Agentic Markets: Equilibrium Effects of Improving Consumer Search

A team of researchers from Microsoft Research, Google Research, and academic institutions has published a significant new paper titled 'Agentic Markets: Equilibrium Effects of Improving Consumer Search.' The study uses game theory to model the future of two-sided markets where both consumers and businesses are assisted by AI tools, creating what they term 'agentic markets.' The core question is how improvements in AI-powered search technology—like smarter shopping assistants—affect long-term market equilibrium, learning, and consumer welfare.

The researchers built a model where consumers use AI agents to conduct costly searches for product fit signals before purchasing. The market tracks these searches and subsequent quality indications from purchases, creating a feedback loop. Their key finding is counterintuitive: while making AI search cheaper (less costly) unambiguously improves consumer surplus, making AI search more *informative* (smarter) can actually harm consumers. This happens because better-informed consumers can become more loyal to products that match their initial search, reducing competitive pressure on businesses, which may then raise prices.

The paper proposes a potential mitigation: this negative effect can be avoided if the market itself learns as much as the consumers do, for example by 'reading the transcripts' of conversations between consumers and their AI agents. This would allow competing businesses to better understand shifting consumer preferences and adjust their offerings, restoring competitive dynamics. The study is a crucial early warning for product designers and economists, highlighting that simply building more powerful AI assistants for consumers can have unintended and adverse economic consequences if not paired with mechanisms that preserve market-wide information symmetry.