分析大模型推荐中品牌占有率，发现头部效应不强且跨模型推荐差异大。
Who Owns the AI Recommendation? A Multi-Industry Empirical Map of Brand Category Ownership Across Large Language Models
- 构建三类指标：品牌提及占比、竞争真空度、品牌替代不对称性。
- 平均集中度为0.28（低于0.60阈值），多数品类有多个推荐品牌。
- 不同行业替代关系差异显著，跨模型榜首一致率仅41.6%。
大语言模型如今影响消费者发现产品与服务的方式，使AI生成推荐的市场竞争格局成为品牌的战略关注点。一个基础问题长期缺乏大规模实证答案：在某一品类中，哪个品牌被模型推荐，其推荐集中度如何？本研究基于3,750次响应，覆盖50个品牌、五个行业及250个无品牌关键词查询，在GPT-5.2、Google Gemini 3 Flash和Perplexity Sonar-Pro三个模型上重复测试五次，采用骰子稳定性协议。提出三项探索性指标：品类拥有指数（COI，品牌在品类中的提及份额）、竞争真空指数（CVI，标识无主导品牌的品类）、位移得分（DS，衡量品牌间非对称替代程度）。结果显示，推荐集中度中等：均值吉尼系数为0.28（95%置信区间[0.16, 0.41]），低于设定的0.60幂律阈值；竞争真空仅出现在8.0%的查询中，说明多数情况下至少有一个样本品牌被提及。跨模型对首位推荐品牌的共识率为41.6%，表明一个模型的榜首在另一模型中并不稳定。位移程度因行业而异，咨询业呈现共推荐（0.4:1），单向替代最高达4.3:1，五行业平均为2.4:1。通过BERTopic检查，仅有4.2%的发现主题簇超出原始品类范围。在研究范围内，结果与“赢家通吃”的主流叙事相矛盾，三类指标为未来可复现的竞争情报分析提供了候选方法。
原文摘要 · Abstract (English)
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.
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