Don't Measure Once: Measuring Visibility in AI Search (GEO)
This addresses the challenge for information access and retrieval practitioners in generative engine optimization, but it is incremental as it builds on existing empirical studies.
The paper tackles the problem of unreliable visibility assessment in AI search due to its probabilistic nature, finding that repeated measurements are necessary to characterize visibility as a distribution rather than a single-point outcome.
As large language model-based chat systems become increasingly widely used, generative engine optimization (GEO) has emerged as an important problem for information access and retrieval. In classical search engines, results are comparatively transparent and stable: a single query often provides a representative snapshot of where a page or brand appears relative to competitors. The inherent probabilistic nature of AI search changes this paradigm. Answers can vary across runs, prompts, and time, making one-off observations unreliable. Drawing on empirical studies, our findings underscore the need for repeated measurements to assess a brand's GEO performance and to characterize visibility as a distribution rather than a single-point outcome.