Big Brand Bias in AI Search

Big brand bias in AI search is the tendency of generative search engines to recommend or cite market-leading brands disproportionately when prompts are broad or unbranded.

Key points

• The paper tests unbranded cola and soda prompts and finds that both ChatGPT and Perplexity skew toward major brands over niche or indie brands ^[src-028].
• The authors interpret this as a combined effect of source prominence and model priors: when prompts are broad, well-known brands are easier for models to justify from the available evidence base ^[src-028].
• Big-brand bias also appears in the broader strategy discussion: niche brands need to overcome default market-leader recommendations by building narrow but verifiable authority ^[src-028].
• The recommended response is not just more owned content; niche brands should pursue specialty earned media, expert coverage, community evidence where relevant, and deep content in a specific category ^[src-028].
• The paper treats big-brand bias as a reason for continuous monitoring because AI-search visibility can erode or improve as competitors earn new citations and authority ^[src-028].

Related entities

• OpenAI
• Perplexity API

Related concepts

• Generative Engine Optimization
• AI Search
• Earned Media Bias in AI Search
• AI Search Citation Network
• Machine-Scannable Content

Source references

• ^[src-028] Mahe Chen, Xiaoxuan Wang, Kaiwen Chen, Nick Koudas — “Generative Engine Optimization: How to Dominate AI Search” (2025-09-10)