Daniel McCarthy Directory Page

We discuss the impact of Apple’s App Tracking Transparency's (“ATT”) on targeted, online advertising. We overview the empirical results of Aridor, Che, Hollenbeck, Kaiser & McCarthy (2025) that measured the impact of ATT on e-commerce firms. The results point to a large reduction in the efficacy of targeted advertising and subsequently large revenue losses, borne primarily by smaller firms. We discuss the competition policy implications of this by highlighting the potentially anticompetitive implications of privacy measures implemented by private firms and the lack of substitutability between advertising networks, despite a large exogenous shock in the efficacy of Meta advertising.

Aridor, Guy: Assistant Professor of Marketing, Northwestern University
Hollenbeck, Brett: Associate Professor of Marketing, UCLA
McCarthy, Daniel: Associate Professor of Marketing, University of Maryland

• Consumer Privacy
• Digital Marketing
• Digital Platforms
• Fairness and Equity

This paper examines competition in the consumer AI assistant market using worldwide iOS and Android app-store data from seven major AI assistants from May 2023 through December 2025. Rather than finding a winner-take-all market, we show that major product launches tend to coincide with growth in the overall category, with little evidence of direct cannibalization across leading models. In other words, the “AI war” appears less zero-sum than commonly assumed.

The analysis identifies three distinct strategic positions that currently appear viable to date. ChatGPT is pursuing scale, with by far the largest market share and substantial revenue generated from a very large user base. Google Gemini is pursuing an ecosystem defense strategy, using broad distribution and low direct monetization to support Google’s wider platform. Claude is pursuing a differentiated premium niche strategy, with a much smaller user base but much higher revenue per user.

There are three practical takeaways for managers and investors. First, firms should not assume that AI markets will necessarily converge to a single dominant winner. Second, the right AI strategy depends on structural advantage: scale, ecosystem leverage, or premium differentiation. Third, as market growth slows and capital becomes less abundant, each strategy will face different risks, making monetization quality and strategic fit more important than raw user growth alone.

Maxime C. Cohen, Professor, Desautels Faculty of Management, McGill University
Eddy Hage-Youssef, McGill University
Daniel M. McCarthy, Associate Professor of Marketing, Robert H. Smith School of Business, University of Maryland, College Park
D. Daniel Sokol, Professor, USC Gould School of Law; USC Marshall School of Business

• Corporate strategy and global competitiveness
• Enterprise impact measurement and valuation
• Markets and enterprise performance
• Artificial intelligence (AI)
• Digital platforms, ecosystems, and omnichannel commerce
• Customer analytics and market insights
• Generative machine learning, data analytics, data fusion, and personalization
• Human–AI collaboration and augmented decision-making
• Corporate governance and accountability
• Institutional trust and role of transparency in promoting well-functioning markets
• Regulation of emerging technologies