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ICLE White Paper Executive Summary Capital One’s proposed acquisition of Discover Financial Services has the potential to transform competition and consumer welfare in the retail banking market. Through . . .
Capital One’s proposed acquisition of Discover Financial Services has the potential to transform competition and consumer welfare in the retail banking market. Through synergies and cost savings, the new entity would compete more vigorously with other banks and payment networks. Not only will this better serve the public in general, by bringing together the firms’ traditional expertise in the development of innovative banking and credit card markets aimed at middle-income consumers, it would also likely expand financial inclusion among underserved communities. And while some critics have expressed concerns that the merger could harm competition, those concerns are speculative and ungrounded in well-established principles of antitrust analysis. Major points to consider include that:
On Feb. 18, 2024, Capital One Financial Corp. announced that it had entered into an agreement to acquire Discover Financial Services in an all-stock transaction valued at $35.3 billion.[1] Before the transaction can be finalized, however, it must be approved by both the U.S. Office of Comptroller of the Currency (OCC) and the Federal Reserve. The two agencies held a July 19, 2024, public meeting on the proposed merger, and have extended public comments on the deal until July 24, 2024.[2]
The proposed acquisition has engendered substantial public and political scrutiny from critics who claim it would have anticompetitive effects. For example, a number of Democratic members of Congress,[3] as well as members of the House Financial Services Committee, specifically,[4] and one Republican senator,[5] have written to the regulators responsible for reviewing the merger to urge that it be blocked on that basis.
These criticisms of the proposed merger, however, are confused. To be sure, the combined bank would be larger than either of the two companies standing alone. Yet its size still would pale in comparison to firms such as JPMorgan Chase, Citibank, and other retail bank companies.
More importantly, reflexive “big is bad” arguments overlook the pro-competitive benefits of the merger to consumers and the banking industry. By combining Capital One’s innovative style and marketing dynamism with Discover’s existing network infrastructure and widespread acceptance, the new company could provide a viable new competitor to both existing large banks and to the payment-card-network space currently dominated by Visa and MasterCard. The result should be enhanced competition across the board, but particularly in the market for payment card networks, about which many of these same critics of this merger have complained lacks adequate competition due to the supposed Visa and MasterCard “duopoly.” Rather than trying to artificially impose a counterproductive scheme of competition on that market through heavy-handed government regulation, such as the Credit Card Competition Act,[6] the merger would do exactly what sponsors of that act claim to desire: foster more robust competition in the payment-card-network space.
This white paper uses the tools of law & economics to evaluate the likely effects of the merger, with a particular focus on two of the key criteria the agencies are required to evaluate: (1) the convenience and needs of the communities to be served by the combined organization and (2) competition in the relevant markets.[7]
The primary communities served by both Capital One and Discover comprise lower-risk low-income and middle-income consumers who have been underserved by other large financial firms. The merged company will presumably continue to seek to attract and maintain such consumers, while also potentially expanding into other market segments. Indeed, the new company may better serve such communities. This could be achieved through synergies that would enable it to invest in innovation and thereby offer better products at a lower cost. In addition, the combined firm plans to issue debit cards on its own proprietary network, enabling it to offer enhanced products to consumers (because it will not be subject to the price controls and routing requirements imposed on debit card issuers subject to the Durbin amendment and related regulations). For example, the company should be better able to market no-fee, no-minimum-balance bank accounts to underserved low- and middle-income consumers. Furthermore, by combining some credit card operations, the new entity should benefit from scale economies and the ability to cross-market products.
On the competition side, the relevant markets are, broadly, banking (deposits and loans) and payments (card issuance and acceptance, and network facilitation). With respect to the former, the combined company would be the sixth-largest bank in the United States by assets, and roughly one-quarter the size of JPMorgan Chase (the nation’s largest bank).[8]If the relevant market is large banks with national reach, the merger will plausibly result in an increase in competition, as the new entity will have greater scope and scale, enabling it to compete more effectively with other large national banks.
Regarding credit card issuance: recent figures suggest that Capital One and Discover combined would be the largest holder of credit card debt in the nation, accounting for nearly 22% of outstanding credit card loans by dollar amount.[9]Even so, and contrary to claims made by some critics of the proposed merger,[10] there is no reason to believe this would harm competition. The increase in market share for credit card debt would not trigger thresholds inviting close scrutiny under federal bank-merger guidelines, or the 2023 Federal Trade Commission (FTC) and U.S. Justice Department (DOJ) merger guidelines.[11] Moreover, as the company notes in its filing with the regulators:
Vertically integrating with Discover’s payments networks will add scale to these credit and debit networks—which respective market shares are in long-term decline— making the networks less costly to operate on a marginal basis and more attractive to consumers and merchants. The combination will also allow Capital One to lower its transaction-related costs and to reinvest those dollars in improved banking products and services, including investments into the payments networks to reduce fraud, improve dispute resolution processes, and lessen information sharing friction to the benefit of consumers and merchants. These network investments will allow Capital One to further scale the networks, improve the actual and perceived acceptance of the networks, and create a credible alternative to the Visa, Mastercard, and Amex payments networks, which dominate the industry today.[12]
Critics of the merger, by contrast, have failed to articulate any tangible harms to competition or consumers from the merger beyond reflexive “big is bad” rhetoric.
From a law & economics perspective, the merger’s potential to create a stronger fourth network aligns with the theory that increased competition can lead to greater market efficiency and consumer welfare. A more competitive network landscape could pressure all players to improve their offerings, potentially resulting in lower fees, better security features, and more innovative payment solutions. This outcome would be consistent with the goals of antitrust law, which seeks to promote competition, rather than protect individual competitors.
In sum, the evidence strongly suggests that this merger would meet the needs and convenience of the communities served by the combined organization and would be pro-competitive in all relevant markets.
The prospective acquisition of Discover by Capital One would bring together Capital One’s savvy marketing and innovation advantages with Discover’s legacy advantage as a payment-card processing network, thereby creating a new viable competitor to both existing banking giants (such as JPMorgan Chase and Citibank) as well as existing payment networks (Visa, MasterCard, and Amex). At the same time, however, the combined entity will remain a fraction of the size of these incumbent banks and networks. The end result should benefit competition and consumers substantially, especially in the network issuing space.
Discover Financial Services originated in 1985 as a subsidiary of Sears, Roebuck, and Co., arising as a general-purpose spinoff of the legendary Sears credit card program. In 1985, Sears was the largest consumer-lending operation in America, with 60 million cardholders and customer receivables of more than $12 billion.[13] The ubiquity of the Sears credit card owed in large part to the department store’s towering presence in the nation’s retail landscape, and particularly the company’s long-established Sears catalog. Sears had 796 retail stores and more than 3,000 branch offices of its subsidiaries: Dean Witter Financial Services, Allstate Insurance, Coldwell Banker real estate, and Sears Saving Bank.[14] The launch of the general-purpose Discover credit card was part of a larger push at the time by Sears into the consumer retail financial services space, including bank accounts, ATMs, and low-cost retirement brokerage accounts offered by Sears’ Dean Witter Reynolds Inc. brokerage subsidiary.[15] The card was issued through Greenwood Trust Co. bank, which was owned by Sears. Sears was able to capitalize on its relationship with those millions of established Sears credit card customers to launch a new general-purpose card to rival Visa and Mastercard.
The Discover Card’s launch illustrates the logic of two-sided payment card markets and the need to attract both consumers and merchants to the platform.[16] Because of Sears’s existing relationships with 60 million cardholders, Discover likewise found it relatively easy to attract cardholders. The company, however, faced greater difficulty in persuading merchants to take up the card, in part because merchants were reluctant to accept a card affiliated with a major retailing rival (a difficulty further compounded by the fact that the original card face featured an image of the Sears Tower). To induce merchant acceptance, Discover offered a lower merchant discount rate than Visa and MasterCard-branded cards.[17] Today, Discover’s average merchant discount rate remains below that of Visa, MasterCard, and American Express.[18]
To encourage consumers to use the card, Discover’s initial strategy was to differentiate itself by offering a card with no annual fee and a cashback-rewards program for purchases (including quarterly “bonus categories”), both of which were novel and innovative concepts at the time. This helped to attract consumers and carve out a niche in the competitive credit card market.[19] Because of Sears’ massive network of retail stores and affiliates, Discover didn’t need to establish a separate system of bank branches to service customers, a distinctive characteristic that remains the case today (although, today, it is all online). The card was introduced with a 1986 Super Bowl commercial.[20]
One of Discover’s key innovations was its approach to the payment network. Like American Express and Diners Club (at the time), but unlike most other issuers, Discover chose to operate a vertically integrated, “three-party” model, acting as card issuer, acquirer, and payment network.[21] This structure enabled Discover to offer merchants lower fees relative to other acquirers, which helped in building acceptance so that it could compete more effectively with “four-party” cards issued on the Visa and Mastercard networks.[22]
In 1993, Sears spun off Dean Witter into a new company and Discover became part of Dean Witter. In 1997, Dean Witter merged with Morgan Stanley and later rebranded itself as Discover Financial Services Inc. In 2007, Discover Financial Services became an independent company. In 2004, Diners Club (then owned by Citigroup) signed an agreement with Mastercard to provide acceptance in the United States and Canada, making it a four-party card issuer in these markets—and leaving Discover and American Express as the only three-party issuers in the United States.[23] In 2008, Discover purchased Diners Club International from Citigroup, giving it an international payment network, albeit one that today has only a tiny share of transactions. (The U.S. and Canadian franchises of Diners Club were not included in the deal, and were sold by Citigroup the following year to BMO International.)[24]
Consistent with its original plan to evolve into a full-service retail banking establishment, in the late 1990s and early 2000s, Discover expanded its product line beyond credit cards. It ventured into personal loans, student loans, and savings accounts, leveraging its brand recognition and customer relationships to compete in broader financial services. In 2005, Discover acquired the Pulse electronic funds transfer (EFT) network, which provides single-message (PIN) ATM and debit payments for around 4,500 smaller banks.[25]
Despite its early distinction as a market innovator, over time, Discover has grown somewhat stagnant. In terms of credit card market share by purchase volume, Discover has been stuck at approximately 4% to 5% of the U.S. market for almost 20 years and has a negligible global presence.[26] While Discover has a slightly larger number of credit cards in circulation than American Express, Amex’s market share by purchase volume is roughly five times that of Discover.[27] As a network competitor, therefore, Discover has neither the large cardholder base of Visa and MasterCard nor Amex’s highly coveted high-spend customer base. As one news report summarized Capital One’s arguments in support of the deal, “Discover’s network has ceded market share over the past decade and Capital One, as a much bigger bank, can provide the additional scale and volume Discover needs to be competitive.”[28]
Capital One Financial Corp. emerged in the early 1990s as a spin-off from Signet Bank, under the leadership of Richard Fairbank and Nigel Morris.[29] Their vision was to revolutionize the credit card industry by applying data analytics and information technology to consumer finance.[30] This approach, often referred to as “information-based strategy,” allowed Capital One to tailor its offerings to specific customer segments, a novel concept at the time.[31]
The company’s key innovation was its use of data-mining techniques to identify and target potential customers with personalized credit card offers.[32] This strategy allowed Capital One to extend credit to a broader range of consumers, including those who might have been overlooked or rejected by traditional banks.[33] By using sophisticated risk-assessment models, they could offer competitive rates to customers across various credit profiles, effectively disrupting the one-size-fits-all approach prevalent in the industry.[34] Writing in the Financial Times, former Federal Deposit Insurance Corp. (FDIC) Chair Sheila Bair noted:
I suspect Capital One’s subprime market share is relatively substantial because other banks simply have less (or no) interest in serving subprime customers. Subprime lending involves higher capital requirements, greater regulatory scrutiny and more resources to underwrite and manage those accounts. Any concentrations in the subprime market are the result of banks’ conscious investment decisions, not barriers to entry.[35]
Capital One’s market entry coincided with the rise of direct marketing in the financial sector.[36] The company leveraged this trend by aggressively promoting its products through direct-mail offers, a strategy that helped it rapidly acquire customers and market share.[37] This direct-to-consumer approach bypassed traditional banking channels and allowed Capital One to build a national presence without the need for an extensive branch network.[38]
As the company grew, it continued to innovate in product design and customer acquisition. Capital One introduced features like balance transfers with low introductory rates, cashback rewards, and no annual fee cards, which were not common at the time.[39] The company was also one of the first banks to offer a secured credit card.[40] These offerings appealed to consumers and forced competitors to adapt, ultimately benefiting the broader market through increased competition and more favorable terms for cardholders.
Capital One’s disruptive influence extends beyond credit cards. The company has expanded into retail banking, auto financing, and savings products, often bringing its data-driven approach to these sectors.[41] For instance, its online savings accounts offer higher interest rates than many traditional banks, challenging the status quo and prompting other institutions to improve their offerings to remain competitive.[42]
Capitol One’s acquisition of Discover will have manifest benefits to consumers, competition, and innovation in the payment-card market. By combining the advantages of Discover’s existing (but somewhat stagnant) presence in the payment-card-network space and its reach into middle-class consumers with Capital One’s innovative culture in payments and data security and its marketing savvy, the deal offers the potential to create a viable competitor to existing mega-banks and the dominant card-processing networks. As noted, the proposed deal has elicited some criticism from politicians, but none of those criticisms have amounted to much more than a reflexive “big is bad” mentality and vague, unspecified concerns about the potential for harm to competition and consumers. By contrast, the potential benefits of the deal are manifest and concrete.
These benefits are explained in greater detail below, but in broad terms comprise the following two components:
This section analyses the various components of the proposed acquisition. From an industrial-organization perspective, this has both “horizontal” and “vertical” components. Both companies accept deposits, issue loans, offer credit and debit cards, and offer other financial services; the combination of these business lines would therefore be considered a horizontal merger. While such mergers have the potential to be anticompetitive, they can also be pro-competitive, as demonstrated by many horizontal “four-to-three” mergers in the wireless industry discussed in the first sub-section below.
While these horizontal aspects are tasty hors d’oeuvres, the main course in Capital One’s acquisition of Discover is its purchase of Discover’s payment networks, which would facilitate vertical integration with many of Capital One’s existing products (including all of its debit cards).[44] The beneficial effects of this vertical aspect of the merger are addressed in the separate subsections on credit cards, debit cards, and banking. This is followed by a more detailed discussion of the effects of the merger on the identification and deterrence of fraud—and the benefits this would bring to consumers and merchants. The final subsection addresses some concerns raised by critics of the merger.
Some lessons may be learned from mergers in other industries where two mid-size firms merge to create a competitor that is similar in size to the market leaders. An example is so-called “four-to-three” mergers in wireless telecommunications. A survey of empirical research on these mergers, undertaken by a team that included two of the authors of this white paper, provides insights that may help to evaluate the merger between Capital One and Discover.[45]
First, the paper notes the importance of considering both price and nonprice effects when assessing mergers. In the case of Capital One and Discover, while price effects (such as interest rates or fees) are crucial, nonprice factors like investments in technology, product innovation, and service-quality improvements should be given substantial weight.[46] The merger will enable the combined entity to increase investments in digital-banking capabilities, artificial intelligence, and data analytics—all areas where both companies have shown strengths. This increased investment capacity could lead to more innovative financial products and improved customer experiences, ultimately benefiting consumers.
Second, the review of empirical research highlights that mergers can lead to more symmetrical market structures (that is, with firms of more equal size), which may result in stronger incentives for individual firms to invest and compete.[47] In the context of the credit card and banking industries, a merged Capital One-Discover entity could become a more formidable competitor to larger players like JPMorgan Chase, Bank of America, and Citigroup. This increased symmetry in market power could drive all players to innovate and compete more aggressively, potentially leading to better offerings for consumers across the industry.
Lastly, the empirical research suggests that the optimal number of competitors in a market depends on various factors, including geographic and demographic considerations.[48] In the U.S. financial-services market, which is both large and geographically diverse, the merger could potentially create a stronger nationwide competitor. By combining Capital One’s extensive customer base and marketing prowess with Discover’s payment network and reputation for customer service, the merged entity could more effectively compete across different regions and customer segments. This could be particularly beneficial in making Discover a more effective competitor, as it would gain access to Capital One’s larger customer base and potentially expand the reach and utilization of its payment network.
Three networks currently account for approximately 96% of credit card purchase volume in the United States: Visa (52%), Mastercard (25%), and American Express (20%).[49] Discover has most of the remaining 4%, a proportion that has declined from 6% in 2011.[50] Capital One’s acquisition of Discover could potentially create a more robust fourth network, aligning with some legislators’ stated desire for an increase in the number of competitors in this market.[51]Unlike current proposed legislative interventions, however, it also would more plausibly lead to a genuine increase in competition, as Capital One would have strong incentives to identify ways to reinvigorate the network. Unlike some legislative proposals ostensibly intended to promote competition, but which likely would lead to increased fraud, the merger would likely improve the detection and prevention of fraud.
Capital One’s extensive cardholder base and innovative approach to payments could provide the scale and technological edge that Discover’s network has been lacking. Capital One’s data analytics capabilities and marketing prowess could be leveraged to expand the network’s reach, potentially making it more attractive to both merchants and consumers. This, in turn, could lead to a more competitive market in which four major players compete, potentially driving down transaction fees and spurring further innovation in payment technologies.
Moreover, whereas mandatory routing regulations—such as those contained in the Durbin amendment and the proposed Credit Card Competition Act—lead to data fragmentation that would undermine fraud detection, the combination of Capital One’s innovative data analytics with Discover’s networks would likely improve fraud detection. For example, Capital One recently partnered with Stripe and Ayden to build an open-source application programming interface (API) that enables any entity in the payment stack to share real-time transaction data, enabling Capital One to better detect fraud.[52]
The transaction also potentially offers an opportunity for Capital One to shift the debit cards of its current and future bank customers over to Discover’s payment networks. Capital One founder and CEO Richard Fairbank has stated that the company intends to transfer all its debit cards to the newly acquired networks.[53]
By moving customers onto Discover’s three-party payment card network, Capital One’s customers will be able to avoid the distortions imposed by the Durbin Amendment’s price controls. In turn, this will enable Capital One to offer rewards and maintain free checking accounts for lower-income consumers. These price controls only apply to debit cards issued on four-party payment networks, so Capital One will be able to avoid them by issuing debit cards on its own newly acquired three-party network.
Under a provision of the Dodd-Frank Wall Street Reform Act of 2010 known commonly as the “Durbin amendment,” the U.S. Federal Reserve imposed caps on debit card interchange fees for banks with more than $10 billion in assets (“covered banks”), as well as routing requirements for all debit card issuers.[54] As a result, debit card interchange fees fell by about 50% for large banks almost immediately. Interchange fees on debit cards issued by smaller banks and credit unions initially fell by a smaller amount, and interchange fees on single-message (PIN) debit cards have now fallen to similar levels as PIN debit cards issued by larger banks.[55]
Estimates suggest that the Durbin amendment initially reduced annual interchange fee revenue for covered banks by between $4.1 and $8 billion.54F[56] In response, covered banks eliminated or reduced card-rewards programs on debit cards.62F[57] They also typically raised monthly account maintenance fees and increased the minimum balance needed for a fee-free account.[58] These changes have resulted in an increase in unbanked and underbanked households in the United States, particularly among lower-income consumers.[59]
As a covered bank, Capital One might have been expected to have been among those that reduced the availability of free checking. But Capital One’s business model is focused on attracting the very clients who would be put off by having to pay a fee for their checking account. So, as noted above, it has kept fee-free checking accounts with zero minimum balances.[60] It has been able to do this, in part, because of its lower costs as a primarily online bank. As with most covered banks, however, Capital One discontinued its debit card rewards program following the implementation of the Durbin amendment.[61]
Capital One’s debit cards currently operate on four-party networks. By contrast, the Discover card network operates as a three-party closed-loop system, in which the issuer and the acquirer are the same and there is, therefore, no interchange fee. As such, debit cards issued directly by Discover are not subject to the Durbin amendment, which is why it is able to continue to offer cashback rewards of 1% on purchases made on those cards.[62] Shifting all of Capital One’s debit cards over to the Discover network (including, in particular, the PULSE single-message PIN-debit network) would allow Capital One to more effectively balance the two sides of the market, using fees charged to merchants to cross-subsidize holders of Capital One current accounts. This might include:
Another potentially significant benefit of the merger is its effect on fraud, which is a challenge for every party in the payments ecosystem: issuers, acquirers, merchants, and cardholders. Global losses from payment-card fraud were estimated to be $34 billion in 2022, of which 36% was attributed to the United States.[63] Discover, in particular, has had various data security breaches and other compliance issues.[64] By combining Capital One’s innovative approach to data management with Discover’s payment networks, the combined entity could help to significantly reduce such fraud.
Issuers and networks have developed increasingly sophisticated systems to reduce fraud. For example, when a card with a chip is dipped or tapped, it transfers a unique one-time token, generated by the chip, that is encrypted and can only be read by the issuer.[65] The implementation of chip-based tokenized transactions has dramatically reduced fraud compared to the simpler magnetic stripe cards. Mobile payments also use tokens in a similar way.
But tokens by themselves can’t solve the problem of stolen cards and hacked online accounts. Issuers and networks have thus implemented other measures, most notably systems of multifactor authentication. An example is 3D-Secure (3DS), which involves using the information sent in the first (authorization) message to check against a cardholder’s profile. If the proposed payment fits the profile, it is permitted; if not, then the cardholder is asked to complete two-factor authentication on the transaction.[66]
3DS would not be possible without cardholder profiles, which are an example of the application of AI to payments. Since the 1990s, Visa and Mastercard have used machine learning to develop cardholders profiles, which then enable them to identify potential instances of fraud.
Payment networks, issuers, and other companies in the card-processing stack have also begun to use biometrics, typically combined with machine learning, as part of the authentication and authorization process.[67] Capital One has been a leading innovator in such methods, going back at least to its pattern-tracing system for accessing mobile accounts.[68] From 2018 to 2020, Capital One applied for 23 biometric-related patents, including one for voice recognition.[69]
One problem that can reduce the effectiveness of AI-based fraud detection (including 3DS) is data fragmentation. When a consumer has cards from multiple issuers on multiple networks, or where the same card is run by different merchants over different networks (which is currently possible with debit cards, due to the Durbin amendment’s routing requirements), it may be difficult for networks and issuers to build a consistent picture of an individual’s payment patterns. This makes it more difficult to identify attempted payments that do not fit a pattern.
The merger might improve fraud detection in several ways. First, when Capital One’s debit cards are moved to Discover’s networks, they will no longer be subject to the Durbin amendment’s routing requirements, and thus all transactions on those cards will be monitored directly by Capital One’s systems. Second, Capital One will be able to implement its highly innovative fraud-detection and prevention systems across all Discover networks. Third, as noted above, Capital One recently partnered with Stripe and Ayden to build an open-source API that enables any entity in the payment stack to share real-time transaction data,[70] which should help Capital One to address fraud more effectively and in a manner comparable to existing larger networks (Visa, Mastercard, American Express) despite of its smaller size.
These improvements in fraud detection and prevention would have both direct and indirect benefits for merchants and consumers. The direct benefits arise from the simple fact of experiencing fewer fraudulent transactions. For consumers, this means not having to identify fraudulent transactions or go through the process of initiating chargebacks. For merchants, it means fewer chargebacks and related disputes with issuers. The indirect benefit is lower costs all around, which can be passed on in the form of lower fees and/or additional account or card benefits. And these increased benefits should be expected to drive an increase in the use of Capital One cards, thereby generating a virtuous cycle of network effects, whereby fraud can be reduced further, while use and acceptance of the cards are further increased.
Critics of the merger have identified ways the proposed merger could harm banking consumers by increasing the cost of credit, increasing fees, and reducing the interest paid to depositors.[71] There is, however, little evidence the merger poses potential antitrust harm to depositors. Of note:
Capital One and Discover have both been industry leaders in increasing financial access for underserved consumers. For example, most bank accounts in the United States today impose monthly maintenance fees, especially for lower-income consumers who cannot meet the stiffer average balance requirements required to be eligible for free checking. Both Capital One’s 360 Checking Account and Discover’s Cashback Debit accounts offer free checking accounts with no minimum balance requirements. Capital One was also one of the first large banks to eliminate overdraft fees.
With such small market shares, it would be a stretch to conclude that a merger between Capital One and Discover would have any noticeable effect on competition for deposits or depositors in the U.S. banking sector. Moreover, as primarily online banks, Capital One and Discover compete nationally against other online banks, as well as “traditional” banks with substantial online presence. Thus, even if the merged firm were to try to charge above-competitive fees or offer below-competitive interest rates to depositors, such efforts would be likely to fail in the face of competition from hundreds of other competing banks and credit unions.
Based on the above analysis, the prospective acquisition of Discover by Capital One augurs well for consumer welfare. As noted, however, some critics have raised concerns regarding certain aspects of the merger. Here, we briefly review these concerns.
The merged firm would be the largest holder of credit card debt, accounting for nearly 22% of outstanding credit card loans by dollar amount.[75] That, in and of itself, is not necessarily a concern; as Capital One points out in its filing, it would not exceed any threshold in a conventional antitrust analysis.[76]
Much of the concern has been focused on potential harms to specific groups of credit card customers, especially the “near-prime” or “subprime” segments of borrowers with FICO scores below 660.[77] A key question for antitrust analysis is whether these constitute a distinct relevant market. One critic of the merger argues that these consumers’ higher risk, as well as Capital One and Discover’s direct-mail marketing to these consumers, suggest they constitute a distinct “submarket.”[78] In contrast, the Bank Policy Institute reports:
No evidence has been put forth by critics of the proposed merger to define the boundaries of the subprime segment and establish that consumers in this segment are sufficiently isolated for it to be considered a distinct submarket for antitrust purposes.[79]
One important consideration in evaluating this concern is that a consumer’s credit status is rarely static over time. Due to changes in income and other circumstances, a subprime borrower today may be a prime borrower next year, and vice versa. Using data from 2014 and 2015, Fair Isaac found that a “notable percentage” of FICO scores migrated up or down more than 20 points in a six-month period, with 14% of accounts decreasing by more than 20 points, and 19% increasing by more than 20 points.[80] Thus, even if a subprime or near-prime market segment can be defined, migration into and out of these segments makes it exceedingly difficult to establish a reliable market definition for antitrust analysis.
Among consumers with at least one credit card, as of 2023, 8.6% were near-prime and 4.4% subprime.[81] The Bank Policy Institute estimates the merged firm would account for a little less than 30% of subprime credit card balances in the United States.[82] Thus, the authors conclude, “If the subprime consumer segment of the credit card market merits separate scrutiny, our analysis indicates that the segment is highly competitive and would remain so even after the proposed merger.”[83]
It is also worth noting that Capital One gained its market share in “subprime” over time through its data-driven strategy. This has enabled the company to identify lower-risk individuals in (otherwise) higher-risk groups, thereby serving otherwise underserved consumers, while limiting default risk.[84] It also provides opportunities for these consumers to migrate toward a lower-risk category by gradually increasing the size of their credit lines as they demonstrate creditworthiness.[85]
Another important aspect of this strategy was the pioneering of two now-widespread credit card offerings: secured credit cards and balance transfers. In 1991, Capital One became the first credit card issuer to introduce a balance-transfer offer.[86] A balance transfer provides a temporarily low interest rate to induce people to move balances from a competing credit card to the card providing the balance-transfer offer. In short, Capital One’s substantial market share in the subprime credit card market is best explained by its innovative culture in meeting the needs of the heterogeneous consumers in this complex market.
Although perhaps not the first to offer a secured credit card, Capital One was arguably the first issuer to implement a major program of such cards.[87] A secured credit card differs from a traditional card in that all or part of the borrower’s credit limit is secured against a cash deposit provided by the consumer at the time of account opening. Secured cards are most useful to consumers seeking to build a credit history or attempting to repair a damaged credit history.[88] Research published by the Philadelphia Fed concludes that a combination of credit-score migration and increased competition has been associated with increasing “graduation rates” over time from secured cards to unsecured cards.[89]
Finally, for credit card issuers, a merger might result in a more effective competitor to the major incumbents, thereby potentially increasing competition, even while reducing the number of competitors. And a smaller number of larger firms facing more intense competition may be better for consumers than a larger number of smaller, less effective firms.
With respect to payment networks, it’s important to note that the proposed merger between Capital One and Discover does not reduce the number of competitors; it merely shifts ownership of Discover’s network to the merged firm, which would presumably adopt Capital One’s more sophisticated technologies, including those related to fraud detection, as discussed above. In this way, it could be argued that Capital One’s acquisition of Discover’s payments network might result in more effective competition to Visa, Mastercard, and American Express, with broad benefits to merchants and consumers.
A major objective of the merger is, as noted above, to switch Capital One’s debit cards over to Discover’s payment network and thereby circumvent the Durbin amendment’s price controls and routing requirements. This vertical integration could allow for more flexibility in fee structures and potentially higher overall revenue per transaction. This would enable Capital One to offer cashback rewards to debit cards and potentially also cross-subsidize accounts in other ways, such as by offering sign-up bonuses.
Consumers would almost certainly benefit from the increased availability of debit card rewards and sign-up bonuses. Cashback rewards may be especially beneficial to lower-income cardholders. Indeed, it is likely that the reintroduction of such rewards will encourage some lower-income consumers, and especially those with poor credit scores and without access to a rewards credit card, to switch to Capital One. Moreover, the prospect of such rewards would likely entice many consumers who are currently unbanked or “underbanked” (i.e., have access to only minimal banking services) to open accounts with Capital One and thereby participate more fully in the banking system.
Of course, if Capital One does charge higher debit card transaction fees than four-party issuers, some merchants may choose to no longer accept its debit cards (and, if Capital One’s terms require merchants to accept all cards operating on its branded three-party network, also its credit cards). And if fewer merchants accept its cards, that will make the cards less attractive to consumers. Capital One will therefore have to balance such potential effects on merchants against the benefits to cardholders, just as Sears did in 1986 when it introduced Discover with lower than prevailing merchant fees in order to incentivize merchant acceptance.
From the perspective of merchants as a whole, the prospect of a larger proportion of consumers having bank accounts, and an even greater proportion paying by card rather than cash, should be attractive, given that card payments can result in increased sales (because consumers are able to spend more than they have in their wallet).[90] Meanwhile, having some consumers use debit cards rather than credit cards should also be attractive. As such, not only does it seem unlikely that many merchants would cease accepting Capital One cards, but it is also unlikely that Capital One switching its debit cards to Discover’s networks would cause net harm to social welfare.
From an antitrust perspective, it appears almost certain that, while some merchants may face higher costs of acceptance, this will be more than balanced by the increase in card-based transactions. Hence, there would be lower net costs for many merchants and an increase in consumer benefits arising from the rewards and other benefits the debit cards would now provide.
A similar issue lay at the heart of the U.S. Supreme Court’s decision in Ohio v. Amex:
Respondent… Amex… operate[s] what economists call a “two-sided platform,” providing services to two different groups (cardholders and merchants) who depend on the platform to intermediate between them. Because the interaction between the two groups is a transaction, credit-card networks are a special type of two-sided platform known as a “transaction” platform. The key feature of transaction platforms is that they cannot make a sale to one side of the platform without simultaneously making a sale to the other. Unlike traditional markets, two-sided platforms exhibit “indirect network effects,” which exist where the value of the platform to one group depends on how many members of another group participate. Two-sided platforms must take these effects into account before making a change in price on either side, or they risk creating a feedback loop of declining demand. Thus, striking the optimal balance of the prices charged on each side of the platform is essential for two-sided platforms to maximize the value of their services and to compete with their rivals.
Visa and MasterCard—two of the major players in the credit-card market—have significant structural advantages over Amex. Amex competes with them by using a different business model, which focuses on cardholder spending rather than cardholder lending. To encourage cardholder spending, Amex provides better rewards than the other credit-card companies. Amex must continually invest in its cardholder rewards program to maintain its cardholders’ loyalty. But to fund those investments, it must charge merchants higher fees than its rivals. Although this business model has stimulated competitive innovations in the credit-card market, it sometimes causes friction with merchants.[91]
Thus, the fact that some merchants may see their costs rise (slightly) as a result of the merger must be weighed against the significant benefits that accrue to consumers and other merchants.
Returning to the criteria by which the Federal Reserve and OCC are required to evaluate this merger, and in service of which this paper has been produced, (1) the convenience and needs of the communities to be served by the combined organization and (2) competition in the relevant markets, the forgoing analysis leads to the following conclusions:
Through these effects, Capital One may attract additional customers, especially those with low incomes or lower credit scores, thereby more effectively meeting the convenience and needs of the communities it serves. At the same time, and for largely the same reasons, it would arguably increase competition in most relevant markets and is unlikely substantially to diminish competition in any markets.
[1] Press Release, Capital One to Acquire Discover, Capital One (Feb. 19, 2024), https://investor.capitalone.com/news-releases/news-release-details/capital-one-acquire-discover.
[2] Press Release, Board of Governors of the Federal Reserve System & Office of the Comptroller of the Currency, Agencies Announce Public Meeting on Proposed Acquisition by Capital One of Discover; Public Comment Period Extended, Federal Reserve Board (May 14, 2024), https://www.federalreserve.gov/newsevents/pressreleases/other20240514a.htm; Press Release, Agencies Announce Public Meeting on Proposed Acquisition by Capital One of Discover; Public Comment Period Extended, Office of the Comptroller of the Currency (May 14, 2024), https://www.occ.gov/news-issuances/news-releases/2024/nr-ia-2024-50.html.
[3] See, Letter to the Honorable Michael Barr, Vice Chair of Supervision for the Board of Governors of the Federal Reserve System and Acting Comptroller Michael Hsu, Office of the Comptroller of the Currency from the Undersigned Members of the Congress of the United States, Office of Sen. Elizabeth Warren (Feb. 25, 2024), available at https://www.warren.senate.gov/imo/media/doc/2024.02.25%20Capital%20One%20Letter1.pdf.
[4] See, Letter to the Honorable Jerome Powell, et. al from the Undersigned Members of the U.S. House of Representatives Committee on Financial Services, House Financial Services Committee Democrats (Feb. 28, 2024), available at https://democrats-financialservices.house.gov/uploadedfiles/02.28_-_ltr_on_ibmr.pdf.
[5] See, Letter to Jonathan Kanter, Assistant Attorney General of the U.S. Department of Justice, Antitrust Division from Senator Josh Hawley, Office of Sen. Josh Hawley (Feb. 21, 2024), available at https://www.hawley.senate.gov/wp-content/uploads/files/2024-02/Hawley-Letter-to-Kanter-re-Capital-One-Discover-Merger.pdf.
[6] See, e.g., Julian Morris & Todd Zywicki, Regulating Routing in Payment Networks (ICLE White Paper 2022-08-17), available at https://laweconcenter.org/wp-content/uploads/2022/08/Regulating-Routing-in-Payment-Networks-final.pdf; Julian Morris, The Credit Card Competition Act’s Potential Effects on Airline Co-Branded Cards, Airlines, and Consumers (ICLE White Paper 2023-11-17), available at https://laweconcenter.org/wp-content/uploads/2023/11/CCCA-Airline-Rewards-Study-v4.pdf.
[7] The list of relevant criteria for consideration includes: the convenience and needs of the communities to be served by the combined organization; each insured depository institution’s performance under the Community Reinvestment Act; competition in the relevant markets; the effects of the proposal on the stability of the U.S. banking or financial system; the financial and managerial resources and future prospects of the companies and banks involved in the proposal; and the effectiveness of the companies and banks in combatting money laundering activities. See Joint Press Release, supra note 4; 12 U.S.C. § 1828(c).
[8] See, Federal Reserve Board, Insured U.S.-Chartered Commercial Banks That Have Consolidated Assets of $300 Million or More, Ranked by Consolidated Assets as of March 31, 2024, Federal Reserve Board (Mar. 31, 2024), https://www.federalreserve.gov/releases/lbr/current.
[9] See, Federal Reserve Bank of New York, Quarterly Report on Household Debt and Credit, 2023:Q4, Federal Reserve Board (Feb. 2024), https://www.newyorkfed.org/medialibrary/interactives/householdcredit/data/pdf/HHDC_2023Q4; 20 Bank Holding Companies With the Largest Credit Card Loan Portfolios, American Banker (Mar. 28, 2024), https://www.americanbanker.com/list/20-bank-holding-companies-with-the-largest-credit-card-loan-portfolios-at-the-end-of-q4.
[10] See Shahid Naeem, Capital One-Discover: A Competition Policy and Regulatory Deep Dive, American Economic Liberties Project (Mar. 2024), available at https://www.economicliberties.us/wp-content/uploads/2024/03/2024-03-20-Capital-One-Discover-Brief-post-design-FINAL.pdf.
[11] See Diana Moss, The Capital One Financial-Discover Financial Services Merger: A Test for the Biden Merger Agenda?, Progressive Policy Institute (Jun. 20, 2024), at 1, available at https://www.progressivepolicy.org/wp-content/uploads/2024/06/PPI-Capitol-One-Discover-Commentary.pdf.
[12] Application to the Board of Governors of the Federal Reserve System for Prior Approval for Capital One Financial Corporation to Acquire Discover Financial Services Pursuant to Section 3 of the Bank Holding Company Act and Section 225.15 of Regulation Y, Federal Reserve Board(Mar. 20, 2024), at 40, available at https://www.federalreserve.gov/foia/files/capital-one-application-20240320.pdf [hereinafter “Capital One Application”].
[13] See, New Sears Credit Card by Year-End, Chicago Tribune (Apr. 25, 1985), https://www.chicagotribune.com/1985/04/25/new-sears-credit-card-by-year-end.
[14] See Nancy Yoshihara, Sears Unveils Its New Credit Card: Multipurpose “Discover” to Get 1st Test Marketing in Fall, Los Angeles Times (Apr. 25, 1985), https://www.latimes.com/archives/la-xpm-1985-04-25-fi-12317-story.html.
[15] Id.
[16] See Todd J. Zywicki, The Economics of Payment Card Interchange Fees and the Limits of Regulation (ICLE Financial Regulatory Program White Paper Series, Jun. 2, 2010), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1624002.
[17] See Chicago Tribune, supra note 13.
[18] See Jack Caporal, Average Credit Card Processing Fees and Costs in 2024, the ascent, https://www.fool.com/the-ascent/research/average-credit-card-processing-fees-costs-america, (last updated Jun. 5, 2024), (noting that “Discover credit card processing fees have the lowest range, excluding outliers.”).
[19] See Eric Schmuckler, Playing Your Cards Right, Forbes (Dec. 28, 1987).
[20] See, Discover—Dawn of Discover, AdAge (Jan. 26, 1986), https://adage.com/videos/discover-dawn-of-discover/1241.
[21] See Frances Denmark, Discover CEO David Nelms Reinvents His Credit Card Firm, Institutional Investor (Dec. 28, 2011), https://www.institutionalinvestor.com/article/2bszspjc02cwjwn5f2pds/portfolio/discover-ceo-david-nelms-reinvents-his-credit-card-firm.
[22] Michael Weinstein, Bankers: DiscoverCard Has Not Hurt Business, American Banker (Mar. 7, 1988).
[23] Diners Club and MasterCard Finalize Alliance, The Payers (Sep. 27, 2004), https://thepaypers.com/payments-general/diners-club-and-mastercard-finalize-alliance–724076.
[24] See, e.g., The Story Behind The Card, Diners Club Int’l, https://www.dinersclubus.com/home/about/dinersclub/story (last accessed Jul. 17, 2024); Press Release, BMO Financial Group Announces Agreement to Acquire the Diners Club North American Franchise From Citigroup, BMO Financial Group (Nov. 24, 2009), https://newsroom.bmo.com/2009-11-24-BMO-Financial-Group-Announces-Agreement-to-Acquire-the-Diners-Club-North-American-Franchise-From-Citigroup.
[25] See Denmark, supra note 21.
[26] See Adam McCann, Market Share by Credit Card Network, WalletHub (May 9, 2024), https://wallethub.com/edu/cc/market-share-by-credit-card-network/25531.
[27] See Fred Ashton, Capital One’s Acquisition of Discover Could Inject Competition Into Payments Market, American Action Forum Insight (Feb. 29, 2024), at fig. 2, https://www.americanactionforum.org/insight/capital-ones-acquisition-of-discover-could-inject-competition-into-payments-market.
[28] Michelle Price, Exclusive: CapOne Tells Regulators Discover Deal will Boost Competition and Stability, Reuters (Mar. 21, 2024), https://www.reuters.com/markets/deals/capone-tells-regulators-discover-deal-will-boost-competition-stability-sources-2024-03-21.
[29] Capital One Financial Corporation, Capturing the Essence of Capital One: 1996 Annual Report 2-3 (1996), available athttps://investor.capitalone.com/static-files/d823fcd3-e1f1-439a-a34f-5296ef58b93c.
[30] See id. at 3, 5-6.
[31] Id. at 3.
[32] See David Morrison & Adrian Slywotzky, Off the Grid, Industry Standard (Oct. 23, 2000).
[33] See Andrew Becker, The Secret History of the Credit Card, Frontline (Nov. 23, 2004), https://www.pbs.org/wgbh/pages/frontline/shows/credit/more/battle.html.
[34] See Morrison & Slywotzky, supra note 32.
[35] Sheila Bair, How the Capital One/Discover Deal Could Boost Competition, Financial Times (May 31, 2024), https://on.ft.com/4640E6h.
[36] See Morrison & Slywotzky, supra note 32.
[37] See Zack Martin, Capital One Makes Big Push to Become a National Brand, Card Marketing (Dec. 2000).
[38] See Jon Prior, Capital One Keeps Closing Branches, Even as Rivals Open Them, American Banker (Jul. 1, 2019), https://www.americanbanker.com/news/capital-one-keeps-closing-branches-even-as-rivals-open-them.
[39] See Lukasz Drozd, Why Credit Cards Played a Surprisingly Big Role in the Great Recession, 6(2) Econ. Insights 10 (Mar. 2021), n. 12, https://fraser.stlouisfed.org/title/6149/item/604454?start_page=ii.
[40] Naomi Snyder, Capital One’s Secret to Success, Bank Director (Aug. 15, 2022), https://www.bankdirector.com/article/capital-ones-secret-to-success (Capital One “invented the secure credit card”); Larry Santucci, The Secured Credit Card Market, Federal Reserve Bank of Philadelphia(Nov. 2016), available at https://www.philadelphiafed.org/-/media/frbp/assets/consumer-finance/discussion-papers/dp16-03.pdf (“While we were unable to identify the first bank to issue a secured card, the innovation is believed to have occurred sometime in the late 1970s.”).
[41] See Alex Woodie, The Modernization of Data Engineering at Capital One, Datanami (Apr. 4, 2022), https://www.datanami.com/2022/04/04/the-modernization-of-data-engineering-at-capital-one.
[42] See, e.g., Sabrina Karl, Best High-Yield Savings Accounts of July 2024—Up to 5.55%, Investopedia, https://www.investopedia.com/best-high-yield-savings-accounts-4770633 (last updated Jul. 17, 2024), (listing Capital One and Discover among the highest-available interest rates for new accounts).
[43] See Anish Kapoor, Capital One-Discover Acquisition: Unpicking [sic] the Consumer and Competitive Benefits, LinkedIn.com (Apr. 15, 2024), available at https://www.linkedin.com/pulse/capital-one-discover-acquisition-unpicking-consumer-benefits-kapoor-53yge.
[44] Capital One itself lists “Combin[ing] Capital One’s scale in credit cards and banking with Discover’s vertically integrated global payments network” and “Enhanc[ing] Capital One’s ability to compete with the national’s largest banks in credit cards and banking” as the top two reasons for its “strategic rationale.” Investor Presentation, Capitol One & Discover (Feb. 20, 2024), at 4 https://investor.capitalone.com/static-files/cfa11729-0aec-43dc-b531-200e250c8413.
[45] See 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, DAF/COMP/GF(2019)13, Dec. 6, 2019), available at https://one.oecd.org/document/DAF/COMP/GF(2019)13/en/pdf.
[46] Id. at 17.
[47] Id. at 8.
[48] Id. at 3.
[49] Caitlin Mullin, Capital One Pledges to Give Discover’s Network a Boost, Payments Dive (Mar. 26, 2024), https://www.paymentsdive.com/news/capital-one-discover-acquisition-federal-reserve-occ-debit-credit-card-network-visa-mastercard/711385.
[50] Id.; see also, Leading Credit Card Issuers in the United States from 2007 to 2023, Based on Value of Transactions for Goods and Services,Statista (Feb. 2024), https://www.statista.com/statistics/1080768/leading-credit-card-issuers-usa-by-purchase-volume.
[51] See, e.g., Press Release, Sen. Dick Durbin, Durbin, Marshall Announce Hawley, Reed as New Cosponsors, Growing Support for Credit Card Competition Act, Office of Sen. Dick Durbin (Feb. 14 2024), https://www.durbin.senate.gov/newsroom/press-releases/durbin-marshall-announce-hawley-reed-as-new-cosponsors-growing-support-for-credit-card-comptition-act (arguing that “[f]or too long, the Visa-Mastercard duopoly alongside the Wall Street megabanks have price-gouged hardworking Americans with little-to-no oversight” and “[f]or years, Visa and Mastercard have taken advantage of their duopoly in the credit market to impose extreme fees on small merchants and retailers.”).
[52] Mary Ann Azevedo, When Foes Become Friends: Capital One Partners with Fintech Giants Stripe, Adyen to Prevent Fraud, TechCrunch (Jun. 5, 2024), https://techcrunch.com/2024/06/05/when-foes-become-friends-capital-one-partners-with-fintech-giants-stripe-adyen-to-prevent-fraud.
[53] Transcript of Conference Call Held by Capital One Financial Corporation and Discover Financial Services on February 20, 2024, Filed by Capital One Financial Corporation (Commission File No.: 001-13300), available at https://investor.capitalone.com/static-files/d7b64c07-9663-4b0a-b382-48792a04c148:
“So, on the debit side with the Discover Global Network, with the Pulse PIN debit network, along with their Discover signature debit network, it’s really well-positioned and in a strong position to just basically take our debit volume at this place and at this point, and we feel comfortable moving our entire business over there.” See also, supra note 49 (“Currently, Capital One’s debit cards run on Mastercard’s network, and all of that volume will move to Discover’s network, Capital One executives said Tuesday. Some portion of Capital One’s credit cards will move to Discover’s payment rails as well, Fairbank said. Capital One issues cards on both the Visa and Mastercard networks, with about 42% of the bank’s credit cards running on Visa and 58% on Mastercard, as of 2022, according to Bank of America Securities analysts.”.
[54] H.R.4173 – Dodd-Frank Wall Street Reform and Consumer Protection Act, s.1075(a)(3); Debit Card Interchange Fees and Routing; Final Rule, 76 Fed. Reg. 43,393-43,475, (Jul. 20, 2011).
[55] Todd J. Zywicki, Geoffrey A. Manne, & Julian Morris, Unreasonable and Disproportionate: How the Durbin Amendment Harms Poorer Americans and Small Businesses, Int’l Cntr For L. & Econ. (Apr. 25, 2017), available at https://laweconcenter.org/wp-content/uploads/2017/08/icle-durbin_update_2017_final-1.pdf.
[56] See, e.g., Benjamin S. Kay, Mark D. Manuszak, & Cindy M. Vojtech, Competition and Complementarities in Retail Banking: Evidence From Debit Card Interchange Regulation, 34 J. Fin. Intermediation 91, 92 (2018) (estimating losses of interchange income between $4.1-$6.5 billion); Vladimir Mukharlyamov & Natasha Sarin, Price Regulation in Two-Sided Markets: Empirical Evidence From Debit Cards (Dec. 2019), https://ssrn.com/abstract=3328579 (estimating $5.5 billion annual revenue loss to banks from interchange-fee reductions); Bradley G. Hubbard, The Durbin Amendment, Two-Sided Markets, and Wealth Transfers: An Examination of Unintended Consequences Three Years Later, SSRN (May 20, 2013), at 20, https://ssrn.com/abstract=2285105 (estimating annual revenue loss of $6.6 billion to $8 billion from the Durbin amendment).
[57] See Darryl E. Getter, Regulation of Debit Interchange Fees, Congressional Research Service (May 16, 2017), at 8. See also Electronic Payments Coalition, Out of Balance: How the Durbin Amendment Has Failed to Meet Its Promises 7 (Dec. 2018), available at https://www.electronicpaymentscoalition.org/wp-content/uploads/2018/12/EPC.DurbinStudiesPaper.pdf (Eliminating rewards, such as cash-back on purchases, is functionally equivalent to a price increase).
[58] Mark D. Manuszak & Krzysztof Wozniak, The Impact of Price Controls in Two-Sided Markets: Evidence From US Debit Card Interchange Fee Regulation (Bd. of Governors of the Fed. Res. Sys. Fin. & Econ. Discussion Series, Working Paper No. 2017-074, 2017); Mukharlyamov & Sarin, supra note 56.
[59] Mukharlyamov & Sarin, supra note 56.
[60] See Aly J. Yale, Everything You Need to Know About Banking with Capital One, Wall Street Journal (May 28, 2024), https://www.wsj.com/buyside/personal-finance/banking/capital-one-bank-review.
[61] See Blake Ellis, Wells Fargo, Chase, SunTrust cancel debit rewards program, CNN Money (Mar. 28, 2011), https://money.cnn.com/2011/03/25/pf/debit_rewards/index.htm (noting the move by major banks to cancel debit-card rewards in anticipation of the Durbin amendment going into effect); Richard Kerr, Where Have All the Rewards Debit Cards Gone?, The Points Guy (Jun. 24, 2015), https://thepointsguy.com/credit-cards/rewards-debit-cards-gone (describing the “slow death of debit cards that earn points and miles.”).
[62] See, e.g., Earn Cash Back Rewards with No Fees, Discover (2024), https://www.discover.com/online-banking/checking-account.
[63] Kalle Radage, Credit Card Fraud in 2023, Clearly Payments (Aug. 13, 2023), https://www.clearlypayments.com/blog/credit-card-fraud-in-2023.
[64] See, e.g., Discover Financial Jumps 7% After Agreeing with FDIC to Improve Consumer Compliance, Reuters (Oct. 2, 2023), https://www.reuters.com/business/finance/discover-financial-jumps-7-after-agreeing-with-fdic-improve-consumer-compliance-2023-10-02; David Lukic, The Discover Breach, Credit Card Companies Nightmare, ID Strong (Dec. 11, 2023), https://www.idstrong.com/sentinel/discover-breach-credit-card-companies-nightmare.
[65] EMV chips use a form of public-key infrastructure. The token is encrypted using the issuer’s public key and can only be decrypted using the issuer’s private key. After decrypting the token (technically, a cryptogram), the issuer can validate the transaction by checking its authenticity and integrity. If the token is validated successfully, the issuer authorizes the transaction. If the token cannot be validated, the transaction is declined.
[66] See Elint Chu, What Is New with EMV 3DS v.2.3?, EMVCo (Nov. 12, 2021), https://www.emvco.com/knowledge-hub/what-is-new-with-emv-3ds-v2-3.
[67] See, e.g., NuData: It’s Time for Businesses to Replace the Old ‘New Normal’ With a New One, PYMNTS (Jun. 30, 2021), https://www.pymnts.com/news/payments-innovation/2021/nudata-time-businesses-replace-old-new-normal; Chris Burt, Smartmetric CEO Claims Progress Towards American Biometric Payment Card Launch, Biometric Update (Jul. 18, 2022), https://www.biometricupdate.com/202207/smartmetric-ceo-claims-progress-towards-american-biometric-payment-card-launch.
[68] See Jim Bruene, Capital One Launches SureSwipe for Gesutre-Based Mobile Login, Finovate (Nov. 11, 2013), https://finovate.com/capital_ones_gesture-based_mobile_login_sureswipe.
[69] Capital One Patent Looks To Bring Voice Recognition Technology To Mobile Payments, CBInsights (Oct. 13, 2020), https://www.cbinsights.com/research/capital-one-patent-voice-recognition-tech-mobile-payments.
[70] See supra note 52 and accompanying text.
[71] See supra notes 3-5 and accompanying text.
[72] See Federal Reserve Board, supra note 8.
[73] Id.
[74] See Capital One Application, supra note 12, at 39.
[75] See Federal Reserve Board, supra note 8.
[76] See Capital One Application, supra note 12, at 39-40.
[77] CFPB provides the following definitions: superprime (800 or greater), prime plus (720 to 799), prime (660 to 719), near-prime (620 to 659), subprime (580 to 619), and deep subprime (579 or less). Consumer Financial Protection Bureau, The Consumer Credit Card Market 12 (Oct. 2023), available at https://files.consumerfinance.gov/f/documents/cfpb_consumer-credit-card-market-report_2023.pdf.
[78] Naeem, supra note 10, at 17.
[79] Haelim Anderson, Paul Calem, & Benjamin Gross, Is the Subprime Segment of the Credit Card Market Concentrated? Bank Policy Institute(May 31, 2024), https://bpi.com/is-the-subprime-segment-of-the-credit-card-market-concentrated.
[80] See Fair Isaac Corporation, FICO Research: Consumer Credit Score Migration (2018), https://www.fico.com/en/latest-thinking/white-paper/fico-research-consumer-credit-score-migration.
[81] CFPB, supra note 77, at 16—17.
[82] See Anderson, Calem, & Gross, supra note 79, at Panel C.
[83] Id. at Conclusion.
[84] Becker, supra note 33 (“By identifying lower-risk individuals in high-risk groups, Capital One was able to market to reliable consumers other companies wouldn’t touch, says [Chris] Meyer [CEO of Monitor Networks]. In just six years, Capital One became the sixth-largest credit card issuer in the country. “When others were attacking the market with blunt instruments, Capital One used a scalpel,” says Meyer.”).
[85] Snyder, supra note 40 (“Sanjay Sakhrani, an equity analyst and managing director at the investment bank Keefe, Bruyette & Woods, says the bank focuses its efforts on the most profitable risk-adjusted return segments. “I think they’ve done a very effective job [of] underwriting and managing risks inside of the subprime population,” he says. The bank starts by offering those customers low credit lines and graduates them over time as they demonstrate their credit worthiness.”).
[86] See Drozd, supra note 39.
[87] See Snyder, supra note 40.
[88] See, e.g., Ian McGroarty, CFI in Focus: Secured Credit Cards, Federal Reserve Bank of Philadelphia (Sep. 2019), at 1-2, available athttps://www.philadelphiafed.org/-/media/frbp/assets/consumer-finance/articles/secured-credit-cards.pdf.
[89] Id. at 6-7.
[90] Sumit Agarwal, Wenlan Qian, Yuan Ren, Hsin-Tien Tsai, and Bernard Yeung, Mobile Wallet and Entrepreneurial Growth, AEA Papers and Proceedings, 109:48–53 (2019); David Bounie and Youssouf Camara, Card-Sales Response to Merchant Contactless Payment Acceptance, Journal of Banking & Finance, Vol. 119, issue C. (2020).
[91] Ohio v. American Express Co., 138, S.Ct. 2274, 2276-77, 585 U.S. 529 (2018).
Regulatory Comments 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 . . .
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.
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.
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 20th-century antitrust to address the allegedly manifest harms of 21st-century technology. These include the notions that data advantages constitute barriers to entry and can be leveraged to project dominance into adjacent markets; that scale itself is a market failure to be addressed by enforcers; and that the use of consumer data is inherently harmful to those consumers.
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]
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.
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]
[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.
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:
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.
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]
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.
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.
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.
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]
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.
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.
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:
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.
[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.
TOTM 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 . . .
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.
Presentations & Interviews ICLE Chief Economist Brian Albrecht participated in a panel hosted by the Competitive Enterprise Institute on the Federal Trade Commission’s investigation of competition and consolidation . . .
ICLE Chief Economist Brian Albrecht participated in a panel hosted by the Competitive Enterprise Institute on the Federal Trade Commission’s investigation of competition and consolidation in the grocery industry. Video of the full event is embedded below.
Popular Media The movement that some call “neo-Brandeisianism,” after its putative inspiration in the works of the late U.S. Supreme Court Justice Louis Brandeis (others have less-charitably . . .
The movement that some call “neo-Brandeisianism,” after its putative inspiration in the works of the late U.S. Supreme Court Justice Louis Brandeis (others have less-charitably termed it “antitrust populism” or “hipster antitrust”), has indisputably taken the competition world by storm. Indeed, it has arguably led to one of the fastest policy swings in antitrust history.
Scholarship In the US wireless communications market, antitrust regulators blocked so-called four-to-three mergers—mergers of two of the four largest competitors—in 2011 and 2014. But authorities did . . .
In the US wireless communications market, antitrust regulators blocked so-called four-to-three mergers—mergers of two of the four largest competitors—in 2011 and 2014. But authorities did allow then-No. 3 carrier T?Mobile to acquire then-No. 4 Sprint in February 2020, after T?Mobile agreed to several conditions. The merger was, and remains, the subject of intense debate over its effects on consumers.
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ICLE Chief Economist Brian Albrecht’s debate with Evan Starr on the Federal Trade Commission’s ban of noncompete agreements was featured in an episode of the American Bar Association Antitrust Law Section’s Our Curious Amalgam podcast. Audio of the full episode is embedded below.
Regulatory Comments 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 . . .
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.
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 20th-century antitrust to address the allegedly manifest harms of 21st-century technology. These include the notions that data advantages constitute barriers to entry and can be leveraged to project dominance into adjacent markets; that scale itself is a market failure to be addressed by enforcers; and that the use of consumer data is inherently harmful to those consumers.
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]
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]
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.
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]
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]
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]
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.
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.
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]
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:
These lessons have important ramifications for competition-policy debates over the competitive implications of data in technologically evolving areas.
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.”).
TOTM What is the UK doing in the field of digital-market regulation, and what do you think it is achieving? There are probably four areas to . . .
There are probably four areas to consider. The first is that the UK’s jurisdiction on mergers increased with Brexit. The UK is not subject to the same turnover threshold as under European law, and this enables it to call in a wider range of deals. It has also been able to look at different theories of harm in digital markets. It has done that in probably more than 10 cases where it examined issues like potential competition, vertical exclusion, etc.
There are probably four areas to consider.
The first is that the UK’s jurisdiction on mergers increased with Brexit. The UK is not subject to the same turnover threshold as under European law, and this enables it to call in a wider range of deals. It has also been able to look at different theories of harm in digital markets. It has done that in probably more than 10 cases where it examined issues like potential competition, vertical exclusion, etc.
