Defne Halil (Maastricht U Law) and Konrad Kollnig (Maastricht U) have posted “Big is Not Bad, but Big AI Might Be: EU Merger Law and the Future of AI Competition” on SSRN. Here is the abstract:

The growing dominance of a few firms in AI increases competition concerns in the EU, as US-based tech firms, specifically Microsoft, Google, Amazon, Nvidia, Meta and Apple (“Big AI”) control critical layers of the AI stack ranging from hardware to applications. Looking at 71 notable AI startup acquisitions by Big AI from 1989 to 2024, we find that, in the past, they largely escaped the EU Merger Regulation scrutiny; most transactions fell below turnover thresholds leaving potential anti-competitive effects unexamined. The CJEU’s 2024 Illumina/Grail judgment further restricts the European Commission’s ability to review non-notifiable mergers, reinforcing thestatus quo of consolidation by Big AI, and relying instead on member state law—which we analyse across all 27 EU member states. A Targeted Approach under Article 14 of the EU’s Digital Markets Act is proposed to create a special regime for Big AI, ensuring systematic review of their acquisitions to safeguard competition in Europe and expanding beyond the current proposals in literature for value-based thresholds or Article 22 EUMR call-in referrals by member states.

Joel B. Eisen (U Richmond Law) and Heather Payne (Ohio State U (OSU) Michael E. Moritz College Law) have posted “Data Centers’ Data Problem” (72 Vill. L. Rev. (forthcoming 2027)) on SSRN. Here is the abstract:

The rapid expansion of data centers to support artificial intelligence and cloud computing is reshaping the electric grid while exposing a fundamental weakness in the legal and regulatory framework that governs it. This Article identifies a previously overlooked problem at the heart of data center development: the absence of a comprehensive public information framework capable of generating and disseminating reliable data at the critical decision points that determine how data centers are planned, built, and powered. Information is fragmented across utilities, developers, regional transmission organizations, regulators, and local governments, while key project assumptions frequently remain confidential. These informational failures impede effective planning, prevent regulators and the public from determining whether data centers bear the costs they impose on the electric grid, and undermine meaningful public participation in decisions with profound consequences for the grid’s future.

The Article argues that existing voluntary disclosures and other proposed means of addressing data gaps cannot remedy these deficiencies. It therefore proposes the creation of a national public registry for data center development administered by the U.S. Energy Information Administration. The registry would provide a continuously updated, centralized repository of standardized project information, furnishing the public with the information necessary to evaluate proposed projects before critical decisions become effectively irreversible. The Article concludes that this form of information governance is an indispensable prerequisite to ensuring that the rapid growth of data centers advances reliability, affordability, and sound energy policy.

Kamil Szostak (Maastricht U) et al. have posted “Mind the Competitiveness Gap: Measuring the AI Act’s Extraterritorial Reach” (Computer Law & Security Review, volume 62, 2026[10.1016/j.clsr.2026.106357]) on SSRN. Here is the abstract:

The 2024 EU Artificial Intelligence Act (AI Act) asserts broad extraterritorial scope: it applies to any AI system whose output is used in the EU, no matter where the company behind the AI is located. The AI Act reflects a wider trend of the EU extending its legislation beyond its borders. But does this mean companies actually comply with the AI Act? And do compliance levels differ between companies based inside and outside of the EU? Empirical evidence on the effectiveness of extraterritorial legislation has so far been limited, focusing mostly on privacy and consumer protection law. This paper provides such evidence by assessing demonstrated compliance with the AI Act’s prohibitions on certain AI practices under Article 5(1), for which high financial penalties started to apply in August 2025. To this end, this study analysed terms of service and acceptable use policies from 53 AI companies. Results indicate that foreign companies without EU subsidiaries rarely comply, with little improvements following the introduction of penalties. Only around 14% of such companies refer to the AI Act’s prohibitions in their documents, whereas significantly more companies with an EU establishment do so. This may result from limited (extraterritorial) enforcement and uncertainty among non-EU companies about the AI Act’s scope. The findings suggest that EU companies may face a competitive disadvantage compared with foreign firms. This concern is highly relevant to current debates on the competitiveness of EU firms and the potential need for legislative simplification.

William J. Magnuson (Texas A&M U Law) has posted “Artificially Intelligent Markets” (Harvard Business Law Review, Vol. 16, Forthcoming) on SSRN. Here is the abstract:

A remarkable transformation is taking place in our financial markets.  The rise of machine learning algorithms and other artificial intelligence models has rapidly overtaken older methods of financial decisionmaking, and the consequences of the revolution are beginning to be felt across the capital markets ecosystem, from stock exchanges to derivatives markets to currency trading.  These new technologies offer great promise, including more accurate prices, faster transactions and more efficient trading.  But they also create risks.  From flash crashes to insider trading algorithms to adversarial attacks, artificial intelligence presents a range of unique vulnerabilities that could lead to significant and wide-ranging harm to our financial system.  Legal frameworks devised to structure and constrain financial institutions, in turn, are ill-equipped to deal with these harms because they were designed based on outdated assumptions about the structure of markets, as well as the nature of its primary actors. This Article offers the first comprehensive account of the economic, political and legal consequences of the rise of artificially intelligent markets.  It demonstrates how the major driver of this shift has been the hedge fund industry, an opaque and lightly regulated sector of the financial ecosystem that has long been an early-adopter of financial technology.  It concludes by proposing a series of escalating regulatory reforms that might better fit financial regulation to our new artificially intelligent markets.

Giuseppe Colangelo (Università degli Studi della Basilicata) has posted “Is AI the End of the DMA as we Know It?” on SSRN. Here is the abstract:

The disruptive potential of AI-enabled applications for competitive dynamics and the core organisational forms of digital intermediation inevitably also has significant implications for the recent regulatory initiatives adopted to govern digital markets. Indeed, because these instruments were conceived without AI specifically in view, they risk becoming outdated within a very short period of time. Notably, while they have been shaped by a Big Tech-centred conception of digital markets, the possible emergence of new gatekeepers in the age of AI marks a turning point that calls into question the very rationale and foundations of these regimes in their present form. As a result, only a few years after its enactment, the role of the DMA, together with its rationale and claimed future-proof character, is already under scrutiny, as the deployment of AI applications raises the question whether policymakers should reopen the legislative framework in order to amend the Regulation. Against this background and in the context of the first review of the DMA, the paper argues that the rise of AI applications calls for a reconsideration of the DMA’s overall architecture and for the development of a distinct competition policy framework, rather than for a merely incremental fine-tuning exercise.

Lars Hornuf (Dresden U Technology) et al. have posted “Regulatory Competition in the Age of AI” on SSRN. Here is the abstract:

Artificial Intelligence (AI) has not only triggered a technology race and a fight for market leadership, but has also become a site of regulatory competition. As governments race to define rules for AI, they embed competing visions of alignment, innovation, and control into digital technology. These regulatory choices are shaping global markets, norms, and institutions. To analyze how regulatory competition unfolds in the AI domain, this article presents a four-part framework that includes value priorities, areas of AI regulation, governance approaches, and strategic openness and control. Building on this framework, the article discusses current developments in AI regulation and their implications for regulatory competition, both in practice and as a research field. Identifying four shifts from traditional models of regulatory competition, the article highlights how Information Systems research can connect global regulatory policy with the organizational realities of AI design, adoption, and governance within organizations.

Giuseppe Colangelo (Università degli Studi della Basilicata) and Ariel Ezrachi (U Oxford Law) have posted “AI Ecosystems, Power Shifts, and EU Competition Law Enforcement” on SSRN. Here is the abstract:

The emergence of AI assistants and agents is transforming market dynamics, favouring a shift in competition away from individual products and towards integrated services, and from competition between firms to competition between ecosystems. This transition is reshaping power relations along the vertical chain; On the one hand, vertically integrated providers may deploy leveraging strategies to favour their own AI tools, and may further use these tools to extract rents from downstream intermediaries. On the other hand, the success of agentic AI and the proliferation of strategic collaborations may facilitate entry, offer new paths to market, and possibly challenge the hegemony of the established ecosystem. This paper explores these developments and the relationship between intra-ecosystem competition and inter-AI (ecosystem) competition. In doing so, it reflects on the emergence of frenemy relationship between market players, and the adequacy of current competition law analysis in evaluating complex inter-and intra AI competition.

Giuseppe Colangelo (Università degli Studi della Basilicata) has posted “Is AI the End of the DMA as we Know It?” on SSRN. Here is the abstract:

The disruptive potential of AI-enabled applications for competitive dynamics and the core organisational forms of digital intermediation inevitably also has significant implications for the recent regulatory initiatives adopted to govern digital markets. Indeed, because these instruments were conceived without AI specifically in view, they risk becoming outdated within a very short period of time. Notably, while they have been shaped by a Big Tech-centred conception of digital markets, the possible emergence of new gatekeepers in the age of AI marks a turning point that calls into question the very rationale and foundations of these regimes in their present form. As a result, only a few years after its enactment, the role of the DMA, together with its rationale and claimed future-proof character, is already under scrutiny, as the deployment of AI applications raises the question whether policymakers should reopen the legislative framework in order to amend the Regulation. Against this background and in the context of the first review of the DMA, the paper argues that the rise of AI applications calls for a reconsideration of the DMA’s overall architecture and for the development of a distinct competition policy framework, rather than for a merely incremental fine-tuning exercise.

Daniel Goicouria (affiliation not provided to SSRN) has posted “Extraterritoriality in AI: Harmonizing the Digital Market Act and US Antitrust Law” (Vanderbilt Journal of Transnational Law, Volume 58, No. 4 pp. 1055-1110) on SSRN. Here is the abstract:

International AI markets currently operate under divergent and often conflicting competition laws. This splintered approach fosters uncertainty, invites regulatory failure, and risks entrenching dominant firms at the expense of emerging innovators. This Note proposes harmonized enforcement mechanisms to safeguard fair competition and minimize extraterritorial effects on global AI platforms. Recent academic discourse has discussed the domestic effects of ex ante regulations in AI markets, but international harmonization and extraterritoriality remain largely undiscussed. 

This Note proposes treaty-based coordination and uniform enforcement guidelines to ensure consistent international oversight. It synthesizes comparative insights from differing competition frameworks to identify best practices and encourage cross-border cooperation. In effect, this analysis closes jurisdictional gaps and mitigates risks of fragmented enforcement in rapidly expanding AI markets. The Note offers an actionable roadmap to unify competition laws globally, protect consumers, and foster continuing innovation.

Xiong Zhang (Beijing Jiaotong U) et al. have posted “Balancing Data-Driven Competition and Privacy Protection: A Duopoly Analysis of AI-Powered Digital Assistants” on SSRN. Here is the abstract:

Artificial Intelligence (AI) is rapidly empowering smart products, enhancing both work efficiency and quality of life. However, these improvements rely heavily on the continuous collection and processing of user data, raising significant concerns about privacy. In response, many countries have enacted regulations to protect personal data and consumer privacy. This study examines how privacy protection influences market competition in AI-powered digital assistant markets. We develop a stylized analytical model of a duopoly where firms differ in their ability to collect and monetize consumer data. The results reveal that stronger AI capabilities amplify the profitability of data-intensive firms, while data-light firms can strategically strengthen privacy protection to remain competitive, thereby generating mutual profit gains and enhancing consumer surplus as well as overall social welfare. These findings contribute to the theoretical understanding of data-driven competition and digital privacy management, while offering actionable insights for firms seeking to balance innovation, consumer trust, and regulatory compliance in smart product markets.