做AI产品评测或品牌策略的团队会发现，用户画像对推荐结果的影响比想象中大得多——Anthropic的模型尤其容易“看人下菜碟”，建议点开了解如何避免测量偏差。
一项针对AI助手品牌推荐的审计研究发现，当用户以不同身份（如初创创始人、企业VP、英国中小企业主）询问“最佳CRM软件”时，模型推荐的品牌集差异显著。在2000次测试中，角色前缀使推荐集相似度下降12-20%，且影响集中在二线品牌（更换率高达75%），而头部品牌几乎不受影响。Anthropic的Sonnet模型比OpenAI更依赖训练数据先验，其推荐中43-52%无检索证据支撑（OpenAI仅8-29%），因此角色影响更大。研究警告，任何AI品牌感知测量都必须考虑用户角色，否则会掩盖真实偏差。
Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit
The same prompt -- "best CRM software" -- reaches AI assistants from buyers in widely different contexts: a solo founder, an enterprise VP, a UK SMB owner. We audit how strongly that contextual variation reshapes which brands the model recommends. The audit samples 2,000 runs over a design space of 10 personas x 8 prompts x 3 model configurations x N=10 reps, with the two OpenAI cells at full 8-prompt coverage and the Anthropic sonnet-4.6 / low cell at 4-prompt coverage. Prefixing the user message with a persona drops the recommendation-set similarity (Jaccard) by Delta = -0.12 to -0.20 relative to a same-persona baseline (clustered 95% CIs exclude zero on all three measured cells; the sonnet cell's CI rests on only 4 prompt clusters and is correspondingly wider). The effect is sharply prominence-stratified: category leaders are persona-resistant (~80% same-brand consistency across personas), but mid-market brands swap up to 75% of the recommendation set as the persona changes. The Anthropic model shows a larger point-estimate effect than the OpenAI configurations, though clustered CIs overlap for the closer contrast (sonnet vs. OpenAI/high); the asymmetry is consistent with Anthropic's more retrieval-unattributed generation route (43-52% recommendations without observed retrieval-layer evidence, vs OpenAI's 8-29%, documented in Jack 2026). Any measurement of AI brand perception must condition on the buyer persona supplying the query: the same prompt produces materially different recommendation sets depending on who the model thinks is asking, and a measurement protocol that aggregates across personas systematically obscures that variation. The effect concentrates at mid-market and is largest on the most priors-reliant generation route in our audit, consistent with persona responsiveness growing as models lean more on training-data priors and richer context integration.