GREDEG Economics Journal Club

The meeting will take place on Tuesday, June 2nd (2:30 PM in Picasso GREDEG) 

The New Gatekeepers: LLMs, Market Concentration, and the Future of the Web.

The rapid diffusion of Large Language Models (LLMs) is fundamentally altering the architecture of digital markets. As generative AI increasingly becomes the primary gateway for information retrieval, a critical structural question arises: are these systems merely complementing traditional search infrastructures, or are they displacing them altogether?

To tackle this issue, Jingyan Wu will lead our next session by presenting the recent paper “The Impact of LLM Adoption on Online User Behavior” by Padilla et al. (2025). Leveraging large-scale web panel data, the authors document how LLM adoption depresses traditional search volumes, disproportionately diverts traffic away from smaller content creators, and undermines standard display-ad monetisation mechanisms.

Beyond the immediate empirical findings, this work opens up a vital debate for innovation economics and industrial organisation. Jingyan will help us unpack how this technological shock might reallocate attention rents, exacerbate platform dependency, and reshape the long-term economic sustainability of digital content production.

Additionally, we attach a foundational paper by Goldfarb and Tucker (2019), “Digital Economics”, which provides a complete theoretical baseline about the role of digitalisation on the economy. In particular, the paper analyses - among other channels - the interaction between the digital markets structures and firms' search costs, which we will reference during the debate.

ABSTRACT: “The Impact of LLM Adoption on Online User Behavior”, by Nicolas Padilla, H. Tai Lam, Anja Lambrecht, and Brett Hollenbeck (2025)

The adoption of AI tools, and especially Large Language Models (LLMs), has the potential to significantly transform how users engage with information online, potentially serving as substitutes or complements to existing digital resources. We use detailed clickstream data from 2022 and 2023 to examine users' online behavior following the adoption of large language models. We document a significant decrease in online search activity, a typical entry point to content consumption. Online searches drop slowly, suggesting a period during which users learn to use LLMs, but eventually adopters' level of online search in traditional search engines is more than 20% below the pre-adoption period, though there is heterogeneity across types of queries. We then turn to the effect of LLM adoption on website traffic. We document that while frequently visited websites are not affected, smaller websites suffer a significant drop in visits. In line with these results, we then report a significant drop in display ad exposures, especially to consumers with high levels of retail activity, though we do not find a reduction in search ad exposures. Last, we study two distinct categories of websites: education-related websites and user-generated content platforms. We document a significant drop in visits to education-related websites and heterogeneity across user-generated content platforms with a pronounced negative effect on Stack Overflow but no significant effect on Wikipedia, Reddit, and social media. We discuss implications for online content creators, for GenAI firms, and for public policy.