Reasonable people may disagree about their merits, but digital-competition regulations are now the law of the land in many jurisdictions, including the EU and the UK. Policymakers in those jurisdictions will thus need to successfully navigate heretofore uncharted waters in order to implement these regulations reasonably. In recent comments that we submitted to the UK’s Competition and Markets Authority on the recently passed Digital Markets, Competition and Consumers (DMCC) bill, we tried to outline precisely that sort of “positive agenda” for digital-competition enforcement.

Read the full piece here.

Executive Summary

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

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

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

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

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

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

I. Anticompetitive Leveraging in AI Markets

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

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

A. Calls for Intervention in AI Markets

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

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

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

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

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

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

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

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

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

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

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

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

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

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

B. Data-Network Effects Theory and Enforcement

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Incentive to foreclose rivals…

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

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

C. Data-Incumbency Advantages in Generative-AI

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

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

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

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

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

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

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

Available evidence suggests that claims of “extreme” returns to scale in the tech sector are greatly overblown. Not only are the largest expenditures of digital platforms unlikely to become proportionally less important as output increases, but empirical research strongly suggests that even data does not give rise to increasing returns to scale, despite routinely being cited as the source of this effect.^[54]

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

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

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

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

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

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

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

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

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

As Andres Lerner has argued, if you wanted to start a travel business, the data from Kayak or Priceline would be far more relevant. Or if you wanted to start a ride-sharing business, data from cab companies would be more useful than the broad, market-cross-cutting profiles Google and Facebook have. Consider companies like Uber, Lyft and Sidecar that had no customer data when they began to challenge established cab companies that did possess such data. If data were really so significant, they could never have competed successfully. But Uber, Lyft and Sidecar have been able to effectively compete because they built products that users wanted to use—they came up with an idea for a better mousetrap. The data they have accrued came after they innovated, entered the market and mounted their successful challenges—not before.[64]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

II. Merger Policy and AI

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

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

A. Existing AI Partnerships Are Unlikely to Be Anticompetitive

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

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

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

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

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

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

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

B. AI Partnerships Increase Competition

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

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

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

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

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

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

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

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

III. Market Definition in AI

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

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

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

A. AI Is Difficult to Define and Not Monolithic

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

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

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

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

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

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

B. Look Beyond the Marketing Hype

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

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

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

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

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

C. Current Lack of Knowledge About Relevant Markets

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

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

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

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

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

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

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

D. Key Questions to Ask

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[15] Id.

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

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

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

[19] See infra Section I.C.

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

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

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

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

[24] Nathan Newman, Search, Antitrust, and the Economics of the Control of User Data, 31 Yale J. Reg. 401, 409 (2014) (emphasis added); see also id. at 420 & 423 (“While there are a number of network effects that come into play with Google, [“its intimate knowledge of its users contained in its vast databases of user personal data”] is likely the most important one in terms of entrenching the company’s monopoly in search advertising…. Google’s overwhelming control of user data… might make its dominance nearly unchallengeable.”).

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

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

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

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

^[29] Id.

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

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

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

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

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

[35] Id. at 34.

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

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

^[38] Id. at 896.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

^[63] Lee, supra note 61.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[85] Id.

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

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

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

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

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

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

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

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

[94] See Amazon Staff, supra note 92.

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

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

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

[98] Id.

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

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

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

[102] Barnett supra note 99.

[103] Id. at 7-8.

[104] Id.

I.  Introduction: Some Guiding Principles for Reasonable Enforcement of Digital Competition Regulation

We thank the Competition and Markets Authority (CMA) for this invitation to comment on its draft guidance for the digital-markets competition regime.[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.

Reasonable people may disagree about their merits, but digital competition regulations are now the law of the land in many jurisdictions, including the UK. Policymakers in those jurisdictions will thus need to successfully navigate heretofore uncharted territory in order to implement these regulations.

Most digital competition regulations, including the Digital Markets, Competition and Consumers (DMCC) Act, give competition authorities new and far-reaching powers. Ultimately, this affords them far greater discretion to shape digital markets according to what they perceive to be consumers’ best interests. But as a famous pop-culture quote has it, “with great power comes great responsibility”.[2]

The CMA’s acquisition of vast new powers does not mean it should wield them indiscriminately. Because these new powers are so broad, they also have the potential to deteriorate market conditions for consumers. Thus, the CMA and other enforcers should consider carefully how best to deploy their newfound prerogatives. Enforcers will need time to identify those enforcement practices that yield the best outcomes for consumers. While this will undoubtedly be an iterative process, some overarching regulatory and enforcement principles appear to us to be essential:

1. Prioritize consumer welfare: Measure success by assessing outcomes for consumers, including price, quality, and innovation;
2. Establish clear metrics and conduct regular assessments: Design specific, measurable indicators of success, and evaluate outcomes frequently to ensure implementation remains effective and relevant;
3. Respect platform autonomy: Ensure that firms remain the primary designers of their platforms;
4. Implement robust procedural safeguards and evidentiary standards: Minimize unintended consequences through sound legal processes and evidence-based decision-making;
5. Foster innovation and technological progress: Ensure regulations do not stifle innovation, but rather encourage it across the digital ecosystem.

In many respects, the draft guidance already incorporates elements of these principles, and the CMA is to be commended for its thoughtful approach. We discuss these principles in greater detail below, followed by a discussion of areas where the guidance can and should be made to better reflect this approach.

A. Prioritize Consumer Welfare

Consumers’ well-being should be the metric by which digital competition enforcement and compliance are ultimately assessed. As the CMA’s Prioritisation Principles proclaim: “The CMA has a statutory duty to ‘promote competition, both within and outside the UK, for the benefit of consumers.’”[3] It is thus essential that DMCC enforcement ultimately benefits, rather than harms, consumers. In this respect, it will be crucial for the CMA to distinguish conduct that “harms” competitors, because a rival brings superior products to the market, from conduct that harms consumers by distorting competition and foreclosing rivals. Preventing the former would penalize consumers by forcing strategic market status (SMS) firms to degrade their products and by dampening their incentives to continue to improve them.

As we explain throughout our comments, some simple procedural and substantive guardrails could ensure that enforcement ultimately delivers the goods for consumers. For example, the CMA’s guidance should make clear that potential SMS firms are allowed to make the case that increases in size, scope, or popularity are due to competition on the merits, rather than a chronic and entrenched position of market power. By the same token, the CMA should be required to show some degree of causation between consumer harm and potential SMS firms’ insulation from competition.

Favoring light-touch remedies over more intrusive alternatives would reduce the risk that DMCC enforcement leads firms to degrade their platforms in order to comply with its provisions. Other principles that would help to ensure the DMCC remains committed to consumer welfare include granting SMS firms freedom to decide how to achieve outcomes mandated by the conduct requirements, thus leveraging their expertise and know-how, and allowing sufficient time for the effects of remedies to become palpable.

B. Establish Clear Metrics and Conduct Regular Assessments

A second important point is that the deployment of new regulation is a discovery process.[4] Regulators (including the CMA) ought to require multiple iterations—learning from each as they proceed—in order to craft optimal rules. Indeed, despite some similarities with competition law, the DMCC largely rests on untested rules and procedures. This is not inherently bad or good, but it does increase the scope for enforcement errors that could harm stakeholders, including consumers and small businesses. These errors can largely be avoided by defining clear metrics for success, repeatedly assessing whether they are met, and learning from identified successes and/or failures to improve the legal regime in the future. In short, DMCC enforcement should be dynamic, with repeated reassessments of its effectiveness.

While there is some evidence in the CMA’s draft digital-markets competition regime guidance[5] that these issues are at the forefront of its thinking, there is scope to incorporate more positive feedback loops into the DMCC’s implementation. This includes establishing clear metrics for success; creating processes—such as regulatory sandboxes, experiments, and structured regulation[6]—to test rules, and to identify and impute potential failures; as well as defining procedures that enable the CMA to act on previously unavailable information and change its regulatory stance accordingly.

A look at the European experience with the DMA may prove enlightening in this respect. At the time of writing, European users still cannot directly click on Google Maps locations from the Google search-engine results page.[7] In a perfect world, regulations like the DMCC need to identify such failures (ideally before the rules are rolled out to hundreds of millions of users), and then determine whether they are inherent in the legal regime or whether they amount to noncompliance by firms. Depending on the answer, this may lead the regulator either to open noncompliance proceedings (if firms are to blame) or to rethink implementation (if degraded service is a direct consequence of the rule). This is much easier said than done. But creating processes that facilitate such assessments, and using them to improve rules going forward, is essential to maximize positive outcomes for consumers.

C. Respect Platform Autonomy

A third guiding principle is that SMS firms, rather than regulators or (even moreso) competitors, should remain the platforms’ central designers. The basic issue is that it is the platforms themselves whose incentives are the most (though not perfectly) aligned with consumers. Indeed, direct competitors will generally stand to benefit if a platform becomes highly degraded, as this may cause consumers to switch platforms. Similarly, while regulators do not benefit from degrading the services of an SMS firm, they are unlikely to suffer severe repercussions if it occurs.

The same does not hold for platforms. To a first approximation, where consumers are dissatisfied, even a monopoly platform may suffer significant losses. Consumers may switch platforms or reduce their time on the platform, which harms the firm’s bottom line and gives it an incentive to avoid offering a degraded service.

In short, platforms have better—though certainly not perfect—incentives than anyone else to design services that are optimal for users. This does not mean other stakeholders shouldn’t have any input into the scope and shape remedies and how they are rolled out, but rather that key platform-design decisions should ultimately reside with a platform’s owner.

In practice, this behooves policymakers, including the CMA, to exhibit some deference toward platforms’ product-design philosophy and key product differentiators. For instance, if a platform has built its success on features like a frictionless user interface or data security, then enforcers should favor remedies that preserve these key differentiators, even if this might entail less than optimal competition at the margin. This is simply a recognition that, if a platform has become highly successful by offering certain features to users, there is a high likelihood that users value them, and enforcers should thus attempt to preserve them.

In other words, there may be tradeoffs between increasing competition (or contestability) and certain platform features. The optimal balance is unlikely to be one where no weight is given to platform features.

D. Implement Robust Procedural Safeguards and Evidentiary Standards

Fourth, enforcers should bear in mind the maxim: “first, do no harm”. Indeed, while unintended consequences are largely unavoidable when intervening in complex systems like digital-platform markets, some procedural and evidentiary safeguards can minimize these undesired consequences. In general, these safeguards should guarantee (i) that enforcers intervene only when necessary, and (ii) that, when interventions occur, they are as surgical as possible.

In practice, this means the CMA should ensure that DMCC remedies do not degrade the usability of online services—as has arguably been the case in the EU under the Digital Markets Act (DMA).[8] Among the ways this be achieved is by granting firms the time (in terms of compliance deadlines) and flexibility (by testing multiple iterations of remedies) to roll out effective remedies. Similarly, there is a sense the CMA should favor simple remedies which only affect one part of an online platform, rather than more complex remedy packages that could have wider-reaching unintended consequences.[9] A corollary is that enforcement actions are only appropriate when enforcers have a clear sense that remedies would enable markets to function better than the status quo.

In general, enforcers should also be open to the notion that DMCC enforcement could have potentially unintended and undesirable effects on consumers.[10] After all, other digital market regulations—notably, the EU’s General Data Protection Regulation (GDPR)—have been shown to harm innovation and competition.[11] There is no reason to assume the DMCC could not suffer from similar issues if enforcers are not cautious.

Finally, enforcers should intervene only when there is a clear sense that the market is not sufficiently disciplining SMS firms; this, in turn, implies that services should only be designated when there is clear evidence that competition is failing, and that a platform has significant market power. This is why, as explained in Section II.A, it is advisable not to dispense with the definition of relevant markets while enforcing the DMCC, and to have in place a procedure that ensures the best assessment possible of market power. This is a “filter” that would allow the CMA to make efficient use of its resources and reduce both the administrative and error costs of the DMCC, benefitting not only those firms offering digital services and products, but also consumers and society overall.

E. Foster Innovation and Technological Progress

Finally, we have not reached the end of digital history. Online platform markets, including those services designated under the DMCC, could (and likely will) continue to evolve and improve dramatically over the coming decades. This is likely to be especially true as generative-AI technology continues to augment these services.

Ensuring this innovation continues requires that enforcers preserve firms’ incentives to invest in their services. These incentives may sometimes be enhanced by boosting competition, but they also depend on firms (even designated services) being able to earn risk-adjusted returns on their investments.[12] Enforcers should thus be particularly vigilant that DMCC enforcement does not expropriate designated firms, or else their incentives to continue innovating may be severely diminished (and these weakened incentives may have a knock-on effect on rivals’ efforts if innovation is seen as a strategic complement). The upshot is that, pushed to their limits, mandated competition and transfers of rents away from gatekeepers could have dramatic effects on the innovative output of some of the world’s leading innovators.

As we explain throughout the rest of our comments, some simple changes to the current guidance could bring DMCC enforcement further in line with these guiding principles, thereby benefiting society as a whole.

Legitimate concerns were raised when the DMCC (and other digital competition regulation) was passed into law. Indeed, if executed poorly, these regulations have the potential to significantly degrade consumers’ online experience, with little to no benefits to competition.[13] This is arguably what has occurred in the European Union under the DMA. That these regulations are now the law of the land should not obscure such challenges. Instead, these early warning signs suggest it is essential to fine-tune guidance and other policy documents that will drive enforcement of these regulations.

The remainder of these comments proceeds as follows. Section II discusses how strategic market status is assessed under the CMA’s draft guidance. Section III discusses the guidance on conduct requirements. Section IV discusses pro-competition intervention.

II. Strategic Market Status Definition Should Be Based on Solid Economic Evidence and Ensure an Efficient Use of the CMA’s Resources

A platform’s designation as an SMS firm is the first step toward application of the DMCC. Hence, this section of the guidance is of utmost importance to provide economic agents with certainty in designing their business models, contracts, and strategies.

The DMCC sensibly contemplates that a digital-services provider should have “substantial and entrenched market power” and “a position of strategic significance in respect of the digital activity” to be designated as an SMS firm.[14] This is appropriate, because only a firm with substantial market power would be able to impose the kind of harms that are generally relevant to competition law.[15]

Of course, the DMCC also has broader objectives, such as the fair-dealing objective, the open-choices objective, and the trust and transparency objective. But even in those scenarios, a firm without substantial market power would most likely not have incentive to treat its customers and business users “unfairly”.

This “filter” also channels the efficient use of the CMA’s resources. Without a requirement of some substantial degree of market power, competition agencies would pursue cases that are not necessarily worth the effort, as the number of citizens or businesses harmed by the alleged anticompetitive or unfair practice would be irrelevant. This would engender many more “false positives” and an over-deterrence effect on economic agents.[16] As Petit and Radic explain, the market-power requirement also filters out claims that would entail mere transfers of surplus, rather than real harms to the competitive process.[17]

The guidance then (S.2.42) specifies that: “The mere holding of market power is not in itself sufficient for an undertaking to meet the first SMS condition which requires that market power is ‘substantial’ and ‘entrenched’,” and that “‘Substantial’ and ‘entrenched’ are distinct elements and each needs to be demonstrated.” This is an important distinction, as any firm may have some market power. As Landes and Posner explained in their seminal article “Market Power in Antitrust Cases”:

[M]arket power must be distinguished from the amount of market power. When the deviation of price from marginal cost is trivial, or simply reflects certain fixed costs, there is no occasion for antitrust concern, even though the firm has market power in our sense of the term.[18]

The guidance then further clarifies, however, that the terms “substantial [and] entrenched … are not entirely separate as the assessment of each will typically draw on a common set of evidence on market power”. While it is fair to assert that the magnitude of market power (substantial or not) and its level of resiliency (entrenched or not) would have to be assessed using similar evidence, the fact that the drafters of the DMCC deliberately chose to include those words in S. 5.20, and connect them with the word “and” cannot be ignored.

Along those lines, the guidance should establish what it means for market power to be “entrenched”. In turn, this word should mean something different than “substantial”, as it should add some meaning to the Section. The concept is not defined in the case law or codified by the United Kingdom, the EU, or the United States. Both the “Online Platforms and Digital Advertising Market Study Final Report”[19] (at 21) and the “Furman Report”[20] (at 75), however, use the term “entrenched market power” to mean “difficult to remove”. The former, for instance, states that:

Google and Facebook have such entrenched market power as a result of these self-reinforcing entry barriers, that we have concluded that the CMA’s current tools, which allow us to enforce against individual practices and concerns, are not sufficient to protect competition. Further, the markets we have reviewed are fast-moving, and the issues arising within them are wideranging, complex and rapidly evolving. Tackling such issues requires an ongoing focus, and the ability to monitor and amend interventions as required.[21]

While these comments do not endorse the findings or conclusions of the abovementioned reports, the CMA may consider them as guidance to define the term “entrenched” and to specify which kind of evidence may be used to substantiate it. Following the logic of said reports, “entrenched” should mean a high degree of market power, which is not only “substantial”, but also hard to dispute. Therefore, the assessment of such quality should involve some long-term evidence of rivals not entering the market (because, for instance, of the existence of regulatory barriers to entry) or at least of a dominant firm with very stable market shares (because entrants can only compete on a small competitive fringe). A recent background note by the Organization for Economic Co-operation and Development (OECD), for instance, acknowledges that “(a)n entrenched market position therefore implies a degree of durability in a dominant position and resistance to changes”.[22]

Therefore, Section 2.52 of the guidance should be revised or eliminated. The section establishes that “where the CMA has found evidence that the firm has substantial market power at the time of the SMS investigation, this will generally support a finding that market power is entrenched”, establishing a relative presumption, rebuttable with “clear and convincing evidence” that such market power is likely to dissipate. As has been explained in the paragraphs above, the word “entrenched” should add some meaning to the section. The term “entrenched market power” cannot be reasonably construed as being generally the same as “market power”.

The guidance establishes (S.2.43) that “…assessing substantial and entrenched market power does not require the CMA to undertake a formal market definition exercise which often involves drawing arbitrary bright lines indicating which products are ‘in’ and which products are ‘out’.” It would be wise, however, not to disregard the relevant market definition when analyzing the existence of substantial and entrenched market power. While contemporary economists may be open to dispensing with the definition of relevant markets where it is possible to directly infer market power,[23] market definition is helpful not only to measure market power, but also to better identify the competitive process being harmed.[24] As Manne explains:

Particularly where novel conduct or novel markets are involved and thus the relevant economic relationships are poorly understood, market definition is crucial to determine “what the nature of [the relevant] products is, how they are priced and on what terms they are sold, what levers [a firm] can use to increase its profits, and what competitive constraints affect its ability to do so.” In this way market definition not only helps to economize on administrative costs (by cabining the scope of inquiry), it also helps to improve the understanding of the conduct in question and its consequences.[25]

Of course, as the same author warns, it is very important, especially in the case of digital markets, not to define relevant markets too narrowly by looking only to past competition in a static way:

Market definition is inherently retrospective—systematically minimizing where competition is going, and locking even fast-evolving digital competitors into the past. Traditional market definition analysis that infers future substitution possibilities from existing or past market conditions will systematically lead to overly narrow markets and an increased likelihood of erroneous market power determinations. This is the problem of viewing Google as a “search engine” and Amazon as an “online retailer,” for example, and excluding each from the other’s market. In reality, of course, both are competing for scarce user attention (and advertising dollars) in digital environments; the specific functionality they employ in order to do so is a red herring. As such (and as is apparent to virtually everyone but antitrust enforcers and advocates of increased antitrust intervention) they invest significantly in new technology, product designs, and business models because of competitive pressures from each other…

Relatively static market definitions may lead systematically to the erroneous identification of such innovation (or other procompetitive conduct) as anticompetitive. And the benefits of innovation aimed at competing with rivals outside an improperly narrow market, or procompetitive effects conferred on users elsewhere on the platform or in another market, will be relatively, if not completely, neglected.[26]

The guidance takes the abovementioned into account in Sections 2.47 and 2.48:

2.47 The CMA’s starting point will be market conditions and market power at the time of the SMS investigation. From that starting position, the CMA will consider the potential dynamics of competition over the next five years, taking into account any expected or foreseeable developments that may affect the firm’s conduct in respect of the digital activity if the firm was not to be designated.

2.48 As with any ex ante assessment, there will necessarily be some uncertainty as to the future evolution of a sector. However, such uncertainty does not preclude the CMA from finding substantial and entrenched market power based on the evidence available to it when making its assessment. If post designation developments or new evidence indicate that a firm’s market power has – contrary to the CMA’s expectations in its initial assessment been significantly diminished, the CMA is able to revisit its previous assessment and can consider whether to revoke the SMS designation.”

It is commendable that the guidelines contemplate procedures to continue or revoke an SMS designation and specify that the CMA would undertake ongoing monitoring and early reassessment of relevant digital markets, considering the submissions from economic agents. This is a good practice or regulatory governance, considering the abovementioned dynamism of digital markets.

At this point, it is relevant to mention that the market definition—or, in any case, the substitutability analysis conducted by the CMA—should consider the possible substitution of a digital product or service from offline markets. While market definition often involves discussion of specific uses or specific features of a product or service, substitutability should be measured in light of customers’ inclination to switch to other producers of the same product or services, or even to other products after the introduction of “small but significant and non-transitory increase in price”.[27] Irrespective of the product’s nature, if customers switch, there is an alternative to the hypothetic monopolist’s product that disciplines any potential exercise of market power.

There is evidence, for instance, that Amazon has faced robust competition from retail stores like Walmart.[28] In Mexico, for instance, there is empirical evidence that Amazon not only competes, but competes intensively with other distribution channels and has a net-positive welfare effect on Mexican consumers. A 2022 paper found that “e-commerce and brick-and-mortar retailers in Mexico operate in a single, highly competitive retail market”; and that “Amazon’s entry has generated a significant pro-competitive effect by reducing brick-and-mortar retail prices and increasing product selection for Mexican consumers”.^[29]

The guidance clarifies in Section 2.45 that:

Substantial and entrenched market power is a distinct legal concept from that of ‘dominance’ used in competition law enforcement cases, reflecting the fact that the digital markets competition regime is a new framework with a different purpose. As a result, the CMA will not typically seek to draw on case law relating to the assessment of dominance when undertaking an SMS assessment.

This wording suggests that the CMA could set a lower standard than that required to infer dominance in the application of competition law. While the DMCC has a different purpose than the Competition Act of 1998 and the Enterprise Act of 2002, it cannot ignored that the DMCC is concerned with the regulation of competition in digital markets,[30] and that it confers power to the CMA “to promote competition where it considers that activities of a designated undertaking are having an adverse effect on competition”.[31] Moreover, by using terms like “market power” (that in turn has to be “substantial”), the DMCC’s text allows us to infer that the bar should be set, at least, at “dominance” (if not higher, if we consider that the market power should be “entrenched”).

The DMCC, in other words, speaks the language of competition law, and competition law tends to equate the concept of dominance with “substantial market power”. As Whish explains:

Paragraph 65 of the Court’s judgment in United Brands can be understood to equate dominance with the economist’s definition of substantial market power; the Commission does so in paragraph 10 of its Guidance on Article 102 Enforcement Priorities where it says that the notion of independence referred to by the Court is related to the degree of competitive constraint exerted on the undertaking under investigation. Where competitive constraints are ineffective, the undertaking in question enjoys ‘substantial market power over a period of time; the Guidance says that an undertaking has substantial market power if it is ‘capable of profitably increasing prices above the competitive level for a significant period of time.[32] (emphasis added).

The 2004 Office of Fair Trading Guidelines on Abuse of a Dominant Position, in the same vein, states that “(a)n undertaking will not be dominant unless it has substantial market power”.[33]

Market power must be assessed case-by-case. Therefore, it is only reasonable that the CMA shouldn’t be constrained by past specific findings of dominance (or findings that there was no dominance). Still, there is no reason to disregard the criteria applied in competition caselaw to assess the dominance of a given economic agent. Such criteria would bring consistency to the CMA’s actions, more predictability to economic agents, and, therefore, more legitimacy to the DMCC.

The guidance also deals with the concept of “a position of strategic significance” of an SMS firm. In that regard, it follows to a great extent the definitions included in the DMCC, establishing that a firm has strategic significance if it has “achieved a position of significant size or scale in respect of the digital activity” and “(a) significant number of other firms use the digital activity as carried out by the firm in carrying on their business”.[34] It does not, however, offer clear guidance, as the following section establishes that “(t)here is no quantitative threshold for when size or scale of the potential SMS firm can be considered as significant, and this may be assessed in terms of the firm’s absolute position and/or relative to other relevant firms”.[35]

Like the concepts of “substantiality” and “entrenchment”, the concept of “strategic significance” should mean something different and additional to “ordinary” market power. Otherwise, we can assume that the DMCC’s drafters would not have included it in its Section 2. Several of the laws and regulations addressing digital markets target firms’ size, scalability, or “strategic significance”. But many investments, business practices, and innovations that benefit consumers—either immediately or over the long term—may also enhance a company’s size, scale, or “strategic significance”. Some of these are possible because of a company’s size. In that vein, targeting size or conduct that bolsters market power, without any accompanying evidence of harm, creates a serious danger of broad inhibition of research, innovation, and investment—all to the detriment of consumers.

Finally, regarding the evidence considered to assess market power, the guidance (Section 2.49) mentions that it may include “a firm’s internal documents, business forecasts, or industry reports”. Later, paragraphs 2.63 to 2.67 below describe how the CMA may assess such evidence. These sections establish, in general, that the CMA does not have a prescriptive list of evidence, and that the standard of proof will be of the “balance of probabilities”. This is correct and according to procedural good practices.

Furthermore, it is why the CMA should be cautious not to rely too heavily on internal business documents to prove anticompetitive behaviour or “dominance”. As Manne and Williamson explain, business documents “are written by business people, for business purposes, and their translation from business to law (and economics) is frequently untenable”.[36] Salespeople, for instance, have strong incentives to communicate to internal stakeholders their efforts to beat competitors and their results, often overstating them. These communications can be mistakenly construed as evidence of “anticompetitive conduct”.

III. Conduct Requirements

Along with pro-competition interventions, discussed in the next section, the CMA’s other primary tool to achieve the DMCC’s goals of “fair dealing”, “transparency”, and “open choices” will be conduct requirements.[37] The guidance generally adopts a reasonable and balanced approach to such requirements, which suggests that the CMA is committed to achieving the DMCC’s goals without unduly burdening SMS firms.

While the CMA should be commended for putting the interests of consumers first and acknowledging the possibility that conduct requirements might not always pan out as expected, the guidance does not always draw a sufficiently clear distinction between the interests of third parties and consumers. To avoid stifling procompetitive conduct, the guidance should explicitly acknowledge that these groups’ interests may not always align. Where they conflict, consumers’ interests must take precedence over those of business users—including, of course, competitors. This is important to ensure that the DMCC is used to bolster competition to the ultimate benefit of consumers, and not as a rent-seeking tool for self-interested third parties.

In addition to this overarching observation, we offer other thoughts on how to improve the guidance’s conduct-requirement provisions. In particular, we think some key terms and concepts could use further clarification; that the CMA should be patient in evaluating measures taken by SMS firms to comply with conduct requirements; and that the CMA should be realistic about its ability to anticipate the effects of complex conduct requirements and, in particular, the interaction of several conduct requirements applying simultaneously. We also appreciate the use of examples and encourage the CMA to provide more such examples when possible.

A primary challenge of ex-ante competition rules is the indeterminacy of some core concepts used to establish the need for prohibitions and obligations to address gatekeeper power. The CMA’s guidance makes important inroads in the direction of much-needed clarity by demonstrating what inherently vague concepts, such as “fairness”, mean in practice. Some key DMCC concepts, however, could benefit from further clarification. For instance, when will the CMA consider that market power has increased “materially”? (S.20(3)(C)). Does any increase in market power count toward satisfying the materiality criterion, or does the increase have to be of a certain magnitude? If so, how much? While a definitive answer likely cannot be given a priori, it would be useful for the CMA to offer more guidance on the factors that will be considered when assessing materiality. Some examples would also be useful to advance legal certainty.

The guidance generally recognizes the importance of protecting consumer welfare and preserving SMS firms’ incentives to continue to innovate and reap the rewards of their business acumen, foresight, and innovation (See, e.g., Points 3.7, 3.22 and 3.23). The guidance is also cognizant of the possibility of unintended consequences, which suggests that the CMA is realistic about the DMCC’s potential to promote—but also potentially to distort—competition, if conduct requirements are poorly designed (see, for instance, Points 3.26 and 3,28). This is to be applauded, as no regulation is without risks and tradeoffs.[38]

In keeping with this sound approach, the CMA should make clear that not every type of conduct that might strengthen a company’s SMS justifies imposing conduct requirements. According to S.20(3)(C) DMCC:

Carrying on activities other than the relevant digital activity in a way that is likely to increase the undertaking’s market power materially, or bolster the strategic significance of its position, in relation to the relevant digital activity. (emphasis added).

As the DMCC indicates, strategic significance can arise from increased scale, size,[39] and popularity,[40] among other factors. Increased size, scale, and popularity can, however, also be the result of increased efficiency or superior products and services. In other words, companies, including those that render “digital activities” as defined by the DMCC,[41] can also gain size, scale and popularity by competing on the merits, not simply by thwarting competition. In a recent interview about competition reform, Aaron Wudrick, senior fellow and director of the Macdonald-Laurier Institute’s Domestic Policy Program, noted thus:

Say you have one competitor, in particular, offering lower prices, higher quality, or newer cutting-edge products, so they end up breaking from the pack. They gain customers, and their market share rises. So this higher concentration is actually signaling more, rather than less, competition![42]

Wudrick was advising against using concentration measures alone—as opposed to market power—as proxy for the level of competition in a given market. The DMCC does not dispense with the market-power requirement, which is generally a good thing.[43] But like concentration, some measures of SMS status—such as size, scale, and popularity—could be equivocal or might point to vigorous competition, rather than the absence thereof.

Sound competition regulation should seek to encourage, not castigate, procompetitive conduct that rewards companies with size, scale, and popularity. Furthermore, so long as entry into the market is possible, size, scale and network effects can yield further procompetitive benefits, thus creating a virtuous cycle. It is therefore important for the guidance to draw a line in the sand between conduct that merely entrenches market power and conduct that increases sales or traffic as a result of competition on the merits—including competition along the consumer-valued dimensions of efficiency, quality, or convenience.

Just as in competition law, the primary criterion here should be whether a certain conduct has negative, neutral, or positive effects for consumers. Where increases in a firm’s size, scale, or sales revenue (or traffic, as appropriate) are accompanied by cognizable consumer benefits (e.g., lower prices, better quality, choice, or curation), the CMA should generally conclude that such growth is the result of competition on the merits. By contrast, an increase in a firm’s size, scale, or sales that runs parallel to long-term depreciating consumer benefits would be a prima-facie indication that the company is using its position to entrench its market power, and that it may therefore be appropriately labelled an SMS firm. Where increases in scale, size, or popularity are not accompanied by any appreciable effects on consumers—positive or negative—the CMA should defer to consumer choice and to companies’ freedom to experiment, reorganize, redesign and, in general, run their enterprise as they see fit.

In any case, the CMA should allow, and the guidance should make clear, that potential SMS firms are allowed to make the case that any increases in size, scope, or popularity are due to competition on the merits, rather than a chronic and entrenched position of market power. By the same token, the CMA should be required to show some degree of causation between consumer harm and a potential SMS firm’s insulation from competition.

For instance, the CMA should be clear about when tying is procompetitive, such as when consumers benefit from increased convenience or when two products/services combine to create synergies are linked. The CMA should clarify how it will interpret S.20(3)(C), which not only prohibits SMS firms from requiring but also incentivising “users or potential users of one of the designated undertaking’s products to use one or more of the undertaking’s other products alongside services or digital content the provision of which is, or is comprised in, the relevant digital activity”. Read literally, this would prohibit any combination of services that comprise one or several digital activities.

Consumers, however, often appreciate and benefit from integrated products and services—such as, e.g., the seamless integration of Google Search and Google Maps. In fact, following the DMA’s entry into force in the EU, many users have complained that they can no longer access Google Maps from Google.[44] Further, tying could reduce consumers’ search costs and improve functionality by integrating complimentary products that work better together.[45] The guidance should clarify that the CMA does not intend to throw the proverbial baby out with the bathwater.

S.20(3)(c) allows the CMA to impose conduct requirements that capture non-designated digital activities for the purpose of preventing a material increase in the SMS firm’s market power or strategic significance in relation to the designated digital activity. As Point 3.13 of the guidance explains:

This would include requirements to prevent the SMS firm from carrying out non-designated activities in a way that is likely to reinforce or embed such market power and/or position of strategic significance.

As indicated in our comment on Point 3.7 of the guidance, however, strategic significance can also result from procompetitive conduct, such as improved efficiency, quality, or innovation. An expansive reading of S.20(3)(c) would prohibit conduct on any market in which the SMS company is active that resulted in or was (according to the CMA) likely to result in an increase in size, scale, or popularity. We fear that this reading is not only overly broad, but risks capturing swathes of procompetitive conduct in markets that are not even covered by the DMCC.

The guidance could, at a minimum, give some sense of the sort of nondigital activities that could be affected by S.20(3)(c)—such as, e.g., through non-exhaustive but illustrative examples (examples are given elsewhere such as, e.g., Points 3.15, 3.14, or 3.8). We believe this is crucial for the sake of legal certainty, as well as to ensure that the DMCC’s scope remains cabined within its natural and legally prescribed limits, thereby reducing the likelihood of regulatory overreach.

In general, the CMA should be clear that the DMCC’s goal is to protect competition and consumers, not to help competitors. To a large extent, the guidance achieves this, and should be commended for doing so (see, e.g., Point 3.10). In Point 3.22, the guidance states that:

The factors that informed the CMA’s decision to designate a firm as having SMS in respect of a relevant digital activity, including its size, market power, and strategic significance, will often be highly relevant in identifying issues that could cause harm to businesses or consumers which the CMA may wish to remedy, mitigate or prevent through the imposition of CRs. (emphasis added).

This might suggest that harms to businesses and to consumers are treated equally under the DMCC, which we strongly advise against (see our comments to Point 3.7 above). In the next point, however, the Guidance clarifies that “in considering what a [conduct requirement] or combination of CRs is intended to achieve, the CMA will have regard in particular to achieving benefits for consumers”. This is the right approach, and a welcome clarification.

As indicated in our response to Point 3.7, however, the CMA should be clear that there may be times when the interests of competing businesses or business users are not equivalent to the interests of consumers. The guidance’s indication that conduct requirements might benefit consumers either directly or indirectly by giving rise to benefits to businesses that are likely to be passed on to consumers should be tempered by acknowledging that some benefits might not be passed down to consumers at all and, more generally, that not everything that harms or benefits competitors will necessarily have the same effect on consumers. This is important to ensure that the DMCC is used to benefit consumers, and not as a rent-seeking tool by self-interested (and, often, less-successful) businesses. We therefore suggest that the guidance explicitly incorporate examples of situations where certain behavior by SMS firms harms business users or competitors but benefits consumers (and vice versa).

It is good that, as in Point 3.26, the CMA is aware of the need to ensure consistency and coherence in designing and implementing conduct requirements, especially given the range of products and services that are encompassed under “digital activities”. Indeed, the “digital activity” blanket term is misleading. “Digital activities” are anything but monolithic. They cover a range of products and services with little in common, except that they are provided via the internet and involve some sort of digital content.[46]

Furthermore, the companies that render such services are also vastly different. For example, some, like Amazon, are primarily logistics operators, while others, like Apple, are primarily hardware companies. In other words, given that SMS firms and their products are anything but homogenous, achieving coherent and consistent outcomes might require the CMA to impose different conduct requirements on different companies for the same digital activity.

Our (somewhat belated) point here is that the CMA should be commended for showing an awareness that achieving coherence and consistency under DMCC is an important, albeit complex, task. To ensure that coherence and consistency remain a top priority—in theory as well as in practice—the guidance could spend more time elaborating how the CMA intends to design conduct requirements such that different products, rendered by different companies, achieve the same goals.

The guidance states that, whenever possible, SMS firms will be free to decide how to achieve outcomes mandated by the conduct requirements (see, e.g., Principle 1, 3). This is the correct approach, as it allows SMS firms sufficient flexibility to leverage their expertise and know-how in designing solutions that do not undermine the core benefits of their products and services, while allowing the CMA to monitor firms’ alignment with the DMCC’s goals. In a similar vein, it is also commendable that the CMA is willing to impose higher-level requirements before escalating “the enforcement pyramid” toward more stringent and detailed conduct requirements (Principle 4). The opposite approach would be unjustified, and more apt to lead to unintended consequences. It could also foster ill will and distrust between the regulator and the regulated companies, which could negatively affect the DMCC’s effectiveness ove the long term.

With reference to Point 3.28, it is unclear what timescale the CMA will consider when assessing whether a conduct requirement or combination of conduct requirements is likely to be effective in achieving its intended aim or aims. To ensure legal certainty and compliance, however, the guidance should provide some sense of how soon the CMA expects a conduct requirement to start producing the desired results. Or, put differently, when will the CMA consider that a conduct requirement has succeeded or failed? Understandably, this may vary from case to case, but the CMA should at least provide general timescales, along with an explanation and, if possible, examples.

Our view is that, in establishing a timescale, the CMA should be patient and allow a reasonable period for the results of changes made pursuant to the conduct requirements to become palpable. For example, if the CMA requires an SMS company to allow third-party app stores on its operating system, it might take some time before consumers start using those alternative app stores. Thus, if the market shares of competing app stores do not immediately surge following the implementation of changes, the CMA should not be too quick to assume that the SMS firm has not complied with its obligations under the DMCC or has “complied maliciously”.[47] It could be that consumers need more time to get acquainted with the new options, or that they ultimately prefer to stick with the first-party app store. It would be useful to underscore this patience in the guidance, as it would provide clarity to SMS firms and help manage the expectations of business users.

On a separate note, the CMA should be commended for considering effects on consumers and taking into account the risk of unintended consequences when assessing whether a conduct requirement would be effective in achieving its aims. As we have argued throughout these comments, the CMA should ensure that it does not lose sight of the DMCC regime’s effects on consumers and that it remain vigilant to the possibility of unintended consequences with every intervention.

In Point 3.29, the guidance states that the CMA will seek to ensure that a conduct requirement or combination of conduct requirements is coherent with conduct requirements imposed on the same or different SMS firms. It also states that the CMA may consider, as appropriate, coherence with other interventions imposed elsewhere within the scope of the authority’s powers. Ensuring coherence generally signals the right approach, but it is easier said than done (see also our comments on Point 3.26).

Conduct requirements are likely to involve complex product-design changes. They are also, by definition, forward-looking, requiring the CMA to anticipate likely outcomes from the confluence of multiple codependent factors. To minimize unintended consequences and error costs, the CMA should start with simpler, individual conduct requirements, rather than complex, combined conduct requirements. During the early stages of the DMCC, in particular, it is risky to start with combinations of conduct requirements, as such requirements might behave differently together than they do individually.

Furthermore, individual conduct requirements make it easier to observe the relationship between the independent variable (the conduct requirement) and the dependent variable (the market outcome sought). Only once the CMA has significant experience with individual conduct requirements should it start tinkering with combinations. Obviously, some combinations of conduct requirements (such as, e.g., conduct requirements aimed at different SMS firms rendering the same digital activity) are inevitable, but we do not advise the CMA to be overly ambitious until it has developed substantial expertise. A commitment to this piecemeal and cautious approach could perhaps be incorporated into the guidance.

When assessing the proportionality of conduct requirements, the guidance does well to consider the likely positive and negative effects on SMS firms (Point 3.30). The DMCC should not seek to punish SMS firms or undercut their incentives to keep investing in products and services. It is important that conduct requirements do not disproportionately encumber SMS firms or impose unnecessary requirements.

When gathering information before imposing a conduct requirement, the guidance states that the CMA will consider information from a range of sources, including responses to invitations to comment, market-monitoring mechanisms, or market studies (Point 3.38). This is good: the CMA should not overly rely on information and complaints submitted by business users and third parties (especially competitors), who may have vested interests that do not align with those of consumers or the DMCC’s public-interest objectives. Moreover, as some have pointed out, business users face a “Stalter and Waldorf problem”, as they have an interest in never being satisfied and always seeking to extract more concessions from the regulated companies.[48]

Generally, the CMA should be commended for its willingness to give SMS firms flexibility in responding to conduct requirements, even in ways that differ from its interpretative note (see, for example, Point 3.55). In doing so, the guidance recognizes that there may be more than one valid way to interpret a conduct requirement.

We also salute the fact that the guidance displays a willingness to grant SMS firms sufficient time to implement the necessary technical or business changes (see Points 3.61-62). As noted throughout these comments, redesigning products or business practices is costly and time-consuming, and the CMA does well to manage expectations regarding how quickly these things can be achieved.

Furthermore, the CMA displays a generally cordial disposition to SMS firms, rather than an antagonistic one. In a future where the CMA is likely to interact repeatedly and work closely with SMS firms, fostering goodwill and trust between the regulator and the regulated is crucial.

IV. Pro-Competition Interventions

Section 44 of DMCC grants the CMA powers to make pro-competitive interventions (PCI or PCIs, in plural). How the CMA deploys these powers will be one of the factors that most determine whether the DMCC achieves its ambitions. The DMCC bill affords the CMA great discretion to design and enforce PCIs, making them something of a double-edged sword. In the best-case scenario, PCIs could be used to swiftly obtain light-touch remedies from SMS firms, while benefiting consumers and other stakeholders. On the other hand, if used heavy-handedly, PCIs have the potential to degrade online platforms, while dragging the CMA into lengthy legal disputes. In other words, PCIs’ greatest potential lies in their use as a surgical tool, not a sledgehammer.

The CMA’s guidance conveys reassuring signals that it will seek to use PCIs even-handedly. For instance, Article 4.12 of the guidance lists a series of indicators the CMA will consider when determining whether a practice has an adverse effect on competition (AEC).[49] To some extent, this mimics the sort of fact-intensive inquiry that firms have come to expect under competition rules. The CMA’s commitment to account for potential efficiencies when investigating potential AECs is also highly commendable.[50]

In that vein, a good additional procedural safeguard to include in the guidance would be to make at least a preliminary assessment of the PCI before initiating any CR procedure. If a competition agency does not have a very good idea how to implement a remedy that would allow the market to function reasonably, and better than the status quo, then it probably is not a good use of resources to initiate a procedure that may affect business models and practices that we know benefit consumers.[51]

Another positive note concerns the CMA’s acknowledgement that PCIs can fail. According to the guidance, this can happen when a PCI fails to increase competition in the intended way or, crucially, because the PCI degrades an SMS firm’s platform to such an extent that consumers are left worse off than if no PCI had been imposed (the latter is an important recognition that other regulators often fail to acknowledge). Indeed, as the draft guidance explains:

The CMA will have regard to a range of factors, including: (a) the PCI’s likely impact on the AEC and, in addition, any detrimental effects, either already arising or expected to arise from it… (c) the risk of the PCI not meeting its intended purpose and/or giving rise to unintended consequences.[52]

The CMA’s proposed PCI trial and testing of PCIs is, in that respect, a welcome addition. If carefully implemented, this should enable the authority to avoid some of the pitfalls that foreign enforcers, such as the European Commission, have encountered when attempting to enforce digital competition regulations. Following the entry into force of the DMA, gatekeepers have, for instance, been forced to degrade their platforms for European users—mostly because the DMA did not provide sufficient timeframes or legal sandboxes for gatekeepers to market test their compliance solutions.[53]

Despite these reassuring statements, there are several areas where we believe the CMA’s guidance could be amended to provide further clarity to firms and better safeguards against the potential unintended effects of DMCC compliance.

For a start, while the CMA understandably wants to leave all remedial options on the table, some additional clarity concerning the respective roles of behavioral and structural remedies would be welcome. There is, indeed, a sense that structural remedies are far more invasive than behavioral ones; as the CMA notes, they will often amount to selling a highly successful line of business into which an SMS firm may have invested billions of pounds to create or acquire. Structural remedies may also be much harder to implement when an online platform’s distinct services are built upon common infrastructure, such as code, that cannot be easily divided.

The guidance appears implicitly to recognize this much. Many of the procedural safeguards outlined in the CMA’s draft guidance are, indeed, impossible to apply to structural remedies. Divestitures cannot, by definition, be market tested, replaced, or revoked.[54] This makes them inherently less compatible with the spirit of the draft guidance than behavioral ones—which, again by definition, are more amenable to these procedural protections.

Given this, we believe a commitment by the CMA to use structural remedies only in exceptional circumstances would have a beneficial impact on SMS firms that may be considering whether to launch new services in the UK (or continue offering them), as they would be assured that the “nuclear option” is a last resort.

Along similar lines, there is also a sense that the CMA should, when possible, favor simple remedies (such as cease-and-desists orders) rather than more complex ones that entail deep product-design changes. Doing so would minimize the risk of unintended consequences and error costs. This is especially true during the early stages of DMCC implementation. Combinations of remedies might have collective effects that are greater than the sum of their parts.

It would also be easier to infer the cause of unintended consequences in the case of individual (rather than combined) remedies. Initially favoring simple remedies will enable the CMA to “learn by doing” by establishing clearer links between conduct requirements and observable outcomes. As it gains enforcement experience, it will be better-positioned to design more intricate remedy packages.

This leads us to a second important consideration. While the CMA’s proposed testing, trialing, replacement, and revocation of pro-competitive orders (PCO or PCOs, in plural) is commendable, we regret that some of these procedural safeguards appear to be merely optional under the guidance:

4.65 The CMA may include specific provisions within a PCO imposing requirements to test and trial different remedies or remedy design options (on a time limited basis) before imposing any PCI on an enduring basis….[55]

This may seem like a detail, but a firmer commitment to systematically trialing new PCOs before they are introduced would signal a desire to protect consumers from unintended negative effects of remedies. It would also give firms more leeway to experiment and identify those compliance solutions that reach the best tradeoff between the sometimes-diverging interests of consumers, competition, and the SMS firms themselves. In other words, trialing remedies is a sign of regulatory humility in the face of complex digital markets.

Third, the guidance seems to underestimate the difficulty of assessing some of the metrics on which it relies. This is notably the case of Section 4.12, which explains that the CMA will consider whether “SMS firms’ profits reflect a reasonable rate of return based on the nature of competition” or “the competitive positions of SMS firms and their rivals are based on the merits of their respective offerings”.[56] Assessing these factors is much easier said than done.

For example, determining whether profits reflect a “reasonable rate of return” amounts to asking what rate of return the firm would earn absent some anticompetitive conduct. This, in turn, requires a robust counterfactual analysis, including, but not limited to, comparative studies of prices for similar products in other countries, etc. This is no easy task. Yet the error costs entailed are significant, as overenforcement could diminish the very price signals on which the competitive process relies. In fast-moving digital markets, the problem is compounded, as what constitutes a “reasonable rate of return” is likely to quickly go out of date.

The guidance should therefore detail how the CMA intends to calculate a “reasonable rate or return”, and how it will weigh various factors to determine whether an SMS firm’s competitive position is based on competitive merits or on entrenched market power.

Finally, and along similar lines, we believe the CMA’s openness to replacing or revoking PCOs based on evidence that they do not sufficiently promote competition should be explicitly complemented by a mirror-image provision that enables replacement or revocation on the basis of evidence that (i) competition has become sufficiently robust to discipline SMS firms, or (ii) that a given PCO’s costs outweigh its benefits.

Explicitly contemplating these scenarios in the guidance would ensure that consumer welfare is ultimately the metric by which DMCC remedies are to be evaluated. There is, indeed, mounting evidence that DMA remedies in the European Union may not be achieving their stated ambitions because they unintendedly degrade the products of online platforms.[57] At the time of writing, it is still not possible to click through to a Google Maps location from the Google Search engine. The DMA’s enforcement has also significantly and negatively impacted traffic to hotel websites.[58] These unintended consequences provide clear evidence that, for the good of consumers, enforcers need to contemplate the possibility that a remedy does more harm than good. By explicitly contemplating these scenarios in guidance, the CMA would exhibit a humility that has, to date, been absent in other jurisdictions enforcing similar regulations.

The upshot is that the CMA’s guidance on PCIs is a step in the right direction. It shows a regulator willing to contemplate the possibility of regulatory failure when dealing with the highly complex world of digital-platform markets. Certain aspects of the guidance could, however, be further clarified to reinforce the CMA’s commitment to even-handed policymaking.

[1] Consultation on Digital Markets Competition Regime Guidance, Competition and Markets Authority (24 May 2024), https://www.gov.uk/government/consultations/consultation-on-digital-markets-competition-regime-guidance.

[2] Spider-Man (Sony Pictures 2002).

[3] CMA Prioritisation Principles, Competition and Markets Authority (Oct. 30, 2023), https://www.gov.uk/government/publications/cma-prioritisation-principles/cma-prioritisation-principles. See also, The Government’s Strategic Steer to the Competition and Markets Authority, Dep’t for Business & Trade Policy Paper (Jul. 18, 2019), https://www.gov.uk/government/publications/governments-strategic-steer-to-the-competition-and-markets-authority-cma/governments-strategic-steer-to-the-competition-and-markets-authority (“The CMA has a key role in helping consumers and benefiting the wider economy.”).

[4] Justin G. Hurwitz & Geoffrey A. Manne, Pigou’s Plumber (or Regulation as a Discovery Process), SSRN (15 Mar. 2024), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4721112.

[5] Draft Digital Markets Competition Regime Guidance, Competition and Markets Authority (2024), available at https://assets.publishing.service.gov.uk/media/6650a56d8f90ef31c23ebaa6/Digital_markets_competition_regime_guidance.pdf (hereinafter “Draft Guidance”).

[6] See Hurwitz & Manne, supra note 4, at 34-35.

[7] See Dirk Auer, The Future of the DMA: Judge Dredd or Juror 8?, Truth on the Market (8 Apr. 2024), https://truthonthemarket.com/2024/04/08/the-future-of-the-dma-judge-dredd-or-juror-8.

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

[9] This is explained in more detail in Section IV on pro-competition interventions.

[10] Margrethe Vestager, A Whack-A-Mole Approach to Big Tech Won’t Do, Says Europe’s Antitrust Chief, The Economist (4 Jun. 2024), https://www.economist.com/by-invitation/2024/06/04/a-whack-a-mole-approach-to-big-tech-wont-do-says-europes-antitrust-chief (“Some argue that opening up involves trade-offs. It does not have to. Asking platforms to open up their ecosystems, for instance, does not mean they have to compromise the security of their service. Technology can deliver an open and safe digital environment, if there is the will and sufficient investment to make that happen. Compliance with the DMA can be achieved without undermining users’ rights to safety and privacy.”); Foo Yun Chee, Exclusive: EU’s Vestager Warns About Apple, Meta Fees, Disparaging Rival Products, Reuters (19 Mar. 2024), https://www.reuters.com/technology/eus-vestager-warns-about-apple-meta-fees-disparaging-rival-products-2024-03-19.

[11] See, e.g., Jian Jia, Ginger Zhe Jin &Liad Wagman, The Short-Run Effects of GDPR on Technology Venture Investment, 40 Marketing Sci. (2021); Garrett Johnson, Economic Research on Privacy Regulation: Lessons From the GDPR and Beyond, in THE ECONOMICS OF PRIVACY (Avi Goldfarb & Catherine Tucker eds., 2024); See also Michal Gal & Oshrit Aviv, The Competitive Effects of the GDPR, 16 J. Comp. L. & Econ. 349 (2020).

[12] See Dirk Auer, Innovation Defenses and Competition Laws: The Case for Market Power 18 (2019), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4667754 (“There is thus a constant tension between antitrust enforcement and the promotion of innovation. And it is this tension which the dissertation seeks to explore. This task is complicated by the fact that the ex ante/ex post tradeoff is mostly intangible. It will generally be the case that no single innovation can be traced back to antitrust authorities’ restraint, nor can a single antitrust intervention easily be associated with reduced innovation. Just like people trying to respect their new year’s resolutions (lose weight, read more, etc.), no single departure is likely to be of pivotal importance. But a slew of small deviations will add up and may ultimately scupper authorities long term plans to bolster firms’ incentives.”).

[13] Dirk Auer, Matthew Lesh, & Lazar Radic, Digital Overload: How the Digital Markets, Competition and Consumers Bill’s Sweeping New Powers Threaten Britain’s Economy, 4 IEA Perspectives (Sep. 2023), available at https://laweconcenter.org/wp-content/uploads/2023/09/Perspectives_4_Digital-overload_web-1.pdf.

[14] Regarding market power, Section 2.40 of the Guidance states that: “Market power arises where a firm faces limited competitive pressure and individual consumers and businesses have limited alternatives to its product or service or, even if they have good ones, they face barriers to shopping around and switching. Therefore, an assessment of market power is largely an assessment of the available alternatives and the extent to which they are substitutable for that product or service. This includes alternatives available in the present and possibilities for entry and expansion.” It is important that the section mentions “possibilities for entry and expansion”, but the text should be amended to clarify that alternatives should be “reasonable substitutes” and not identical substitutes, with every feature of the product or service offered by the firm whose market power is being assessed.

[15] Hay, for instance, describes the concept of market power as a “filter” or “screen” in antitrust cases. “If we accept the notion that the point of antitrust is promoting consumer welfare, then it is clear why the concept of market power plays such a prominent role in antitrust analysis. If the structure of the market is such that there is little potential for consumers to be harmed, we need not be especially concerned with how firms behave because the presence of effective competition will provide a powerful antidote to any effort to exploit consumers.” See George A. Hay, Market Power in Antitrust, 60 Antitrust L.J. 807, 808 (1991).

[16] See, e.g., Geoffrey A. Manne, Error Costs in Digital Markets, in The Global Antitrust Institute Report On The Digital Economy 103 (Joshua D. Wright & Douglas H. Ginsburg eds., Nov. 11, 2020), https://gaidigitalreport.com (“Market definition is similarly employed as a function of error-cost minimization. One of its primary functions is to decrease administrative costs: analysis of total effects of a proposed conduct would be inordinately expensive or impossible without reducing the scope of analysis. Market definition defines the geographic and product areas most likely to be affected by challenged conduct, sacrificing a degree of analytical accuracy for the sake of tractability.”).

[17] See Nicolas Petit & Lazar Radic, The Necessity of a Consumer Welfare Standard in Antitrust Analysis, Promarket (18 Dec. 2023), https://www.promarket.org/2023/12/18/the-necessity-of-a-consumer-welfare-standard-in-antitrust-analysis (“In general, excessive prices, discriminatory conduct, or unfair trading conditions reflect transaction or mobility costs that can coexist with free and open competition for entry. They only very faintly and ambiguously suggest harm to competition. In such cases, a market power requirement will filter out mere surplus transfers reflecting asymmetries in bargaining power or insignificant distortions in the level playing field, both of which represent the essence of the competitive process in all but name. Without a market power filter, abusive conduct cases blur the line between protecting competition and protecting competitors, since competition by definition consists in putting competitors at a disadvantage and, ultimately, in facilitating their exit from the market.”).

[18] Richard A. Posner & William M. Landes, Market Power in Antitrust Cases, 94 Harv. L. Rev. 937, 939 (1980) (emphasis added).

[19] Online Platforms and Digital Advertising Market Study Final Report, Competition and Markets Authority (1 Jul. 2020), available at https://assets.publishing.service.gov.uk/media/5fa557668fa8f5788db46efc/Final_report_Digital_ALT_TEXT.pdf.

[20] Jason Furman, et al., Unlocking Digital Competition: Report of the Digital Competition Expert Panel (Mar. 2019), available at https://assets.publishing.service.gov.uk/media/5c88150ee5274a230219c35f/unlocking_digital_competition_furman_review_web.pdf.

[21] CMA, supra note 19, at 75 (emphasis added).

[22] Moat Building and Entrenchment Strategies, OECD Background Note (11 Jun. 2004) at 8, available at https://one.oecd.org/document/DAF/COMP/WP3(2024)1/en/pdf.

[23] See, e.g., Louis Kaplow, Market Definition: Impossible and Counterproductive, 79 Antitrust L.J. 361 (2013).

[24] Gregory J. Werden, Why (Ever) Define Markets? An Answer to Professor Kaplow, 78 Antitrust L.J. 729, 741 (2013).

[25] Manne, Error Costs, supra note 16, at 48.

[26] Id. at 104-05.

[27] Richard Whish & David Bailey, Competition Law (8^th Ed., 2015) at 31-32.

[28] Jonathan Barnett, Does the European Union’s Digital Markets Act Provide an Appropriate Model for Maintaining Competition in California’s Innovation Economy?, Report Submitted to the California Law Revision Commission (Jan. 2024) at 17, available at http://www.clrc.ca.gov/pub/2024/MM24-05.pdf.

[29] Raymundo Campos, Alejandro Castañeda, Aurora Ramírez & Carlos Ruiz, Amazon’s Effect on Prices: The Case of Mexico, Centro de Estudios Economicos Working Paper No. II-2022 (2022), available at https://cee.colmex.mx/dts/2022/DT-2022-2.pdf.

[30] DMCC, S.1, (1), (a).

[31] DMCC, S.1, (4).

[32] Whish and Bailey, supra note 27, at 190.

[33] Abuse of a Dominant Position: Understanding Competition Law, Office of Fair Trading (2004) at 13, available at https://assets.publishing.service.gov.uk/media/5a74c497ed915d4d83b5ecd7/oft402.pdf.

[34] Sections 2.53-2.56.

[35] Section 2.57.

[36] Geoffrey A. Manne & E. Marcellus Williamson, Hot Docs vs Cold Economics: The Use and Misuse of Business Documents in Antitrust Enforcement and Adjudication, 47 Ariz. L. Rev., 609, 610 (2005).

[37] DMCC, S.19(5).

[38] In the context of the DMA, see, e.g., Carmelo Cennamo & Juan Santaló, Potential Risks and Unintended Effects of the New EU Digital Markets Act, Esade Ctr. Econ. Pol’y. (Open Internet Governance Inst. Working Paper Series No. 4, 2023), available at https://www.esade.edu/ecpol/wp-content/uploads/2023/02/AAFF_EcPol-OIGI_PaperSeries_04_Potentialrisks_ENG_v5.pdf; see also Lazar Radic & Mario Zúñiga, Comments of the International Center for Law & Economics, Ministry of Finance Public Consultation – Economic and Competitive Aspects of Digital Platforms, Int’l Ctr. L. & Econ., 2 (2024), available at https://laweconcenter.org/wp-content/uploads/2024/05/ICLE-Brazil-MoF-Consultation-on-Digital-Competition-1.pdf (“Ex-ante regulations like the European Union’s Digital Markets Act (DMA) can have unintended consequences, such as stifling innovation, reducing consumer welfare, and increasing compliance costs. They can also lead to increased risks of regulatory capture and rent seeking, as the verdict on whether a gatekeeper has complied with the law often comes down to the degree to which rivals are satisfied. Of course, rivals have a clear personal stake in never being satisfied. By tethering intervention to a comparatively clear public-benefit standard—consumer welfare—competition laws minimize the potential for error costs and decrease the chances that the law will be coopted for private gain.”); and Dirk Auer, The Broken Promises of Europe’s Digital Regulation, Truth on the Mkt. (12 Mar. 2024), https://truthonthemarket.com/2024/03/12/the-broken-promises-of-europes-digital-regulation.

[39] DMCC, S.6(1)(a).

[40] DMCC, S.6(1)(b).

[41] DMCC, S.3.

[42] Aaaron Wudrick, The View from Canada: A TOTM Q&A with Aaron Wudrick, Truth on the Mkt. (12 Jun. 2024), https://truthonthemarket.com/2024/06/12/the-view-from-canada-a-totm-qa-with-aaron-wudrick.

[43] By contrast, the DMA does not require gatekeepers to have market power.

[44] Edith Hancock, Severe Pain in the Butt: EU’s Digital Competition Rules Make New Enemies on the Internet, Politico (25 Mar. 2024), https://www.politico.eu/article/european-union-digital-markets-act-google-search-malicious-compliance.

[45] Andrew Mercado, The Paradox of Choice Meets the Information Age, Truth on the Mkt. (19 Apr. 2022), https://truthonthemarket.com/2022/04/19/the-paradox-of-choice-meets-the-information-age; Kay Jebelli, Confronting the DMA’s Shaky Suppositions, Truth on the Mkt. (16 Apr. 2024), https://truthonthemarket.com/2024/04/16/confronting-the-dmas-shaky-suppositions; Dirk Auer & Lazar Radic, What Have the Intermediaries Ever Done for Us, CPI Antitrust Chronicle (Jun. 2022), available at https://laweconcenter.org/wp-content/uploads/2022/06/4-WHAT-HAVE-THE-INTERMEDIARIES-EVER-DONE-FOR-US-Dirk-Auer-Lazar-Radic.pdf.

[46] DMCC, S.3.

[47] A term popular among critics of gatekeepers’ compliance efforts with the DMA. See, e.g., Andy Yen, Apple’s DMA Compliance Plan Is a Trap and a Slap in the Face for the European Commission, Proton Blog (5 Feb. 2024), https://proton.me/blog/apple-dma-compliance-plan-trap.

[48] Adam Kovacevich, The Digital Markets Act’s “Statler & Waldorf” Problem, Chamber of Progress (7 Mar. 2024), https://medium.com/chamber-of-progress/the-digital-markets-acts-statler-waldorf-problem-2c9b6786bb55.

[49] Draft Guidance, Section 4.12 (“4.12 Typically, however, the indicators that the CMA will consider may include (but are not limited to) whether: (a) SMS firms’ profits reflect a reasonable rate of return based on the nature of competition; (b) the competitive positions of SMS firms and their rivals are based on the merits of their respective offerings; (c) SMS firms and their competitors flex parameters of competition in response to rivals and wider developments; (d) SMS firms’ users and customers can make effective decisions between a range of alternatives and are able to switch between these; (e) SMS firms and their competitors are rewarded for operating efficiently, innovating and competing to supply the products that users and customers want; and/or (f) competitors and potential competitors to SMS firms face limited barriers to entry and expansion.”)

[50] Draft Guidance, Section 4.13 (“When assessing whether a factor or combination of factors is having an AEC, the CMA will also consider in its assessment any competition-enhancing efficiencies that have resulted, or may be expected to result, from such factor(s).”)

[51] Although written with antitrust litigation in mind, this passage from Herbert Hovenkamp is relevant to our point: “Every complex antitrust case must begin by considering the remedy. Anticipating the appropriate fix is like having an exit strategy in battle. Court injunctions that prohibit a specific behavior or action are easier to obtain, but they may also accomplish less. “Structural” relief, such as a breakup, requires proof of conduct that only a structural change can fix, as well as proof that the new structure will be better. The recent platform monopolization cases raise a recurring issue in antitrust law: creating the right remedy is often more difficult than establishing unlawful conduct.” See Herbert Hovenkamp, Fixing Platform Monopoly in the Google Search Case, ProMarket (6 Jun. 2023), https://www.promarket.org/2023/10/06/fixing-platform-monopoly-in-the-google-search-case.

[52] Draft Guidance, Section 4.31.

[53] See, e.g., Auer, The Future of the DMA, supra note 7; Auer, Broken Promises, supra note 38.

[54] Draft Guidance, Sections 4.65 to 4.81.

[55] Id. Section 4.65.

[56] Id. Sections 4.12 (a) and (b).

[57] See Auer, Future of the DMA, supra note 38; Auer, Broken Promises, supra note 7.

[58] Kate Harden-England, European Digital Markets Act Law Should be Rethought, Says Mirai, Travolution (28 May 2024), https://www.travolution.com/news/travel-sectors/accommodation/european-digital-markets-act-law-should-be-rethought-says-mirai.

While last year’s labor disputes between the Writers Guild of America (WGA) and the Screen Actors Guild (SAG-AFTRA), on the one hand, and Hollywood’s major movie studios, on the other, have been settled for months now, lingering questions remain about competitive conditions in the industry.

Read the full piece here.

Executive Summary

We thank the Competition Bureau Canada for promoting this dialogue on competition and artificial intelligence (AI) by publishing its Artificial Intelligence and Competition Discussion Paper (“Discussion Paper”)[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 in several jurisdictions. 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 Macdonald-Laurier Institute (MLI) is an independent and nonpartisan think tank based in Ottawa with the ambition to drive the national conversation and make Canada the best-governed country in the world.

In our comments, we express concern that policymakers may equate the rapid rise of AI services and products with a need to intervene in these markets—when, in fact, the opposite is true. As we explain, the rapid growth of AI markets (or, more precisely, products and services based on AI technology), as well as the fact that new market players are thriving, suggests that 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 AI markets are not important—quite the opposite. AI is already changing the ways that many firms do business and improving employee productivity in many industries.[2] The technology is also increasingly useful in the field of scientific research, where it has enabled creation of complex models that expand scientists’ reach.[3] Against this backdrop, EU 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.”[4]

But while sensible enforcement is of vital importance to maintain competition and consumer welfare, kneejerk reactions may yield the opposite outcome. As our comments explain, overenforcement in the field of 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 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 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 comments proceed as follows. Section I summarizes recent calls for competition intervention in 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 AI markets. Section IV explains why current merger policy is sufficient to address any potential anticompetitive acquisition or partnership in the AI sector without need for any special rules, like presumptions or inverse burdens of proof. Section V explains how balancing user protection with innovation in AI markets is particularly important in the Canadian context. Finally, Section VI concludes by offering five key takeaways to help policymakers and agencies (including the Competition Bureau Canada) 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.”[5] Similar claims of data dominance have been attached to nearly all large online platforms, including Facebook (Meta), Amazon, and Uber.[6]

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 ad-tech antitrust suits),[7] 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.[8] 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.^[9] 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.”^[10]

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 reassess 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.^[11]

It is increasingly common for competition enforcers to argue that so-called “data-network effects” serve not only to entrench incumbents in those markets where the data is collected, but also to confer similar, self-reinforcing benefits in adjacent markets. Several enforcers have, for example, prevented large online platforms from acquiring smaller firms in adjacent markets, citing the risk that they could use their vast access to data to extend their dominance into these new markets.^[12]

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?”^[13] 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.[14]

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 AI services. After all, it is widely recognized that data is an essential input for generative AI.^[15] 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 Llama have routinely made headlines.[16] Apple and Amazon also have vast experience with AI assistants, and all of these firms use AI technology throughout their platforms.[17]

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,[18] 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 20^th-century antitrust to address the allegedly manifest harms of 21^st-century technology. These include the notions that data advantages constitute barriers to entry and can be leveraged to project dominance into adjacent markets; that scale itself is a market failure to be addressed by enforcers; and that the use of consumer data is inherently harmful to those consumers.

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).^[19] 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.”^[20] 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.”^[21]

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.[22]

Meanwhile, actual economic studies of data-network effects have been few and far between, with scant empirical evidence to support the theory.^[23] Andrei Hagiu and Julian Wright’s theoretical paper offers perhaps the most comprehensive treatment of the topic to date.[24] The authors ultimately conclude that data-network effects can be of different magnitudes and have varying effects on firms’ incumbency advantage.^[25] 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.”^[26]

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.”^[27] 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.^[28]

This possibility is also implicit in Hagiu and Wright’s paper.[29] 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.^[30]

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.[31] Nevertheless, the Furman Report concludes that data “may confer a form of unmatchable advantage on the incumbent business, making successful rivalry less likely,”[32] and adopts without reservation “convincing” evidence from non-economists that have no apparent empirical basis.[33]

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.^[34]

As a result, the Commission cleared the merger on the condition that Google refrain from using data from Fitbit devices for its advertising platform.^[35] The Commission will likely focus on similar issues during its ongoing investigation of Microsoft’s investment into OpenAI.[36]

Along similar lines, the FTC’s complaint to enjoin Meta’s purchase of the virtual-reality (VR) fitness app 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.”^[37]

The DOJ’s twin cases against Google also implicate data leveraging and data barriers to entry. The agency’s ad-tech complaint charges that “Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry.”[38] 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.[39]

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.”^[40] 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….^[41]

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 like 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.”[42]

To date, however, this is not how things have unfolded—although it bears noting that 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).[43] 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.^[44] 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.^[45] In April 2023, ChatGPT reportedly registered 206.7 million unique visitors, compared to 19.5 million for Google’s Bard.^[46] 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.^[47]

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.^[48] 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.^[49]

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.”^[50]

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.^[51]

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,[52] or may even outperform real-world data.[53] 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.^[54]

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.”[55]

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, but 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.[56] In one important example:

The model’s performance is particularly remarkable, given its small size. “This is not a large language model trained on the whole Internet; this is a relatively small transformer trained for these tasks,” says Armando Solar-Lezama, a computer scientist at the Massachusetts Institute of Technology, who was not involved in the new study…. The finding implies that instead of just shoving ever more training data into machine-learning models, a complementary strategy might be to offer AI algorithms the equivalent of a focused linguistics or algebra class.[57]

Platforms’ current efforts are thus focused on improving the mathematical and logical reasoning of large language models (LLMs), rather than maximizing training datasets.^[58] Two points stand out. The first is that firms like OpenAI rely largely on publicly available datasets—such as GSM8K—to train their LLMs.^[59] 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.^[60]

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.[61]

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 that these are ultimately decisive.[62] 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.[63] 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 operating-system 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,^[64] 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 custom versions of their generative-AI technology will arguably play a much larger role than (and contribute to) their ownership of data.[65] 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.^[66] 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.[67] 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 merely possessing 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. Merger Policy and AI

According to the Discussion Paper, some mergers that involve firms offering AI services or products deserve special scrutiny:

Mergers, of any form, involving a firm who supplies compute inputs, such as AI chips and cloud services, could warrant additional scrutiny due to the existing high levels of concentration in these markets. Mergers in AI markets may require additional scrutiny as large established firms may seek to acquire emerging competitors as a means of preventing or lessening competition.[68]

The Discussion Paper does not explain what form this “additional scrutiny” may take. It may entail anything from prioritization of resources to procedural rules (presumptions, burden of proof). In any case, while we understand why the two mentioned instances of mergers may raise competition concerns, it is important to acknowledge that these are theoretical concerns. To date, there is no evidence to support differentiated scrutiny for mergers involving AI firms or, in general, firms working with information technology. The view that so-called “killer acquisitions,” for instance, pose a significant competition risk is not supported by solid evidence.[69] To the contrary, the evidence suggests that acquisitions increase competition by allowing larger firms to acquire abilities relevant to innovation and by generating incentives for startups.[70]

Companies with “deep pockets” that invest in AI startups may provide those firms the resources to compete with current market leaders. Firms like Amazon, Google, Meta, and Microsoft, for instance, are investing in creating their own chips for building AI systems, aiming to be less dependent on Nvidia.[71] The availability of this source of funding may thus increase competition at all levels of the AI industry.[72]

There has been also some concern in other jurisdictions regarding recent partnerships among and investments by Big Tech firms into AI “unicorns,”[73] in particular, Amazon’s partnership with Anthropic; Microsoft’s partnership with Mistral AI; and Microsoft’s hiring of former Inflection AI employees (including, notably, founder Mustafa Suleyman) and related arrangements with the company.

Publicly available information, however, suggests that these transactions may not warrant merger-control investigation, let alone the heightened scrutiny that comes with potential Phase II proceedings. At the very least, given the AI industry’s competitive landscape, there is little to suggest these transactions merit closer scrutiny than similar deals in other sectors.

Overenforcement in the field of generative AI could paradoxically engender the very harms that policymakers currently seek to avert. Preventing Big Tech firms from competing in these markets (for example, by threatening competition intervention as soon as they build strategic relationships with AI startups) may thwart an important source of competition needed to keep today’s leading generative-AI firms in check. In short, competition in AI markets is important,[74] but trying naïvely to hold incumbent (in adjacent markets) tech firms back, out of misguided fears they will come to dominate this space, is likely to do more harm than good.

At a more granular level, there are important reasons to believe these kinds of agreements will have no negative impact on competition and may, in fact, benefit consumers—e.g., by enabling those startups to raise capital and deploy their services at an even larger scale. In other words, they do not bear any of the prima facie traits of “killer acquisitions” or even of the acquisition of “nascent potential competitors.”[75]

Most importantly, these partnerships all involve the acquisition of minority stakes and do not entail any change of control over the target companies. Amazon, for instance, will not have “ownership control” of Anthropic. The precise amount of shares acquired has not been made public, but a reported investment of $4 billion in a company valued at $18.4 billion does not give Amazon a majority stake or sufficient voting rights to control the company or its competitive strategy. [76] It has also been reported that the deal will not give Amazon any seats on the Anthropic board or special voting rights (such as the power to veto some decisions).[77] There is thus little reason to believe Amazon has acquired indirect or de facto control over Anthropic.

Microsoft’s investment in Mistral AI is even smaller, in both absolute and relative terms. Microsoft is reportedly investing just $16 million in a company valued at $2.1 billion.[78] This represents less than 1% of Mistral’s equity, making it all but impossible for Microsoft to exert any significant control or influence over Mistral AI’s competitive strategy. Likewise, there have been no reports of Microsoft acquiring seats on Mistral AI’s board or special voting rights. We can therefore be confident that the deal will not affect competition in AI markets.

Much the same applies to Microsoft’s dealings with Inflection AI. Microsoft hired two of the company’s three founders (which currently does not fall under the scope of merger laws), and also paid $620 million for nonexclusive rights to sell access to the Inflection AI model through its Azure Cloud.[79] Admittedly, the latter could entail (depending on deal’s specifics) some limited control over Inflection AI’s competitive strategy, but there is currently no evidence to suggest this will be the case.

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

V. Balancing Innovation and Regulation in Canada’s AI Landscape

AI presents significant opportunities and challenges for competition policy in Canada. As the technology continues to evolve, it is crucial to establish a regulatory framework that promotes innovation, while safeguarding competition and consumer protection.

The European AI Act, for example, categorizes AI systems into different risk levels—unacceptable risk, high risk, limited risk, and minimal risk. This framework allows for regulation proportional to the potential impact of the AI system. By adopting a similar risk-based approach, Canada could ensure that high-risk AI systems are subject to stringent requirements, while lower-risk systems benefit from lighter-touch regulations that encourage innovation.

To foster a competitive AI market in Canada, it is essential to avoid overly restrictive regulations that could stifle technological progress. If implemented reasonably, the EU AI Act’s flexible framework may support the development and deployment of innovative AI technologies by imposing rigorous requirements only on high-risk systems. In turn, this could support innovation by balancing the need for public safety and the protection of fundamental rights with the imperative to maintain a dynamic and competitive market environment. Overenforcement, in contrast, could lead to the opposite outcome.

Canada is currently a world leader in AI talent concentration[80] and Canada’s existing AI strategy has, to date, created significant social and economic benefits for the nation. Overly restrictive regulation (such as the proposed Artificial Intelligence and Data Act (AIDA)[81]) could lead to challenges in attracting and retaining talent, which would inevitably hamper competition.[82] Meta’s response to the proposed AIDA serves as a practical example to illustrate the potential impact of overregulation. Meta has indicated that the proposed laws could prevent the company from launching certain products in Canada due to onerous compliance costs.[83] Other tech companies share similar concerns, warning that misaligned regulations could place Canada at a competitive disadvantage globally and undermine robust competition at home.

The need to retain and attract top AI talent is another critical issue. Canada faces challenges in keeping AI talent due to more attractive opportunities abroad. To maintain its competitive edge, Canada must ensure that its regulatory frameworks do not discourage local talent from contributing to the domestic AI landscape.[84]

The Canadian government has recently committed in its federal budget to invest $2.4 billion for AI, focused primarily on computing power. Unfortunately, Meta’s subsequent release of Llama 3, a powerful open-source LLM, and Microsoft’s €4 billion investment in France’s AI capabilities highlight the need for a reassessment. Rather than computing power, Canada should instead focus on AI applications, education, and industry adoption.[85]

VI. 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.[86]

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,”[87] 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.[88] 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.[89] 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.[90]

In sum, it is a flawed understanding of the economics and practical consequences of large agglomerations of data that leads 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] Competition Bureau Canada, Artificial Intelligence and Competition, Discussion Paper (Mar. 2024), https://competition-bureau.canada.ca/how-we-foster-competition/education-and-outreach/artificial-intelligence-and-competition#sec00.

[2] 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.

[3] 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).

[4] 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.

[5] 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.

[6] 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.”).

[7] 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.”).

[8] See, e.g., Press Release, European Commission, supra note 4; 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).

[9] 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.

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

[11] See, e.g., Press Release, European Commission, supra note 4.

[12] 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.”).

[13] Press Release, European Commission, supra note 4.

[14] 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).

[15] 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.”).

[16] 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 also, 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; 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; 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).

[17] 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; Kathleen Walch, The Twenty Year History Of AI At Amazon, Forbes (Jul. 19, 2019), https://www.forbes.com/sites/cognitiveworld/2019/07/19/the-twenty-year-history-of-ai-at-amazon.

[18] See infra Section III.

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

[20] 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.

[21] 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.”).

[22] See also Yun, supra note 20 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.”).

^[23] 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).

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

^[25] 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…”.

^[26] Id.

^[27] Bruno Jullien & Wilfried Sand-Zantman, The Economics of Platforms: A Theory Guide for Competition Policy, 54 Info. Econ. & Pol’y 10080, 101031 (2021).

^[28] Daniele Condorelli & Jorge Padilla, Harnessing Platform Envelopment in the Digital World, 16 J. Comp. L. & Pol’y 143, 167 (2020).

[29] See Hagiu & Wright, supra note 24.

^[30] 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 23, at 1330.

[31] 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.

[32] Id. at 34.

[33] Id. at 35. To its credit, it should be noted, the Furman Report counsels caution before mandating access to data as a remedy to promote competition. See id. at 75. With that said, the Furman Report maintains 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.

^[34] 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.

^[35] Id. at 896.

[36] 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.

^[37] 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.

[38] Amended Complaint (D.D.C), supra note 7 at ¶37.

[39] Amended Complaint (E.D. Va), supra note 7 at ¶8.

^[40] 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.

[41] 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.

[42] Furman Report, supra note 31, at ¶4.

[43] 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.

^[44] 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.

^[45] 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).

^[46] 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.

^[47] 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.

^[48] 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.

^[49] 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.

^[50] Catherine Tucker, Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility, 54 Rev. Indus. Org. 683, 686 (2019).

^[51] Manne & Auer, supra note 23, at 1345.

[52] 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.”).

[53] 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.).

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

[55] 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.

[56] 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.

[57] 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)).

^[58] 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.

^[59] Id.; see also GSM8K, Papers with Code, available at https://paperswithcode.com/dataset/gsm8k (last visited Jan. 18, 2023); MATH Dataset, GitHub, available at https://github.com/hendrycks/math (last visited Jan. 18, 2024).

^[60] Lee, supra note 58.

[61] 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).

[62] 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.”).

[63] Or, as John Yun puts it, data is only a small component of digital firms’ production function. See Yun, supra note 20, 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.”).

^[64] 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.

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

^[66] 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.

[67] See Yun, supra note 20 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.’”).

[68] Discussion Paper, Section 3.1.6, “Consideration for mergers”.

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

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

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

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

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

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

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

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

[77] Id.

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

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

[80] Canada Leads the World in AI Talent Concentration, Deloitte (Sep. 27, 2023),  https://www2.deloitte.com/ca/en/pages/press-releases/articles/impact-and-opportunities.html.

[81]Government of Canada, Bill C-27, https://www.parl.ca/DocumentViewer/en/44-1/bill/C-27/first-reading.

[82] See e.g., Aaron Wudrick, Government Overregulation Could Jeopardize Canada’s Artificial Intelligence Chances, Globe and Mail (Apr. 1, 2024), https://www.theglobeandmail.com/business/commentary/article-government-overregulation-could-jeopardize-canadas-artificial.

[83] Howard Solomon, Meta May Not Bring Some Products to Canada Unless Proposed AI Law Changed, Parliament Told, IT World Canada (Feb. 8, 2024), https://www.itworldcanada.com/article/meta-may-not-bring-some-products-to-canada-unless-proposed-ai-law-changed-parliament-told/558406.

[84] Elissa Strome, Canada’s Got AI Talent. Let’s Keep It Here, Policy Opinions (Feb. 2, 2024), https://policyoptions.irpp.org/magazines/february-2024/ai-talent-canada.

[85] Joel Blit & Jimmy Lin, Canada’s Planned $2.4-Billion Artificial Intelligence Investment Is Already Mostly Obsolete, Globe and Mail (May 19, 2024), https://www.theglobeandmail.com/business/commentary/article-canadas-planned-24-billion-artificial-intelligence-investment-is.

[86] Lerner, supra note 61, at 4-5 (emphasis added).

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

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

[89] See Hagiu & Wright, supra note 24, at 24 (“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 61.

[90] 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.”).

Margrethe Vestager, the European Union’s commissioner for competition, posits that competition law has not addressed “the structural entrenchment of companies holding market power”, and that sweeping regulations like the European Union’s Digital Markets Act (DMA) are therefore justified (By invitation, June 3rd). She compares the case-by-case approach of competition enforcement to “playing a never-ending game of whack-a-mole”. However, enforcement is often slow and complex, especially in the kinds of “abuse of dominance” cases that have been brought against large online platforms. This deliberate pace is necessary, as the companies’ business models and the consequences of their behaviour are themselves complex.

Read the full piece here.

I. Introduction/Summary

We appreciate the opportunity to respond to this request for information on consolidation in health-care markets, Docket No. ATR-102, issued by the U.S. Justice Department (DOJ), the U.S. Department of Health and Human Services (HHS), and the Federal Trade Commission (FTC) (collectively, “the agencies”).[1] We agree wholeheartedly that robust competition in health-care markets is critical to consumer welfare and the U.S. economy. As an FTC staff policy paper summarized, “[c]ompetition in health care markets benefits consumers by helping to control costs and prices, improve quality of care, promote innovative products, services, and service delivery models, and expand access to health care services and goods.”[2] Conversely, anticompetitive health-care acquisitions can harm competition and consumer welfare; there is a substantial economic literature to that effect.[3] The agencies, over decades, have done well to oppose such mergers and, as a related matter, attempts to insulate such mergers from federal antitrust scrutiny.[4] Hence, the agencies have important roles to play in protecting health-care competition, as they enforce key federal statutes relevant to it, including the general antitrust laws that are enforced by the FTC and the DOJ,[5] and that apply across health-care markets, among others. Effective and accurate antitrust enforcement is a key component of health-care policy, and one that tends to benefit patients and other health-care consumers, including both private and public payers.

We recognize that the FTC also has a distinctive research and reporting mission assigned by Congress under Section 6 of the FTC Act, and that it has decades of experience engaging in policy and economic research, both internally and in cooperation with DOJ and HHS.[6] Acknowledging express statutory limits—such as the restriction on studies and reports regarding the business of insurance under Section 6(l) of the FTC Act[7]—the fulfillment of that research mission across health-care markets has been wide-ranging; and, as described below, it has often found salutary application in antitrust enforcement.

Our response to the agencies’ RFI comprises, at the highest level of generality, one observation and one recommendation. The observation is that, while health-care-provider acquisitions remain an extremely important domain of merger scrutiny, neither enforcement experience nor the economic literature support any fundamental changes in procedural or substantive antitrust law or regulation, whether for provider acquisitions generally or any of the categories of acquirers specified in the RFI. Competition policy is not, and should not be, static. At the same time, sound policy reform is a difficult, stepwise process, and one that requires a firm foundation in both research and enforcement experience, along with attention to established precedent. Information submitted in response to the RFI may well contribute to the agencies’ aggregate knowledge base on provider transactions. But the present inquiry does not appear designed to move that body of knowledge much beyond the margin. Indeed, as explained below, FTC research and enforcement experience underscore not just the importance of health-care competition, but also how complex the tasks of merger scrutiny and reform are.

Correspondingly, our overarching recommendation is that the agencies build on the substantial body of research regarding mergers and acquisitions in the health-care sector that has been conducted over the course of several decades by agency staff and others. That body of research includes, notably, contributions made by the staff of the FTC Bureau of Economics (BE).[8] More specifically, to that end, we recommend that economic and policy staff at the agencies synthesize the extant body of research at their disposal. To be sure, market developments, and developments in research methods and available data, may suggest new avenues of research, as well as those in need of significant updates. But a serious, critical synthesis of the available literature will only help to sharpen the agencies’ sense of new research demands, just as it will provide a basis on which to contemplate new enforcement initiatives. Such a synthesis can also ground more focused and productive requests for information on critically important issues in health-care competition going forward.

Our recommendation of such a research synthesis or review is consistent with the agencies’ acknowledgement that they may require “additional proceedings, including workshops or other public engagement, to learn more about [concerns identified in response to the RFI].”[9] While such workshops and other engagements have been a useful component of the agencies’ understanding of health-care-competition policy, we stress that they should be seen as complements to rigorous systematic research. And both should be seen as complements to building on case-specific agency enforcement experience, which typically scrutinizes the specific facts and circumstances of transactions and other firm conduct.

For example, in many smaller markets, independent providers of hospital-based services, such as anesthesiology, may be highly concentrated on any standard for “highly concentrated” markets.[10] Further research might aid the agencies in examining highly concentrated provider markets to develop filters or screens for provider acquisitions below the Hart-Scott-Rodino filing threshold, so that the agencies might identify and investigate those sub-threshold filings that are the most likely candidates for investigations and, depending on the results of those investigations, enforcement actions. Efficient matter-selection tools will be critical to that effort, less the agencies commit scarce resources to small, unpromising investigations and impose undue costs on health-care providers.

In our comments below, we recognize that the agencies themselves have established models for building the sort of “policy R&D” contemplated by the RFI in a way that complements their enforcement mandates. Also, we understand that the RFI is but one of the tools the agencies use to further their understanding of provider consolidation.[11] And, indeed, the RFI may contribute to the larger health-care-competition R&D programs at the agencies, if only at the margin.

At the same time, we write to note certain concerns about the agencies’ framing of their RFI, including, specifically, elements of the RFI that appear to be in tension with learning from agency-sponsored research and agency-enforcement experience.

Some of our concerns may be summarized as follows:

First, evidence and enforcement experience do not identify categories of health-care acquisitions that “always or almost always” impede competition and reduce output. That militates against per se prohibitions. Absent an express charge from Congress, new competition regulations regarding health-care acquisitions are not justified.

Second, agency experience—and, indeed, the FTC’s landmark success in the 2^nd U.S. Circuit Court of Appeals in the American Medical Association case[12]—suggests legitimate competition concerns about undue restraints on “the corporate practice of medicine.” More broadly—and consistent with longstanding agency practice—the agencies should be cautious about drawing general conclusions about whole industries, business models, or methods of health-care delivery, and more cautious still in condemning them.

Third, while there is no doubt that provider consolidation can be anticompetitive, the relationship between concentration and competition is complex, both as a general matter and—on current understanding—across the various sectors and subsectors identified in the RFI. That general point has been illustrated by BE staff research, even as that research has helped to refine and strengthen appropriate antitrust scrutiny of health-care provider mergers and acquisitions.

Fourth, it is well-understood that vertical acquisitions can harm competition and consumers under certain conditions. At the same time, vertical mergers are not generally—or even typically—anticompetitive. Vertical mergers may entail certain efficiencies, and are commonly procompetitive or benign on net, as research by agency personnel and the larger academic community has demonstrated. Analogously, while conglomerate mergers may raise competition concerns, they are not generally anticompetitive.

Fifth, the RFI’s framing seems problematic—both uncharacteristic of open inquiry and in tension with antitrust experience and its economic foundations. For example, we question a statement at the outset of the RFI: “Given recent trends, we are concerned that transactions may generate profits for those firms at the expense of patients’ health, workers’ safety, and affordable health care for patients and taxpayers.”[13] To be sure, some provider transactions, under particular facts and circumstances, may harm competition and consumer welfare, in violation of the antitrust laws.[14] But the agencies understand that antitrust law and economics do not recognize any general or fundamental tension between firm profits on the one hand, and the consumer benefits typically associated with competition on the other. Indeed, FTC research and enforcement have specifically undermined the notion that not-for-profit provider mergers should be treated differently under the antitrust laws.[15]

More generally, health-care acquisitions can prove anticompetitive, procompetitive, or benign, but the RFI pointedly does not request information on potential patient or payor benefits that may be associated with consolidation. More generally, the RFI does not seem to recognize that health-care acquisitions commonly entail tradeoffs of benefits and costs. Such tradeoffs are well-documented in the literature and are recognized in U.S. merger jurisprudence.

As a related matter, the FTC’s recent workshop, “Private Capital, Public Impact: An FTC Workshop on Private Equity in Health Care,”[16] seemed uncharacteristically lax and imbalanced. The workshop was announced and timed to make it appear a complement to the RFI.[17] While sponsored by the FTC, leadership from the DOJ and HHS also participated. But there were no participants from the industry in question or from insurers, large health plans, or other private payors. Instead, participants—including those providing largely anecdotal evidence—appear to have been chosen exclusively for the purpose of representing agency and third-party criticism of private equity in health care. That is, the workshop seems to have been conclusory by design.

II. Discussion

A. Economic Research and Other Forms of Policy R&D Provide a Critical Foundation for Enforcement Policy

A 2009 report by then-FTC Chairman William Kovacic defines “policy R&D” broadly in a way that comprises, but is not limited to, original, author-initiated academic research by BE staff.[18] It also incorporates diverse forms of policy inquiries, including, e.g., hearings,[19] workshops,[20] conferences,[21] and, indeed, requests for public information.[22] These can all be mutually reinforcing, providing expert input that range from issue-spotting to literature review, to the presentation of new data and studies, as well as diverse perspectives on agency interests and activities. They can, in turn, help to inform case selection and enforcement, just as enforcement experience can yield data and other inputs into subsequent policy R&D. But one need not gainsay concerns about health-care competition or specific types of acquisitions to appreciate the difficulty of grounded, systematic reform of enforcement policy in these areas. We appreciate the agencies’ recent extension of the deadline for submissions in response to the RFI; that will likely increase the utility of the inquiry. Still, while the present RFI may be a useful endeavor, it is just one tool—in itself, a limited one—in the agencies’ “policy R&D” toolbox.

Below, we sketch some of the long-running developments in the agencies’ policy R&D pertinent to provider acquisitions and health-care consolidation. Our description of the many pertinent agency endeavors focuses on work by FTC staff and leadership, in large part because of the FTC’s enforcement experience with provider mergers and its sustained health-care-competition research program. We recognize, of course, that DOJ and HHS have also made substantial contributions of their own and, in turn, that the empirical literature regarding health-care consolidation is considerable, if not vast. We recognize, too, that inquiries are ongoing, and not restricted to the RFI. Our sketch is an abridged one, partly because the agencies—and, certainly, the FTC Bureau of Economics—are well familiar with their own research programs, just as they are familiar with the challenges of building lasting enforcement reforms.

On the one hand, we mean to underscore the advances made in understanding the competitive effects of provider consolidation and its potential—both realized and residual—for application in rigorous enforcement. At the same time—and based in no small part on their own contributions to understanding health-care consolidation—the agencies should appreciate the complexity and challenge of the Policy R&D project, both across health-care sectors and within them. That complexity and challenge militate against hasty conclusions about, e.g., specific sectors, business models, and competitive trends.

1. Policy investigations

Varied hearings, workshops, RFIs, and other agency policy tools have played a significant role in developing competition policy at the agencies, even if no single agency workshop or RFI is likely to generate a record adequate to justify a significant change in enforcement policy. For example, from February through October 2003, FTC and DOJ jointly conducted 27 days of hearings on health-care-competition issues, with testimony from diverse stakeholders from academia, industry, and, not incidentally, agency staff.[23] Although HHS did not cosponsor those hearings, representatives from various HHS agencies—including the Centers for Medicare & Medicaid Services (CMS) and the Agency for Healthcare Research and Quality (AHRQ)—provided testimony and otherwise consulted on the hearings.

Based on the record of those hearings, an FTC-sponsored workshop in September 2002, and independent research (including applied-industrial-organization research conducted within and without the agencies), FTC and DOJ jointly published a substantial policy report in 2004.[24] The report reviewed systematic research, diverse stakeholder perspectives, and numerous health-care-competition policy issues. It also presented concrete policy recommendations by FTC and DOJ, drawn from that review.[25]

Follow-up workshops conducted by FTC staff, such as the 2008 workshop “Innovations in Health Care Delivery,”[26] also included participation by HHS personnel, including that of the national coordinator for health information technology and the deputy director for health information privacy at the HHS Office for Civil Rights.[27] A 2014 FTC workshop[28] and 2015 joint FTC/DOJ workshop[29] on health-care-competition issues both similarly involved officials and other personnel from HHS, FTC, DOJ, and other agencies, as well as academics, practitioners, and diverse industry stakeholders.

More focused health-care-competition and policy workshops have also informed agency enforcement policy. For example, a 2010 workshop on accountable care organizations (ACOs) jointly conducted by the FTC, the DOJ, and HHS, together with a 2011 FTC workshop on ACOs (with participation from DOJ staff),[30] informed the joint FTC/DOJ enforcement-policy statement on ACOs,[31] which was developed in consultation with the HHS Centers for Medicare and Medicaid Services, and which applied to specific forms of provider collaborations (not mergers) under the Medicare Shared Savings program.

2. Economic research on provider consolidation

The wide-ranging policy inquiries described above were not conducted in a vacuum. Rather, they build on a larger body of economic research and enforcement experience, including, notably, research on health-care competition from within and without BE, coupled with enforcement by the FTC Bureau of Competition. Staff and management in BE have made substantial contributions to the study of competition in health-care markets, with a focus on the study of provider consolidation;[32] and the FTC’s longstanding, multi-pronged investigation of provider consolidation represents a signal model of the application of applied-industrial-organization research to policy development and law enforcement.[33]

Many—including current leadership at the antitrust agencies,[34] among others[35]—have recognized that BE research, specifically, has had a significant impact on the courts’ treatment of provider mergers. Between 1993 and 2000, antitrust enforcers challenged eight hospital mergers, losing all eight challenges.[36] Hospital-merger challenges waned, and might have been abandoned, but the losing streak spurred renewed research efforts, both within the bureau and across the academy.[37] Critically, BE staff undertook a series of merger-retrospective studies, with then-FTC Chairman Timothy Muris initiating a program of merger-review studies that built on, for example, Vita & Sacher’s 2001 study, “The Competitive Effects of Not-for-Profit Hospital Mergers: A Case Study.”[38]

Subsequent provider-merger-retrospective studies have ranged from individual case studies to reviews of dozens of consummated provider mergers.[39] These are, in essence, forensic investigations, aiming “to determine ex post how, if at all, a particular merger affected equilibrium behavior in one or more markets.”[40] The retrospectives have helped to refine merger-screening methods employed within the FTC; and they have been widely credited with reversing the way that hospital mergers are viewed in the courts.[41] As Michael Salinger observes in a recent article in the Review of Industrial Organization, the retrospective studies grounded testimony in, e.g.:

the FTC’s successful challenge to Evanston-Northwestern Healthcare’s acquisition of the Highland Park Hospital . . . and the empirical methods the Bureau of Economics developed (in conjunction with noted academic health care economists) were essential to subsequent success of the Agencies in challenging hospital mergers.[42]

Especially important to litigation challenges were results on the price effects of not-for-profit hospital mergers (which some courts had supposed were generally benign) and on methods of geographic-market definition (where some courts had been inclined toward very broad geographic markets).[43] Subsequent provider retrospectives have extended the scope of the body of work, considering, e.g., nonprice effects,[44] and merger-screening methods more broadly.[45] Indeed, subsequent studies have not been confined to hospital mergers, but have examined, for example, mergers of physician practice groups[46] and the acquisition of physician practices by hospitals.[47]

Of course, retrospective studies of provider mergers at the enforcement margin have limitations, as well as advantages.[48] Critically, the retrospective studies are not conducted or considered in isolation; rather, they complement methodologically diverse studies of hospital mergers and other forms of provider consolidation, including observational studies based on panel data and cross-sectional data,[49] event studies,[50] and theoretical work.[51] Research has also examined the interaction between providers and third-party payors, as it shapes the nature of competition in health-care-provider markets,[52] as well as vertical[53] and cross-market acquisitions.[54]

Several of the annual review papers published by BE (first, by the FTC, and subsequently by the Review of Industrial Organization) provide brief reviews and, importantly, sketch the application of the academic research to provider merger reviews.[55] Learning from the body of research has, in turn, informed investigations of transactions involving, e.g., outpatient kidney-dialysis centers and specialty surgical centers, as well as physician and hospital mergers.[56]

B. There Is No Sound Basis for New Substantive Competition Regulations Regarding Health-Care Acquisitions, and the RFI Seems Unlikely to Provide One

The agencies state that the RFI will inform, inter alia, “new regulations aimed at promoting and protecting competition in health care markets.”[57] Absent a notice of proposed rulemaking (NPRM), it is unclear what is being contemplated, and correspondingly unclear how the RFI might lead to an NPRM from any of the three agencies. Certainly, FTC and HHS already enforce consumer-protection regulations, issued under express congressional charges, that may have procompetitive effects.[58] These include, for example, the FTC’s Contact Lens Rule (CLR),[59] implementing the Fairness to Contact Lens Consumers Act,[60] and FDA regulations regarding over-the-counter (OTC) hearing aids,[61] implementing certain provisions of the FDA Reauthorization Act of 2017 (FDARA).[62] We would welcome reporting from the FTC on the question of whether it has brought any cases to enforce the central prescription-release provision of the CLR, initially adopted in 2004. More broadly, study of the competitive effects of these regulations may be salutary, to the extent that it might inform proposals to amend the rules. Still, we recognize that enforcement of these regulations is a proper part of the congressional charges to the agencies, and we do not propose changes to either rule.

The prospect of new competition regulations seems, at best, premature. First, the agencies may lack the authority to promulgate such competition rules. The question of whether Congress has granted the FTC substantive or “legislative” competition-rulemaking authority is contentious;[63] and we are aware of no legal basis on which the DOJ could adopt substantive competition regulations. We also are unaware of any amenable statutory charge to HHS. Certainly, HHS can and should consider competitive effects when implementing health-care statutes, but statutory charges for health-care regulations to HHS tend to be specific ones—as was the charge to promulgate regulations for OTC hearing aids noted in the preceding paragraph—and not commonly related to merger scrutiny.

Cognizant of the agencies’ substantial enforcement experience and a significant body of academic literature regarding health-care consolidation, it is difficult to imagine how submissions to the RFI could establish the prerequisites to competition rulemaking regarding health-care acquisitions, even if FTC were deemed to have the requisite rulemaking authority. At present, the agencies do not enforce any such health-care regulations and, to the best of our knowledge, none of the agencies has ever adopted a rule regarding health-care acquisitions under a general grant of legislative rulemaking authority. Specific health-care acquisitions, whether proposed or consummated, can, of course, be blocked, if found anticompetitive, under an administrative ruling, by the Federal Trade Commission, or a judicial ruling, by a federal court. To the best of our knowledge, such decisions have always been case-specific.

Contemporary antitrust law reserves broad rule-like prohibitions for a very limited number of “naked” restraints on trade, such as horizontal price-fixing. For more than 40 years, the U.S. Supreme Court has been clear that general, per se, prohibitions are reserved for the types of matters that “always or almost always tend to restrict competition and decrease output.”[64] None of the types of acquisitions listed in the RFI can demonstrably meet that standard and, absent an express statutory charge from Congress, there is no evident ground for regulating categories of health-care acquisitions under a lesser standard.

Again, we do not—and cannot—impugn ex ante competition concerns that may be raised by specific health-care acquisitions. But, for example, a given study suggesting that certain private-equity acquisitions of hospitals are associated with poorer quality in-patient care, at least on certain measures (chiefly, falls and central-line infections for Medicare beneficiaries)[65] may indeed inform merger scrutiny, but such average effects from a single noncausal study,[66] driven by select effects in a select patient population, cannot suffice to establish that such acquisitions are anticompetitive on net, on average, much less that they “always or almost always tend to restrict competition and reduce output.”

Of course, by noting the potential for new regulations, the agencies might contemplate not only—or even primarily—the promulgation of regulations sui generis, but research and advocacy reported to lawmakers that could inform subsequent and specific statutory charges for regulations.[67] Such research and advocacy can indeed have a salutary effect on policy, although, again, we caution that the present RFI seems unlikely to lead to well-founded policy recommendations, even if it does advance agency learning at the margin.

C. Vertical Transactions Are Not Generally Anticompetitive

The RFI raises broad questions about vertical acquisitions, both in questioning the impact of “[t]ransactions conducted by private equity funds or other alternative asset managers,”[68] (some of which might be considered conglomerate mergers) and in questioning the impact of “[t]ransactions conducted by health systems.”[69]

There is no doubt that vertical mergers can be anticompetitive under certain circumstances. For example, an integrated firm may have an incentive to exclude rivals,[70] and a vertical merger can have an anticompetitive effect if the upstream firm has market power and the ability, post-acquisition, to foreclose its competitors’ access to a key input.[71] In that regard, raising rivals’ costs can “represent[] a credible theory of economic harm” if other conditions of exclusionary conduct are met.”[72] But the implications of vertical mergers are theoretically ambiguous: anticompetitive effects are possible, but they are neither necessary nor, for that matter, typical: “The circumstances… in which [raising rivals’ costs] can occur are usually so limited that [it] almost always represents a minimal threat to competition.”[73] Moreover:

[a] major difficulty in relying principally on theory to guide vertical enforcement policy is that the conditions necessary for vertical restraints to harm welfare generally are the same conditions under which the practices increase consumer welfare.[74]

This structural ambiguity weighs against any presumption against vertical mergers, and it suggests the importance of empirical research in formulating standards to evaluate vertical transactions.

The economics literature is, to borrow a phrase from Leegin, “replete with procompetitive justifications” for vertical integration. Vertical integration typically confers benefits, such as eliminating double marginalization,[75] increasing R&D investment,[76] and creating operational and transactional efficiencies.[77]

Empirical evidence further supports the established legal distinctions between horizontal mergers and vertical mergers (as well as other forms of vertical integration), indicating that vertical integration tends to be procompetitive or benign. For example, a meta-analysis of more than 70 studies of vertical transactions analyzed groups of studies for their implications for various theories or models of vertical integration, and for the effects of vertical integration. From that analysis:

a fairly clear empirical picture emerges. The data appear to be telling us that efficiency considerations overwhelm anticompetitive motives in most contexts. Furthermore, even when we limit attention to natural monopolies or tight oligopolies, the evidence of anticompetitive harm is not strong.[78]

On the contrary, “under most circumstances, profit-maximizing vertical integration decisions are efficient, not just from the firms’ but also from the consumers’ points of view.”[79] And “[a]lthough there are isolated studies that contradict this claim, the vast majority support it….”[80] Lafontaine & Slade accordingly concluded that “faced with a vertical arrangement, the burden of evidence should be placed on competition authorities to demonstrate that that arrangement is harmful before the practice is attacked.”[81] Another study of vertical restraints finds that, “[e]mpirically, vertical restraints appear to reduce price and/or increase output. Thus, absent a good natural experiment to evaluate a particular restraint’s effect, an optimal policy places a heavy burden on plaintiffs to show that a restraint is anticompetitive.”[82]

Subsequent research has reinforced these findings. Reviewing the more recent literature from 2009-18, John Yun concluded “the weight of the empirical evidence continues to support the proposition that vertical mergers are less likely to generate competitive concerns than horizontal ones.”[83]

Leading contributors to the empirical literature, reviewing both new studies and critiques of the established view of vertical mergers, maintain a consistent view. For example, testifying at a 2018 FTC hearing, former FTC Bureau of Economics Director Francine Lafontaine acknowledged that some of the early empirical evidence is less than ideal, in terms of data and methods, but reinforced the overall conclusions of her earlier research “that the empirical literature reveals consistent evidence of efficiencies associated with the use of vertical restraints (when chosen by market participants) and, similarly, with vertical integration decisions.”[84]

The empirical literature regarding vertical acquisitions involving health-care providers, specifically, remains unclear.[85] One study of hospital acquisitions of large physician groups, employing Medicare claims data, finds significant changes at the physician level, with acquired physicians delivering substantially more care in the acquirers’ hospitals post-acquisition (and less at other hospitals and via office-based care).[86] It also finds increased billing at the hospital level, although observed hospital-level effects are smaller, and estimates less precise, than those at the physician level.[87] Here, increased costs—at least for these acquisitions on these measures—appear to be “consistent with the hypothesis that acquired physicians are responding to CMS’ location-based billing policy, which provides higher compensation for care delivered in hospital settings relative to doctors’ offices.”[88] Another study fails to find systematic clinical benefits to vertical integration across diverse quality-of-care metrics.[89]

Such studies may tend to impugn the notion that vertical acquisitions of physician practices by hospitals tend to provide efficiencies that offset cost or price increases, but they cannot be regarded as comprehensive. Further, they suggest the role that public health-care programs and regulations may play in distorting competitive dynamics for both utilization and costs. That raises the question of where policy reform might best be located, supposing that it is called for.

Finally, such studies do not resolve the larger question of why so many physicians—both individually and through their practice groups—are leaving independent practice for hospital- and system-based employment. While the extant literature can certainly inform provider-merger scrutiny in individual matters, it does not appear to implicate general policy reforms for vertical acquisitions of health-care providers and, indeed, suggests equal concern with the design of federal programs and regulations beyond antitrust.

In short, empirical research confirms that the law properly does not presume that vertical mergers have anticompetitive effects; rather, it requires specific evidence of both harms and efficiencies.

The preceding comments apply a fortiori to conglomerate mergers. Whereas vertical mergers combine firms in the same supply chain, conglomerate mergers combine firms that are neither engaged in head-to-head competition nor operating in the same supply chain. Such mergers thus do not inherently reduce competition in any market. The government has explained that conglomerate mergers can produce many of the same “procompetitive benefits” of vertical mergers if the combined firms’ “production or distribution uses the same assets, inputs, or know-how.”[90] That is so “even if the merged firm will become a more effective competitor or gain [market] share.”[91] The resulting economies of scope can increase consumer welfare.

Conglomerate mergers between large, established firms and smaller innovators also play an important role in fostering innovation—and, thus, product competition—in, for example, the pharmaceutical industry. As the Congressional Budget Office (CBO) explains:

The acquisition of a small company by a larger one can create efficiencies that might increase the combined value of the firms by allowing drug companies of different sizes…to specialize in activities in which they have a comparative advantage. Small companies—with relatively fewer administrative staff, less expertise in conducting clinical trials, and less physical and financial capital to manage—can concentrate primarily on research. For their part, large drug companies are much better capitalized and can more easily finance and manage clinical trials. They also have readier access to markets through established drug distribution networks and relationships with buyers.[92]

Conglomerate mergers in the pharmaceutical industry thus can realize the procompetitive effects of vertical combinations (creating efficiencies) while avoiding the anticompetitive effects of horizontal mergers (eliminating competition).

That is not to say conglomerate mergers can never lead to higher prices. Recent research on bargaining models indicates it is possible for cross-market acquisitions to facilitate a price increase. Such models do not, however, suggest that is a likely result. Instead, empirical research indicates that, generally, “cross-market acquisitions by larger companies do not have a significant effect on price.”[93] Moreover, a common theory of competitive harm holds only “as long as” the parties’ “products have common customers.”[94] Hospital acquisitions may provide a special case, in that they may cross geographic markets even if they do not cross product markets. Moreover, geographic markets may have different boundaries from the perspectives of patients and third-party payors.[95] Put another way, certain provider mergers that may be deemed cross-market transactions from a patient perspective may also alter provider bargaining with health plans for whom the providers are substitutes (or complements); hence, from another perspective, they are within the same geographic market. In that regard, additional research[96] and additional enforcement experience may, in time, lead to further refinements in hospital-merger scrutiny.

Of course, none of this is to say that the agencies should not scrutinize vertical or conglomerate mergers involving health-care providers. Further research might sharpen the agencies’ understanding of specific industries in which, or circumstances under which, provider acquisitions may be more or less likely to raise competitive concerns. Ongoing research by the agencies—including BE staff research[97]—will no doubt further that goal.

But, as we note above, a single study is not a body of literature, much less one that is mature or settled. Indeed, a single study suggesting that certain private-equity acquisitions of hospitals are associated with poorer quality in-patient care, at least on certain measures (chiefly, falls and central-line infections for Medicare beneficiaries)[98] may, indeed, inform merger scrutiny, but such average effects from a single noncausal study, driven by select effects in a select patient population, cannot suffice to establish that such acquisitions are anticompetitive on net, on average, much less that they “always or almost always tend to restrict competition and reduce output.” More plausibly, it may be pertinent to questions of, e.g., when to issue a second request or commence a formal investigation, at least at the margin.

Similarly, future research may sharpen the agencies’ understanding of the conditions under which vertical (or conglomerate) acquisitions by third-party payors are more, or less, likely to raise competition concerns. Such research might be bolstered by additional enforcement experience with such acquisitions, but we expect that the present RFI is not well-designed to further agency understanding much beyond that already available to agency staff.

D.  A Provider’s For-Profit or Not-for-Profit Status May Say Little About the Likely Competitive Effects of Mergers or Acquisitions Involving that Provider

As noted above, we were struck by a statement at the outset of the RFI: “Given recent trends, we are concerned that transactions may generate profits for those firms at the expense of patients’ health, workers’ safety, and affordable health care for patients and taxpayers.”[99] To be sure, some transactions do just that. There is no doubt that the antitrust laws are broadly applicable to health-care transactions or that particular provider mergers, under particular facts and circumstances, may violate the antitrust laws, harming competition and consumer welfare.[100] And nonprice effects, such as quality of care, may factor in antitrust scrutiny of a provider merger.[101]

Nonetheless, the agencies understand that antitrust law and economics do not recognize any general or fundamental tension between firm profits, on the one hand, and the consumer benefits typically associated with price and nonprice competition in goods and services markets, on the other. Moreover, considerable research militates against the suggestion that for-profit and not-for-profit providers should be distinguished for the purposes of merger scrutiny. As Martin Gaynor—former director of the FTC Bureau of Economics and presently special advisor to the assistant U.S. attorney general for antitrust—summarized in testimony before the Senate Judiciary Committee: “Research evidence shows not-for-profit hospitals exploit market power just as much as for-profits.”[102]

In fact, as noted above, two early foci for the FTC’s hospital-merger retrospective studies were, one, the question of how best to approach geographic-market definition (not least, because some courts were inclined toward very broadly drawn hospital markets, at odds with established methods) and, two, the question of whether not-for-profit hospitals were less likely than for-profit hospitals to exploit market power, when they had it, within the relevant geographic boundaries (not least, because some courts were accepting what amounted to a “not-for-profit defense” to hospital-merger challenges).[103] The merger retrospectives consistently demonstrated that not-for-profit status did not make a difference.[104]

Our point is not that the extant literature is definitive or that it is easily generalized across different types of providers. Rather, there are good reasons to think that not-for-profit providers are not special from a competition standpoint, and substantial evidence on that point in a well-investigated provider domain.

Further research and enforcement experience might suggest a different perspective on one or more specific subcategories of provider acquisitions. Still, the agencies should be mindful of the hospital findings as a background matter. And as the agencies’ research staff are likely aware, research regarding the question of whether for-profit provider status in, e.g., hemodialysis treatment is associated with different treatment quality has provided mixed results, with some agency research failing to find any statistically significant indication that it is.[105] Results also are observed to vary across empirical specifications and available datasets.[106] In addition, given the large number of hemodialysis acquisitions nationwide associated with two acquiring firms, there is an open question, even with regard to kidney dialysis, how best to parse for-profit status from the management practices of two very large for-profit acquirers.[107]

E. Various Models of Health-Care Delivery May Be Associated with Complex Tradeoffs

Whereas some of the interests or concerns in the RFI focus on transactions’ structural features—e.g., on the competitive effects of horizontal, vertical, or conglomerate mergers—an overlapping set of questions focuses on the type of acquiring firm. For example, the RFI notes “concerning trends” in, e.g., “transactions in the health care market conducted by private equity funds or other alternative asset managers, health systems, and private payers .”[108] The RFI suggests that there is “recent research indicating these categories of transactions may harm health care quality, access, and/or costs.”[109]

But the suggestion about “recent research” has no attached citation. An earlier footnote substantiates the claim that “[a]cademic research and agency experience in enforcement actions has shown that patients, health care workers, and others may suffer negative consequences as a result of horizontal and vertical consolidation of a range of different types of providers—including not-for-profit providers.”[110] While the agencies cite only two primary research articles and one policy review for that general proposition—and while one of the articles suggests limited results[111]—we take it that the far more general claim is (or should be) uncontroversial. The dozens of papers cited above in Section II.A.2. of these comments tend to substantiate those broad claims. That is, diverse provider acquisitions can raise competitive concerns; and, moreover, competitive concerns can be raised equally by transactions (or other conduct) involving not-for-profit and for-profit providers.

Not incidentally, many provider markets are highly concentrated, pre-acquisition, on any notion of “highly concentrated.”[112] For example, the FTC’s defense of its authority in the Phoebe-Putney matter concerned what was, in effect, a merger to monopoly;[113] and several surrounding counties—like many outside metropolitan areas across the country—had no general hospital at all.[114] No policy reform is needed to provide that two-to-one hospital mergers will be carefully scrutinized by antitrust authorities. Similarly high provider concentration can be observed across diverse specialty practices in many rural and other small markets.[115]

But the research base and enforcement experience regarding specific types of acquiring (or target) firms is considerably less well-developed. We do not mean to impugn specific studies, so much as to place available results in context. For example, as we also discuss above, there are studies suggesting negative health-care-quality effects—cognizable harms—associated with certain for-profit acquisitions of hemodialysis-treatment facilities.[116] But as we note there, results in that space are somewhat mixed, varying across empirical specifications and available data, and there is some research that fails to find any statistically significant indication that acquisitions by for-profit firms are associated with different treatment quality.[117]

Turning to private-equity acquisitions, the RFI cites a single study suggesting that certain private-equity acquisitions of hospitals are associated with poorer quality in-patient care, at least on certain measures (chiefly, falls and central-line infections for Medicare beneficiaries).[118] We cannot gainsay competition concerns about such acquisitions, and the study may, indeed, inform merger scrutiny going forward. Such average effects from a single non-causal study, driven by select effects in a select patient population, cannot, however, suffice to establish that such acquisitions are anticompetitive on net, on average, much less ground a fundamentally different approach to private-equity acquisitions of health-care providers. We note, too, that another study (with two of the same coauthors) found more mixed results, including some suggesting improved quality of care:

In our main analysis, we observed greater improvements in process quality measures among private equity–acquired hospitals relative to controls, which may reflect better care for patients. However, it could also be consistent with better adherence to compliance standards or efforts to maximize opportunities for quality bonuses under pay-for-performance contracts.[119]

Positive income and profitability were also observed. Both studies evidence some heterogeneity of findings across the private-equity and control hospitals. Our point is not that the agencies should be unconcerned about nonprice effects, such as quality of care. Rather, it is that the understanding of this class of transactions is incomplete, and unlikely to be resolved by submissions in response to this RFI. Also, the research does not resolve the fundamental question of when or under what conditions hospitals may be targets for private-equity acquisitions. And to the extent it suggests new management practices, it may suggest not just concerns but tradeoffs in the management capacity associated with different acquirers.[120]

Given mixed results and a lacunae in the literature, further research is warranted, as well as case-specific investigation using established methods. To the extent that specific findings on specific categories of provider mergers are mixed, unclear, or conspicuously limited, more general economic learning and precedent regarding, e.g., horizontal, vertical, and conglomerate mergers may be especially informative. So, too, may be agency experience with undue restraints on the “corporate practice of medicine” or other undue restraints on new models of distribution for health care, dating at least to the FTC’s landmark 1980 case against the American Medical Association, which addressed various restraints on physician and nonphysician contracting.[121] Analogously, in 1992, based on its research regarding the eye-care industry, FTC staff advocated for the repeal of “prohibitions against practicing in retail settings and against corporate affiliations.”[122]

Finally, given results suggesting the confounding effects of health-care programs and regulations, from Medicare reimbursement policies to state-based certificate-of-public-advantage and certificate-of-need regulations, the agencies should be ever alert to the question of the best locus for policy reform.

F. The Framing of a Request for Information Can Influence the Quality of the Response

As we explain above, we appreciate the importance of the agencies’ efforts to protect and foster competition in diverse health-care markets; and we appreciate the mutually reinforcing roles that policy studies and enforcement experience can play in health-care and antitrust policy. Still, one need not gainsay concerns about health-care competition or specific types of acquisitions to appreciate the difficulty of grounded, systematic reform of enforcement policy in these areas. The agencies’ extension of the deadline for submissions in response to the RFI will, no doubt, increase the utility of the inquiry. But while recognizing that the present RFI may be a useful endeavor, it is just one tool—in itself, a limited one—in the agencies “policy R&D”[123] toolbox. Moreover, the RFI’s framing seems, in many ways, unfortunate: not conducive to the most constructive use of agency resources or third-party contributions.

First, the scope of the RFI is unclear. The agencies note that they are:

particularly interested in information on transactions in the health care market conducted by private equity funds or other alternative asset managers, health systems, and private payers, especially those transactions that would not be noticed to the Department of Justice and the Federal Trade Commission under the Hart-Scott-Rodino Antitrust Improvements Act, 15 USC 18(a).[124]

Such transactions may be both numerous and diverse, with or without a restriction on HSR-reportable transactions. The scope and heterogeneity of agency interests is only underscored by the RFI’s elaboration on the transactions of interest:

These transactions could involve dialysis clinics, nursing homes, hospice providers, primary care providers, hospitals, home health agencies, home- and community-based services providers, behavioral health providers, billing and collections services, revenue cycle management services, support for value-based care, data/analytics services, and other types of health care payers, providers, facilities, Pharmacy Benefit Managers (PBMs), Group Purchasing Organizations (GPOs), or ancillary products or services.[125]

If the RFI is meant to be but one inquiry in a much larger project—say, for example, something akin to the 2003 FTC/DOJ health-care hearings that led to the 2024 Dose of Competition report—some sense of the scope of the larger project would be helpful to the public. On its own, the reference to acquisitions across such diverse health-care entities seems extremely broad, and not well-suited to the production of usefully focused submissions.

Whatever the scope of the RFI, its framing is critical to its utility. Given the agencies’ considerable contributions to health-care competition over the course of several decades,[126] we regret to comment on a conspicuous deficit in the RFI’s framing. As we note in our introductory summary, the agencies’ RFI seems, at times, to prejudge the answers to its own questions. That may be unproductive for research purposes and, specifically, may bias submissions to the public record.

The most egregious example of this may be the FTC’s press release announcing the RFI, which informed both the press and stakeholders that the FTC, DOJ, and HHS “Launch Cross-Government Inquiry on Impact of Corporate Greed in Health Care.”[127] That framing would seem overly dramatic if it announced allegations of antitrust violations; it seems an especially poor way to announce a request for information from the diverse stakeholders constituting “the public.”

Of course, a press release is just that, but the language is repeated in the FTC’s May 1 announcement that the agencies had extended the RFI comment period;[128] and at least some readers may have noticed that the language in the FTC’s press release mirrors that of a White House “fact sheet” noting that the administration was “[l]aunching a cross-government public inquiry into corporate greed in health care.”[129] Some individuals may be “greedy,” in a colloquial sense, whether in their personal capacities or acting as corporate agents. But “corporate greed” has no clear meaning in antitrust law or industrial-organization economics. It is hardly a subject for systematic investigation by expert agencies; it seems, at best, an atmospheric distraction.

Announcements of the RFI from DOJ and HHS seem similarly, if less steeply, slanted, describing a “cross-government public inquiry into private-equity and other corporations’ increasing control over health care.”[130] Identifying legitimate competition concerns is not, in itself, problematic. But suggesting such concerns about broad categories of transactions, without any acknowledgment of potential merger benefits, and without any acknowledgment that most provider mergers and acquisitions are not challenged, much less blocked, and should be presumed lawful until established otherwise, seems to suggest a general hostility to provider acquisitions with no basis in legal precedent, economic research, or agency practice. Similarly, any suggestion that profits in highly differentiated product and service markets are inconsistent with the fruits of vigorous health-care competition—lower prices, higher quality, and greater availability of health care—would appear fundamentally at odds with both established antitrust law and economic learning.

Similarly, as we note above, we were struck by a statement at the outset of the RFI itself: “Given recent trends, we are concerned that transactions may generate profits for those firms at the expense of patients’ health, workers’ safety, and affordable health care for patients and taxpayers.”[131] To be sure, some transactions do just that. But, as we discuss at some length above,[132] antitrust law and economics do not recognize any general or fundamental tension between firm profits, on the one hand, and the consumer benefits typically associated with price and nonprice competition in goods and services markets, on the other.

And while there is some research suggesting that some categories of for-profit provider acquisitions may be associated with competitive harms, at least in some circumstances, a considerable body of research, reinforced by agency-enforcement experience, militates against the suggestion that for-profit and not-for-profit providers should be distinguished for the purposes of merger scrutiny. As Martin Gaynor—former director of the FTC Bureau of Economics and presently special advisor to the assistant U.S. attorney general for antitrust—summarized in testimony before the Senate Judiciary Committee: “Research evidence shows not-for-profit hospitals exploit market power just as much as for-profits.”[133]

We wonder, too, about the design of a March 5, 2024, workshop titled “Private Capital, Public Impact: An FTC Workshop on Private Equity in Health Care,”[134] which was hosted by the FTC with leadership from the DOJ and HHS also participating. The workshop was timed to coincide with the RFI and, not incidentally, was noted identically by all three agencies in their press releases for the RFI.[135] The posted agenda specifies a brief event—less than half a day—at which roughly half of the participants represented the agencies themselves, and none obviously worked in or for the industry in question or, e.g., for large health plans or other private payors. Several participants were individual practitioners relating their own perceptions of specific acquisitions.[136]

To be sure, providers and other stakeholders might well be interested in the perspectives of agency officials. At the same time, the airing of agency views and concerns seems ill-timed, given the timing of the RFI itself, as submissions in response to the RFI were initially due just one day after the workshop.

Moreover, the FTC’s announcement of its workshop echoes the apparent imbalance of the RFI itself:

In recent years, the Commission has become increasingly concerned about the effects of private equity investment in this sector. We are convening a workshop bringing together experts and affected individuals to discuss their insights. The workshop will consist of several panels and feature remarks from government officials, academics, economists, and practitioners, as well as members of the public who have experienced, first-hand, the effects of private equity investment in the health care system.[137]

Again, we do not take any issue with the identification of legitimate competition concerns. Merger scrutiny is the proper purview and, indeed, obligation of the antitrust agencies; and we do not write to opine on open matters or potential acquisitions. But the workshop design, description, and timing suggest an information-gathering exercise distinct from an open-minded public inquiry, if not the prejudgment of myriad fact-dependent potential enforcement matters.

III. Conclusion

Health-care-provider consolidation is an important area of concern for antitrust enforcers, and there is no doubt that specific provider acquisitions can prove anticompetitive. For those reasons, the RFI may indeed prompt the submission of useful materials to the antitrust agencies and, perhaps, to HHS. To the extent that the RFI is considered but one more step in the agencies’ ongoing competition R&D program, it may be salutary. At the same time, the RFI does not seem designed to move agency learning much beyond the margin—certainly not across the broad swath of issues it raises; and the RFI’s framing seems likely to skew, rather than focus, the information submitted.

Further, while competition concerns may be important to how the agencies implement various congressional charges to promulgate specific regulations (and, by statute, are implicated in any FTC rulemaking regarding unfair or deceptive acts or practices), neither enforcement experience nor economic literature militate in favor of new competition regulations regarding provider mergers and acquisitions.

While there may be ample reasons for diverse competitive concerns, such concerns do not establish categories of acquisitions that warrant per se condemnation, via regulation or otherwise. To the contrary, agency experience and expertise with, e.g., restraints on the “corporate practice of medicine” and with other regulatory restraints on diverse methods or models of health-care delivery illustrate the competitive (and welfare) tradeoffs implicated by many types of provider acquisitions and, indeed, by specific transactions. Such tradeoffs can have—and have had—directionally different competition implications on a case-by-case basis.

More specifically, while extant research and enforcement experience may identify or heighten competitive concerns about certain transactions, they militate against, rather than for, new policies regarding for-profit providers, overly simple structural approaches to health-care-merger screening, and the conflation of considerations for horizontal, vertical, and conglomerate acquisitions.

Emerging concerns may prompt reallocation of screening resources and priorities within the agencies, although the importance of building experience cumulatively may suggest caution there, too.

As a related matter, concerns about provider acquisitions—single transactions or clusters of them—below the HSR reporting threshold may be justified in many markets, especially in rural or other underserved areas. That suggests a complex of inquiries, however, and not new rules or general policies. Given the myriad factors driving consolidation—especially in small (and, often, shrinking) markets—and given the fact that the large majority of mergers, above or below the threshold, are not anticompetitive, how can further research and enforcement experience identify filters by which the agencies might identify and screen those sub-threshold acquisitions most likely to raise competitive concerns?

Finally, as we suggest in the introduction to these comments, further policy inquiries—from RFIs to workshops to systematic research—might best be served by agency economists conducting a serious critical synthesis of the extant body of research regarding health-care-provider acquisitions. That is a nontrivial project, but it should be prologue to consideration of or recommendations regarding policy reforms in the area.

 

[1] Request for Information on Consolidation in Health Care Markets, Docket No. ATR-102, Dep’t Justice, Dep’t Health & Human Servs., & Fed. Trade Comm’n (Mar. 5, 2024), https://www.regulations.gov/docket/FTC-2024-0022 [hereinafter “RFI”].

[2] Daniel J. Gilman & Tara Isa Koslov, Policy Perspectives: Competition and the Regulation of Advanced Practice Nurses, Fed. Trade Comm’n, 1 (Mar. 2014), available at https://www.ftc.gov/system/files/documents/reports/policy-perspectives-competition-regulation-advanced-practice-nurses/140307aprnpolicypaper.pdf.

[3] For an overview, see, e.g., Hearing on Antitrust Applied: Hospital Consolidation Concerns and Solutions, Testimony before the Subcomm. on Competition Pol’y, Antitrust, and Consumer Rights, S. Comm. on the Judiciary, 117^th Cong. (2021) (statement of Martin Gaynor, E.J. Barone University Professor of Economics and Public Policy Heinz College, Carnegie Mellon University), https://www.judiciary.senate.gov/download/martin-gaynor-testimony.

[4] For successful cases against provider mergers, see, e.g., Fed. Trade Comm’n v. Hackensack Meridian Health, Inc., 30 F.4^th 160 (3d Cir. 2022); ProMedica Health Sys., Inc., FTC Docket No. 9346, 2012 WL 2450574 (Jun. 25, 2012), aff’d, ProMedica Health Sys., Inc., v. FTC, 749 F.3d 559 (6th Cir. 2014); FTC v. Penn State Hershey Med. Ctr., 185 F. Supp. 3d 552 (M.D. Pa. 2016), rev’d, 838 F.3d 327, 343 (3d Cir. 2016); Saint Alphonsus Med. Ctr.-Nampa Inc. v. St. Luke’s Health Sys., Ltd., No. 1:12-cv-00560, 2014 WL 407446 (D. Idaho Jan. 24, 2014), aff’d, 778 F.3d 775 (9th Cir. 2015). Regarding the authority to review provider mergers, see Fed. Trade Comm’n v. Phoebe Putney Health Sys., Inc., 586 U.S. 216 (2013) (acquisition not immune from scrutiny under state-action doctrine).

[5] We refer to the Sherman and Clayton Acts and, by extension, the Federal Trade Commission Act, and recognize that other parties, including state attorneys general and private parties, may sue to enforce certain provisions of the antitrust laws, while recognizing that there is no private right of action under the FTC Act.

[6] 15 U.S.C. § 46. See infra., text accompanying notes 23-31, for constructive examples.

[7] Id. at § 46(l).

[8] Links to economic research, including reports, working papers, issue papers, and articles in peer-reviewed journals can be found at Fed. Trade Comm’n, Bureau of Econ., Research in the Bureau of Economics, https://www.ftc.gov/about-ftc/bureaus-offices/bureau-economics/research-bureau-economics. See also infra. Section II.A.2.

[9] RFI at 11.

[10] Sub-threshold acquisitions may well be 3-to-2 or merger-to-monopoly transactions for critical services. “Any standard” would include, for example, those described in any or all editions of the horizontal merger guidelines.

[11] We note that, e.g., in January 2021, the FTC issued orders, under its FTC Act Section 6(b) authority to six health-insurance companies to provide information to facilitate the agency’s study of the effects of physician group and health-care-facility consolidation from 2015 through 2020. See Press Release, FTC to Study the Impact of Physician Group and Healthcare Facility Mergers, Fed. Trade Comm’n (Jan. 14, 2021), https://www.ftc.gov/news-events/news/press-releases/2021/01/ftc-study-impact-physician-group-healthcare-facility-mergers. While the information collected under such orders is limited partly by restrictions imposed under the Paperwork Reduction Act, and not merely available data and methodological concerns, it may nonetheless help advance understanding of provider consolidation. We assume that this project, initiated at the tail end of the last administration, is ongoing.

[12] AMA v FTC, 638 F.2d 443 (2d Cir. 1980).

[13] Id. at 1.

[14] See generally, e.g., Fed. Trade Comm’n v. Phoebe Putney Health Sys., Inc., 568 U.S. 216 (2013). In its unanimous decision, the Court noted that the 11^th U.S. Circuit Court of Appeals had, as an initial matter, “‘agreed with the [FTC] that, on the facts alleged, the joint operation of Memorial and Palmyra would substantially lessen competition or tend to create, if not create, a monopoly’” 568 U.S. at 222-3 (quoting Fed. Trade Comm’n v. Phoebe Putney Health Sys., Inc., 663 F.3d 1369, 1375 (2011). The Court’s holding in Phoebe Putney upheld the FTC’s jurisdiction over the hospital merger, notwithstanding the grant of certain powers to hospital authorities by the state of Georgia. 568 U.S. at 224. For a discussion of various FTC research, advocacy, and enforcement activities in health care, including scrutiny of provider mergers, see, e.g., Maureen K. Ohlhausen, The First Wealth is Health: Protecting Competition in Healthcare Markets, Remarks at the 2017 ABA Fall Forum (Nov. 16, 2017), available at https://www.ftc.gov/system/files/documents/public_statements/1275573/mko_fall_forum_2017.pdf. Although the FTC and the DOJ have concurrent jurisdiction over mergers under Section 7 of the Clayton Act, health-care-provider mergers are typically assigned to the FTC under the FTC/DOJ clearance process. For a list of health-care-enforcement matters, see FTC, The FTC’s Health Care Work: Cases, https://www.ftc.gov/news-events/topics/competition-enforcement/health-care-competition (last accessed May 1, 2024).

[15] See infra. Section II.D.

[16] Private Capital, Public Impact: An FTC Workshop on Private Equity in Health Care, Fed. Trade Comm’n (May 5, 2024), https://www.ftc.gov/news-events/events/2024/03/private-capital-public-impact-ftc-workshop-private-equity-health-care. The workshop webpage includes a description, along with links to the agenda, participant biographies, and a transcript of the proceedings.

[17] Press Release, Federal Trade Commission, the Department of Justice and the Department of Health and Human Services Launch Cross-Government Inquiry on Impact of Corporate Greed in Health Care, Fed. Trade Comm’n (Mar. 5, 2024), https://www.ftc.gov/news-events/news/press-releases/2024/03/federal-trade-commission-department-justice-department-health-human-services-launch-cross-government (noting that, “[i]n addition to the launch of the RFI, all three agencies will also be participating today in a virtual public workshop that will explore the impact of private equity in health care and will discuss what the government is doing to address any harmful effects.”). The announcement of the FTC workshop was repeated verbatim in DOJ and HHS announcements of the RFI. Press Release, Justice Department, Federal Trade Commission and Department of Health and Human Services Issue Request for Public Input as Part of Inquiry into Impacts of Corporate Ownership Trend in Health Care, Dep’t Justice (Mar. 5, 2024), https://www.justice.gov/opa/pr/justice-department-federal-trade-commission-and-department-health-and-human-services-issue; Press Release, HHS, DOJ, and FTC Issue Request for Public Input as Part of Inquiry into Impacts of Corporate Ownership Trend in Health Care, Dep’t Health & Human Servs. (Mar. 5, 2024), https://www.hhs.gov/about/news/2024/03/05/issue-request-for-public-input-as-part-of-inquiry-into-impacts-of-corporate-ownership-trend-in-health-care.html.

[18] William e. Kovacic, The Federal Trade Commission at 100: Into Our Second Century, 91-92 (Jan. 2009), available at https://www.ftc.gov/sites/default/files/documents/public_statements/federal-trade-commission-100-our-second-century/ftc100rpt.pdf.

[19] Public Hearings: Health Care and Competition Law and Policy Hearings, Fed. Trade Comm’n & Dep’t Justice (2023), https://www.ftc.gov/news-events/events/2003/02/health-care-competition-law-policy-hearings (hearings page with links to agendas and transcripts); Hearings on Competition and Consumer Protection in the 21^st Century, Fed. Trade Comm’n (2018-19), https://www.ftc.gov/enforcement-policy/hearings-competition-consumer-protection (hearings page with links to agendas, transcripts, and submissions).

[20] See, e.g., Now Hear This: Competition, Innovation, and Consumer Protection Issues in Hearing Health Care, Fed. Trade Comm’n (Apr. 18, 2017), https://www.ftc.gov/news-events/events/2017/04/now-hear-competition-innovation-consumer-protection-issues-hearing-health-care (FTC Workshop); Examining Health Care Competition, Fed. Trade Comm’n (Mar. 2014), https://www.ftc.gov/news-events/events/2014/03/examining-health-care-competition (FTC Workshop); Innovations in Health Care Delivery, Fed. Trade Comm’n (Apr. 24, 2008), https://www.ftc.gov/news-events/events/2008/04/innovations-health-care-delivery (FTC Workshop).

[21] See, e.g., 16^th Annual Microeconomics Conf., Fed. Trade Comm’n (Nov. 2023), https://www.ftc.gov/news-events/events/2023/11/sixteenth-annual-microeconomics-conference (annual conference hosted by FTC Bureau of Economics; 2023 conference was cosponsored by FTC and Tobin Ctr., Yale Univ.).

[22] See, e.g., RFI; FTC Seeks Comment on Contact Lens Rule Review, 16 CFR Part 315, Fed. Trade Comm’n (May 28, 2019), https://www.regulations.gov/document/FTC-2019-0041-0001.

[23] Public Hearings: Health Care and Competition Law and Policy, Dep’t Justice (last updated Aug. 21, 2023), https://www.justice.gov/archives/atr/event/public-hearings-health-care-and-competition-law-and-policy (describing hearings jointly conducted by DOJ and FTC, and providing links to agendas and transcripts for individual hearings, submissions to the public record, and various supporting materials).

[24] Fed. Trade Comm’n & U.S. Dep’t of Justice (“DOJ”), Improving Health Care: A Dose of Competition (2004), available at https://www.ftc.gov/sites/default/files/documents/reports/improving-health-care-dose-competition-report-federal-trade-commission-and-department-justice/040723healthcarerpt.pdf.

[25] Id. at exec. summ., 20-29.

[26] Innovations in Health Care Delivery (workshop), Fed. Trade Comm’n (Apr. 24, 2008), https://www.ftc.gov/news-events/events/2008/04/innovations-health-care-delivery.

[27] The workshop agenda is available at https://www.ftc.gov/sites/default/files/documents/public_events/innovations-health-care-delivery/agenda-5.pdf.

[28] Examining Health Care Competition (workshop), Fed. Trade Comm’n (Mar. 2014), https://www.ftc.gov/news-events/events/2014/03/examining-health-care-competition.

[29] Examining Health Care Competition, Fed. Trade Comm’n & Dep’t Justice (Feb. 2015), https://www.ftc.gov/news-events/events/2015/02/examining-health-care-competition.

[30] Another Dose of Competition: Accountable Care Organizations and Antitrust, FTC Workshop (May 2011), https://www.ftc.gov/news-events/events/2011/05/another-dose-competition-accountable-care-organizations-antitrust.

[31] See, e.g., Statement of Antitrust Enforcement Policy Regarding Accountable Care Organizations Participating in the Medicare Shared Savings Program, 76 Fed. Reg. 67026 (Oct. 28, 2011), (Final Policy Statement, Fed. Trade Comm’n and Dep’t Justice Antitrust Div.).

[32] See, e.g., Devesh Raval et al., Using Disaster Induced Closures to Evaluate Discrete Choice Models of Hospital Demand, 53 RAND J. Econ. 561 (2022); Thomas Koch & Shawn W. Ulrick, Price Effects of a Merger: Evidence from a Physicians’ Market, 59 Econ. Inquiry 790 (2021); Keith Brand & Ted Rosenbaum, A Review of the Economic Literature on Cross-Market Healthcare Mergers, 82 Antitrust L.J. 533 (2019); Thomas Koch et al., Physician Market Structure, Patient Outcomes, and Spending: An Examination of Medicare Beneficiaries, 53 Health Servs. Res. 3549 (2018); Thomas G. Koch, Brett W. Wendling, & Nathan E. Wilson, How Vertical Integration Affects the Quantity and Cost of Care for Medicare Beneficiaries, 52 J. Health Econ. 19 (2017); Julie A. Carlson et al., Economics at the FTC: Physician Acquisitions, Standard Essential Patents, and Accuracy of Credit Reporting, 43 Rev. Indus. Org. 303 (2013); See also, e.g., Martin Gaynor & Robert J. Town, The Impact of Hospital Consolidation—Update, Robert Wood Johnson Foundation, The Synthesis Project (2012) (Gaynor is a former director of the FTC’s Bureau of Economics who serves presently as a special advisor to the assistant U.S. attorney general for antitrust); Martin Gaynor & William B. Vogt, Competition Among Hospitals, 34 RAND J. Econ. 764 (2003); Maximillian J. Pany, Michael E. Chernew, & Leemore S. Dafny, Regulating Hospital Prices Based on Market Concentration Is Likely to Leave High-Price Hospitals Unaffected, 40 Health Aff. 1386 (Sept. 2021) (Dafny was deputy director for health care antitrust in the FTC’s Bureau of Economics from 2012-13); Leemore S. Dafny, Hospital Industry Consolidation—Still More to Come?, 370 New Eng. J. Med. 198 (2014).

[33] We focus here on research associated with the FTC’s Bureau of Economics, which comprises a significant body of pertinent research. We recognize, of course, that diverse empirical research from DOJ economists and, indeed, various HHS agencies, may be pertinent to provide antitrust scrutiny as well. Stepping back, the larger and still-developing body of academic literature regarding health-care competition is considerable and complex. We do not attempt to review it here.

[34] See, e.g., Letter from Lina Khan, Chair, Fed. Trade Comm’n & Jonathan Kanter, Asst. Atty. General, Antitrust Div., Dept. Justice to the Hon. François-Philippe Champagne, Canada Ministry Innovation, Sci. & Indus. (Mar. 31, 2023), https://www.justice.gov/atr/page/file/1578296/dl?inline. See also id. at 3, n. 11 and 9, n. 40 (highlighting specific hospital-merger retrospective studies and merger retrospectives more generally).

[35] See, e.g., Michael A. Salinger, The 2023 Merger Guidelines and the Role or Economics, Rev. Indus. Org. (May 3, 2024), https://doi.org/10.1007/s11151-024-09957-x; see also, Prepared Opening Remarks of Chairman Joseph J. Simons, Hearings on Competition and Consumer Protection in the 21st Century, Merger Retrospectives, Fed. Trade Comm’n (Apr. 12, 2019), available at https://www.ftc.gov/system/files/documents/public_statements/1513555/merger_retrospectives_hearing_opening_remarks_chairman.pdf. Numerous injunctions obtained by the FTC in provider matters since commencement of the hospital-merger retrospective study program can be found at https://www.ftc.gov/legal-library/browse/cases-proceedings?search=hospital+clinic&sort_by=search_api_relevance.

[36] See, e.g., Thomas L. Greaney, Whither Antitrust? The Uncertain Future of Competition Law in Health Care, 21 Health Affs. 185 (2002); Christopher Garmon, Hospital Mergers—Retrospective Studies to Improve Prediction, CPI Antitrust Chronicle (Jul. 2017).

[37] Orly Ashenfelter, Daniel Hosken, Michael Vita, & Matthew Wienberg, Retrospective Analysis of Hospital Mergers, 18 Int. J. Econ. & Bus. 5, 6 (2011).

[38] Michael G. Vita & Seth Sacher, The Competitive Effects of Not?For?Profit Hospital Mergers: A Case Study, 49 J. Indus. Econ. 63 (2001).

[39] See, e.g., Christopher Garmon & Laura Kmitch, Hospital Mergers and Antitrust Immunity: The Acquisition of Palmyra Medical Center by Phoebe Putney Health, 14 J. Comp. L. & Econ. 433 (2018); Christopher Garmon, The Accuracy of Hospital Screening Methods, 48 RAND J. Econ. 1068 (2017) (reviewing post-merger price changes for 28 hospital mergers, initially published as BE Working Paper); Deborah Haas?Wilson & Christopher Garmon, Hospital Mergers and Competitive Effects: Two Retrospective Analyses, 18 Int. J. Econ. Bus. 17 (2011); Steven Tenn, The Price Effects of Hospital Mergers: A Case Study of the Sutter–Summit Transaction, 18 Int. J. Econ. Bus. 65 (2011) (originally published as BE Working Paper); Aileen Thompson, The Effect of Hospital Mergers on Inpatient Prices: A Case Study of the New Hanover-Cape Fear Transaction, 18 Int. J. Econ. Bus. 91 (2011) (originally published as BE Working Paper); Ashenfelter et al., supra note 37; Patrick S. Romano & David J. Balan, A Retrospective Analysis of the Clinical Quality Effects of the Acquisition of Highland Park Hospital by Evanston Northwestern Healthcare, 18 Int. J. Econ. Bus. 45 (2010); John Simpson, Geographic Markets in Hospital Mergers: A Case Study, 10 Int. J. Econ. Bus. 291 (2003); Vita & Sacher, supra note 38. A bibliography of merger-retrospective studies compiled by the Bureau of Economics comprises more than 30 provider-merger retrospectives, with contributors from within and without BE. Those, in turn, inform and are informed by the larger body of research regarding health-care merger retrospectives. Fed. Trade Comm’n, Merger Retrospectives Bibliography, https://www.ftc.gov/policy/studies/merger-retrospective-program/bibliography (last visited May 10, 2024).

[40] Joseph Farrell, Paul Pautler, & Michael Vita, Economics at the FTC: Retrospective Merger Analysis with a Focus on Hospitals, 35 Rev. Indus. Org. 369 (2009).

[41] See, Overview of the Merger Retrospective Program in the Bureau of Economics, Fed. Trade Comm’n (last visited Apr. 12, 2023), https://www.ftc.gov/policy/studies/merger-retrospective-program/overview; see also, Simons, supra note 35; Khan & Kanter, supra note 34.

[42] Salinger, supra note 35, at note 10.

[43] Ashenfelter et al., supra note 37, at 6-7.

[44] See, e.g., Romano & Balan, supra note 39 (regarding impact on clinical quality).

[45] See, e.g., Hass-Wilson & Garmon, supra note 39.

[46] Koch & Ulrick, supra note 32.

[47] See, e.g., Thomas G. Koch, Brett W. Wendling, & Nathan E. Wilson, The Effects of Physician and Hospital Integration on Medicare Beneficiaries’ Health Outcomes, 103 Rev. Econ. & Stats. 725 (2021) (initially published as BE Working Paper).

[48] See, e.g., Dennis W. Carlton, Why We Need to Measure the Effect of Merger Policy and How to Do It, 5 Comp. Pol’y Int. 77 (2009); Ashenfelter et al., supra note 37; Farrell, Pautler, & Vita, supra note 40.

[49] Matthew Panhans, Ted Rosenbaum, & Nathan E. Wilson, Prices for Medical Services Vary Within Hospitals, But Vary More Across Them, 78 Med. Care Res. Rev. 157 (2021, initially published as BE Working Paper); Koch, Wendling, & Wilson, Medicare Beneficiaries’ Health Outcomes, supra note 47 (initially published as BE Working Paper); Asako S. Moriya, William B. Vogt, & Martin Gaynor, Hospital Prices and Market Structure in the Hospital and Insurance Industry, 5 Health Econ, Pol’y & Law 1 (2010) (Martin Gaynor is a former director of the FTC’s Bureau of Economics presently serving as a special advisor to the assistant U.S. attorney general for antitrust); Martin Gaynor & William B. Vogt, Competition Among Hospitals, 34 RAND J. Econ. 764 (2003); Maximillian J. Pany, Michael E. Chernew, & Leemore S. Dafny, Regulating Hospital Prices Based on Market Concentration Is Likely to Leave High-Price Hospitals Unaffected, 40 Health Aff. 1386 (September 2021) (Dafny was deputy director for health care antitrust in the FTC’s Bureau of Economics from 2012-13); Leemore S. Dafny, Hospital Industry Consolidation—Still More to Come?, 370 New Eng. J. Med. 198 (2014); Devesh Raval et al., Using Disaster Induced Closures to Evaluate Discrete Choice Models of Hospital Demand, 53 RAND J. Econ. 561 (2022) (initially published as BE Working Paper); Nathan E. Wilson, Market Structure as a Determinant of Patient Care Quality, 2 Amer. J. Health Econ. 241 (2016) (studying hemodialysis care) (initially published as BE Working Paper).

[50] See, e.g., Devesh Raval, Ted Rosenbaum, & Nathan E. Wilson, Using Disaster-Induced Closures to Evaluate Discrete Choice Models of Hospital Demand, 53 RAND J. Econ. 561 (2022). Event studies are, of course, also observational studies, even if they—and merger retrospectives—may be considered in some regards “quasi-experimental.”

[51] David J. Balan & Keith Brand, Simulating Hospital Merger Simulations, 71 J. Indus. Econ. 47 (2023) (initially published as BE Working Paper); see also Leemore Dafny, Katherine Ho, & Robin Lee, The Price Effects of Cross-Market Mergers: Theory and Evidence from the Hospital Industry, 50 RAND J. Econ. 286 (2019) (theoretical analysis with empirical extension).

[52] See, e.g., Carlson et al., supra note 32 (citing Cory Capps, David Dranove, & Mark Satterthwaite, Competition and Market Power in Option Demand Markets, 34 RAND J. Econ. 737 (2003); Robert Town & Gregory Vistnes, Hospital Competition in HMO Networks, 20 J. Health Econ. 753 (2001)).

[53] Koch, Wendling, & Wilson, Quantity and Spending, supra note 32; Koch, Wendling, & Wilson, Health Outcomes, supra note 47.

[54] Dafny, Ho, & Lee, supra note 51; see also, Keith Brand & Ted Rosenbaum, A Review of the Economic Literature on Cross-Market Healthcare Mergers, 82 Antitrust L.J. 533 (2019).

[55] Keith Brand, Martin Gaynor, Patrick McAlvanah, David Schmidt, & Elizabeth Scheirov, Economics at the FTC: Office Supply Retailers Redux, Healthcare Quality Efficiencies Analysis, and Litigation of an Alleged Get-Rich-Quick Scheme, 45 Rev. Indus. Org. 325 (2014); Julie A. Carlson, Leemore S. Dafny, Beth A. Freeborn, Pauline M. Ippolito, & Brett W. Wendling, Economics at the FTC: Physician Acquisitions, Standard Essential Patents, and Accuracy of Credit Reporting, 43 Rev. Indus. Org. 303 (2013); Joseph Farrell, David J. Balan, Keith Brand, & Brett W. Wendling, Economics at the FTC: Hospital Mergers, Authorized Generic Drugs, and Consumer Credit Markets, 39 Rev. Indus. Org. 271 (2011). Cf. Martin Gaynor, Kate Ho, & Robert J. Town, The Industrial Organization of Health-Care Markets, 53 J. Econ. Lit. 235 (2015); Brand & Rosenbaum, supra note 54 (review of cross-market health-care mergers literature).

[56] See Carlson et al., supra note 32.

[57] RFI at 4.

[58] See, e.g., FTC Staff Comment to the Food and Drug Administration in Docket No. FDA-2021-N-0555 Concerning Over-the-Counter Hearing Aids, Fed. Trade Comm’n (Jan. 28, 2022), available at https://www.ftc.gov/system/files/documents/advocacy_documents/ftc-staff-comment-federal-drug-administration-docket-no-fda-2021-n-0555-concerning-over-counter/v220000staffcommentotchearingaids2.pdf (noting the likely procompetitive effect of rule).

[59] 16 C.F.R. § 315.

[60] 15 U.S.C. 7601-7610.

[61] 21 C.F.R. § 800.30.

[62] Pub. L. 115-52, 131 Stat. 1005, Aug. 18, 2017.

[63] See, e.g., Thomas W. Merrill, Antitrust Rulemaking: the FTC’s Delegation Deficit, 75 Admin Law Rev. 277 (2023) (“the FTC has no legal authority to engage in legislative rulemaking on competition matters.” Id. at 278); see also, Thomas W. Merrill et al., Agency Rules with the Force of Law: The Original Convention, 116 Harv. L. Rev. 467 (2002).

[64] Broadcast Music, Inc. v. Columbia Broad. Sys., Inc., 441 U.S. 1, 19-20 (1979) (citations omitted).

[65] Sneha Kannan, Joseph Dov Bruch, & Zirui Song, Changes in Hospital Adverse Events and Patient Outcomes Associated with Private Equity Acquisition, 330 JAMA 2365 (2023).

[66] As discussed below, other studies suggest mixed results. See, e.g., infra. note 118, and accompanying text.

[67] Regarding competition advocacy generally, see, e.g., James C. Cooper, Paul A. Pautler, & Todd J. Zywicki, Theory and Practice of Competition Advocacy at the FTC, 72 Antitrust L.J. 1091 (2005); Maureen K. Ohlhausen, Identifying, Challenging, and Assigning Political Responsibility for State Regulation Restricting Competition, 2 Comp. Pol’y Int. 151 (2006); Daniel J. Gilman, Advocacy, in SAGE Encyclopedia of Political Behavior 8 (Fathali M. Moghaddam ed., 2017). Links to numerous studies, reports, and advocacy documents by the FTC and its staff are at https://www.ftc.gov/advice-guidance/competition-guidance/industry-guidance/competition-health-care-marketplace. We note that FTC and DOJ jointly issued many such documents. See, e.g., Joint Statement of the Federal Trade Commission and the Antitrust Division of the U.S. Department of Justice Regarding Certificate-of-Need (CON) Laws and Alaska Senate Bill 62, Which Would Repeal Alaska’s CON Program (Apr. 12, 2017), available at https://www.ftc.gov/system/files/documents/advocacy_documents/joint-statement-federal-trade-commission-antitrust-division-us-department-justice-regarding/v170006_ftc-doj_comment_on_alaska_senate_bill_re_state_con_law.pdf.

[68] RFI at 5.

[69] RFI at 6.

[70] Steven C. Salop & David T. Scheffman, Cost-Raising Strategies, 36 J. Indus. Econ. 19 (1985).

[71] Janusz A. Ordover, Garth Saloner, & Steven C. Salop, Equilibrium Vertical Foreclosure, 80 Am. Econ. Rev. 127 (1990).

[72] Malcolm B. Coate & Andrew N. Kleit, Exclusion, Collusion, and Confusion: The Limits of Raising Rivals’ Costs (FTC Bureau of Economics, Working Paper No. 179, 1990).

[73] Id. at 3.

[74] James C. Cooper et al., Vertical Antitrust Policy as a Problem of Inference, 23 Int’l. J. Indus. Org. 639, 643 (2005).

[75] David Reiffen & Michael Vita, Comment: Is There New Thinking on Vertical Mergers? 63 Antitrust L. J. 917 (1995); see also, e.g., Daniel O’Brien, The Antitrust Treatment of Vertical Restraint: Beyond the Possibility Theorems, in THE PROS AND CONS OF VERTICAL RESTRAINTS 22, 36 (Konkurrensverket ed., 2008); Michael A. Salinger, Vertical Mergers and Market Foreclosure, 103 Q. J. Econ. 345 (1988).

[76] Henry Ogden Armour & David Teece, Vertical Integration and Technological Innovation, 62 Rev. Econ. & Stat. 470 (1980).

[77] Dennis W. Carlton, Transaction Costs and Competition Policy, 73 Int’l J. Indus. Org. 1 (2020).

[78] Francine Lafontaine & Margaret Slade, Vertical Integration and Firm Boundaries: The Evidence, 45 J. Econ. Lit. 629, 677 (2007).

[79] Id.

[80] Id.

[81] Id.

[82] Cooper et al., supra note 74, at 639.

[83] John M. Yun, Vertical Mergers and Integration in Digital Markets, in THE GAI REPORT ON THE DIGITAL ECONOMY (Joshua D. Wright & Douglas H. Ginsburg eds., 2020) at 245.

[84] Francine Lafontaine, Vertical Mergers (Presentation Slides), in FTC, Competition and Consumer Protection in the 21^st Century; FTC Hearing #5: Vertical Merger Analysis and the Role of the Consumer Welfare Standard in U.S. Antitrust Law, Presentation Slides 93 (Nov. 1, 2018), available at https://www.ftc.gov/system/files/documents/public_events/1415284/ftc_hearings_5_georgetown_slides.pdf. See also Francine Lafontaine & Margaret E. Slade, Presumptions in Vertical Mergers: The Role of Evidence, 59 Rev. Indus. Org. 255 (2021).

[85] See Koch, Wendling, & Wilson, Outcomes, supra note 47 (discussing research challenges and mixed results in the literature).

[86] Koch, Wendling, & Wilson, Quantity and Cost of Care, supra note 32

[87] Id. at 20.

[88] Id. at 20.

[89] Koch, Wendling, & Wilson, Outcomes, supra note 47.

[90] Conglomerate Effects of Mergers – Note by the United States 2, OECD (Jun. 10, 2020), available at https://www.ftc.gov/system/files/attachments/us-submissions-oecd-2010-present-other-international-competition-fora/oecd-conglomerate_mergers_us_submission.pdf (“Conglomerate Effects”).

[91] Id. at 2-3.

[92] Research and Development in the Pharmaceutical Industry, Cong. Budget Off. (April 2021),
https://www.cbo.gov/publication/57126.

[93] Josh Feng et al., Mergers that Matter: The Impact of M&A Activity in Prescription Drug Markets 6 (SSRN Working Paper, Jul. 25, 2023), https://ssrn.com/abstract=4523015.

[94] Id. at 5-6.

[95] Leemore Dafny, Katherine Ho, & Robin Lee, The Price Effects of Cross-Market Mergers: Theory and Evidence from the Hospital Industry, 50 RAND J. Econ. 286 (2019) (finding price effects for mergers across geographic markets, but within state boundaries); see also Keith Brand & Ted Rosenbaum, A Review of the Economic Literature on Cross-Market Healthcare Mergers, 82 Antitrust L.J. 533 (2019) (reviewing several studies and noting observed competitive effects and issues for further study).

[96] See, e.g., Brand & Rosenbaum, supra note 95 (regarding possible application to hospital-merger cases, among others, as well as issues for further research).

[97] We note that, e.g., in January 2021, the FTC issued orders under its FTC Act Section 6(b) authority to six health-insurance companies to furnish information in order to facilitate the agency’s study of the effects of physician group and health-care-facility consolidation from 2015 through 2020. Press Release, FTC to Study the Impact of Physician Group and Healthcare Facility Mergers, Fed. Trade Comm’n (Jan.14, 2021), https://www.ftc.gov/news-events/news/press-releases/2021/01/ftc-study-impact-physician-group-healthcare-facility-mergers. While the information collected under such orders is limited partly by restrictions imposed under the Paperwork Reduction Act, and not merely available data and methodological concerns, it may nonetheless help advance understanding of provider consolidation.

[98] Kannan et al., supra note 65.

[99] RFI at 1 (the second sentence of the summary).

[100] See generally, e.g., FTC v. Phoebe Putney Health Sys., Inc., 568 U.S. 216 (2013). In its unanimous decision, the Court noted that the 11^th U.S. Circuit Court of Appeals had, as an initial matter, “‘agreed with the [FTC] that, on the facts alleged, the joint operation of Memorial and Palmyra would substantially lessen competition or tend to create, if not create, a monopoly’” 568 U.S. at 222-3 (quoting FTC v. Phoebe Putney Health Sys., Inc., 663 F.3d 1369, 1375 (2011). The Court’s holding in Phoebe Putney upheld the FTC’s jurisdiction over the hospital merger, notwithstanding the grant of certain powers to hospital authorities by the State of Georgia. 568 U.S. at 224. For a discussion of various FTC research, advocacy, and enforcement activities in health care, including scrutiny of provider mergers, see, e.g., Maureen K. Ohlhausen, The First Wealth is Health: Protecting Competition in Healthcare Markets, Remarks at the 2017 ABA Fall Forum (Nov. 16, 2017), available at https://www.ftc.gov/system/files/documents/public_statements/1275573/mko_fall_forum_2017.pdf. While the FTC and the DOJ have concurrent jurisdiction over mergers under Section 7 of the Clayton Act, health-care-provider mergers are typically assigned to the FTC under the FTC/DOJ clearance process. For a list of health-care-enforcement matters, see FTC, The FTC’s Health Care Work: Cases, https://www.ftc.gov/news-events/topics/competition-enforcement/health-care-competition (last visited May 1, 2024).

[101] See, e.g., David J. Balan & Patrick S. Romano, A Retrospective Analysis of the Clinical Quality Effects of the Acquisition of Highland Park Hospital by Evanston Northwestern Healthcare, 18 Int. J. Econ. Bus. 45 (2011) (initially published as a BE Working Paper, available at https://www.ftc.gov/sites/default/files/documents/reports/retrospective-analysis-clinical-quality-effects-acquisition-highland-park-hospital-evanston/wp307.pdf).

[102] Gaynor testimony, supra note 3.

[103] Ashenfelter et al., supra note 37, at 12.

[104] Id.

[105] See, e.g., Wilson, supra note 49 (studying hemodialysis care and finding no statistically significant indication that for-profit status is associated with a different quality of care; and comparing, e.g., Paul Grieco, & Ryan C. McDevitt, Productivity and Quality in Health Care: Evidence from the Dialysis Industry, 84 Rev. Econ. Studs. 1071 (2006) with John M. Brooks et al., Effect of Dialysis Center Profit-Status on Patient Survival: A Comparison of Risk-Adjustment and Instrumental Variable Approaches, 41 Health Servs. Res. (2006)). As we note below, it may be difficult to generalize observations from the U.S. dialysis industry because of both variation in the quality of care and the degree to which two firms account for for-profit acquisitions of independent facilities.

[106] See Wilson, supra note 49.

[107] For example, in a 2020 paper, Eliason et al. observed that only 21% of dialysis facilities were independently owned, and that two large, publicly traded companies owned 60% of the facilities and 90% of the revenue in the space. Paul J. Eliason et al., How Acquisitions Affect Firm Behavior and Performance: Evidence from the Dialysis Industry, 135 Q. J. Econ. 221, 222 (220). We note, too, that the FTC already has consent orders in place with both of those firms. Under one such order, DaVita, Inc. was required to divest certain facilities and limit its use of noncompete agreements; it must also get prior approval for future acquisitions from the FTC. See, In the Matter of DaVita, Inc., and Total Renal Care, FTC File No. 211-0013 (Oct. 25, 2021), (agreement containing consent orders).

[108] RFI at 3.

[109] RFI at 5.

[110] RFI at 4-5.

[111] Elena Praeger & Matt Schmitt, Employer Consolidation and Wages: Evidence from Hospitals, 111 Am. Econ. Rev. 397–427 (2021). Praeger & Schmitt examine whether hospital employees’ wage growth slows following consolidation. While they observe some slowing wage growth under limited conditions (large increases in concentration, plus industry-specific skills), they fail to reject zero wage effects in most cases.

[112] See supra note 10.

[113] FTC v. Phoebe Putney Health Sys., Inc., 568 U.S. 216 (2013).

[114] See, e.g., FTC Staff Comment Before the Georgia Department of Community Health Regarding the Certificate of Need Application Filed by Lee County Medical Center, Fed. Trade Comm’n (2017), available at https://www.ftc.gov/system/files/documents/advocacy_documents/ftc-staff-comment-georgia-department-community-health-regarding-certificate-need-application-filed/v180001gaconleecounty_and_attachments.pdf (discussing ongoing dearth of competition for hospital services in surrounding five-county area).

[115] For example, in a 2019 letter to the Texas Medical Board, FTC staff noted that most of the critical-access hospitals in Texas were located in counties where there were no practicing anesthesiologists, with 37 of those hospitals located in counties where certified-registered-nurse anesthetists were the only licensed, specialized providers of anesthesia and anesthesia-related services. FTC Comment to Texas Medical Board on Its Proposed Rule 193.13 to Add Supervision Requirements for Texas-Certified Nurse Anesthetists, 2, Fed. Trade Comm’n (2019), available at https://www.ftc.gov/system/files/documents/advocacy_documents/ftc-comment-texas-medical-board-its-proposed-rule-19313-add-supervision-requirements-texas-certified/v200004_texas_nurse_anesthetists_advocacy_letter.pdf.

[116] See supra text accompanying notes 105-107.

[117] Id. (citing Wilson, supra note 49).

[118] Kannan et al., supra note 65.

[119] Joseph D. Bruch, Suhas Gondi, & Zirui Song, Changes in Hospital Income, Use, and Quality Associated with Private Equity Acquisition, 180 JAMA Intern. Med. 1 (2020).

[120] Agency staff have no doubt also noticed that the studies regard limited numbers of private-equity acquirers. For example, the Bruch, Gondi, & Song study, id., incorporates numerous acquisitions by the Hospital Corporation of America (HCA), which may provide a sharper picture of HCA acquisitions, but may or may not generalize across the industry.

[121] American Medical Assn. v. FTC, 638 F.2d 443 (2d Cir. 1980) (aff’d per curiam American Medical Assn. v. FTC, 455 U.S. 676 (1982)); cf., e.g., Matthew Mandelberg et al., Reconsidering the Ban on Physician-Owned Hospitals to Combat Consolidation, and Matthew Mandelberg, Michael Smith, Jesse Ehrenfeld, & Brian Miller, Reconsidering the Ban on Physician-Owned Hospitals to Combat Consolidation (Feb. 5, 2023). Forthcoming in N.Y.U. J. Leg. & Pub. Pol’y, available at SSRN: https://ssrn.com/abstract=4350105.

[122] Statement on L.D. 1866 to the Committee on Bus. Leg., Maine House of Representatives (Jan. 8, 1992), available at https://www.ftc.gov/sites/default/files/documents/advocacy_documents/ftc-staff-comment-maine-house-representatives-committee-business-legislation-concerning-l.d.1866-repeal-prohibitions-against-optometry-practice-retail-settings-and-corporate-affiliations/af-21.pdf; see also, FTC Staff Comment Before the North Carolina State Board of Opticians Concerning Proposed Regulations for Optical Goods and Optical Goods Businesses, Fed. Trade Comm’n (2011), https://www.ftc.gov/legal-library/browse/advocacy-filings/ftc-staff-comment-north-carolina-state-board-opticians-concerning-proposed-regulations-optical-goods. Cf. FTC Staff Comment to the Food & Drug Admin. in Docket No. FDA-2021-N-055 Concerning Over-the-Counter Hearing Aids, Fed. Trade Comm’n (Jan. 8, 2022), available at https://www.ftc.gov/system/files/documents/advocacy_documents/ftc-staff-comment-federal-drug-administration-docket-no-fda-2021-n-0555-concerning-over-counter/v220000staffcommentotchearingaids2.pdf.

[123] A 2009 report by then-FTC Chair William Kovacic defines “policy R&D” broadly in a way that comprises, but is not limited to, original, author-initiated academic research by BE staff. It also includes various review, issue-spotting, and synthetic endeavors, such as policy workshops and, indeed, requests for public information. William E. Kovacic, The Federal Trade Commission at 100: Into Our Second Century, 91-92 (Jan. 2009), available at https://www.ftc.gov/sites/default/files/documents/public_statements/federal-trade-commission-100-our-second-century/ftc100rpt.pdf.

[124] RFI at 3.

[125] Id. at 3-4.

[126] For a review of diverse endeavors, see, e.g., Ohlhausen, supra note 3.

[127] Press Release, Federal Trade Commission, Department of Justice, and Department of Health and Human Services Launch Cross-Government Inquiry on Impact of Corporate Greed in Health Care, Fed. Trade Comm’n (Mar. 5, 2024), https://www.ftc.gov/news-events/news/press-releases/2024/03/federal-trade-commission-department-justice-department-health-human-services-launch-cross-government.

[128] Press Release, FTC, DOJ, and HHS Extend Comment Period on Cross-Government Inquiry on Impact of Corporate Greed in Health Care, Fed. Trade Comm’n (May 1, 2024), https://www.ftc.gov/news-events/news/press-releases/2024/05/ftc-doj-hhs-extend-comment-period-cross-government-inquiry-impact-corporate-greed-health-care?utm_source=govdelivery.

[129] Press Release, Fact Sheet: Biden-Harris Administration Announces New Actions to Lower Health Care and Prescription Drug Costs by Promoting Competition, The White House (Dec. 7, 2023), https://www.whitehouse.gov/briefing-room/statements-releases/2023/12/07/fact-sheet-biden-harris-administration-announces-new-actions-to-lower-health-care-and-prescription-drug-costs-by-promoting-competition.

[130] See, e.g., Press Release, Justice Department, Federal Trade Commission and Department of Health and Human Services Issue Request for Public Input as Part of Inquiry into Impacts of Corporate Ownership Trend in Health Care, Dep’t Justice, (Mar. 5, 2024), https://www.justice.gov/opa/pr/justice-department-federal-trade-commission-and-department-health-and-human-services-issue.

[131] RFI at 3.

[132] See supra Section II.D.

[133] Gaynor statement, supra note 3.

[134] Private Capital, Public Impact: An FTC Workshop on Private Equity in Health Care, Fed. Trade Comm’n (May 5, 2024), https://www.ftc.gov/news-events/events/2024/03/private-capital-public-impact-ftc-workshop-private-equity-health-care. The workshop webpage includes a description, along with links to the agenda, participant biographies, and a transcript of the proceedings.

[135] Id.; Press Release, Justice Department, Federal Trade Commission and Department of Health and Human Services Issue Request for Public Input as Part of Inquiry into Impacts of Corporate Ownership Trend in Health Care, Dep’t Justice (Mar. 5, 2024), https://www.justice.gov/opa/pr/justice-department-federal-trade-commission-and-department-health-and-human-services-issue; Press Release, HHS, DOJ, and FTC Issue Request for Public Input as Part of Inquiry into Impacts of Corporate Ownership Trend in Health Care, Dep’t Health & Human Servs. (Mar. 5, 2024), https://www.hhs.gov/about/news/2024/03/05/issue-request-for-public-input-as-part-of-inquiry-into-impacts-of-corporate-ownership-trend-in-health-care.html.

[136] Their testimony is confined to their own perceptions of, as the agencies themselves put it in the RFI, “how their experiences . . . changed after a facility or other provider where they work or receive treatment or services was acquired or underwent a merger.” Such perceptions may help make certain policy concerns vivid or accessible, but there is no credible argument that they were either randomly selected or representative of practitioner experience, much less that they represent legal or economic analyses of the acquisitions under discussion. That they may be considered as part of a larger policy inquiry is uncontroversial. That three such participants were selected for such a brief workshop—absent industry participants, and given the dearth of economic evidence and legal perspectives beyond those of enforcers—strains credulity.

[137] See supra note 135.

California does not have a reputation for business-friendly legislation. This makes it all the more surprising that a California legislative report rejected a New York bill as too anti-business for the Golden State. That bill, the 21^st Century Antitrust Act, championed by New York State Senate Deputy Majority Leader Michael Gianaris (D-Queens), would import European competition-policy principles and expand on them, ultimately making New York an outlier in U.S. antitrust enforcement.

In its current form, Gianaris’ bill would lead enforcers to punish the mere possession of monopoly power, rather than anti-competitive behavior that harms consumers. This marks a firm rejection of longstanding U.S. antitrust principles. Indeed, not punishing monopolization has been a longstanding concern of U.S. antitrust law. As Albany native and Second Circuit Court of Appeals Judge Learned Hand wrote in 1945: “The successful competitor, having been urged to compete, must not be turned upon when he wins.”

I. Introduction

In mid-2021, the International Center for Law & Economics (ICLE) published a white paper on the state of broadband competition in the United States,[1] which concluded that:

• The U.S. broadband market was generally healthy and competitive, with 95.6% of the population having access to high-speed broadband;
• Concentration metrics are poor predictors of competitiveness—broadband markets can be dynamic and competitive even with only a few providers. Indeed, in some cases, increased concentration can result from efficiency gains and innovation, benefiting consumers through better services; and
• Municipal broadband often requires significant taxpayer subsidies or cross-subsidies from other municipal enterprises, and is thus an example of “predatory entry,” rather than market competition.[2]

Rather than repeat the analysis conducted in the 2021 report, in this report, we investigate the extent to which broadband competition has evolved over the past three years. We find that it has been a rapid evolution:

• More households are connected to the internet;
• Broadband speeds have increased, while prices have fallen;
• More households are served by multiple providers; and
• New technologies like satellite and 5G have expanded internet access and intermodal competition among providers.

When the 2021 ICLE white paper was published, the worst of the COVID-19 pandemic appeared to be over, but the virus’ Delta variant was surging.[3] With pandemic precautions keeping people at home to work, go to school, visit health-care providers, or be entertained, broadband access and use was seen by many as a necessity, rather than a luxury. At the time, Congress considered whether to devote significant federal resources toward promoting broadband access in underserved communities. Toward this end, in November 2021, Congress passed the Infrastructure Investment and Jobs Act (IIJA), which includes three key provisions to foster greater broadband access:[4]

1. The COVID-era Emergency Broadband Benefit’s temporary subsidy was extended indefinitely and renamed the Affordable Connectivity Program (ACP). The IIJA allocated an additional $14 billion to provide subsidies of $30 a month to eligible households;
2. The IIJA also created and funded the Broadband Equity, Access, and Deployment Program (BEAD), which provides $42 billion to expand high-speed internet access to “unserved” and “underserved” locations; and
3. The law required the Federal Communications Commission (FCC) to adopt final rules to prevent “digital discrimination” in broadband access based on income level, race, ethnicity, color, religion, or national origin, while also instructing the commission to consider issues of technical and economic feasibility.

These three policies were intended to intertwine in order to foster greater broadband competition. ACP subsidies are intended to boost consumer demand for broadband and generate revenue to support providers’ profitable deployment of broadband investments.[5] BEAD investments are intended to reduce the costs of broadband deployment.[6] The law’s digital-discrimination provisions were intended to prevent discrimination by broadband providers that serves to deny or limit consumers’ access to broadband internet.[7]

Alas, today, we find that each of these provisions faces headwinds. With Congress failing to extend appropriations beyond a May 31 deadline, the ACP has run out of funding.[8] States attempting to implement the BEAD program have complained of tight timelines, restrictive rules, limited coordination, and administrative burdens that may undermine effectiveness.[9] Providers and local jurisdictions report that BEAD’s Buy America rules are particularly onerous.[10] Smaller internet service providers say BEAD’s financial requirements exclude them from projects they would otherwise be able to complete successfully.[11] Complying with Buy America rules regarding attaching equipment to utility poles and railroad crossings also threatens deployment timelines.[12] And, in November 2023, the FCC approved rules to apply a disparate-impact approach toward the IIJA’s digital-discrimination mandate, which could raise constitutional issues over the major questions doctrine.[13]

In addition to these programs, the FCC appears dead set to regulate more stringently much of the broadband-internet industry. First, the agency’s sweeping digital-discrimination rules cover nearly every aspect of the deployment and delivery of internet services and nearly every entity associated—even tangentially—with deployment and delivery.[14] Next, the agency approved Title II common-carrier regulation with its recently adopted Safeguarding and Securing the Open Internet Order.[15]^,[16]

The current state of broadband competition policy appears to be one of confusion. Some policies foster competition, while others hinder it. Programs such as the ACP and BEAD could do much to encourage competition by simultaneously generating demand for broadband and helping to build out supply. At the same time, these programs—especially BEAD—attempt to micromanage competition with stifling conditions and de facto rate regulation. Similarly, the FCC’s digital-discrimination rules explicitly subject broadband pricing to ex post scrutiny and enforcement. The FCC’s reclassification of broadband internet-access services under Title II of the Communications Act raises the specter of common-carrier rate regulation that will hang over the industry unless either vacated by the courts, or a future administration once again reverses course.

Put simply, broadband competition in the United States is currently robust, innovative, and successful. But this state of vibrant competition is at risk from recent and forthcoming regulations. Without a course correction, we are likely to see slowing or shrinking broadband investment, reduced innovation, and the exit of small and rural providers.

II. The Broadband Market Is Competitive and Dynamic

By all relevant measures, U.S. broadband competition is vibrant and has increased dramatically since the COVID-19 pandemic. Since 2021, more households are connected to the internet, broadband speeds have increased, prices have fallen, more households are served by more than a single provider, and new technologies like satellite and 5G have expanded internet access and intermodal competition among providers.

A. Access and Adoption

By any reasonable measure today’s U.S. broadband market is an incredible success. Nearly the entire country has access to at-home internet, a vast majority has access to high-speed internet, and much of the country has access to these speeds from three or more providers. Nevertheless, criticisms of the current state of broadband deployment claim that too few Americans have affordable access to adequate broadband speed and capacity and that this, in turn, is the result of insufficient competition among broadband providers.[17] For example, in her speech announcing the FCC’s most recent process to regulate internet services under Title II, Chair Rosenworcel claimed that 80% of the country faces a monopoly or duopoly for download speeds of 100 Mbps or greater.[18] These claims are belied by widespread broadband adoption and competitive markets.

FIGURE 1: US At-Home Internet Access and Adoption, 2021

SOURCE: U.S. Census Bureau, American Community Survey

The U.S. Census Bureau’s American Community Survey reports that 97.6% of households have access to at-home internet and 92.6% use the internet at home (Figure 1).[19] While a large majority with at-home internet get it through a broadband subscription, a substantial minority access the internet from their mobile wireless providers. A small number (2.3%) claim they can access the internet at home without paying for a subscription. This likely includes multi-family units, as well as student and senior housing in which broadband access is included in the rent. Among the 7.4% who do not use an at-home internet connection, two-thirds indicate that internet access is available, but they have chosen not to adopt it.[20]

In 2021, approximately 97 percent of 3- to 18-year-olds had home internet access, according to the National Center for Education Statistics. This represents a five-percentage-point increase since 2016.[21]

Until March 2024, the FCC defined high-speed broadband as internet service that offered speeds of at least 25/3 Mbps.[22] The IIJA defines a location as “unserved” if it has no internet connection available or only has a connection offering speeds of less than 25/3 Mbps.[23] A location is considered “underserved” if the only options available offer speeds of less than 100/20 Mbps.[24]

As shown in Figure 2, smaller households with relatively simple needs can generally access the internet productively with download speeds of less than 100 Mbps, or even 25 Mbps. The third iteration of the National Broadband Map, released in November 2023, indicated:[25]

• 8% of locations have access to connections of 25/3 Mbps or greater;
• 5% of locations have access to speeds of 200/25 Mbps or greater;
• 5% of locations have access to 1000/100 Mbps speeds; and
• Only 6.2% of locations are unserved, and 2.6% are “underserved” with connections of less than 100/20 Mbps, as those terms are defined in the IIJA.

FIGURE 2: FCC Recommended Internet Speeds and US Household Access, 2021

SOURCE: Allconnect, ‘Everything You Need to Know;’ FCC, ‘Fixed Broadband Deployment’

FIGURE 3: Typical Maximum Download Speed by Connection Type, 2021 (Mbps)

SOURCE: HighSpeedInternet.com, ‘What Type of Internet Do You Have?’

The FCC reports that more than 90% of U.S. households have access to speeds of 100 Mbps or greater, and nearly 90% have access to 1 Gbps or greater (Table 1).[26] Fewer than 4% of U.S. households lack access to at least 30 Mbps download speeds via fixed broadband.

TABLE 1: US Household Internet Access by Download Speed, 2021

SOURCE: FCC, ‘International Broadband Data Report’[27]

Some note that, while high-speed connections are available across nearly the entire country, in many cases, only a single provider offers such speeds. This, such critics assert, suggests insufficient competition among providers of high-speed internet. For example, regarding 100 Mbps service, FCC Chair Rosenworcel claimed that “only half of us can get it from more than a single provider. Only one-fifth of the country has more than two choices at this speed.”[28]

This provides a misleading sense of the rate of high-speed broadband deployment and the scope of availability. The most recent information from the FCC on broadband deployment across the United States suggests that 90% of the population in 2021 was served by one or more providers offering 250/25 Mbps or higher speeds (Table 2).[29] That is more than double the population share five years earlier, when only 44% of the population had access to such speeds.[30] In 2019, the FCC did not report the share of population with access to 1,000/100 Mbps speeds or greater. By 2021, 28% of the population had access to gigabit download speeds.[31]

Moreover, Table 2 shows that, in 2021, more than 85% of the population was covered by two or more fixed-broadband providers offering 25/3 Mbps or greater speeds, and more than 60% of the country was covered by three or more providers providing such speeds. Moreover, if satellite and 5G providers are included, close to 100% of the country is served by two or more high-speed providers.

TABLE 2: US Population Fixed-Broadband Access by Number of Providers, 2021

SOURCE: FCC, ‘Fixed Broadband Deployment’

At the same time, the evidence indicates that broadband competition has increased over time, as measured by the number of competing high-speed providers (Figure 4).[32]

• In 2018, 73.0% of households had access to 25/3 Mbps speeds from only one or two fixed-broadband providers, and only 21.6% had access from three or more providers. In 2021, only 29.1% of households had access from one or two providers while 69.3% were served by three or more providers. Thus, the number of households served by three or more providers increased by 47.7 percentage points from 2018 through 2021.
• In 2018, 11.6% of households had no access to 100/20 Mbps speeds and 14.8% had access from three or more fixed broadband providers. In 2021, 5.4% of households had no access, while 21.3% were served by three or more providers. Thus, the number households served by three or more providers increased by 6.5 percentage points from 2018 through 2021.

FIGURE 4: Percentage of US Households Living in Census Blocks with Multiple Provider Options for Fixed-Terrestrial Services (2018 vs 2021)

SOURCE: FCC, ‘2022 Communications Marketplace Report’

Additionally, intermodal competition among providers is only improving. Starlink satellite service has been made available to all locations in the United States.[33] Starlink’s reported speeds are between 25/5 Mbps and 220/25 Mbps.[34] And Project Kuiper has successfully launched its first test satellites,[35] with commercial service expected to begin in the second half of 2024.[36]

B. Broadband Prices Continued to Fall, Even as Speeds Increased and Demand Grew During the Pandemic

After accounting for speed and data usage, the United States has some of the lowest broadband prices in the world. Even so, critics of the current state of U.S. broadband competition claim that U.S. prices are among the highest in the developed world because, they claim, the U.S. market is not as competitive as other jurisdictions. For example, the Community Tech Network asks rhetorically, “[s]o why does the internet cost so much more in the U.S. than in other countries? One possible answer is the lack of competition.”[37] Their article included a graphic in which U.S. internet service is described as “expensive and slow” while Australia is categorized as “fast and cheap.” Yet none of these claims hold up under scrutiny, such as adjusting for consumption and download speeds.

It’s true the United States has the third-highest average monthly broadband costs among OECD countries, according to Cable.co.uk (Figure 5). Australia, however, has the seventh-highest.[38] On a cost-per-megabit basis, Australia has the second-highest costs in the OECD, while the United States is in the bottom third of the distribution (Figure 6).[39] Speedtest’s Global Index of median speeds reports that the United States has the second-fastest median speed, and Australia the third-slowest median speed, among OECD countries (Figure 7).[40]

FIGURE 5: Average Monthly Cost of Broadband (OECD, in $US)

SOURCE: Cable.co.uk, ‘Global Broadband Pricing League Table 2023’

FIGURE 6: Average Monthly Cost of Broadband (OECD, Per Megabit $US)

SOURCE: Cable.co.uk, ‘Global Broadband Pricing League Table 2023’

FIGURE 7: Median Download Speed (OECD, Mbps)

SOURCE: Speedtest, Global Index

Cross-country comparisons of broadband pricing are especially fraught, due to country-by-country variations in factors that drive the costs of delivering broadband and the prices paid by consumers.[41] Deployment costs are driven largely by population density and terrain, as well as each country’s unique regulatory and tax policies.[42] Consumer choices often drive the prices paid by subscribers. These include choices regarding the mix of fixed broadband and mobile, speed preferences, and data consumption.[43]

For example, Figure 8 demonstrates a clear relationship between the average monthly cost for broadband and the monthly cost per megabit; a higher monthly cost tends to be associated with a higher cost per megabit. But there are outliers. The United States is well below the trendline, but Canada is well above it. While the average monthly cost in the two countries is similar, the information provided by Cable.co.uk suggests that U.S. consumers use 9-10 times more megabits per month than Canadian consumers. In addition, as shown in Figure 7, the median U.S. download speed is about 35% faster than the median in Canada.

FIGURE 8: Relationship Between Average Monthly Cost of Broadband and Cost Per-Megabit Per-Month (OECD, in $US)

SOURCE: Cable.co.uk, ‘Global Broadband Pricing League Table 2023’

FIGURE 9: Relationship Between Average Monthly Cost of Broadband and Median Download Speed

SOURCE: Cable.co.uk, ‘Global Broadband Pricing League Table 2023’; Speedtest, Global Index

A broadband-pricing index published annually by USTelecom reports that inflation-adjusted broadband prices for the most popular speed tiers fell by 54.7% from 2015 to 2023, or 5.6% annually.[44] Prices for the highest speed tiers fell by 55.8% over the same period. The Producer Price Index for residential internet-access services fell by 11.2% from 2015 through July 2023.[45] The median fixed-broadband connection in the United States delivers more than 207 Mbps download service, an 80% increase over pre-pandemic median speeds (Figure 10).[46]

FIGURE 10: Median Download Speed in the US (Mbps)

SOURCE: Speedtest, Global Index (July of each year)

Evidence from large surveys suggests that price is not a dominant factor driving adoption for the currently unconnected. For example, among the 7% of households who do not use the internet at home, more than half of Current Population Survey respondents indicated that they “don’t need it or [are] not interested.”[47] About one-third of respondents indicated that price is a factor, with responses such as “can’t afford it” or “not worth the cost.”[48]

Of course, cost and interest are not mutually exclusive factors.[49] A common response to CPS surveys among those who do not subscribe to internet service is that it is “not worth the cost.” This is an unhelpful response to guide policymakers because it doesn’t answer whether the cost is “too high,” the value is “too low,” or a combination of both. Another common response is “not interested.” This, too, is unhelpful, as it does not identify the price at which a potential consumer might become interested, if such a price exists. For example, surveys suggest that some nonadopters may become interested in subscribing to internet services or find it worth the cost at a price of zero.

• A National Telecommunications and Information Administration (NTIA) survey of internet use reported the average monthly price that offline households wanted to pay for internet access was approximately $10 per month; roughly 75% of households gave $0 or “none” as their answer.[50]
• Another NTIA publication reports that households with “no need/interest” in home internet are willing to pay about $6 a month, while those who indicate it is “too expensive” are willing to pay approximately $16 a month.[51]

In addition, as shown in Figure 1, about a quarter of households without a broadband or smartphone subscription claim that they can access the internet at home without paying for a subscription.

Jamie Greig & Hannah Nelson note that low-income households are more likely to use smartphones than computers for internet access.[52] According to Pew Research, 19% of adults who do not have at-home broadband report that their smartphone does everything they need to do online.[53] Colin Rhinesmith et al. summarize the response of a Detroit focus group participant: “[I]f he had to choose between home access and mobile access, the latter is more desirable as it allows him to be reachable and flexible for job interviews and the like”[54]

C. Investment by Broadband Providers Has Remained High

When the FCC issued the Open Internet Order (OIO) in 2015 to reclassify broadband internet-access service under Title II, opponents claimed the policy would diminish broadband investment. Similarly, when the FCC repealed the reclassification in 2018, opponents claimed the repeal would diminish broadband investment. While U.S. broadband capital expenditures have been relatively stable for the past two decades, there was a noticeable drop in the wake of the 2015 OIO (Figure 11).[55]

FIGURE 11: US Broadband Provider Capital Expenditures ($B)

SOURCE: USTelecom

Recent peer-reviewed econometric research from economist Wolfgang Briglauer and his coauthors—indicates that net-neutrality rules do, in fact, slow broadband investment, as measured by the number of fiber connections deployed.[56] The study analyzed 2000-2021 data across OECD countries. Thus, it includes both 2015’s imposition of Title II regulations in the United States and the 2017 repeal. It found that introducing net-neutrality rules was associated with a 22-25% decrease in fiber investments.

Briglauer’s study isolated the effects of net neutrality from other factors that might have affected investment, such as general economic conditions. It focused on new fiber connections as representing growth in network capacity, rather than short-term fluctuations in spending. Even controlling for other variables, net neutrality had an independent negative relationship with fiber deployments.

ICLE’s 2021 white paper argued that broadband markets are dynamic and characterized by ongoing innovation in technologies and business models. Investment and innovation do not solely come from new entrants, as incumbents often are important sources of innovation while they try to stay competitive and avoid disruption. In this way, providers compete through new product introductions and disruption, not just on price. Because of these dynamics, mergers and increased concentration can sometimes be associated with increased investment, in that they may allow firms to achieve greater economies of scale and scope.[57] In addition, firms make long-term investments to upgrade networks and deploy new technologies even amid just a few competitors.[58]

Since ICLE’s white paper, Kenneth Flamm & Pablo Varas published research examining the relationship between the change in a territory’s number of providers and changes in service-plan quality (e.g., upload and download speeds).[59] They examine Census blocks that were served by only two “legacy” broadband providers in 2014, which they define as cable and digital subscriber line (DSL) providers. Their study tracked entry and exit of providers in these blocks through 2018, and evaluated the change in maximum download speeds available in those blocks over time. They find that blocks with no entry or exit (what they call “unchanged duopoly”) experienced an increase of 750 Mbps in maximum download speeds (Figure 12). Blocks that transitioned from duopoly to monopoly experienced a relatively modest 430 Mbps increase, while blocks that transitioned from two to three providers experienced an 810 Mbps increase. Blocks that transitioned from three to four providers experienced an 854 Mbps increase.

They also noted that internet providers may be highly motivated to introduce new, higher-quality speed tiers as technology improves. These results comport with research summarized in the 2021 ICLE white paper, which found the most significant incremental benefits in broadband quality came from adding a second service provider (relative to monopoly), with some marginal benefit from adding a third provider, and a much smaller benefit from adding a fourth.

FIGURE 12: Increase in Maximum Download Speed Associated with Cable or Digital Subscriber Line Provider Entry or Exit, 2014-2018 (Mbps)

SOURCE: Flamm & Varas (2022)

Another recent study is Andrew Kearns’ analysis of the Seattle market.[60] In contrast to Flamm & Varas, Kearns concluded that competition among broadband providers might weaken the incentive to increase quality, which he measured as a provider upgrading a Census block to fiber. He argued that improvements in quality often require significant investment, and the returns on this investment may be uncertain in a competitive market. Thus, in a competitive market, providers may prioritize attracting customers with lower prices and a wider range of product options, rather than investing in improvements to the quality of their service. Even so, Kearns concluded that increased competition offers substantial benefits to consumers related to increased product choice and lower prices.

The latest published research supports ICLE’s earlier observation that whether adding or removing a competitor is associated with more or less investment depends greatly on various factors, including the market’s initial conditions.[61] Thus, a case can be made that competition (as judged by counting the number of competitors in a market) may be, in and of itself, of only lesser importance relative to other factors that guide investment decisions, such as population density, terrain, and demand, as well as the local regulatory and tax environment.[62]

III. Current and Anticipated Policies Affecting Broadband Competition

Broadband internet has become a service that many Americans—and U.S. policymakers—consider essential. But new and forthcoming regulations imposed in an effort to promote equal access to broadband may actually risk dampening innovation and investment in this critical sector. In this section, we discuss the Affordable Connectivity Program and Broadband Equity, Access, and Deployment subsidy programs, which could foster broadband competition by stimulating both demand and supply. Even so, administration of both of these programs have erected significant hurdles that may damage their effectiveness if not remedied by Congress or the regulatory agencies.

We also discuss other programs that are likely to reduce broadband competition by diminishing the incentives to invest and innovate. Though motivated by a desire to prevent discriminatory access, rigid rules to correct “disparate impact” in broadband-deployment decisions fail to account for the dynamic efficiencies of differentiated service models calibrated to consumer demand. At the same time, attempts to impose common-carrier obligations on broadband providers ignore the truly competitive nature of modern broadband markets, which are thriving under light-touch regulation.

Going forward, policymakers should resist the temptation to micromanage a sector as dynamic as U.S. broadband internet. Instead, they should focus their attention on interventions to address genuinely unfair or anticompetitive conduct, while trusting that innovation and investment will be maximized when companies retain the flexibility to respond to consumer demand, while constrained by economic and technical realities.

A. ACP More Effective at Reducing Broadband Costs Than Connecting the Unconnected

The ACP is a federal subsidy program that provides eligible low-income households with monthly broadband-service discounts of up to $30, or up to $75 for households on tribal lands.[63] It also provides a one-time $100 discount for the purchase of a computer or tablet. ICLE has argued that well-designed subsidies targeted to underserved consumers can be an effective way to increase broadband deployment and adoption.[64] Subsidies help make providing service in high-cost, low-density areas more financially viable for providers. They also make broadband more affordable for lower-income consumers, stimulating demand.[65]

Proponents of the ACP identify two main goals for the program:

1. to increase at-home internet adoption by unconnected households; and
2. to maintain internet connections for low-income households at risk of “unadoption” due to unaffordability.[66]

Through the ACP, the federal government absorbs part of the cost of providing broadband service to these households, making them more financially attractive customers for broadband providers. The program also creates an incentive for providers to expand their networks to reach eligible households, as they can now potentially recover more revenue from serving those users.[67] For example, if ACP subsidies stimulate consumer demand, providers may find it profitable to deploy broadband to areas that would not otherwise generate a sufficient return on investment to justify deployment. In some cases, a new provider might be able to offer services to a market currently served by a single incumbent firm.

To date, however, the ACP and its predecessors do not appear to have been as successful in increasing at-home internet adoption by unconnected households as was hoped when such programs were created. Due to what appears to be inelastic demand, ACP has faced difficulties in stimulating sufficient interest among the 5% of unconnected households who could access the internet, but fail to take up service.[68] These households may not be aware of the program or may lack digital literacy; may be able to access the internet without a subscription; or may have no interest in subscribing to an internet service at any price.

On the other hand, the ACP’s subsidies appear to have successfully enabled already-subscribed households to maintain at-home internet service through the COVID-19 pandemic, thereby proving effective in enabling economically vulnerable inframarginal consumers to remain connected. More than 23 million U.S. households (about 17%) were enrolled in the ACP before the program lapsed at the end of May 2024.[69] It is currently unknown how many of these households will unsubscribe now that ACP subsidies are unavailable. In turn, it’s also unknown how providers will respond should large number of households unsubscribe from their internet services.

In March 2024, the FCC announced that April 2024 would be the program’s last fully funded month, with partial subsidies through May 2024.[70] Without ACP subsidies, one expects some households will unsubscribe from internet service, and the decreased demand may even lead to consolidation in some markets through exits or mergers. Moreover, Congress’ failure to renew the ACP risks other long-term policy responses that could waste already-invested funds.

In the face of another economic downturn, the inframarginal households that unadopt internet service will likely spur future rounds of congressional appropriations to bring these households back online. This turmoil, meanwhile, stands to erode providers’ investment incentives, due to lack of demand. This threatens to create a vicious cycle that requires periodic reinvestment from Congress just to stand these programs back up. Over the long term, it would almost certainly be more efficient to extend and focus the ACP program to ensure that truly needy households receive the subsidy (including those that would otherwise unadopt), rather than construing the program as strictly focused on convincing the last 5% of households with inelastic demand to adopt.

B. Red Tape and Regulation May Stymie BEAD’s Efforts to Expand Broadband Access

In 2023, the NTIA awarded more than $42 billion in grants to state governments under the Broadband Equity, Access, and Deployment (BEAD) program,[71] whose primary purpose is to expand high-speed internet access in areas that currently lack it.[72] Congress focused the BEAD program on connecting “unserved” and “underserved” territories. The law requires that those areas lacking connections with speeds of at least 100/20 Mbps must be helped first before addressing other priorities, such as upgrades, adoption programs, and middle-mile infrastructure.[73] Funding is distributed directly to states, which are required to develop plans tailored to connect their unserved and underserved locations.[74]

But much of that congressional intent got muddled in the NTIA’s implementation of BEAD funding. The NTIA’s notice of funding opportunity (“NOFO”) introduced conflicting priorities beyond connecting the unserved. These additional priorities include “middle-class affordability” requirements, the provision of “low cost” plans, and a ban on data caps.[75] The NOFO also gave clear preference to fiber networks over wireless and satellite providers, and to governmental and municipal providers over private companies.[76]

The NTIA’s NOFO prompted each participating U.S. state or territory to include a “middle-class affordability plan to ensure that all consumers have access to affordable high-speed internet” (emphasis in original).[77] The notice provided several examples of how this could be achieved, including:

1. Requiring providers to offer low-cost, high-speed plans to all middle-class households using the BEAD-funded network; and
2. Providing consumer subsidies to defray subscription costs for households ineligible for the Affordable Connectivity Benefit or other federal subsidies.

Despite the IIJA’s explicit prohibition of price regulation, the NTIA’s approval process appears to envision exactly this. The first example provided above is clear rate regulation. It specifies a price (“low-cost”); a quantity (“all middle-class households”); and imposes a quality mandate (“high-speed”). Toward these ends, the notice provides an example of a “low-cost” plan that would be acceptable to NTIA:

• Costs $30 per month or less, inclusive of all taxes, fees, and charges, with no additional non-recurring costs or fees to the consumer;
• Allows the end user to apply the Affordable Connectivity Benefit subsidy to the service price;
• Provides download speeds of at least 100 Mbps and upload speeds of at least 20 Mbps, or the fastest speeds the infrastructure is capable of if less than 100 Mbps/20 Mbps;
• Provides typical latency measurements of no more than 100 milliseconds; and
• Is not subject to data caps, surcharges, or usage-based throttling.[78]

A policy bulletin published by the Phoenix Center for Advanced Legal & Economic Public Policy Studies notes that the NTIA did not conclude that broadband was unaffordable for middle-class households.[79] George Ford, the bulletin’s author, collected data on broadband adoption by income level. The data indicate that, in general, internet-adoption rates increase with higher income levels (Figure 12). Higher-income households have higher adoption rates (97.3%) than middle-income households (92.9%), which in turn have higher adoption rates than lower-income households (78.1%).

FIGURE 13: Internet Adoption and Income

SOURCE: Adapted from Ford (2022), Table 2 and Figure 2.

For each of the 50 states and the District of Columbia, the Phoenix bulletin finds that middle-income internet-adoption rates are, to a statistically significant degree, higher than lower-income adoption. Thus, the Phoenix bulletin concludes that broadband currently is “affordable” to middle-class households and that “no direct intervention is required” to ensure affordability to the middle class.[80]

John Mayo, Greg Rosston, & Scott Wallsten point out that BEAD’s key purpose of providing high-speed internet access to locations that lack it (presumably because it’s too expensive to deploy to these areas without investment subsidies) conflicts with NTIA’s focus on affordability:

A substantial portion of the unserved and underserved areas of the country that are the likely targets of the BEAD program, however, are rural, low-population density areas where deployment costs will be high. These high deployment costs may seem to indicate that even “cost-based” rates—normally seen as an attractive competitive benchmark—may be high, violating the IIJA’s “affordability” standard.[81]

The only effective way to simultaneously reduce broadband prices, increase access, and improve quality is to increase supply. But the NTIA’s attempts at rate regulation work at cross-purposes with BEAD’s objective to increase supply. Therefore, attempts to use BEAD funding to impose price controls may act to reduce broadband competition, rather than preserve or increase it.

The potential harm to competition is worsened by NTIA’s preference for government or municipal providers over private providers, which we discuss in more detail in Section III.G. The NTIA’s funding notice required states to ensure the participation of “non-traditional broadband providers,” such as municipalities and cooperatives. Municipal broadband networks might make sense in some rare cases where private providers are unable to deploy, but such systems have generally mired taxpayers in expensive projects that failed to deliver on promises.

In addition to these challenges, BEAD applications must come with a letter of credit issued by a qualified bank for 25% of the grant amount.[82] This is a guarantee to the grant administrator (e.g., a state broadband office) that there is liquid cash in an account that it can claw back should the applicant not deliver on their grant requirements. To receive a letter of credit, applicants will be required by the issuing bank to provide collateral—which could be cash or cash equivalents equal to the full value of the letter of credit. The letter-of-credit requirement is separate and in addition to BEAD’s match requirement, which demands that applicants contribute a minimum 25% of the total build cost. The letter-of-credit and matching requirements may hinder competition by favoring large and well-capitalized providers over smaller internet-service providers (ISPs) that may be better positioned to serve rural areas.

In November 2023, NTIA released a waiver for the letter-of-credit requirement because of industry concerns about how the rule may prevent smaller ISPs from participating in the BEAD program.[83] The “programmatic waiver” describes several alternatives to the letter of credit. For example, subgrantees can obtain the letter of credit from a credit union instead of a bank. The expectation is that credit unions would offer lower interest rates for loans and lower fees. Alternatively, applicants can provide a performance bond “equal to 100% of the BEAD subaward amount.” In addition, the NTIA is allowing states and territories to reduce the percentage requirement of the performance bond or letter of credit over time, as service providers meet certain project milestones.

Congress set an ambitious goal with BEAD: To expand high-speed internet access in areas that currently lack it. The $42 billion appropriated for the program could have been used to deploy broadband to underserved areas and to foster broadband implementation. However, NTIA’s implementation of the program appears designed to dampen private investment and stifle competition among broadband, wireless, and satellite providers.

C. Digital-Discrimination Rules

One of the most problematic new regulations to hit the broadband sector is the FCC’s digital-discrimination rules. While well-intentioned, these rules are virtually certain to curtail broadband investment and adoption. In late 2023, the FCC adopted final rules facilitating equal access to broadband internet under Section 60506 of the IIJA.[84] The statutory text directs the FCC to prevent discrimination in broadband access based on income level, race, ethnicity, color, religion, or national origin, while also directing the commission to consider issues of technical and economic feasibility.

The rules prohibit digital discrimination of access, which is defined as policies or practices that differentially affect or are intended to differentially affect consumers’ access to broadband internet-access service based on their income level, race, ethnicity, color, religion or national origin, unless justified by genuine issues of technical or economic feasibility.[85] The are two key provisions that will disrupt broadband competition, namely:

1. Adopting a disparate-impact standard to define “digital discrimination of access;” and
2. Subjecting a “broad range” of service characteristics to digital-discrimination rules, including pricing, promotional conditions, terms of service, and quality of service.

The rules apply to entities that provide, facilitate, and/or affect consumer access to broadband internet-access service. This includes typical broadband providers, as well as entities that “affect consumer access to broadband internet access service.”[86] Under this broad definition, local governments, nonprofits, and even apartment-building owners all may be subject to the FCC’s digital-discrimination rules.

The rules also revise the commission’s informal consumer-complaint process to accept complaints of digital discrimination of access, and to authorize the commission to initiate investigations and impose penalties and remedies for violations of the rules.[87]

The FCC also proposed additional rules that would require providers to submit annual reports on their major deployment, upgrade, and maintenance projects, and to establish and maintain internal compliance programs to assess whether their policies and practices advance or impede equal access to broadband internet-access service within their service areas.[88] In essence, these proposed rules would require providers to prepare their own disparate-impact analysis every year.

Because of the expansive definition of covered entities and services subject to the digital-discrimination rules, providers will face legal uncertainty and litigation risks.[89] The most obvious of these involve the likelihood of complaints or investigations based on allegations of disparate impact, which may be difficult to disprove. Comments to the FCC from the U.S. Chamber of Commerce highlight these concerns:[90]

These policies would render it impossible for businesses and the marketplace to make rational investment decisions. The scope of the services that the Draft covers is so broad that it does not provide meaningful guidance for how to comply. And because the Draft fails to grant sufficient guidance, it does not give fair notice of how to avoid liability. Consequently, investment in broadband innovation would disappear and consumers would have to pay higher costs for less efficient services.

The digital-discrimination rules also may discourage innovation and differentiation in broadband service offerings, as providers could avoid service offerings that may be perceived as discriminatory or having a differential impact on certain consumers or communities. Providers could also be reluctant to invest in new technologies or platforms that, while improving broadband service quality or availability, might also create disparities in service characteristics among consumers or areas. As FCC Commissioner Brendan Carr has noted:[91]

Another telling last minute addition is a new advisory opinion process. This is the very definition of swapping out permissionless innovation for a mother-may-I pre-approval process. What’s more? The FCC undermines whatever value that type of process could provide because, to the extent the FCC does—at some point in the future—authorize your conduct, the Order says that the agency reserves the right to rescind an advisory opinion at any time and on a moment’s notice. At that time, the covered provider “must promptly discontinue” the practice or policy. That does not provide the confidence necessary to invest and innovate.

Private, public, and nonprofit entities may even face allegations of intentional discrimination for policies and practices designed to increase internet adoption and use by protected groups. In particular, programs intended to increase broadband adoption among low-income and price-sensitive consumers could run afoul of the digital-discrimination rules. George Ford provides an example of such a program:[92]

For example, Cox Communications offers 100 Mbps broadband service for $49.99 per month, but ACP eligible households can get the same service for $30 per month. Higher-income households may not avail themselves of the discounted price.

In Tennessee, Hamilton County Schools’ EdConnect program offers free high-speed internet access to eligible students, where eligibility is based on income level—i.e., students who receive free or reduced-cost lunch, attend any school where every student receives free or reduced-cost lunch, or whose family participates in the Supplemental Nutrition Assistance Program (SNAP) or other economic-assistance programs.[93] Both the school district and the nonprofit that runs the program would also be covered entities. The fact that the price (free) is available only to those of a certain income level is explicit, intentional discrimination.

The FCC’s digital-discrimination rules will almost surely increase the regulatory burden and compliance costs for providers. Small and rural providers may be disproportionately burdened, as these providers tend to have more limited resources and face technical and economic challenges in deploying and maintaining broadband networks in unserved and underserved areas. The FCC’s proposal that broadband providers submit an annual report on their substantial broadband projects could likewise give larger providers an advantage, as they are more likely to have the resources to comply with this requirement. For example, the Wireless Internet Service Providers Association commented to the FCC:[94]

Annual reporting and record retention rules and the requirement to adopt and certify to the existence and compliance with an internal digital discrimination compliance plan would impose significant burdens on broadband providers, especially smaller providers that may not track investment data and lack the resources to develop a compliance program with ongoing obligations. The burdens are overly egregious given that smaller providers do not have any record of engaging in digital discrimination.

Further complicating the evaluation of digital-discrimination claims based on income is that, not only is income a key factor influencing whether a given consumer will adopt broadband, but it is also highly correlated with race, ethnicity, national origin, age, education level, and home-computer ownership and usage. The FCC’s digital-discrimination rules fail to recognize this “income conundrum” and will invite costly and time-consuming litigation based on allegations of digital discrimination either where it does not exist or where it is excused by economic-feasibility considerations. Moreover, by specifying pricing as an area subject to digital-discrimination scrutiny, the FCC’s rules allow for ex-post regulation of rates, prompting Commissioner Carr to characterize the agency’s digital-discrimination rules and Title II rules as “fraternal twins.”[95]

D. Title II and Net Neutrality

In 2015, the FCC issued the Open Internet Order (OIO), which reclassified broadband internet-access service as a telecommunications service subject to Title II of the Communications Act. Proponents of the OIO contend that the Title II classification was necessary to ensure net neutrality—that is, that internet service providers (ISP) would treat all internet traffic equally. In 2018, the Title II classification was repealed by the FCC’s Restoring Internet Freedom Order (RIFO).

One month after ICLE’s white paper was published in 2021, President Joe Biden issued an executive order that “encouraged” the FCC to “[r]estore Net Neutrality rules undone by the prior administration.” Last year, Anna Gomez was confirmed as an FCC commissioner, providing the commission a 3-2 Democratic majority. One day after her confirmation, FCC Chair Rosenworcel announced the agency’s proposal to reimpose Title II regulation on internet services. Soon thereafter, the FCC issued its “Notice of Proposed Rulemaking for the Safeguarding and Securing the Open Internet Order,” which would again reclassify broadband under Title II.[96] On April 25, 2024, the commission approved the order on a 3-2 party-line vote.[97]

While the FCC provides several reasons for reclassifying broadband, most of the justifications are built on the same underlying premise: That broadband is an essential public utility and should be regulated as such. Of course, many other essentials—shelter, food, clothing—are provided by various suppliers in competitive markets. Utilities are considered distinct because they tend to have significant economies of scale such that:

1. a single monopoly provider can provide the goods or services at a lower cost than multiple competing firms; and/or
2. market demand is insufficient to support more than a single supplier.[98]

Under this definition, water, sewer, electricity, and natural gas constitute examples typically cited as “natural” monopolies.[99] In some cases, not only are these industries treated as regulated monopolies, but their monopoly status is solidified by laws forbidding competition.

At one time, local and long-distance telephone services were similarly treated as natural monopolies, as was cable television.[100] Various innovations eroded the “natural” monopolies in telephone and cable service over time.[101] As of the year 2000, 94% of U.S. households had a landline telephone, while only 42% had a mobile phone.[102] By 2018, those numbers flipped. In 2015, 73% of households subscribed to cable or satellite television service.[103] Today, fewer than half of U.S. households subscribe.[104] Much of that transition has been due to the enormous improvements in broadband speed, reliability, and affordability discussed in Section II. Similarly, innovations in 5G, fixed wireless, and satellite are eroding the already-tenuous claims that broadband internet service is akin to a utility.

The FCC’s latest reclassification of broadband under Title II prohibits blocking, throttling, or engaging in paid or affiliated prioritization arrangements.[105] In addition, it imposes “a general conduct standard that would prohibit unreasonable interference or unreasonable disadvantage to consumers or edge providers.” Under the OIO, the FCC invoked the general conduct standard to scrutinize providers’ “zero rating” programs.[106] Although Title II regulation explicitly allows for rate regulation of covered entities, the 2024 order forebears rate regulation.[107]

Critics of Title II regulation have argued that some of the conduct prohibited under the FCC’s proposal may be pro-competitive practices that benefit consumers. For example, Hyun Ji Lee & Brian Whitacre found that low-income consumers were willing to pay for an extra GB of data each month, but were not willing to pay extra for a higher speed.[108] This data-speed tradeoff suggests those consumers would benefit from a plan that offered a larger data allowance, but throttled speeds if the allowance is exceeded. In 2014 comments to the FCC, ICLE and TechFreedom described a pro-competitive benefit of paid prioritization:[109]

Prioritization at least requires content providers to respond to incentives—to take congestion into account instead of using up a common resource without regard to cost. It also allows the gaming company to buy better service, which isn’t an option at all with neutrality, under which it just has to suffer congestion. The truth is that, if the game developer can’t afford to pay for clear access, then it may have a bad business model if it is built on an expectation that it will have unfettered, free access to a scarce, contestable resource.

Aside from the likely pro-competitive effects of the conduct the FCC seeks to prohibit, in the face of robust competition, consumers can readily switch away from providers who charge anticompetitive prices or impose harmful terms and conditions. In its 2019 Mozilla decision, the U.S. Circuit Court of Appeals for the D.C. Circuit concluded:[110]

[M]any customers can access edge provider’s content from multiple sources (i.e., fixed and mobile). In this way, there is no terminating monopoly. Additionally, the Commission argued that even if a terminating monopoly exists for some edge providers the commenters did not offer sufficient evidence in the record to demonstrate that the resulting prices will be inefficient. Given these reasons, we reject Petitioners’ claim that the Commission’s conclusion on terminating monopolies is without explanation.

In addition, the court noted:[111]

More importantly, the Commission contends that low churn rates do not per se indicate market power. Instead, they could be a function of competitive actions taken by broadband providers to attract and retain customers. And such action to convince customers to switch providers, the Commission argues, is indicia of material competition for new customers.

Regardless of the FCC’s intent in imposing Title II regulation, the effect will be a stifling of innovation in the delivery and pricing of broadband-internet service. In tandem with the agency’s digital-discrimination rules, the proposed “net neutrality” rules attempt to transition broadband to a commodity service with little differentiation between providers. In so doing, the FCC is eliminating, piece-by-piece, the dimensions among which broadband providers compete, resulting in both higher prices for consumers and lower returns for providers. Rather than a “virtuous cycle” of growth and innovation, the U.S. broadband market may instead experience a “doom loop” of stagnant internet adoption, depressed investment in deployment, and diminished broadband competition.

E. De-Facto Rate Regulation

Rate regulation—any mechanism whereby government intervenes in the pricing process—has long been a contentious issue in the realm of broadband services.[112] Historically, the FCC has been deeply involved in rate regulation, tasked with ensuring fair rates, reliable service, and universal access to telecommunications since 1934.[113] As the telecommunications landscape has evolved, however, so too has the FCC’s approach, increasingly moving toward deregulatory approaches. That is, until recently.[114] Unfortunately, there are multiple ways that rates can be regulated, and—despite public disavowals—policymakers already appear to be implementing some forms of rate regulation on broadband providers.

Explicit rate regulation manifests primarily in two forms: price ceilings and floors.[115] Price ceilings limit the maximum price that can be charged, a common example being rent control. Price floors, on the other hand, set a minimum price, akin to minimum wage laws. Each of these forms impacts the broadband sector differently, potentially altering market dynamics and influencing consumer access and provider revenues.[116]

Policymakers can also resort to less-obvious means of regulating prices—de-facto rate regulation—such as rent stabilization or inflation-linked wage increases, which control the rate of price changes rather than the prices themselves.[117] Moreover, as discussed further infra, price controls are sometimes introduced laterally as requirements to participate in various federal programs, with the effect remaining that government agents assume broad control over prices. Still other regulations may not explicitly regulate rates, but act in much the same way as direct rate regulation, as explained by Jonathan Nuechterlein and Howard Shelanski:[118]

Finally, but no less important, the line between “price” and “non-price” regulation is thin, and regulatory obligations can amount to rate regulation even when regulators do not perceive themselves as setting rates at either the retail or wholesale level.

The FCC’s 2015 OIO, while explicitly eschewing rate regulation, indirectly influenced pricing strategies in the broadband market.[119] By imposing common-carriage obligations, the OIO impacted how ISPs invested and priced their services. In this respect, the FCC’s 2024 rules are identical to the 2015 rules. But this time, Title II regulation will work hand-in-hand with the agency’s digital-discrimination rules. While the proposed common-carrier rules explicitly eschew ex-ante rate regulation through forbearance, the digital-discrimination rules explicitly subject pricing policies and practices to ex-post discrimination scrutiny.

In some ways, the FCC may be imposing among the worst of possible rate-regulation regimes. Under an ex-ante approach to rate regulation, providers have—at a minimum—a framework to form their expectations about whether and how rates will be regulated. As discussed in Section III.C, however, under the ex-post approach that the FCC has adopted in its digital-discrimination rules, providers and any other “covered entity” lack any meaningful framework regarding how the agency may regulate rates or how to avoid liability.

Specifically, the FCC’s Digital Discrimination Order states:

The Commission need not prescribe prices for broadband internet access service, as some commenters have cautioned against, in order to determine whether prices are “comparable” within the meaning of the equal access definition. The record reflects support for the Commission ensuring pricing consistency as between different groups of consumers. We also find that the Commission is well situated to analyze comparability in pricing, as we must already do so in other contexts.[120]

While assessing the comparability of prices is not explicit rate regulation, a policy that holds entities liable for those disparities, such that an ISP must adjust its prices until it matches the FCC definitions of “comparable” and “consistency,” is tantamount to setting that rate.[121]

In addition to the FCC digital-discrimination and Title II rules, recent developments in broadband policy have introduced other forms of de-facto rate regulation. The BEAD program itself mandates a “low-cost” option be made available to recipients of the Affordable Connectivity Program by providers that receive a BEAD grant.[122] The NTIA’s NOFO for the BEAD program further mandates that participating states include an affordability plan that ensures access to affordable high-speed internet for all middle-class consumers.[123] This initiative might require providers to offer low-cost plans or to provide consumer subsidies. Similarly, the U.S. Department of Agriculture’s (USDA) ReConnect Loan and Grant Program awards funding preferences to applicants that adhere to net-neutrality rules and offer “affordable” options.[124] New York’s Affordable Broadband Act is another example of broadband rules that mandate ISPs provide low-cost internet-access plans to qualifying low-income households.[125]

Rate regulation, de facto or otherwise, has a major effect on providers’ ability to enter new markets and to improve service in those markets in which they already operate. Rate regulations lead to market distortions. By capping prices below the market rate, such regulations can increase demand without a corresponding increase in supply, potentially leading to shortages and discouraging providers from making output-improving investments.[126] For broadband providers, this can translate into reduced investment in network expansion and quality improvement, particularly in less profitable or more challenging areas. Moreover, binding rate regulations can lower the returns on investment, thereby discouraging deployments and slowing overall broadband expansion. Quality and service also may suffer under rate regulation. A regulated provider, constrained by price ceilings, cannot fully reap the benefits of service-quality improvements, leading to a reduced incentive to enhance that service quality.[127]

F. Pole Attachments

The importance of pole attachments cannot be overstated in the context of expanding broadband connectivity, even if utility-pole issues often fly under the radar. This is particularly true due to their implications for competition in the relevant local broadband markets. Access to physical infrastructure is critical, and where providers cannot readily access this physical infrastructure, it can delay deployment or make it more costly.

The FCC has recognized the crucial role of pole attachments in a pending proceeding that seeks to address inefficiencies in access to pole attachments that lead to cost overruns and delays in deployment.[128] In December 2023, in an effort to expedite broadband deployment, the commission adopted several important pole-attachment reform measures.[129] These included introducing a streamlined process to resolve utility-pole attachment disputes, which could be pivotal to hasten broadband rollouts, especially in underserved areas.[130] The FCC also mandated that utilities provide comprehensive pole-inspection information to broadband attachers, which is expected to facilitate more informed planning and to reduce delays.[131] The commission has also refined its procedural rules to foster quicker resolutions through mediation and expedited adjudication via the Accelerated Docket.[132]

The FCC is on the right track: ensuring timely access to pole infrastructure is crucial to ensure that broadband markets remain competitive, and that the substantial investments in broadband infrastructure directed by programs like BEAD yield the intended benefits.

The goal of pole-attachment rules should be to equitably assess costs in ways that avoid inefficient rent extraction and ensure the smooth deployment of broadband infrastructure.[133] The FCC’s current rules, however, can impose on a requesting attacher the entire cost of pole replacement, which is economically suboptimal.[134] There is therefore a need to revisit the current formula to ensure that the incremental costs and benefits are appropriately allocated to each relevant party. In its recent order, the FCC expanded the definition of what constitutes as a “red tagged” pole in need of replacement.[135] The extent to which this works in practice will, however, depend on how the FCC processes applications under its new “red tag” policy.

One critical concern is the emergence of hold-up and hold-out problems.[136] Section 224 of the Communications Act authorizes the FCC to ensure that the costs of pole attachments are just and reasonable.[137] This provision, however, also allows pole owners to deny access when there is insufficient capacity, creating a potential imbalance in bargaining power.[138] This imbalance is exacerbated by the pole owners’ superior knowledge of their cost structures and their ability to impose “take it or leave it” offers on prospective attachers.[139] Consequently, attachers might be, at the margin, discouraged from deploying in areas with capacity-constrained poles. Further, the “last attacher pays” model can inadvertently create a disincentive for pole owners to replace or upgrade poles until a new attacher is obligated to bear the full cost. This scenario may lead to delays in broadband deployment, especially in areas where the cost of deployment is already high. The recent FCC order aims to address these concerns by clarifying cost-causation principles and ensuring more equitable cost sharing for pole replacements and modifications.[140] But there again remains interpretive room within the framework the commission has established. Thus, it remains to be seen how effectively the new rules will mitigate the problem.

Any reconsideration of pole-attachment rules also must account for the fact that the pole market is highly regulated.[141] The actual cost for pole replacements in a free market, without regulatory intervention, would likely be some middle ground between the total replacement cost and the new rental price charged to attachers. The FCC must judiciously leverage its ability to set reasonable rental rates to approach the ideal price that would otherwise be discovered through market mechanisms.

Toward this end, the upfront “make-ready” charges for pole replacement should be limited to a pole owner’s incremental cost.[142] This approach acknowledges that early replacements simply shift the timing of the expense, rather than adding additional costs. The formula could incorporate the depreciated value of the pole being replaced and allocate the costs associated with increased capacity across all beneficiaries, including new attachers as well as the pole owner, who may realize additional revenue from the increased capacity.

Beyond disputes over privately owned poles, a lacuna in the FCC’s authority over poles owned by certain public entities threatens to erect large roadblocks to deployment. This is particularly the case for poles owned by the Tennessee Valley Authority (TVA).[143]  Such common TVA practices as refusing reasonable and nondiscriminatory pole-attachment agreements risk significantly slowing the deployment of broadband, especially in the rural areas the TVA services.[144]

The source of this problem is a provision of Section 224 of the Communications Act that exempts municipal and electric-cooperative (coop) pole owners from FCC oversight.[145] This exemption allows the TVA to set its own rates for pole attachments, which are notably higher than FCC rates, and often sidestep access requirements typically mandated by states and the FCC.[146]

Municipally owned electricity distributors constitute what economists call state-owned enterprises. As such, they face significantly different restraints than privately owned enterprises.[147] Private businesses must pass the profit-and-loss test on the market, while state-owned enterprises are not similarly constrained. Municipally owned electricity distributors are usually monopolies, either because private competitors are not allowed to compete, or because they receive government benefits not available to potential private competitors. As a result, they may pursue other goals in the “public interest,” such as providing their products and services at below-market prices.[148] This includes the ability to leverage their electricity monopolies to enter into broadband provision. The problem is that these municipally owned electricity distributors also have strong incentives to refuse to deal with private competitors in the broadband market who need access to the electric poles they own.[149]

Rural electric cooperatives (RECs), particularly those distributing electricity from the TVA, also hold a privileged position that allows them to act in potentially anticompetitive ways toward broadband providers seeking pole attachments. Unlike municipally owned electricity distributors, RECs need to earn sufficient revenues to remain operational. They are also, however, much more like state-owned enterprises in the governmental benefits they receive, including the immense difficulty of normal oversight from the market for corporate control.[150] This similarly incentivizes them to act anticompetitively, particularly as many enter or plan to enter the broadband market.[151]

These circumstances often lead RECs to refuse to deal with private broadband providers, thereby stifling competition and deployment in rural areas.[152] Furthermore, RECs often face little oversight from rate regulators regarding pole attachments, leading to significantly higher costs for broadband companies seeking to attach to poles owned by co-ops and municipalities outside FCC jurisdiction.[153]

This regulatory loophole not only leads to higher costs for broadband providers, but also raises concerns about the application of antitrust laws to these entities. Sen. Mike Lee (R-Utah) has argued that the U.S. Justice Department (DOJ) should examine the antitrust implications of these practices, emphasizing that these government-owned entities should be subject to antitrust laws when acting as market participants.[154] And FCC Commissioner Brendan Carr has noted ongoing concerns about delays and costs associated with attaching to poles owned by municipal and cooperative utilities.[155] Addressing this loophole is crucial to bridge the digital divide and ensure that the IIJA’s goals are met effectively.

G. Municipal/Co-Op Broadband

As previously noted, despite persistent interest in some quarters to promote municipal broadband,[156] there are many challenges that contribute to such projects’ poor record. In particular, the financial prospects of municipal networks are typically dim, as many such projects generate negative cash flow and are unsustainable without substantial improvements in operations.[157] Only a small subset of municipalities—usually those with existing municipal-power utilities—might be well-positioned to venture into municipal broadband, due to potential cross-subsidization opportunities.[158] Even among those municipal-broadband projects that have been deemed successful, however, the repayment of project costs is daunting, often requiring substantial subsidies and cross-subsidization.[159] The prospects for municipal broadband have not improved since ICLE’s 2021 white paper.

In a study by Christopher Yoo et al., the authors examine the financial performance of every municipal fiber project operating in the United States from 2010 through 2019 that provided annual financial reports for its fiber operations.[160] Each of the 15 projects was located in an urban area, as defined by the U.S. Census Bureau. In addition, each project was built in areas already served by one or more private broadband providers—none were designed to serve previously unserved areas. In every case, the municipality issued revenue bonds to fund construction and initially expected the projects to repay their construction and operating costs from project revenues, rather than from taxes or interfund transfers. In some cases, the cities anticipated the projects would generate surpluses that would, in turn, allow the cities to lower taxes.

In contrast to these expectations, every project either needed infusions of cash from outside sources or debt relief through refinancing. Three projects defaulted on their debt, two of which were liquidated at significant losses.

Yoo et al. employed two measures of financial performance:

1. adjusted net cash flow (ANCF), which measures the actual cash collected and spent by a fiber project; and
2. net present value of cash flow from operations (NPV), which discounts cash flow using the project’s weighted average cost of capital.

Based on ANCF, only two of the 15 projects have broken even or are expected to break even by the time their initial debt matures. Based on NPV, more than half of the projects were not on track to break even—even assuming a theoretical best-case performance in terms of capital expenditures and debt service.

Municipalities that are unable to cover their broadband projects’ costs of debt and operations must make up the shortfall from general tax revenues or default on their debt. Making up a shortfall from tax revenues means the city must enact some combination of tax increases or service cuts. A default will result in a downgrade in the municipality’s bond rating, which will increase the costs of financing all of the city’s operations, not just the broadband project. These additional costs must ultimately be paid the municipality’s taxpayers.

In a separate analysis, George Ford notes that many municipal-broadband projects are located in cities that operate their own electric utilities.[161] Such an arrangement allows the broadband network’s debt and other expenses to be placed on the electric utility’s books, thereby improving the apparent financial condition of the broadband network. As electricity rates are based on cost of service, Ford argues that a shift of broadband costs to the electric utility would be expected to increase electricity rates.

To evaluate this hypothesis, he compares municipal electricity rates among four Tennessee cities that own and operate municipal broadband. Two cities financed the projects with general-obligation bonds funded by tax revenues and other sources of the municipality’s income. The other two cities used electric-utility profits to cover the broadband project’s financial losses. One of these cities is Chattanooga, which received $111 million in subsidies and in which the city’s electric utility assumed $162 million of debt to construct the broadband network and made $50 million of loans to the broadband division.

Ford’s statistical analysis calculates broadband projects are associated with a 5.4% increase in electricity rates in cities with utility-funded projects, relative to cities that issued general-obligation bonds. It should be emphasized that the higher rates are imposed on all electricity ratepayers, not just those who subscribe to the city’s broadband. These higher electricity rates are used to cross-subsidize municipal-broadband subscribers. For example, Ford reports that, in Chattanooga, the average monthly revenue per broadband subscriber was $147 in 2015. In addition, the average subscriber was associated with a monthly subsidy of $30. Thus, cross-subsidies from electricity ratepayers account for about 17% of the average monthly broadband-subscriber cost.

The conclusions from ICLE’s 2021 white paper remain valid today. Proposals to offer municipal broadband as a means to increase broadband adoption—either by attempting to increase supply, or to suppress prices—put the cart before the horse. That’s because private supply and demand conditions are usually sufficient to guarantee creation of adequate broadband networks throughout most of the country.

Some uneconomic locations (i.e., the unserved areas) may require interventions to ensure broadband access. In some cases, municipal broadband may be an effective option to subsidize hard-to-reach consumers. Municipal broadband should not, however, be considered the best or only option. Indeed, the evidence demonstrates that municipal broadband might best be considered a solution of last resort, used only when no private provider finds it economically viable to serve a particular area.

IV. Conclusion

By most measures, U.S. broadband competition is vibrant and has increased dramatically since the COVID-19 pandemic. Since 2021, more households are connected to the internet; broadband speeds have increased, while prices have declined; more households are served by more than a single provider, and new technologies like satellite and 5G have expanded internet access and intermodal competition among providers.

Broadband competition policy currently appears to be in a state of confusion: Some policies foster competition, while others hinder it. Programs such as the ACP and BEAD could do much to encourage competition by simultaneously increasing the demand for broadband and facilitating the buildout of supply. At the same time, some facets of these programs’ implementation act to stifle competition with onerous rules, reporting requirements, and—in some cases—de-facto rate regulation.

In addition, the FCC’s digital-discrimination rules explicitly subject broadband pricing and other dimensions of competition to ex-post scrutiny and enforcement. In reclassifying broadband internet-access services under Title II of the Communications Act, the FCC has rendered nearly every aspect of broadband deployment and delivery subject to its regulation or scrutiny.

Put simply, today, U.S. broadband competition is robust, innovative, and successful. At the same time, new and forthcoming regulations threaten broadband competition by eliminating or proscribing the policies and practices by which providers compete. As a result, the United States is at risk of slowing or shrinking broadband investment—thereby reducing innovation and harming the very consumers that policymakers claim they seek to help.

[1] Geoffrey A. Manne, Kristian Stout, & Ben Sperry, A Dynamic Analysis of Broadband Competition: What Concentration Numbers Fail to Capture, Int’l Ctr. for L. & Econ. (Jun. 2021), available at https://laweconcenter.org/wp-content/uploads/2021/06/A-Dynamic-Analysis-of-Broadband-Competition.pdf.

[2] See id. at 2-3; 35-37.

[3] CDC Museum COVID-19 Timeline, Ctr. for Disease Control and Prevention (Mar. 15, 2023), https://www.cdc.gov/museum/timeline/covid19.html.

[4] H.R. 3684, 117th Cong. (2021).

[5] Eric Fruits & Kristian Stout, Finding Marginal Improvements for the ‘Good Enough’ Affordable Connectivity Program, Int’l Ctr. for L. & Econ. (Sep. 15, 2023), available at https://laweconcenter.org/wp-content/uploads/2023/09/ACP-Subsidies-Paper.pdf.

[6] Eric Fruits & Geoffrey A. Manne, Quack Attack: De Facto Rate Regulation in Telecommunications, Int’l Ctr. for L. & Econ. (Mar. 30, 2023), available at https://laweconcenter.org/wp-content/uploads/2023/03/De-Facto-Rate-Reg-Final-1.pdf.

[7] Eric Fruits & Kristian Stout, The Income Conundrum: Intent and Effects Analysis of Digital Discrimination, Int’l Ctr. for L. & Econ. (Nov. 14, 2022), available at https://laweconcenter.org/wp-content/uploads/2022/11/The-Income-Conundrum-Intent-and-Effects-Analysis-of-Digital-Discrimination.pdf.

[8] Wireline Competition Bureau Announces the Final Month of the Affordable Connectivity Program, WC Docket No. 21-450 (Mar. 4, 2024), available at https://docs.fcc.gov/public/attachments/DA-24-195A1.pdf; see also Brian Fung, FCC Ends Affordable Internet Program Due to Lack of Funds, CNN (May 31, 2024), https://www.cnn.com/2024/05/31/tech/fcc-affordable-connectivity-program-acp-close/index.html.

[9] Anthony Hennen, More Money, More Problems for National Broadband Expansion, The Center Square (Aug. 15, 2023), https://www.thecentersquare.com/pennsylvania/article_3124e98c-3bb3-11ee-ad87-7361f3872110.html.

[10] Lindsay McKenzie, BEAD Waiver Information Coming This Summer, NTIA Says, StateScoop (Aug. 17, 2023), https://statescoop.com/bead-broadband-waiver-summer-2023-ntia.

[11] BEAD Letter of Credit Concerns, $4.3M in ACP Outreach Grants, FCC Waives Rules for Hawaii Wildfires, Broadband Breakfast (Aug. 21, 2023), https://broadbandbreakfast.com/2023/08/bead-letter-of-credit-concerns-4-3m-in-acp-outreach-grants-fcc-waives-rules-for-hawaii-wildfires.

[12] Eric Fruits, Red Tape and Headaches Plague BEAD Rollout, Truth on the Market (Aug. 17, 2023), https://truthonthemarket.com/2023/08/17/red-tape-and-headaches-plague-bead-rollout.

[13] Fruits & Stout, supra note 6; see also Eric Fruits, Kristian Stout, & Ben Sperry, ICLE Reply Comments on Prevention and Elimination of Digital Discrimination, Notice of Proposed Rulemaking, In the Matter of Implementing the Infrastructure, Investment, and Jobs Act: Prevention and Elimination of Digital Discrimination, No. 22-69, at Part III, Int’l Ctr. for L. & Econ. (Apr. 20, 2023), https://laweconcenter.org/resources/icle-reply-comments-on-prevention-and-elimination-of-digital-discrimination.

[14] FCC, Report and Order and Further Notice of Proposed Rulemaking on Implementing the Infrastructure Investment and Jobs Act: Prevention and Elimination of Digital Discrimination, GN Docket No. 18-238, FCC 19-44 (Nov. 20, 2023), available at https://docs.fcc.gov/public/attachments/FCC-23-100A1.pdf [hereinafter “Digital Discrimination Order”]. See also Eric Fruits, Everyone Discriminates Under the FCC’s Proposed New Rules, Truth on the Market (Oct. 30, 2023), https://truthonthemarket.com/2023/10/30/everyone-discriminates-under-the-fccs-proposed-new-rules (reporting that, under the rules, “broadband service” includes every element of a consumer’s broadband-internet experience, including speeds, data caps, pricing, and discounts, and that the rules broadly apply to broadband providers as well as to “entities outside the communications industry” that “provide services that facilitate and affect consumer access to broadband,” which may include municipalities and property owners).

[15] Notice of Proposed Rulemaking, Safeguarding and Securing the Open Internet, WC Docket No. 23-320 (Sep. 28, 2023). [hereinafter “Title II NPRM”]

[16] Declaratory Ruling, Order, Report and Order, and Order on Reconsideration, Safeguarding and Securing the Open Internet, WC Docket No. 23-320, WC Docket No. 17-108 (adopted Apr. 25, 2024), available at https://docs.fcc.gov/public/attachments/DOC-401676A1.pdf [hereinafter “SSOIO” or “2024 Order”].

[17] See, e.g., Karl Bode, Colorado Eyes Killing State Law Prohibiting Community Broadband Networks, TechDirt (Mar. 30, 2023), https://www.techdirt.com/2023/03/30/colorado-eyes-killing-state-law-prohibiting-community-broadband-networks (local broadband monopolies are a “widespread market failure that’s left Americans paying an arm and a leg for what’s often spotty, substandard broadband access.”).

[18] FCC Chair Rosenworcel on Reinstating Net Neutrality Rules, C-Span (Sep. 25, 2023), https://www.c-span.org/video/?530731-1/fcc-chair-rosenworcel-reinstating-net-neutrality-rules (“Only one-fifth of the country has more than two choices at [100 Mbps download] speed. So, if your broadband provider mucks up your traffic, messes around with your ability to go where you want and do what you want online, you can’t just pick up and take your business to another provider. That provider may be the only game in town.”).

[19] U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates, Table Id. S2801 (2021); U.S. Census Bureau, ACS 1-Year Estimates Public Use Microdata Sample 2021, Access to the Internet (ACCESSINET) (2021).

[20] In contrast, a 2021 NTIA survey reports that 14.4% of households do not use the internet at home, with three-quarters of these households indicating they have “no need/interest” and one quarter indicating it is “too expensive.” See, Michelle Cao & Rafi Goldberg, Switched Off: Why Are One in Five U.S. Households Not Online?, National Telecommunications and Information Administration (2022), https://ntia.gov/blog/2022/switched-why-are-one-five-us-households-not-online.

[21] National Center for Education Statistics, Children’s Internet Access at Home, Condition of Education, (U.S. Department of Education, Institute of Education Sciences, Aug. 2023), https://nces.ed.gov/programs/coe/indicator/cch.

[22] See FCC, 2015 Broadband Progress Report (2015), https://www.fcc.gov/reports-research/reports/broadband-progressreports/2015-broadband-progress-report (upgrading the standard speed from 4/1 Mbps to 25/3 Mbps). In March 2024, the FCC approved a report increasing the fixed-speed benchmark to 100/20 Mbps and setting an “aspirational goal” of 1 Gbps/500 Mbps. See, FCC, In the Matter of Inquiry Concerning the Deployment of Advanced Telecommunications Capability to All Americans in a Reasonable and Timely Fashion, GN Docket No. 22-270 (Mar. 14, 2024), available at https://docs.fcc.gov/public/attachments/DOC-400675A1.pdf. In November 2023, FCC Chair Jessica Rosenworcel proposed reaching a 1 Gbps/500 Mbps benchmark by the year 2030. See Eric Fruits, Gotta Go Fast: Sonic the Hedgehog Meets the FCC, Truth on the Market (Nov. 3, 2023), https://truthonthemarket.com/2023/11/03/gotta-go-fast-sonic-the-hedgehog-meets-the-fcc.

[23] Infrastructure Investment and Jobs Act, Pub. L. No. 117-58, § 60102 (a)(1)(A)(ii), 135 Stat. 429 (Nov. 15, 2021), available at https://www.congress.gov/117/plaws/publ58/PLAW-117publ58.pdf; Jake Varn, What Makes a Community “Unserved” or “Underserved” by Broadband?, Pew Charitable Trusts (May 3, 2023), available at https://www.pewtrusts.org/-/media/assets/2023/06/un–and-underserved-definitions-ta-memo-pdf.pdf.

[24] Id., IIJA.

[25] Mike Conlow, New FCC Broadband Map, Version 3, Mike’s Newsletter (Nov. 20, 2023), https://mikeconlow.substack.com/p/new-fcc-broadband-map-version-3.

[26] FCC, Communications Marketplace Report, GN Docket No 22-203, FCC 22-103, Appendix G (Dec. 20, 2022), https://www.fcc.gov/document/2022-communications-marketplace-report.

[27] Pursuant to the IIJA, the FCC and providers are working to provide new broadband-coverage maps. These numbers will change over time, but FCC Chair Jessica Rosenworcel noted: “Looking ahead, we expect that any changes in the number of locations will overwhelmingly reflect on-the-ground changes such as the construction of new housing.” See Brad Randall, FCC’s Updated Broadband Map Shows Increasing National Connectivity, Broadband Communities (Nov. 27, 2023), https://bbcmag.com/fccs-new-broadband-map-shows-increasing-national-connectivity.

[28] FCC Chair Rosenworcel on Reinstating Net Neutrality Rules, C-Span (Sep. 26, 2023), https://www.c-span.org/video/?530731-1/fcc-chair-rosenworcel-reinstating-net-neutrality-rules.

[29] FCC, Fixed Broadband Deployment (Jun. 2021), https://broadband477map.fcc.gov/#/area-summary?version=jun2021&type=nation&geoid=0&tech=acfw&speed=25_3&vlat=27.480205324799257&vlon=-41.52925368904516&vzoom=5.127403622197149.

[30] FCC, 2019 Broadband Deployment Report, GN Docket No. 18-238, FCC 19-44 at Fig. 4 (May 29, 2019), available at https://docs.fcc.gov/public/attachments/FCC-19-44A1.pdf.

[31] The FCC does not explain the differences between the information summarized in Table 1 and Table 2. The differences likely reflect different methodologies. For example, Table 1 may be at the household level and Table 2 at the population level.

[32] 2022 Communications Marketplace Report, GN Docket No. 22-203 (Dec. 30, 2022) at Fig. II.A.28, available at https://docs.fcc.gov/public/attachments/FCC-22-103A1.pdf.

[33] Dan Heming, Starlink No Longer Has a Waitlist for Standard Service, and 10 MPH Speed Enforcement Update, Mobile Internet Resource Center (Oct. 3, 2023), https://www.rvmobileinternet.com/starlink-no-longer-has-a-waitlist-for-standard-service-and-10-mph-speed-enforcement-update/#:~:text=In%20the%20latest%20update%2C%20the,order%20anywhere%20in%20the%20USA.

[34] Starlink Specifications, Starlink, https://www.starlink.com/legal/documents/DOC-1400-28829-70.

[35] Amazon Shares an Update on How Project Kuiper’s Test Satellites Are Performing, Amazon (Oct. 16, 2023), https://www.aboutamazon.com/news/innovation-at-amazon/amazon-project-kuiper-test-satellites-space-launch-october-2023-update.

[36] Kuiper Service to Start by End of 2024: Amazon, Communications Daily (Oct. 12, 2023), https://communicationsdaily.com/news/2023/10/12/Kuiper-Service-to-Start-by-End-of-2024-Amazon-2310110007.

[37] Why Is the Internet More Expensive in the USA than in Other Countries?, Community Tech Network (Feb. 2, 2023), https://communitytechnetwork.org/blog/why-is-the-internet-more-expensive-in-the-usa-than-in-other-countries.

[38] Dan Howdle, Global Broadband Pricing League Table 2023, Cable.co.uk (2023), https://www.cable.co.uk/broadband/pricing/worldwide-comparison, data available at https://www.cable.co.uk/broadband/worldwide-pricing/2023/broadband_price_comparison_data.xlsx.

[39] This is qualitatively consistent with the FCC’s finding that United States has the seventh-lowest prices per gigabit of data consumption, and that Australia has the 12th-lowest among OECD countries. FCC, 2022 Communications Marketplace Report, Docket No. 22-103, Appendix G (Dec. 30, 2022), available at https://docs.fcc.gov/public/attachments/FCC-19-44A1.pdf.

[40] Median Country Speeds, Speedtest Global Index (Oct. 2023), https://www.speedtest.net/global-index (last visited Dec. 7, 2023).

[41] See Christian Dippon, et al., Adding a Warning Label to Rewheel’s International Price Comparison and Competitiveness Rankings (Nov. 30, 2020), available at https://laweconcenter.org/wp-content/uploads/2020/11/Rewheel_Review_Final.pdf.

[42] Fruits & Stout, supra note 6; see also Giuseppe Colangelo, Regulatory Myopia and the Fair Share of Network Costs: Learning from Net Neutrality’s Mistakes, Int’l Ctr. for L. & Econ. (Comments to European Commission Exploratory Consultation, The Future of the Electronic Communications Sector and Its Infrastructure, May 18, 2023), https://laweconcenter.org/resources/regulatory-myopia-and-the-fair-share-of-network-costs-learning-from-net-neutralitys-mistakes.

[43] Id. at 14.

[44] Arthur Menko Business Planning Inc., 2023 Broadband Pricing Index, USTelecom (Oct. 2023), available at https://ustelecom.org/wp-content/uploads/2023/10/USTelecom-2023-BPI-Report-final.pdf.

[45] U.S. Bureau of Labor Statistics, Producer Price Index by Commodity: Telecommunication, Cable, and Internet User Services: Residential Internet Access Services [WPU374102], retrieved from FRED, Federal Reserve Bank of St. Louis (Aug. 29, 2023), https://fred.stlouisfed.org/series/WPU374102.

[46] United States Median Country Speeds July 2023, Speedtest Global Index (2023), https://www.speedtest.net/global-index/united-states. Prior years retrieved from Internet Archive. See also Camryn Smith, The Average Internet Speed in the U.S. Has Increased by Over 100 Mbps since 2017, Allconnect (Aug. 4, 2023), https://www.allconnect.com/blog/internet-speeds-over-time (average download speed in the United States was 30.7 Mbps in 2017 and 138.9 Mbps in the first half of 2023).

[47] George S. Ford, Confusing Relevance and Price: Interpreting and Improving Surveys on Internet Non-adoption, 45 Telecomm. Pol’y 102084 (2021).

[48] Smaller surveys and focus groups that allow more opportunities for follow-up questions, however, suggest that price may be more important than is suggested by Census Bureau surveys. For example, one study in Detroit, Michigan, used surveys and focus groups to examine internet adoption and use in three low-income urban neighborhoods. Participants who reported lacking at-home internet mentioned lack of interest and high costs at roughly equal rates. See, Colin Rhinesmith, Bianca Reisdorf, & Madison Bishop, The Ability to Pay For Broadband, 5 Comm. Res. Pract. 121 (2019).

[49] Ford, supra note 9.

[50] Michelle Cao & Rafi Goldberg, New Analysis Shows Offline Households Are Willing to Pay $10-a-Month on Average for Home Internet Service, Though Three in Four Say Any Cost Is Too Much, National Telecommunications and Information Administration (Oct. 6, 2022), https://ntia.gov/blog/2022/new-analysis-shows-offline-households-are-willing-pay-10-month-average-home-internet.

[51] Michelle Cao & Rafi Goldberg, Switched Off: Why Are One in Five U.S. Households Not Online?, National Telecommunications and Information Administration (2022), https://ntia.gov/blog/2022/switched-why-are-one-five-us-households-not-online.

[52] Jamie Greig & Hannah Nelson, Federal Funding Challenges Inhibit a Twenty-First Century “New Deal” for Rural Broadband, 37 Choices 1 (2022).

[53] Andrew Perrin, Mobile Technology and Home Broadband 2021, Pew Research Center (Jun. 3, 2021), https://www.pewresearch.org/internet/2021/06/03/mobile-technology-and-home-broadband-2021.

[54] Rhinesmith, et al., supra note 10.

[55] 2022 Broadband Capex Report, USTelecom (Sep. 8, 2023), available at https://ustelecom.org/wp-content/uploads/2023/09/2022-Broadband-Capex-Report-final.pdf.

[56] Wolfgang Briglauer, Carlo Cambini, Klaus Gugler, & Volker Stocker, Net Neutrality and High-Speed Broadband Networks: Evidence from OECD Countries, 55 Eur. J. L. & Econ. 533 (2023).

[57] Eric Fruits, Justin (Gus) Hurwitz, Geoffrey A. Manne, Julian Morris, & Alec Stapp, Static and Dynamic Effects of Mergers: A Review of the Empirical Evidence in the Wireless Telecommunications Industry, (OECD Directorate for Financial and Enterprise Affairs Competition Committee, Global Forum on Competition, DAF/COMP/GF(2019)13, Dec. 6, 2019), available at https://one.oecd.org/document/DAF/COMP/GF(2019)13/en/pdf.

[58] Manne, Stout, & Sperry, supra note 1.

[59] Kenneth Flamm & Pablo Varas, Effects of Market Structure on Broadband Quality in Local U.S. Residential Service Markets, 12 J. Info. Pol’y 234 (2022).

[60] Andrew Kearns, Does Competition From Cable Providers Spur the Deployment of Fiber? (Jul. 27, 2023), https://ssrn.com/abstract=4523529 or http://dx.doi.org/10.2139/ssrn.4523529.

[61] Manne, Stout, & Sperry, supra note 1.

[62] Fruits, et al., supra note 55.

[63] FCC, Affordable Connectivity Program (Oct. 2, 2023), https://www.fcc.gov/acp.

[64] Eric Fruits & Kristian Stout, Finding Marginal Improvements for the ‘Good Enough’ Affordable Connectivity Program (Int’l. Ctr. for L. & Econ. Issue Brief, Sep. 15, 2023), available at https://laweconcenter.org/wp-content/uploads/2023/09/ACP-Subsidies-Paper.pdf.

[65] See Paul Winfree, Bidenomics Goes Online: Increasing the Costs of High-Speed Internet, Econ. Pol’y Innovation Ctr (Jan. 8, 2024), available at https://epicforamerica.org/wp-content/uploads/2024/01/Bidenomics-Goes-Online_01.08.24-1.pdf (Finding ACP subsidies are associated with higher prices for all broadband plans, especially lower-speed plans, but these costs are more than offset by the subsidies for those who receive them. Thus, the ACP provides lower prices net of subsidy to ACP beneficiaries, but higher prices for those who are not.).

[66] Id.

[67] Id.

[68] Fruits & Stout, supra note 4.

[69] Universal Service Administrative Co., ACP Enrollment and Claims Tracker (Feb. 8, 2024), https://www.usac.org/about/affordable-connectivity-program/acp-enrollment-and-claims-tracker. Beginning Feb. 8, 2024, the ACP ceased enrollment.

[70] Wireline Competition Bureau Announces the Final Month of the Affordable Connectivity Program, WC Docket No. 21-450 (Mar. 4, 2024), available at https://docs.fcc.gov/public/attachments/DA-24-195A1.pdf.

[71] Biden-Harris Administration Announces State Allocations for $42.45 Billion High-Speed Internet Grant Program as Part of Investing in America Agenda, Nat’l Telecomms and Info. Admin. (Jun. 26, 2023), https://www.ntia.gov/press-release/2023/biden-harris-administration-announces-state-allocations-4245-billion-high-speed.

[72] Id.

[73] U.S. Dep’t of Com., Internet For All Frequently Asked Questions and Answers Draft Answers Version 2.0 Broadband, Equity, Access, and Deployment (BEAD) Program, Nat’l Telecomms and Info. Admin. (Sep. 2022), available at https://broadbandusa.ntia.doc.gov/sites/default/files/2022-09/BEAD-Frequently-Asked-Questions-%28FAQs%29_Version-2.0.pdf.

[74] Infrastructure Investment and Jobs Act Overview, BroadbandUSA, https://broadbandusa.ntia.doc.gov/resources/grant-programs (last visited Dec. 7, 2023).

[75] U.S. Dep’t of Com., Notice of Funding Opportunity, Broadband Equity, Access, and Deployment Program, NTIA-BEAD-2022, Nat’l Telecomms and Info. Admin. (May 2022), available at https://broadbandusa.ntia.doc.gov/sites/default/files/2022-05/BEAD%20NOFO.pdf. [hereinafter “BEAD NOFO”]

[76] Id. See also, Ted Cruz, Red Light Report, Stop Waste, Fraud, and Abuse in Federal Broadband Funding, U.S. S. Comm. on Com., Science, and Transp. (Sep. 2023), https://www.commerce.senate.gov/services/files/0B6D8C56-7DFD-440F-8BCC-F448579964A3.

[77] U.S. Dep’t of Com., Notice of Funding Opportunity, Broadband Equity, Access, and Deployment Program, NTIA-BEAD-2022, NTIA (May 2022), available at https://broadbandusa.ntia.doc.gov/sites/default/files/2022-05/BEAD%20NOFO.pdf (note that the IIJA itself did not include this requirement, as it was an addition by NTIA as part of the NOFO process; thus, it is unclear the extent to which this represents a valid requirement by NTIA under the BEAD program).

[78] Id. at 67.

[79] George S. Ford, Middle-Class Affordability of Broadband: An Empirical Look at the Threshold Question, Phoenix Ctr. for Adv. Leg. & Econ. Pub. Pol’y Stud., Pol’y Bull. No. 61 (Oct. 2022), available at https://phoenix-center.org/PolicyBulletin/PCPB61Final.pdf.

[80] Id.

[81] John W. Mayo, Gregory L. Rosston & Scott J. Wallsten, From a Silk Purse to a Sow’s Ear? Implementing the Broadband, Equity, Access and Deployment Act, Geo. U. McDonough Sch. of Bus. Ctr. for Bus. & Pub. Pol’y (Aug. 2022), https://georgetown.app.box.com/s/yonks8t7eclccb0fybxdpy3eqmw1l2da?mc_cid=95d011c7c1&mc_eid=dc30181b39.

[82] BEAD Letter of Credit Concerns, $4.3M in ACP Outreach Grants, FCC Waives Rules for Hawaii Wildfires, Broadband Breakfast (Aug. 21, 2023), https://broadbandbreakfast.com/2023/08/bead-letter-of-credit-concerns-4-3m-in-acp-outreach-grants-fcc-waives-rules-for-hawaii-wildfires.

[83] NTIA, Ensuring Robust Participation in the BEAD Program (Nov. 1, 2023), https://www.internetforall.gov/blog/ensuring-robust-participation-bead-program.

[84] FCC, Report and Order and Further Notice of Proposed Rulemaking, GN Docket No. 22-69, FCC 23-100 (Nov. 20, 2023), available at https://docs.fcc.gov/public/attachments/FCC-23-100A1.pdf

[85] Id. at 3.

[86] Id.

[87] Id.

[88] Id.

[89] Fruits, supra note 13.

[90] U.S. Chamber of Commerce, In the Matter of Implementing the Infrastructure Investment and Jobs Act: Prevention and Elimination of Digital Discrimination, GN Docket No. 22-69 (Nov. 6, 2023), https://www.fcc.gov/ecfs/document/110620347626/2 (citations omitted).

[91] FCC, Dissenting Statement of Commissioner Brendan Carr Regarding the Implementing the Infrastructure Investment and Jobs Act: Prevention and Elimination of Digital Discrimination, GN Docket No. 22-69, Report and Order and Further Notice of Proposed Rulemaking, FCC 23-100 (2023), available at https://docs.fcc.gov/public/attachments/FCC-23-100A3.pdf.

[92] George S. Ford, Will Digital Discrimination Policies End Discount Plans for Low-Income Consumers? (Phoenix Ctr. for Advanced Legal & Econ. Pub. Pol’y Stud., Nov. 1, 2023), https://www.fcc.gov/ecfs/document/1103079827403/5.

[93] HCS EdConnect, Welcome to HCS EdConnect (2023), https://www.edconnect.org.

[94] WISPA, In the Matter of Implementing the Infrastructure Investment and Jobs Act: Prevention and Elimination of Digital Discrimination, GN Docket No. 22-69 (Nov. 8, 2023), https://www.fcc.gov/ecfs/document/1108944918538/1.

[95] Testimony of Brendan Carr, Commissioner, Federal Communications Commission, Before the Subcommittee on Communications and Technology of the United States House of Representatives Committee on Energy and Commerce, “Oversight of President Biden’s Broadband Takeover” (Nov. 30, 2023), available at https://d1dth6e84htgma.cloudfront.net/11_30_23_Carr_Testimony_3163ea4363.pdf.

[96] Title II NPRM, supra note 14.

[97] SSOIO, supra note 16.

[98] See, Paul Krugman & Robin Wells, Economics (4th ed. 2015) at 389 (“So the natural monopolist has increasing returns to scale over the entire range of output for which any firm would want to remain in the industry—the range of output at which the firm would at least break even in the long run. The source of this condition is large fixed costs: when large fixed costs are required to operate, a given quantity of output is produced at lower average total cost by one large firm than by two or more smaller firms.”)

[99] Id. (“The most visible natural monopolies in the modern economy are local utilities—water, gas, and sometimes electricity. As we’ll see, natural monopolies pose a special challenge to public policy.”)

[100] Richard H. K. Vietor, Contrived Competition (1994) at 167 (“[I]n the early part of the twentieth century, American Telephone and Telegraph (AT&T) set itself the goal of providing universal telephone services through an end-to-end national monopoly. … By [the 1960s], however, the distortions of regulatory cross-subsidy had diverged too far from the economics of technological change.”). Thomas W. Hazlett, Cable TV Franchises as Barriers to Video Competition, 2 Va. J.L. & Tech. 1 (2007) (“Traditionally, municipal cable TV franchises were advanced as consumer protection to counter “natural monopoly” video providers. …  Now, marketplace changes render even this weak traditional case moot. … [V]ideo rivalry has proven viable, with inter-modal competition from satellite TV and local exchange carriers (LECs) offering “triple play” services.”)

[101] Id.

[102] Share of United States Households Using Specific Technologies, Our World in Data (n.d.), https://ourworldindata.org/grapher/technology-adoption-by-households-in-the-united-states.

[103] Edward Carlson, Cutting the Cord: NTIA Data Show Shift to Streaming Video as Consumers Drop Pay-TV, NTIA (2019), https://www.ntia.gov/blog/2019/cutting-cord-ntia-data-show-shift-streaming-video-consumers-drop-pay-tv.

[104] Karl Bode, A New Low: Just 46% Of U.S. Households Subscribe to Traditional Cable TV, TechDirt (Sep. 18, 2023), https://www.techdirt.com/2023/09/18/a-new-low-just-46-of-u-s-households-subscribe-to-traditional-cable-tv. See also, Shira Ovide, Cable TV Is the New Landline, New York Times (Jan. 6, 2022), https://www.nytimes.com/2022/01/06/technology/cable-tv.html.

[105] SSOIO, supra, note 16.

[106] FCC, Wireless Telecommunications Bureau Report: Policy Review of Mobile Broadband Operators’ Sponsored Data Offerings for Zero-Rated Content and Services (Jan. 2017), available at https://transition.fcc.gov/Daily_Releases/Daily_Business/2017/db0111/DOC-342987A1.pdf.

[107] SSOIO, supra, note 16.

[108] Hyun Ji Lee & Brian Whitacre, Estimating Willingness-to-Pay for Broadband Attributes among Low-Income Consumers: Results from Two FCC Lifeline Pilot Projects, 41 Telecomm. Pol’y. 769 (Oct. 2017).

[109] Geoffrey A. Manne, Ben Sperry, Berin Szóka, & Tom Struble, ICLE & TechFreedom Policy Comments (Jul. 14, 2014), available at https://laweconcenter.org/images/articles/icle-tf_nn_policy_comments.pdf.

[110] Mozilla Corp. v. Fed. Commc’ns Comm’n, 940 F.3d 1 (D.C. Cir. 2019) (citations omitted).

[111] Id.

[112] In 2015, when the FCC voted to enact the 2015 Open Internet Order, Chair Tom Wheeler promised to forebear from applying such rate regulation, stating flatly that “we are not trying to regulate rates.” FCC Reauthorization: Oversight of the Commission, Hearing Before the Subcommittee on Communications and Technology, Committee on Energy and Commerce, House of Representatives, 114 Cong. 27 (Mar. 19, 2015) (Statement of Tom Wheeler). Standing as a nominee to the FCC, Gigi Sohn was asked during a 2021 confirmation hearing before the U.S. Senate Commerce Committee if she would support the agency’s regulation of broadband rates. She responded: “No. That was an easy one.” David Shepardson, FCC Nominee Does Not Support U.S. Internet Rate Regulation, Reuters (Dec. 1, 2021), https://www.reuters.com/world/us/fcc-nominee-does-not-support-us-internet-rate-regulation-2021-12-01. In September 2023, in a speech announcing the FCC’s proposal to regulate broadband internet under Title II of the Communications Act, Chair Jessica Rosenworcel was emphatic: “They say this is a stalking horse for rate regulation. Nope. No how, no way.” FCC Chair Rosenworcel on Reinstating Net Neutrality Rules, C-Span (Sep. 26, 2023), https://www.c-span.org/video/?530731-1/fcc-chair-rosenworcel-reinstating-net-neutrality-rules.

[113] Vietor, supra note 89.

[114] Id. See also, Illinois Economic and Fiscal Commission, Telecommunications Deregulation Issues and Impacts: A Special Report (Apr. 2001), available at https://www.ilga.gov/commission/cgfa/archives/telecom_dereg.PDF and Kevin J. Martin, Balancing Deregulation and Consumer Protection, 17 Commlaw Conspectus (2008), available at https://transition.fcc.gov/commissioners/previous/martin/MartinSpeech011609.pdf.

[115] Fruits & Manne, supra note 5, at 1.

[116] Id.

[117] Id. at 7.

[118] Jonathan E. Nuechterlein & Howard Shelanski, Building on What Works: An Analysis of U.S. Broadband Policy, 73 Fed. Comm. L.J. 219 (2021)

[119] Fruits & Manne, supra note 5, at 13.

[120] Digital Discrimination Order, supra note 15 [emphasis added].

[121] Brief of the International Center for Law & Economics and the Information Technology & Innovation Foundation as Amici Curiae in Support of Petitioners and Setting Aside the Commission’s Order, Minnesota Telecom Alliance v. FCC, No. 24-1179 (8^th Cir. Apr. 29, 2024) available at https://laweconcenter.org/wp-content/uploads/2024/04/2024-04-29-ICLE-ITIF-Amicus-Brief.pdf.

[122] IIJA 60102 (h)(4)(B).

[123] U.S. Dep’t of Com., supra note 66, at 66. States have begun to follow this lead by prescribing obligations to local providers for quality and price on deployments that have speeds and capabilities far above what BEAD and the FCC consider as the baseline for a “served” household. See, e.g., ConnectLA, BEAD Initial Proposal, vol. 2 (Aug. 2023), available at https://connect.la.gov/media/3gylvrgc/bead-vol-2-final.pdf (prescribing a complex system for preferencing providers that deploy “affordable” fiber and other high-speed service to middle-class homes).

[124] RUS Vol. 87, No. 149, Notice of Availability of the Draft Programmatic Environmental Assessment for the Partnerships for Climate-Smart Commodities Funding Opportunity, Docket No. NRCS–2022–0009 (U.S.D.A., Aug. 4, 2022), https://www.federalregister.gov/documents/2022/08/04/2022-16694/rural-econnectivity-program and RD, Preparing for ReConnect Round 4, (USDA) available at https://www.rd.usda.gov/sites/default/files/Preparing-for-ReConnect-Round-4.pdf.

[125] New York State Telecommunications Association, Inc. v. James, No. 21-1075 (2^nd Cir. Apr. 26, 2024), available at https://www.courthousenews.com/wp-content/uploads/2024/04/ny-broadband-law-opinion-second-circuit.pdf. See also, Randolph J. May & Seth L. Cooper, Second Circuit Hears Preemption Challenge to New York’s Broadband Rate Regulation Law, FedSoc Blog (Feb. 7, 2023), https://fedsoc.org/commentary/fedsoc-blog/second-circuit-hears-preemption-challenge-to-new-york-s-broadband-rate-regulation-law.

[126] Fruits & Manne, supra note 5, at 16.

[127] Id. at 1.

[128] FCC, Fourth Report and Order, Declaratory Ruling, and Third Further Notice of Proposed Rulemaking Accelerating Wireline Broadband Deployment by Removing Barriers to Infrastructure Investment, WC Docket No. 17-84 (Dec. 15, 2023), available at https://docs.fcc.gov/public/attachments/FCC-23-109A1.pdf [hereinafter “Poles Order”].

[129] Id.

[130] Id. at ¶ 7.

[131] Id.

[132] Id.

[133] Kristian Stout & Eric Fruits, Reply Comments of the International Center for Law & Economics, In the Matter of Accelerating Wireline Broadband Deployment by Removing Barriers to Infrastructure Investment, WC Docket No. 17-84 at 4 (submitted Aug. 26, 2022), available at https://laweconcenter.org/wp-content/uploads/2022/08/Pole-Attachments-Reply-Comments-2022-08-27-v2.pdf.

[134] Id.

[135] See Poles Order at ¶ 42.

[136] Id.

[137] Id. at 8.

[138] Id. at 9.

[139] Id.

[140] See Poles Order at ¶ 42.

[141] Id.

[142] Id. at 10.

[143] Ben Sperry, Geoffrey A. Manne, & Kristian Stout, The Role of Antitrust and Pole-Attachment Oversight in TVA Broadband Deployment (Int’l Ctr. for L. & Econ. Issue Brief 2023-09-04, 2023), available at https://laweconcenter.org/wp-content/uploads/2023/08/TVA-Pole-Attachments-Issue-Brief.pdf.

[144] Id. at 2.

[145] Id. at 3.

[146] Id.

[147] Id. at 4.

[148] Id.

[149] Id.

[150] Id. at 6-9.

[151] Id. at 10.

[152] Id.

[153] Id. at 11.

[154] Sen. Michael S. Lee, Letter to DOJ Re: Tennessee Valley Authority (TVA) – Supporting Broadband Deployment (June 22, 2023), in Ben Sperry, Geoffrey A. Manne, & Kristian Stout, The Role of Antitrust and Pole-Attachment Oversight in TVA Broadband Deployment (Int’l. Ctr. for L. & Econ. Issue Brief, Sep. 4, 2023) available at https://laweconcenter.org/wp-content/uploads/2023/08/TVA-Pole-Attachments-Issue-Brief.pdf.

[155] Sperry, Manne, & Stout, supra note 124, at 16.

[156] See, e.g., BEAD NOFO, supra note 71.

[157] Manne, Stout, & Sperry, supra note 1.

[158] Id.

[159] Id.

[160] Christopher S. Yoo, Jesse Lambert & Timothy P. Pfenninger, Municipal Fiber in the United States: A Financial Assessment, 46 Telecomm. Pol. 102292 (Jun. 2022).

[161] George S. Ford, Electricity Rates and the Funding of Municipal Broadband Networks: An Empirical Analysis, 102 Energy Econ. 105475 (2021).