A study of 3,750 queries across five industries finds moderate recommendation concentration, with a mean Gini coefficient of 0.28. Cross-model agreement on top-recommended brands was only 41.6%, and displacement scores varied by industry, ranging from 0.4:1 to 4.3: 1. The results challenge the 'winner-takes-all' narrative and introduce three reproducible metrics for competitive-intelligence analysis.
AI-generated brand reputations vary significantly by language, with Uralic and Baltic languages showing more positive sentiment and Germanic languages, including English, being more critical. Query language impacts which brands are recommended, especially for local champions, where home-language queries increase visibility by 0.80 points compared to English queries. English-only monitoring fails to capture the full AI visibility of locally headquartered brands, creating a measurable language blind spot.
A large-scale study of 100K+ AI prompt responses across 100+ brands reveals a three-tier brand visibility ladder: global brands appear in 73% of answers, mid-market in 44%, and niche brands in just 11%. AI engines primarily cite corporate websites, with YouTube leading non-corporate sources, and best-of listicles accounting for 21% of citations. Sentiment in brand mentions is unstable, flipping six times more often than mere mention.