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.

Tim W. Dornis (Leibniz U Hannover) and Nicola Lucchi (Universitat Pompeu Fabra Law) have posted “Generative AI and the Scope of EU Copyright Law: A Doctrinal Analysis in Light of C-250/25” (IIC (International Review of Intellectual Property and Competition Law) vol. 56 (2025), forthcoming November (issue 10)) on SSRN. Here is the abstract:

This article offers a doctrinal analysis of the copyright implications raised by Like Company v. Google Ireland (C-250/25), the first case to bring generative AI before the CJEU. It examines whether the training and output of systems like Gemini infringe exclusive rights under EU copyright law. We argue that AI model training may involve acts of reproduction under Article 2 of the InfoSoc Directive, while the dissemination of AI-generated outputsespecially through public interfaces-may trigger the right of communication to the public under Article 3. Particular concerns arise when protected content is recognisably reproduced or when AI outputs serve as functional substitutes for original works, thereby affecting the normal exploitation of those works. While not a formal infringement criterion, such functional substitution is relevant in assessing the application of exceptions and compliance with the three-step test. The paper also challenges the applicability of the text and data mining exception to generative uses, highlighting its incompatibility with the limitations imposed by the three-step test. Ultimately, the analysis supports a technologically neutral, rights-based interpretation that safeguards the economic viability of creative production in the algorithmic age.

Ai Deng (Berkeley Research Group) has posted “As AI Regulations and Price-Fixing Allegations Pick Up, New Research on Algorithmic Collusion Offers Insights for Executives and Attorneys” (BRG ThinkSet, Spring, 2025) on SSRN. Here is the abstract:

This is a two-part series on the topic of algorithmic collusion. In Part One, I delve into how algorithms influence pricing, the feasibility of algorithmic collusion, and the impact of algorithmic design on whether a pricing algorithm sets supracompetitive prices. In Part Two, I explore the closely related subject of third-party pricing algorithms, which have attracted significant attention. Throughout these articles, I draw lessons for executives and attorneys from the latest academic research.

Emmie Hine (Yale U Digital Ethics Center) et al. have posted “The Impact of Modern Big Tech Antitrust on Digital Sovereignty” on SSRN. Here is the abstract:

This article examines the history of antitrust cases against Big Tech companies in the United States. It highlights a shift in the attitudes of enforcers away from the economic-analysis-informed Chicago and post-Chicago schools of antitrust thought, which are informed by economic analysis, towards New Brandeisian thinking, which emphasizes structural concerns and broader consumer welfare. However, it has yet to catch on in courtrooms. By contrasting the US’s antitrust strategy with those of the European Union and China, we argue that antitrust enforcement may hinder economic and technological competitiveness in the short term, but may have long-term benefits. Regarding global digital sovereignty, the US increasing enforcement likely would not impact its global competitiveness, as it still presents a more favorable regulatory environment than the EU, and targeted economic measures prevent Chinese companies from being competitive in the US. New legislation may help address the complexities of modern digital markets so that the US can maintain its competitive edge in technology while enhancing consumer welfare.

Walid Chaiehloudj (U Côte d’Azur) has posted “Musk v. OpenAI: Antitrust and the Boundaries of Strategic Litigation in the AI Sector” (European Competition and Regulatory Law Review (CoRe), forthcoming) on SSRN. Here is the abstract:

This paper analyzes the recent decision in Musk v. Altman (N.D. Cal., March 2025), in which the United States District Court denied a preliminary injunction sought by Elon Musk and his company xAI against OpenAI and Microsoft. The plaintiffs alleged that OpenAI and Microsoft had entered into an unlawful group boycott by pressuring investors not to fund competing AI companies, in violation of Section 1 of the Sherman Act. The court rejected the claim on both procedural and substantive grounds, notably finding that Musk lacked standing, and that the evidence presented-consisting mainly of media articles-was insufficient to establish a plausible antitrust violation or irreparable harm.

Beyond its procedural lessons, Musk v. Altman illustrates the intensifying global battle for dominance in AI markets and the legal complexities accompanying it. The court’s decision ultimately favors a model of competition based on innovation rather than speculative or strategic litigation.