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 Indeed, because these instruments were conceived without AI specifically in view, they risk becoming outdated within a very short period of time.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

Sean Norick Long (Georgetown U Law Center) has posted “The Mirror Test for AI agents: A path to regulate autonomous algorithmic collusion” on SSRN. Here is the abstract:

A US federal judge recently reasoned that a pricing algorithm learns “no different” from an attorney. This comparison is flawed in its immediate context, but it poses a greater danger: entrenching a mental model that blinds antitrust enforcement to the emergent threat of autonomous algorithmic collusion, where AI agents coordinate without human instruction. To prove collusion, courts cannot look directly into the human mind for intent, so they rely on an indirect proxy: evidence of observable communication between competitors. This paper argues the proxy is obsolete for AI agents, because their initial design and behavioral patterns are directly observable-offering a new basis to rule out independent action. In its place, I propose a two-part Mirror Test: an ex ante Design Test examines initial conditions for collusive bias, while an ex post Pattern Test detects coordinated pricing patterns inconsistent with independent action. This test can be implemented through agency guidance rather than new legislation, protecting the competitive process while giving companies predictable standards for compliance.

Giovanna Massarotto (U Pennsylvania) has posted “Algorithmic Remedies for Google’s Data Monopoly” on SSRN. Here is the abstract:

Algorithms and data are the building blocks of the digital economy. From Google’s search engine to Meta’s Instagram and OpenAI’s ChatGPT, all “Big Tech” rely on algorithms to collect and process vast amounts of data that power their services and AI models. While algorithms themselves can be efficient and impartial tools, Google’s strategic use of them, combined with exclusionary practices, has landed the company in federal court for monopolizing critical digital markets. On September 2, 2025, a judge required Google to grant rivals access to its data to address the company’s monopolization of critical digital markets that rely on data. Another judge is expected to impose remedies on Google in a separate antitrust proceeding, which could encompass data-sharing measures, including data facilities. This remedy would de facto regulate data-driven markets and influence the future of the emerging AI industry.

However, such data-sharing obligations in antitrust law create a classic resource allocation problem: who gets access, and how can courts ensure that access is fair and non-discriminatory? This article demonstrates that this legal challenge mirrors a problem computer science solved decades ago: ensuring multiple parties can use a shared resource without conflict. Thereafter, drawing on those algorithmic solutions, it proposes a framework with systems that operate like a digital ‘take-a-number’ machine or a formal voting process to manage data distribution efficiently and fairly.

This article makes three important contributions to the existing scholarship in this field. First, it explains how data-sharing remedies can be designed and implemented, whether to address specific anticompetitive conduct or as part of broader regulatory frameworks. Second, it develops a comprehensive framework with three algorithmic approaches for resource allocation, translating computer science solutions into legal mechanisms. Third, this framework is applied to Google’s ongoing monopolization cases, guiding data-sharing remedies and promoting competition in AI and other data-driven markets.