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 Indeed, because these instruments were conceived without AI specifically in view, they risk becoming outdated within a very short period of time.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.

Pinar Yildirim (U Pennsylvania The Wharton) has posted “On Artificial Intelligence and Network Effects” on SSRN. Here is the abstract:

Network effects have long been identified as a significant driver of growth for digital platforms. Emergence of artificial intelligence (AI) technologies stands to interact with network effects in significant ways. While several scholars argued that network effects can accelerate the success of AI, it remains less clear how AI-enabled tools themselves might reshape the competitive advantage digital platforms gain from network effects. In this article, I examine the implications of AI tools for network effects. I argue that while some use cases of AI can amplify network effects, others may weaken them. In particular, when the AI tools reduce search and production costs and reduces shared experiences among consumers, AI may reduce the importance of network effects to a digital platform. The paper concludes with the note that new technologies such as AI can have important implications for competition policy and antitrust enforcement.

Srikanth Thudumu (Institute Applied Artificial Intelligence and Robotics (IAAIR)) has posted “How to Measure ROI for AI” on SSRN. Here is the abstract:

Return on Investment (ROI) is often used as the primary metric for evaluating Artificial Intelligence (AI) projects. However, conventional ROI calculations tend to focus narrowly on short-term, directly attributable savings while overlooking enabling capabilities, strategic options, and risk reduction. Historical technology shifts such as the automobile, electrification, and the internet reveal that value typically emerges after complementary investments and operational redesign. This working paper explains why conventional ROI lenses can be misleading, distills lessons from past transformations, and proposes a simple “Smart ROI” framework with a practical measurement playbook for organizations.

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.

Adam Feher (U Lausanne) et al. have posted “Is AI Trained on Public Money? Evidence from US Data Centers” on SSRN. Here is the abstract:

Rapid data center growth has raised concerns about rising energy demand and its effects. Leveraging a novel dataset of U.S. data center energy loads, utility prices, and establishment-level outcomes, we quantify local spillover effects on electricity prices, firm performance, and emissions. Using an IV continuous DiD exploiting exogenous variation in data center location attractiveness, we find no local spillovers over 2010–2024. A regional model calibrated to the empirical null suggests that shocks larger than those observed through 2024 could still result in noticeable increases in household utility bills if not offset by regulation or external supply.