AI summaryⓘ
The authors studied how three large language models recommend brands across different industries by analyzing 3,750 responses. They created three new measures to understand which brands are most mentioned (Category Ownership Index), whether any brand dominates a category (Competitive Vacuum Index), and how brands replace each other in recommendations (Displacement Score). Their results showed that brand recommendations were moderately concentrated, with no strong single winners dominating most categories, and that different models often disagreed on the top brand. This challenges the idea that AI recommendations lead to a winner-takes-all market. The authors suggest their metrics can help future research on brand competition in AI recommendations.
Large Language ModelsBrand RecommendationCategory Ownership IndexCompetitive Vacuum IndexDisplacement ScoreGini CoefficientCompetitive StructureCross-model AgreementBERTopicPower-law Distribution
Abstract
Large language models now mediate how buyers discover products and services, making the competitive structure of AI-generated recommendations a strategic concern for brands. A basic question has lacked large-scale empirical answers: in a given category, which brand does a model recommend, and how concentrated is that ownership? Across 3,750 responses spanning 50 brands, five industries, and 250 brand-free category queries on three models (GPT-5.2, Google Gemini 3 Flash, and Perplexity sonar-pro), each query repeated five times under a dice-roll stability protocol, we propose three exploratory metrics: the Category Ownership Index (COI), a brand's share of mentions within a category; the Competitive Vacuum Index (CVI), flagging categories with no single leader; and the Displacement Score (DS), quantifying asymmetric substitution between brand pairs. In this sample, recommendation concentration was moderate: the mean Gini coefficient was 0.28 (95% CI [0.16, 0.41]), below the 0.60 power-law threshold we set. Competitive vacuums were rare, appearing in 8.0% of queries, so the models named at least one sampled brand in most cases. Cross-model agreement on the top-recommended brand was 41.6%: a top position on one model did not reliably hold on another. Displacement was industry-dependent, from co-recommendation in consulting (0.4:1) to one-directional substitution up to 4.3:1, with an unweighted mean of 2.4:1 across the five industries. A BERTopic check placed only 4.2% of discovered topic clusters outside the original categories. Within the scope studied, these results sit in tension with a strong winner-takes-all narrative around AI recommendation, and the three metrics offer a candidate, reproducible procedure for competitive-intelligence analysis that future work can validate.