Beyond Search: LLMs Adoption and Web Traffic Concentration

Best Student Paper Award, WISE 2025

with Samira Gholami, Cristobal Cheyre, and Alessandro Acquisti

This paper studies how the diffusion of large language models (LLMs) as consumer-facing information intermediaries reshapes web traffic allocation and market concentration. Using large-scale behavioral data, we document substitution patterns between traditional search engines, LLM-based interfaces, and downstream content providers. We show that LLM adoption alters referral flows and reduces direct traffic to certain classes of websites, with heterogeneous effects across content categories. These shifts have implications for market power, content sustainability, and the measurement of concentration in digital markets. The results inform ongoing antitrust and platform governance debates around AI-driven intermediaries.

An Experimental Infrastructure for Ecologically Valid Studies of Online Advertising, Tracking, and Targeting

Journal of Marketing Research (Invited to Resubmit), 2025

with Cristobal Cheyre, Li Jiang, Florian Schaub, Zhiwei Ding, Yifan Li, and Alessandro Acquisti

This manuscript introduces a novel experimental methodology for studying the impacts of online advertising, tracking, and targeting on users. Addressing limitations in observational and platform-specific research, the infrastructure deploys a client–server architecture that enables randomized field experiments by assigning participants to ad-blocking, anti-tracking, or full ad-exposure conditions. The system captures rich longitudinal data across browsers, emails, and mobile devices while preserving ecological validity. By integrating fine-grained behavioral measures with repeated self-report surveys, the approach provides a rigorous framework for causal inference on consumer welfare, information-seeking behavior, and privacy outcomes in digital advertising markets.

Advertising and the Cost of Search

with Cristobal Cheyre and Alessandro Acquisti

This project examines how advertising affects consumer search costs, consideration sets, and market outcomes in digital environments. Leveraging a large-scale field experiment with detailed browsing and advertising exposure data, we study how personalized and contextual ads shape information acquisition and navigation behavior. We quantify changes in search intensity, set composition, and downstream purchase decisions across experimental conditions. The analysis sheds light on the dual role of advertising as both an informational device and a potential distortion in consumer decision-making. The findings speak to debates in industrial organization, consumer protection, and digital advertising regulation.

The Cursed Equilibrium of Algorithmic Traumatization

Solo-authored

This paper develops a dynamic model of interaction between a profit-maximizing digital platform and users who may be vulnerable to certain categories of content. The platform optimizes engagement through adaptive recommendation algorithms that learn from past user behavior, while users exhibit myopia and limited awareness of long-run utility consequences. We show that engagement-based optimization can lead to a “cursed” equilibrium in which platforms rationally amplify triggering content, generating persistent welfare losses for users. The equilibrium arises even in the absence of malicious intent or informational asymmetries on the platform side. The model provides a theoretical foundation for understanding algorithmic harm and motivates policy interventions beyond standard transparency or disclosure tools.

Media coverage: Featured in an interview on Tech Policy Press discussing algorithmic recommendation systems and platform-induced harms.

Unlocking Growth: The Economic Effects of GDPR in Europe

with Francesco Decarolis

This paper studies the economic effects of the General Data Protection Regulation (GDPR) on firm behavior, market outcomes, and innovation across European countries. Using cross-country data and institutional variation in enforcement and compliance, we analyze how GDPR affected entry, investment, and growth, with a particular focus on heterogeneous impacts across firm size and sector. The project contributes to the empirical literature on data regulation and digital markets and informs current policy debates on proportional and risk-based regulatory design.