers (and, ultimately, the distribution of revenue between those sellers and Amazon).[24] In both cases, authorities are ultimately trying to limit the extent to which firms can propertize their assets.

Finally, both of the EU’s Google cases sought to bring more openness to the company’s main platform. The Google Shopping decision sanctioned Google for purportedly placing its services more favorably than those of its rivals.[25] The separate Android decision sought to facilitate rival search engines’ and browsers’ access to the Android ecosystem. The same appears to be true of ongoing litigation brought by state attorneys general in the United States.[26]

Much of the same can be said of the numerous regulatory initiatives pertaining to digital markets. Indeed, draft regulations being contemplated around the globe mimic the features of the antitrust/competition interventions discussed above. For instance, it is widely accepted that Europe’s DMA effectively transposes and streamlines the enforcement of the theories harm described above.[27] Similarly, several scholars have argued that the proposed American Innovation and Choice Online Act (“AICOA”) in the United States largely mimics European competition policy.[28] The legislation would ultimately require firms to open up their platforms, most notably by forcing them to treat rival services as they would their own and to make their services more interoperable with those rivals.[29]

What is striking about these decisions and investigations is the extent to which authorities are pushing back against the very features that distinguish the platforms they are investigating. Closed (or relatively closed) platforms are forced to open up, and firms with highly propertized assets are made to share them (or, at the very least, monetize them less aggressively).

B. The Empty Quadrant

All of this would not be very interesting if it weren’t for a final piece of the puzzle: the model of open and shared platforms that authorities apparently favor has traditionally struggled to gain traction with consumers. Indeed, there seem to be vanishingly few successful consumer-oriented products and services in this space.

There have been numerous attempts to introduce truly open consumer-oriented operating systems in both the mobile and desktop segments. Most have ended in failure. Ubuntu and other flavors of the Linux operating system remain fringe products. There have been attempts to create open-source search engines, but they have not met with success.[30] The picture is similar in the online retail space. Amazon appears to have beaten eBay, despite the latter being more open and less propertized. Indeed, Amazon has historically charged higher fees than eBay and offers sellers much less freedom in the ways in which they may sell their goods.[31]

This theme is repeated in the standardization space. There have been innumerable attempts to impose open, royalty-free standards. At least in the mobile-internet industry, few (if any) of these have taken off. Instead, proprietary standards such as 5G and WiFi have been far more successful. That pattern is repeated in other highly standardized industries, like digital-video formats. Most recently, the proprietary Dolby Vision format seems to be winning the war against the open HDR10+ format.[32]

Figure II: Open and Shared Platforms

This is not to say that there haven’t been any successful examples of open, royalty-free standards. Internet protocols, blockchain, and Wikipedia all come to mind. Nor does it mean that we will not see more decentralized goods in the future. But by and large, firms and consumers have not yet taken to the idea of fully open and shared platforms. Or, at least, those platforms have not yet achieved widespread success in the marketplace (potentially due to supply-side considerations, such as the difficulty of managing open platforms or the potentially lower returns to innovation in weakly propertized ones).[33] And while some “open” projects have achieved tremendous scale, the consumer-facing side of these platforms is often dominated by intermediaries that opt for much more traditional business models (think of Coinbase in the blockchain space, or Android’s use of Linux).

C. Potential Explanations

The preceding section posited a recurring reality: the digital platforms that competition authorities wish to bring into existence are fundamentally different from those that emerge organically. But why have authorities’ ideal platforms, so far, failed to achieve truly meaningful success?

Three potential explanations come to mind. First, “closed” and “propertized” platforms might systematically—and perhaps anticompetitively—thwart their “open” and “shared” rivals. Second, shared platforms might fail to persist (or grow pervasive) because they are much harder to monetize, and there is thus less incentive to invest in them. This is essentially a supply-side explanation. Finally, consumers might opt for relatively closed systems precisely because they prefer these platforms to marginally more open ones—i.e., a demand-side explanation.

In evaluating the first conjecture, the key question is whether successful “closed” and “propertized” platforms overcame their rivals before or after they achieved some measure of market dominance. If success preceded dominance, then anticompetitive foreclosure alone cannot explain the proliferation of the “closed” and “propertized” model.[34]

Many of today’s dominant platforms, however, often overcame open/shared rivals, well before they achieved their current size. It is thus difficult to make the case that the early success of their business models was due to anticompetitive behavior. This is not to say these business models cannot raise antitrust issues, but rather that anticompetitive behavior is not a good explanation for their emergence.

Both the second and the third conjectures essentially ask whether “closed” and “propertized” might be better adapted to their environment than “open” and “shared” rivals.

In that respect, it is not unreasonable to surmise that highly propertized platforms would generally be easier to monetize than shared ones. For example, to monetize open-source platforms often requires relying on complementarities, which tend to be vulnerable to outside competition and free-riding.[35] There is thus a natural incentive for firms to invest and innovate in more propertized environments. In turn, competition enforcement that limits a platform’s ability to propertize their assets may harm innovation.

Similarly, authorities should reflect on whether consumers really want the more “competitive” ecosystems that they are trying to design. The European Commission, for example, has a long track record of seeking to open digital platforms, notably by requiring that platform owners do not preinstall their own web browsers (the Microsoft decisions are perhaps the most salient example). And yet, even after these interventions, new firms have kept using the very business model that the commission reprimanded, rather than the “pro-consumer” model it sought to impose on the industry. For example, Apple tied the Safari browser to its iPhones; Google went to some length to ensure that Chrome was preloaded on devices; and Samsung phones come with Samsung Internet as default.[36] Yet this has not ostensibly steered consumers away from those platforms.

Along similar lines, a sizable share of consumers opt for Apple’s iPhone, which is even more centrally curated than Microsoft Windows ever was (and the same is true of Apple’s MacOS). In other words, it is hard to claim that opening platforms is inherently good for consumers when those same consumers routinely opt for platforms with the very features that policymakers are trying to eliminate.

Finally, it is worth noting that the remedies imposed by competition authorities have been anything but successes. Windows XP N (the version of Windows that came without Windows Media Player) was an unmitigated flop, selling a paltry 1,787 copies.[37] Likewise, the internet-browser “ballot box” imposed by the commission was so irrelevant to consumers that it took months for authorities to notice that Microsoft had removed it, in violation of the commission’s decision.[38]

One potential inference is that consumers do not value competition interventions that make dominant ecosystems marginally more open and less propertized. There are also many reasons why consumers might prefer “closed” systems (at least, relative to the model favored by many policymakers), even when they must pay a premium for them.

Take the example of app stores. Maintaining some control over the apps that can access the store enables platforms to easily weed out bad actors. Similarly, controlling the hardware resources that each app can use may greatly improve device performance. Indeed, it may be that a measure of control facilitates the very innovations that consumers demand. Therefore, “authorities and courts should not underestimate the indispensable role control plays in achieving coordination and coherence in the context of systemic ef?ciencies. Without it, the attempted novelties and strategies might collapse under their own complexity.”[39]

Relatively centralized platforms can eliminate negative externalities that “bad” apps impose on rival apps and consumers.[40] This is especially true when consumers will tend to attribute dips in performance to the overall platform, rather than to a particular app.[41] At the same time, they can take advantage of positive externalities to improve the quality of the overall platform.

And it is surely the case that consumers prefer to make many of their decisions at the inter-platform level, rather than within each platform. In simple terms, users arguably make their most important decision when they choose between an Apple or Android smartphone (or a Mac and a PC, etc.). In doing so, they can select their preferred app suite with one simple decision. They might thus purchase an iPhone because they like the secure App Store, or an Android smartphone because they like the Chrome Browser and Google Search. Absent false information at the time of the initial platform decision, this decision will effectively incorporate expectations about subsequent constraints.[42]

Furthermore, forcing users to make too many “within-platform” choices may undermine a product’s attractiveness. Indeed, it is difficult to create a high-quality reputation if each user’s experience is fundamentally different.[43] In short, contrary to what antitrust authorities appear to believe, closed platforms might give most users exactly what they desire.

All of this suggests that consumers and firms often gravitate spontaneously toward both closed and highly propertized platforms, the opposite of what the commission and other competition authorities tend to favor. The reasons for this trend are still misunderstood, and mostly ignored. Too often it is simply assumed that consumers benefit from more openness, and that shared/open platforms are the natural order of things. Instead, what some regard as “market failures” may in fact be features that explain the rapid emergence of the digital economy.

When considering potential policy reforms targeting the metaverse, policymakers would be wrong to assume openness (notably, in the form of interoperability) and weak propertization are always objectively superior. Instead, these platform designs entail important tradeoffs. Closed metaverse ecosystems may lead to higher consumer safety and better performance, while interoperable systems may reduce the frictions consumers face when moving from one service to another. There is little reason to believe policymakers are in a better position to weigh these tradeoffs than consumers, who vote with their virtual feet.

III. Conclusion: Competition Intervention Would be Premature

A final important argument against intervening today is that the metaverse industry is nowhere near mature. Tomorrow’s competition-related challenges and market failures might not be the same as today’s. This makes it exceedingly difficult for policymakers to design appropriate remedies and increases the risk that intervention might harm innovation.

As of 2023, the entire metaverse industry (both hardware and software) is estimated to be worth somewhere in the vicinity of $80 billion, and projections suggest this could grow by a factor of 10 by 2030.[44] Growth projections of this sort are notoriously unreliable. But in this case, they do suggest there is some consensus that the industry is not fully fledged.

Along similar lines, it remains unclear what types of metaverse services will gain the most traction with consumers, what sorts of hardware consumers will use to access these services, and what technologies will underpin the most successful metaverse platforms. In fact, it is still an open question whether the metaverse industry will foster any services that achieve widespread consumer adoption in the foreseeable future.[45] In other words, it is not exactly clear what metaverse products and services the Commission should focus on in the first place.

Given these uncertainties, competition intervention in the metaverse appears premature. Intervening so early in the industry’s life cycle is like aiming at a moving target. Ensuing remedies might end up being irrelevant before they have any influence on the products that firms develop. More worryingly, acting now signals that the metaverse industry will be subject to heightened regulatory scrutiny for the foreseeable future. In turn, this may deter large platforms from investing in the European market. It also may funnel venture-capital investments away from the European continent.

Competition intervention in burgeoning industries is no free lunch. The best evidence concerning these potential costs comes from the GDPR. While privacy regulation is obviously not the same as competition law, the evidence concerning the GDPR suggests that heavy-handed intervention may, at least in some instances, slow down innovation and reduce competition.

The most-cited empirical evidence concerning the effects of the GDPR comes from a paper by Garrett Johnson and co-authors, who link the GDPR to widespread increases to market concentration, particularly in the short-term:

We show that websites’ vendor use falls after the European Union’s (EU’s) General Data Protection Regulation (GDPR), but that market concentration also increases among technology vendors that provide support services to websites…. The week after the GDPR’s enforcement, website use of web technology vendors falls by 15% for EU residents. Websites are relatively more likely to retain top vendors, which increases the concentration of the vendor market by 17%. Increased concentration predominantly arises among vendors that use personal data, such as cookies, and from the increased relative shares of Facebook and Google-owned vendors, but not from website consent requests. Although the aggregate changes in vendor use and vendor concentration dissipate by the end of 2018, we find that the GDPR impact persists in the advertising vendor category most scrutinized by regulators.[46]

Along similar lines, an NBER working paper by Jian Jia and co-authors finds that enactment of the GDPR markedly reduced venture-capital investments in Europe:

Our findings indicate a negative differential effect on EU ventures after the rollout of GDPR relative to their US counterparts. These negative effects manifest in the overall number of financing rounds, the overall dollar amount raised across rounds, and in the dollar amount raised per individual round. Specifically, our findings suggest a $3.38 million decrease in the aggregate dollars raised by EU ventures per state per crude industry category per week, a 17.6% reduction in the number of weekly venture deals, and a 39.6% decrease in the amount raised in an average deal following the rollout of GDPR.[47]

In another paper, Samuel Goldberg and co-authors find that the GDPR led to a roughly 12% reduction in website pageviews and e-commerce revenue in Europe.[48] Finally, Rebecca Janssen and her co-authors show that the GDPR decreased the number of apps offered on Google’s Play Store between 2016 and 2019:

Using data on 4.1 million apps at the Google Play Store from 2016 to 2019, we document that GDPR induced the exit of about a third of available apps; and in the quarters following implementation, entry of new apps fell by half.[49]

Of course, the body of evidence concerning the GDPR’s effects is not entirely unambiguous. For example, Rajkumar Vekatesean and co-authors find that the GDPR had mixed effects on the returns of different types of firms.[50] Other papers also show similarly mixed effects.[51]

Ultimately, the empirical literature concerning the effects of the GDPR shows that regulation—in this case, privacy protection—is no free lunch. Of course, this does not mean that competition intervention targeting the metaverse would necessarily have these same effects. But in the absence of a clear market failure to solve, it is unclear why policymakers should run such a risk in the first place.

In the end, competition intervention in the metaverse is unlikely to be costless. The metaverse is still in its infancy, regulation could deter essential innovation, and the commission has thus far failed to identify any serious market failures that warrant public intervention. The result is that the commission’s call for contributions appears premature or, in other words, that the commission is putting the meta-cart before the meta-horse.

 

[1] Competition in Virtual Worlds and Generative AI – Calls for contributions, European Commission (Jan. 9, 2024) https://competition-policy.ec.europa.eu/document/download/e727c66a-af77-4014-962a-7c9a36800e2f_en?filename=20240109_call-for-contributions_virtual-worlds_and_generative-AI.pdf (hereafter, “Call for Contributions”).

[2] Jonathan Vaian, Meta’s Reality Labs Records $3.99 Billion Quarterly Loss as Zuckerberg Pumps More Cash into Metaverse, CNBC (Apr. 26, 2023), https://www.cnbc.com/2023/04/26/metas-reality-labs-unit-records-3point99-billion-first-quarter-loss-.html.

[3] Alan Truly, Horizon Worlds Leak: Only 1 in 10 Users Return & Web Launch Is Coming, Mixed News (Mar. 3, 2023), https://mixed-news.com/en/horizon-worlds-leak-only-1-in-10-users-return-web-launch-coming; Kevin Hurler, Hey Fellow Kids: Meta Is Revamping Horizon Worlds to Attract More Teen Users, Gizmodo (Feb. 7, 2023), https://gizmodo.com/meta-metaverse-facebook-horizon-worlds-vr-1850082068; Emma Roth, Meta’s Horizon Worlds VR Platform Is Reportedly Struggling to Keep Users, The Verge (Oct. 15, 2022),
https://www.theverge.com/2022/10/15/23405811/meta-horizon-worlds-losing-users-report; Paul Tassi, Meta’s ‘Horizon Worlds’ Has Somehow Lost 100,000 Players in Eight Months, Forbes, (Oct. 17, 2022), https://www.forbes.com/sites/paultassi/2022/10/17/metas-horizon-worlds-has-somehow-lost-100000-players-in-eight-months/?sh=57242b862a1b.

[4] Call for Contributions, supra note 1. (“6) Do you expect the technology incorporated into Virtual World platforms, enabling technologies of Virtual Worlds and services based on Virtual Worlds to be based mostly on open standards and/or protocols agreed through standard-setting organisations, industry associations or groups of companies, or rather the use of proprietary technology?”).

[5] Less Lawrence Lessig, The Law of the Horse: What Cyberlaw Might Teach, 113 Harv. L. Rev. 508 (1999).

[6] Virtual Worlds (Metaverses) – A Vision for Openness, Safety and Respect, European Commission, https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/13757-Virtual-worlds-metaverses-a-vision-for-openness-safety-and-respect/feedback_en?p_id=31962299H.

[7] Catherine Thorbecke, What Metaverse? Meta Says Its Single Largest Investment Is Now in ‘Advancing AI’, CNN Business (Mar. 15, 2023), https://www.cnn.com/2023/03/15/tech/meta-ai-investment-priority/index.html; Ben Marlow, Mark Zuckerberg’s Metaverse Is Shattering into a Million Pieces, The Telegraph (Apr. 23, 2023), https://www.telegraph.co.uk/business/2023/04/21/mark-zuckerbergs-metaverse-shattering-million-pieces; Will Gendron, Meta Has Reportedly Stopped Pitching Advertisers on the Metaverse, BusinessInsider (Apr. 18, 2023), https://www.businessinsider.com/meta-zuckerberg-stopped-pitching-advertisers-metaverse-focus-reels-ai-report-2023-4.

[8] Mansoor Iqbal, Fortnite Usage and Revenue Statistics, Business of Apps (Jan. 9, 2023), https://www.businessofapps.com/data/fortnite-statistics; Matija Ferjan, 76 Little-Known Metaverse Statistics & Facts (2023 Data), Headphones Addict (Feb. 13, 2023), https://headphonesaddict.com/metaverse-statistics.

[9] James Batchelor, Meta’s Flagship Metaverse Horizon Worlds Struggling to Attract and Retain Users, Games Industry (Oct. 17, 2022), https://www.gamesindustry.biz/metas-flagship-metaverse-horizon-worlds-struggling-to-attract-and-retain-users; Ferjan, id.

[10] Richard Lawler, Decentraland’s Billion-Dollar ‘Metaverse’ Reportedly Had 38 Active Users in One Day, The Verge (Oct. 13, 2022), https://www.theverge.com/2022/10/13/23402418/decentraland-metaverse-empty-38-users-dappradar-wallet-data; The Sandbox, DappRadar, https://dappradar.com/multichain/games/the-sandbox (last visited May 3, 2023); Decentraland, DappRadar, https://dappradar.com/multichain/social/decentraland (last visited May 3, 2023).

[11] David J. Teece, Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy, 15 Research Policy 285-305 (1986), https://www.sciencedirect.com/science/article/abs/pii/0048733386900272.

[12] Geoffrey Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 Geo. Mason L. Rev. 1279 (2021).

[13] Roblox, Wikipedia, https://en.wikipedia.org/wiki/Roblox (last visited May 3, 2023); Minecraft, Wikipedia, https://en.wikipedia.org/wiki/Minecraft (last visited May 3, 2023); Fortnite, Wikipedia, https://en.wikipedia.org/wiki/Fortnite (last visited May 3, 2023); see Fiza Chowdhury, Minecraft vs Roblox vs Fortnite: Which Is Better?, Metagreats (Feb. 20, 2023), https://www.metagreats.com/minecraft-vs-roblox-vs-fortnite.

[14]  Marc Rysman, The Economics of Two-Sided Markets, 13 J. Econ. Perspectives 134 (2009) (“First, if standards can differentiate from each other, they may be able to successfully coexist (Chou and Shy, 1990; Church and Gandal, 1992). Arguably, Apple and Microsoft operating systems have both survived by specializing in different markets: Microsoft in business and Apple in graphics and education. Magazines are an obvious example of platforms that differentiate in many dimensions and hence coexist.”).

[15] Id. at 134 (“Second, tipping is less likely if agents can easily use multiple standards. Corts and Lederman (forthcoming) show that the fixed cost of producing a video game for one more standard have reduced over time relative to the overall fixed costs of producing a game, which has led to increased distribution of games across multiple game systems (for example, PlayStation, Nintendo, and Xbox) and a less-concentrated game system market.”).

[16] What Are Fortnite, Roblox, Minecraft and Among Us? A Parent’s Guide to the Most Popular Online Games Kids Are Playing, FTC Business (Oct. 5, 2021), https://www.ftc.net/blog/what-are-fortnite-roblox-minecraft-and-among-us-a-parents-guide-to-the-most-popular-online-games-kids-are-playing; Jay Peters, Epic Is Merging Its Digital Asset Stores into One Huge Marketplace, The Verge (Mar. 22, 2023), https://www.theverge.com/2023/3/22/23645601/epic-games-fab-asset-marketplace-state-of-unreal-2023-gdc.

[17] Luke Winkie, Inside Roblox’s Criminal Underworld, Where Kids Are Scamming Kids, IGN (Jan. 2, 2023), https://www.ign.com/articles/inside-robloxs-criminal-underworld-where-kids-are-scamming-kids; Fake Minecraft Updates Pose Threat to Users, Tribune (Sept. 11, 2022), https://tribune.com.pk/story/2376087/fake-minecraft-updates-pose-threat-to-users; Ana Diaz, Roblox and the Wild West of Teenage Scammers, Polygon (Aug. 24, 2019) https://www.polygon.com/2019/8/24/20812218/roblox-teenage-developers-controversy-scammers-prison-roleplay; Rebecca Alter, Fortnite Tries Not to Scam Children and Face $520 Million in FTC Fines Challenge, Vulture (Dec. 19, 2022), https://www.vulture.com/2022/12/fortnite-epic-games-ftc-fines-privacy.html; Leonid Grustniy, Swindle Royale: Fortnite Scammers Get Busy, Kaspersky Daily (Dec. 3, 2020), https://www.kaspersky.com/blog/top-four-fortnite-scams/37896.

[18] See, generally, David Evans & Richard Schmalensee, Matchmakers: The New Economics of Multisided Platforms (Harvard Business Review Press, 2016).

[19] David S. Evans, Governing Bad Behaviour By Users of Multi-Sided Platforms, Berkley Technology Law Journal 27:2 (2012), 1201.

[20] See Case COMP/C-3/37.792, Microsoft, OJ L 32 (May 24, 2004). See also, Case COMP/39.530, Microsoft (Tying), OJ C 120 (Apr. 26, 2013).

[21] See Complaint, Epic Games, Inc. v. Apple Inc., 493 F. Supp. 3d 817 (N.D. Cal. 2020) (4:20-cv-05640-YGR).

[22] See European Commission Press Release IP/20/1073, Antitrust: Commission Opens Investigations into Apple’s App Store Rules (Jun. 16, 2020); European Commission Press Release IP/20/1075, Antitrust: Commission Opens Investigation into Apple Practices Regarding Apple Pay (Jun. 16, 2020).

[23] See European Commission Press Release IP/18/421, Antitrust: Commission Fines Qualcomm €997 Million for Abuse of Dominant Market Position (Jan. 24, 2018); Federal Trade Commission v. Qualcomm Inc., 969 F.3d 974 (9th Cir. 2020).

[24] See European Commission Press Release IP/19/4291, Antitrust: Commission Opens Investigation into Possible Anti-Competitive Conduct of Amazon (Jul. 17, 2019).

[25] See Case AT.39740, Google Search (Shopping), 2017 E.R.C. I-379. See also, Case AT.40099 (Google Android), 2018 E.R.C.

[26] See Complaint, United States v. Google, LLC, (2020), https://www.justice.gov/opa/pr/justice-department-sues-monopolist-google-violating-antitrust-laws; see also, Complaint, Colorado et al. v. Google, LLC, (2020), available at https://coag.gov/app/uploads/2020/12/Colorado-et-al.-v.-Google-PUBLIC-REDACTED-Complaint.pdf.

[27] See, e.g., Giorgio Monti, The Digital Markets Act: Institutional Design and Suggestions for Improvement, Tillburg L. & Econ. Ctr., Discussion Paper No. 2021-04 (2021), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3797730 (“In sum, the DMA is more than an enhanced and simplified application of Article 102 TFEU: while the obligations may be criticised as being based on existing competition concerns, they are forward-looking in trying to create a regulatory environment where gatekeeper power is contained and perhaps even reduced.”) (Emphasis added).

[28] See, e.g., Aurelien Portuese, “Please, Help Yourself”: Toward a Taxonomy of Self-Preferencing, Information Technology & Innovation Foundation (Oct. 25, 2021), available at https://itif.org/sites/default/files/2021-self-preferencing-taxonomy.pdf. (“The latest example of such weaponization of self-preferencing by antitrust populists is provided by Sens. Amy Klobuchar (D-MN) and Chuck Grassley (R-IA). They introduced legislation in October 2021 aimed at prohibiting the practice.2 However, the legislation would ban self-preferencing only for a handful of designated companies—the so-called “covered platforms,” not the thousands of brick-and-mortar sellers that daily self-preference for the benefit of consumers. Mimicking the European Commission’s Digital Markets Act prohibiting self-preferencing, Senate and the House bills would degrade consumers’ experience and undermine competition, since self-preferencing often benefits consumers and constitutes an integral part, rather than an abnormality, of the process of competition.”).

[29] Efforts to saddle platforms with “non-discrimination” constraints are tantamount to mandating openness. See Geoffrey A. Manne, Against the Vertical Discrimination Presumption, Foreword, Concurrences No. 2-2020 (2020) at 2 (“The notion that platforms should be forced to allow complementors to compete on their own terms, free of constraints or competition from platforms is a species of the idea that platforms are most socially valuable when they are most ‘open.’ But mandating openness is not without costs, most importantly in terms of the effective operation of the platform and its own incentives for innovation.”).

[30] See, e.g., Klint Finley, Your Own Private Google: The Quest for an Open Source Search Engine, Wired (Jul. 12, 2021), https://www.wired.com/2012/12/solar-elasticsearch-google.

[31] See Brian Connolly, Selling on Amazon vs. eBay in 2021: Which Is Better?, JungleScout (Jan. 12, 2021), https://www.junglescout.com/blog/amazon-vs-ebay; Crucial Differences Between Amazon and eBay, SaleHOO, https://www.salehoo.com/educate/selling-on-amazon/crucial-differences-between-amazon-and-ebay (last visited Feb. 8, 2021).

[32] See, e.g., Dolby Vision Is Winning the War Against HDR10 +, It Requires a Single Standard, Tech Smart, https://voonze.com/dolby-vision-is-winning-the-war-against-hdr10-it-requires-a-single-standard (last visited June 6, 2022).

[33] On the importance of managers, see, e.g., Nicolai J Foss & Peter G Klein, Why Managers Still Matter, 56 MIT Sloan Mgmt. Rev., 73 (2014) (“In today’s knowledge-based economy, managerial authority is supposedly in decline. But there is still a strong need for someone to define and implement the organizational rules of the game.”).

[34] It is generally agreed upon that anticompetitive foreclosure is possible only when a firm enjoys some degree of market power. Frank H. Easterbrook, Limits of Antitrust, 63 Tex. L. Rev. 1, 20 (1984) (“Firms that lack power cannot injure competition no matter how hard they try. They may injure a few consumers, or a few rivals, or themselves (see (2) below) by selecting ‘anticompetitive’ tactics. When the firms lack market power, though, they cannot persist in deleterious practices. Rival firms will offer the consumers better deals. Rivals’ better offers will stamp out bad practices faster than the judicial process can. For these and other reasons many lower courts have held that proof of market power is an indispensable first step in any case under the Rule of Reason. The Supreme Court has established a market power hurdle in tying cases, despite the nominally per se character of the tying offense, on the same ground offered here: if the defendant lacks market power, other firms can offer the customer a better deal, and there is no need for judicial intervention.”).

[35] See, e.g., Josh Lerner & Jean Tirole, Some Simple Economics of Open Source, 50 J. Indus. Econ. 197 (2002).

[36] See Matthew Miller, Thanks, Samsung: Android’s Best Mobile Browser Now Available to All, ZDNet (Aug. 11, 2017), https://www.zdnet.com/article/thanks-samsung-androids-best-mobile-browser-now-available-to-all.

[37] FACT SHEET: Windows XP N Sales, RegMedia (Jun. 12, 2009), available at https://regmedia.co.uk/2009/06/12/microsoft_windows_xp_n_fact_sheet.pdf.

[38] See Case COMP/39.530, Microsoft (Tying), OJ C 120 (Apr. 26, 2013).

[39] Konstantinos Stylianou, Systemic Efficiencies in Competition Law: Evidence from the ICT Industry, 12 J. Competition L. & Econ. 557 (2016).

[40] See, e.g., Steven Sinofsky, The App Store Debate: A Story of Ecosystems, Medium (Jun. 21, 2020), https://medium.learningbyshipping.com/the-app-store-debate-a-story-of-ecosystems-938424eeef74.

[41] Id.

[42] See, e.g., Benjamin Klein, Market Power in Aftermarkets, 17 Managerial & Decision Econ. 143 (1996).

[43] See, e.g., Simon Hill, What Is Android Fragmentation, and Can Google Ever Fix It?, DigitalTrends (Oct. 31, 2018), https://www.digitaltrends.com/mobile/what-is-android-fragmentation-and-can-google-ever-fix-it.

[44] Metaverse Market Revenue Worldwide from 2022 to 2030, Statista, https://www.statista.com/statistics/1295784/metaverse-market-size (last visited May 3, 2023); Metaverse Market by Component (Hardware, Software (Extended Reality Software, Gaming Engine, 3D Mapping, Modeling & Reconstruction, Metaverse Platform, Financial Platform), and Professional Services), Vertical and Region – Global Forecast to 2027, Markets and Markets (Apr. 27, 2023), https://www.marketsandmarkets.com/Market-Reports/metaverse-market-166893905.html; see also, Press Release, Metaverse Market Size Worth $ 824.53 Billion, Globally, by 2030 at 39.1% CAGR, Verified Market Research (Jul. 13, 2022), https://www.prnewswire.com/news-releases/metaverse-market-size-worth–824-53-billion-globally-by-2030-at-39-1-cagr-verified-market-research-301585725.html.

[45] See, e.g., Megan Farokhmanesh, Will the Metaverse Live Up to the Hype? Game Developers Aren’t Impressed, Wired (Jan. 19, 2023), https://www.wired.com/story/metaverse-video-games-fortnite-zuckerberg; see also Mitch Wagner, The Metaverse Hype Bubble Has Popped. What Now?, Fierce Electronics (Feb. 24, 2023), https://www.fierceelectronics.com/embedded/metaverse-hype-bubble-has-popped-what-now.

[46] Garret A. Johnson, et al., Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR, Forthcoming Management Science 1 (2023).

[47] Jian Jia, et al., The Short-Run Effects of GDPR on Technology Venture Investment, NBER Working Paper 25248, 4 (2018), available at https://www.nber.org/system/files/working_papers/w25248/w25248.pdf.

[48] Samuel G. Goldberg, Garrett A. Johnson, & Scott K. Shriver, Regulating Privacy Online: An Economic Evaluation of GDPR (2021), available at https://www.ftc.gov/system/files/documents/public_events/1588356/johnsongoldbergshriver.pdf.

[49] Rebecca Janßen, Reinhold Kesler, Michael Kummer, & Joel Waldfogel, GDPR and the Lost Generation of Innovative Apps, Nber Working Paper 30028, 2 (2022), available at https://www.nber.org/system/files/working_papers/w30028/w30028.pdf.

[50] Rajkumar Venkatesan, S. Arunachalam & Kiran Pedada, Short Run Effects of Generalized Data Protection Act on Returns from AI Acquisitions, University of Virginia Working Paper 6 (2022), available at: https://conference.nber.org/conf_papers/f161612.pdf. (“On average, GDPR exposure reduces the ROA of firms. We also find that GDPR exposure increases the ROA of firms that make AI acquisitions for improving customer experience, and cybersecurity. Returns on AI investments in innovation and operational efficiencies are unaffected by GDPR.”)

[51] For a detailed discussion of the empirical literature concerning the GDPR, see Garrett Johnson, Economic Research on Privacy Regulation: Lessons From the GDPR And Beyond, NBER Working Paper 30705 (2022), available at https://www.nber.org/system/files/working_papers/w30705/w30705.pdf.

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Antitrust & Consumer Protection

ICLE Comments to European Commission on AI Competition

Regulatory Comments Executive Summary We thank the European Commission for launching this consultation on competition in generative AI. The International Center for Law & Economics (“ICLE”) is . . .

Executive Summary

We thank the European Commission for launching this consultation on competition in generative AI. 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 our comments, we express concern that policymakers may equate the rapid rise of generative AI services with a need to intervene in these markets when, in fact, the opposite is true. As we explain, the rapid growth of AI markets, as well as the fact that new market players are thriving, suggests competition is intense. If incumbent firms could easily leverage their dominance into burgeoning generative AI markets, we would not have seen the growth of generative AI unicorns such as OpenAI, Midjourney, and Anthropic, to name but a few.

Of course, this is not to say that generative AI markets are not important—quite the opposite. Generative AI is already changing the ways that many firms do business and improving employee productivity in many industries.[1] The technology is also increasingly useful in the field of scientific research, where it has enabled creation of complex models that expand scientists’ reach.[2] Against this backdrop, Commissioner Margrethe Vestager was right to point out that it “is fundamental that these new markets stay competitive, and that nothing stands in the way of businesses growing and providing the best and most innovative products to consumers.”[3]

But while sensible enforcement is of vital importance to maintain competition and consumer welfare, knee-jerk reactions may yield the opposite outcomes. As our comments explain, overenforcement in the field of generative AI could cause the very harms that policymakers seek to avert. For instance, preventing so-called “big tech” firms from competing in these markets (for example, by threatening competition intervention as soon as they embed generative AI services in their ecosystems or seek to 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, competition in AI markets is important, but trying naïvely to hold incumbent tech firms back out of misguided fears they will come to dominate this space is likely to do more harm than good.

Our comment proceeds as follows. Section I summarizes recent calls for competition intervention in generative AI markets. Section II argues that many of these calls are underpinned by fears of data-related incumbency advantages (often referred to as “data-network effects”). Section III explains why these effects are unlikely to play a meaningful role in generative-AI markets. Section IV concludes by offering five key takeaways to help policymakers (including the Commission) better weigh the tradeoffs inherent to competition intervention in generative-AI markets.

I. 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.”[4] Similar claims of data dominance have been attached to nearly all large online platforms, including Facebook (Meta), Amazon, and Uber.[5]

While some of these claims continue even today (for example, “big data” is a key component of the U.S. Justice Department’s (DOJ) Google Search and adtech antitrust suits),[6] 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 in the early stages of mainstream adoption and remain 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 were purportedly made during the formative years of Web 2.0.[7] 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.[8] As Lina Khan, chair of the U.S. Federal Trade Commission (FTC), 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”.[9]

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.[10]

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 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.[11]

They have also launched consultations to ascertain the role that data plays in AI competition. For instance, in an ongoing consultation, the European Commission asks: “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?”[12] Unsurprisingly, the FTC has likewise been bullish about the risks 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.[13]

Certainly, it stands to reason 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.[14] 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 have routinely made headlines.[15] Apple and Amazon also have vast experience with AI assistants, and all of these firms use AI technology throughout their platforms.[16]

Contrary to what one might expect, however, the tech giants have, to date, been largely unable to leverage their vast data troves to outcompete startups like OpenAI and Midjourney. At the time of writing, OpenAI’s ChatGPT appears to be, by far, the most successful chatbot,[17] despite the large tech platforms’ apparent access to far more (and more up-to-date) data.

In these comments, we suggest that 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 largely focused on the rueful after-effects of the purported failure of 20th-century antitrust to address the allegedly manifest harms of 21st-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.

II. Data-Network Effects Theory and Enforcement

Proponents of tougher interventions 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).[18] 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.”[19] This self-reinforcing cycle purportedly leads to market domination by a single firm. Thus, it is argued, for example, 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.”[20]

Right off the bat, it is important to note the conceptual problem these claims face. Because data can be used to improve the quality of products and/or to subsidize their use, the idea of data as an entry barrier suggests that any product improvement or price reduction made by an incumbent could be a problematic entry barrier to any new entrant. 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.[21]

Meanwhile, actual economic studies of data-network effects have been few and far between, with scant empirical evidence to support the theory.[22] Andrei Hagiu and Julian Wright’s theoretical paper offers perhaps the most comprehensive treatment of the topic to date.[23] The authors ultimately conclude that data-network effects can be of different magnitudes and have varying effects on firms’ incumbency advantage.[24] 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.”[25]

This is echoed by other 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.”[26] 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.[27]

This possibility is also implicit in Hagiu and Wright’s paper.[28] 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, however, 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.[29]

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.[30] Nevertheless, the Furman Report concludes that data “may confer a form of unmatchable advantage on the incumbent business, making successful rivalry less likely,”[31] and adopts without reservation “convincing” evidence from non-economists that have no apparent empirical basis.[32]

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.[33]

As a result, the Commission cleared the merger on the condition that Google refrain from using data from Fitbit devices for its advertising platform.[34] The Commission will likely focus on similar issues during its ongoing investigation of Microsoft’s investment into OpenAI.[35]

Along similar lines, the FTC’s complaint to enjoin Meta’s purchase of a virtual-reality (VR) fitness app called “Within” relied, among other things, on 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.”[36]

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.”[37] Similarly, in its search complaint, the agency argues 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.[38]

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 newly published 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.”[39] Likewise, the UK Competition and Markets Authority (CMA) warns 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….[40]

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

III. Data-Incumbency Advantages in Generative-AI Markets

Given the assertions canvassed in the previous section, it would be reasonable to assume that firms such as Google, Meta, and Amazon should be in pole position to dominate the burgeoning market 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.”[41]

To date, however, this is not how things have unfolded—although it bears noting these markets 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 holds an estimated 60% of the market (though reliable numbers are somewhat elusive).[42] 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 the previous record holder, TikTok.[43] 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.[44] In April 2023, ChatGPT reportedly registered 206.7 million unique visitors, compared to 19.5 million for Google’s Bard.[45] 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 market position.[46]

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.[47] 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.[48]

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.”[49]

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.[50]

In other words, being the firm with the most data appears to be far less important than having enough data. This lower bar may be accessible to far more firms than one might initially think possible. And obtaining enough data could become even easier—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,[51] or may even outperform real-world data.[52] 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.[53]

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.”[54]

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.[55] In one important example:

[t]he 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.[56]

Platforms’ current efforts are thus focused on improving the mathematical and logical reasoning of large language models (LLMs), rather than maximizing training datasets.[57] Two points stand out. The first is that firms like OpenAI rely largely on publicly available datasets—such as GSM8K—to train their LLMs.[58] Second, the real challenge to create cutting-edge AI is not so much in collecting data, but rather 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.[59]

Furthermore, it is worth noting that the data most relevant to startups in a given market may not be those data held by large incumbent platforms in other markets, but rather 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.[60]

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 sense these are ultimately decisive.[61] As a result, incumbent platforms’ access to vast numbers of users and data in their primary markets might only marginally affect their AI competitiveness.

A related observation is that firms’ capabilities and other features of their products arguably play a more important role than the data they own.[62] Examples of this abound in digital markets. Google overthrew Yahoo, despite initially having access to far fewer users and far less data; Google and Apple overcame Microsoft in the smartphone OS market despite having comparatively tiny ecosystems (at the time) to leverage; and TikTok rose to prominence despite intense competition from incumbents like Instagram, which had much larger user bases. In each of these cases, important product-design decisions (such as the PageRank algorithm, recognizing the specific needs of mobile users,[63] and TikTok’s clever algorithm) appear to have played a far more significant role than 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 what data it did (or did not) own. Going forward, OpenAI and its rivals’ ability to offer and monetize compelling stores offering custom versions of their generative-AI technology will arguably play a much larger role than (and contribute to) their ownership of data.[64] In other words, the ultimate challenge is arguably to create a valuable platform, of which data ownership is a consequence, but 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.[65] 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 extract and organize the right information is more important than simply owning vast troves of data.[66] 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 firms would not be where they are today. This does not mean that data is worthless, of course. 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.

IV. 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.[67]

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 main 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 effects stemming 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 competition-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 failure suggests that, in a process akin to Clayton Christensen’s “innovator’s dilemma,”[68] something about the incumbent platforms’ existing services and capabilities was holding them back in those markets. Of course, this does not necessarily mean that those same services or capabilities could not become an advantage when the generative-AI market starts addressing issues of monetization and scale.[69] But it does mean that assumptions about a firm’s market power based on its possession of data are 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 desires with reality. While there currently exists a vibrant AI-startup ecosystem, there is at least a case to be made that the most 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.

Finally, even if there were a competition-related market failure to be addressed in the field of generative AI (which is anything but clear), it is unclear that the remedies being contemplated would do more good than harm. 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 markets.[70] 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.[71]

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

 

[1] See, e.g., Michael Chui, et al., The Economic Potential of Generative AI: The Next Productivity Frontier, McKinsey (Jun. 14, 2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-AI-the-next-productivity-frontier.

[2] See, e. g., Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, & Animashree Anandkumar, State-Specific Protein–Ligand Complex Structure Prediction with a Multiscale Deep Generative Model, 6 Nature Machine Intelligence, 195-208 (2024); see also, Jaemin Seo, Sang Kyeun Kim, Azarakhsh Jalalvand, Rory Conlin, Andrew Rothstein, Joseph Abbate, Keith Erickson, Josiah Wai, Ricardo Shousha, & Egemen Kolemen, Avoiding Fusion Plasma Tearing Instability with Deep Reinforcement Learning, 626 Nature, 746-751 (2024).

[3] 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.

[4] 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.

[5] 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.”).

[6] 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.”).

[7] See, e.g., Press Release, European Commission, supra note 3; 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).

[8] 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.

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

[10] See, e.g., Press Release, European Commission, supra note 3.

[11] See infra, Section II. 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.”).

[12] Press Release, European Commission, supra note 3.

[13] 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).

[14] 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.”).

[15] 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).

[16] 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 (July 19, 2019), https://www.forbes.com/sites/cognitiveworld/2019/07/19/the-twenty-year-history-of-ai-at-amazon.

[17] See infra Section III.

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

[19] 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.

[20] 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.”).

[21] See also Yun, supra note 19 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.”).

[22] 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).

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

[24] 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…”.

[25] Id.

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

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

[28] See Hagiu & Wright, supra note 23.

[29] 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 22, at 1330.

[30] 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.

[31] Id. at 34.

[32] 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 does maintain that such a remedy should certainly be 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. In fact, the evidence does not show this.

[33] 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.

[34] Id. at 896.

[35] 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.

[36] 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.

[37] Amended Complaint (D.D.C), supra note 6 at ¶37.

[38] Amended Complaint (E.D. Va), supra note 6 at ¶8.

[39] 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.

[40] 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.

[41] Furman Report, supra note 30, at ¶4.

[42] 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.

[43] 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.

[44] 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).

[45] 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.

[46] 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.

[47] 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.

[48] 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.

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

[50] Manne & Auer, supra note 22, at 1345.

[51] 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.”).

[52] 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.).

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

[54] 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.

[55] 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.

[56] 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)).

[57] 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.

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

[59] Lee, supra note 57.

[60] 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.).

[61] 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.”).

[62] Or, as John Yun puts it, data is only a small component of digital firms’ production function. See Yun, supra note 19, 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.”).

[63] 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.

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

[65] 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.

[66] See Yun, supra note 19 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.’”).

[67] Lerner, supra note 60, at 4-5 (emphasis added).

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

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

[70] See Hagiu & Wright, supra note 23, at 23 (“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 60.

[71] 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.”).

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Antitrust & Consumer Protection

Rising Markups and Declining Business Dynamism: Evidence From the Industry Cross Section

Popular Media In recent decades, various measures of “business dynamism”—such as new business entry rates and gross job or worker flows—have seen significant declines in the U.S. . . .

In recent decades, various measures of “business dynamism”—such as new business entry rates and gross job or worker flows—have seen significant declines in the U.S. (figure 1, right panel). Over a similar time frame, there is evidence that an important measure of market power—the average markup—has risen significantly (figure 1, left panel; De Loecker, Eeckhout, and Unger 2020). A natural question is whether these patterns are related.

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Antitrust & Consumer Protection

A Competition Law & Economics Analysis of Sherlocking

ICLE White Paper 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 . . .

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 117th 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.

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