A new study from arXiv challenges conventional wisdom about search ranking economics by examining how AI agents behave when delegated shopping tasks. Researchers randomized 100 hotel listings across 5,000 AI agent sessions and compared four large language models against human behavior, finding that while position bias still exists, it operates in fundamentally different ways than it does for human consumers.

Unlike humans, who tend to examine results sequentially and stop early, AI agents scan entire results pages at once and never abandon a search without making a selection. The study found a surprising non-monotonic pattern: listings in the middle of a results page suffered the lowest inspection probability — not those at the bottom, as traditional search behavior research would predict. Crucially, whether position influenced the final purchasing decision varied significantly across different AI models, with no clear pattern tied to model provider or general capability benchmarks.

The most actionable finding for practitioners: all four models converged on the same top-performing, 'undominated' listing regardless of where it appeared on the page. This suggests that in an agentic AI shopping environment, the quality and completeness of listing attributes displayed on the results page matters far more than achieving a high search ranking. For businesses and platform designers, this is a significant strategic signal as AI-mediated commerce continues to scale.