Agentic markets.
1Department of Computer Science, Technical University of Munich, Boltzmannstr. 3, Garching, Germany.
Electronic Markets
|June 9, 2026
Summary
Generative AI agents in digital marketplaces may not automatically reduce consumer search costs. Their suboptimal behavior and AI-generated content can create market inefficiencies, hindering optimal outcomes.
Area of Science:
- Economics
- Artificial Intelligence
- Marketplace Dynamics
Background:
- Generative AI facilitates autonomous agents for digital marketplace transactions, potentially lowering consumer search costs and improving market matching.
- Economic search theory provides a framework for analyzing market efficiency and the impact of search costs.
Purpose of the Study:
- To analyze the economic implications of generative AI agents in digital marketplaces.
- To identify factors that may prevent AI agents from achieving optimal market outcomes.
- To explore the interaction between AI behavior, content generation, and market efficiency.
Main Methods:
- Application of economic search theory to analyze AI agent behavior.
- Examination of empirical studies on AI agent deviations from optimal search strategies.
- Analysis of signal dilution and product differentiation effects from AI-generated content.
Main Results:
- AI agents exhibit behavioral search costs, deviating from optimal search even with low technical costs.
- AI-generated content dilutes signals, weakening product differentiation and offer informativeness.
- Low entry costs combined with AI behaviors can lead to excessive, low-value entry and inefficient market equilibria.
Conclusions:
- The anticipated gains from generative AI in digital marketplaces are not guaranteed due to inherent agent behaviors and market dynamics.
- Frictions introduced by AI agents, including behavioral search costs and signal dilution, can limit competitive outcomes.
- Further research is needed to understand and mitigate inefficiencies in agentic markets to ensure effective electronic marketplaces.
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