Why generative search expands competition from individual webpages to entities, third-party evidence, and the wider information ecosystem.
The unit of competition is expanding
Traditional SEO often treats the webpage as the primary object: which URL ranks, for which query, and at what position? Generative answers frequently operate at the level of entities. Which companies are recommended? Which products make the shortlist? Which organization is described as trusted?
A brand can gain visibility through information that does not originate on its own domain. Review publications, retailers, government sites, videos, industry articles, community discussions, and competitors can all become part of the evidence used to construct an answer.
GEO is not only about optimizing a website. It is also about improving the information environment surrounding an entity.
Owned, earned, and community evidence
Chen and colleagues found substantial differences between conventional search and AI search in the source categories engines used, along with differences among AI platforms themselves. Their study emphasizes the importance of earned media—third-party sources such as independent reviews and publications—within generative source ecosystems.
Owned media remains essential because it supplies accurate first-party facts, product details, policies, evidence, and a canonical description of the business. Earned media provides independent context and authority. Community and social sources can reveal experience, language, and demand. A strong information ecosystem is coherent across these layers without trying to control every voice.
Recognition is not discovery
Asking “What is Acme Analytics?” measures whether a model recognizes a named brand. Asking “What are the best analytics platforms for a small ecommerce company?” measures whether that brand competes in unbranded discovery. These are different tests.
Kumar’s 2026 study of more than 100,000 prompt responses across more than 100 brands found a large visibility gap by brand stature in first-run unbranded prompts: roughly 73% for household-name brands, 44% for established mid-market or regional brands, and 11% for niche and small brands. The figures belong to that study’s tracked panel and should not be treated as universal platform constants. They nevertheless illustrate why smaller brands must measure discovery rather than take recognition as proof of competitiveness.
Questions about this topic
Can a brand be visible when its own website is not cited?+
Yes. A generative system may learn about or retrieve the brand through retailers, reviews, publications, videos, government sources, or other third parties.
What is an unbranded prompt?+
It describes a category, need, or use case without naming the target brand—for example, “best inventory system for a small retailer.”
Should a company focus on owned or earned media?+
Both. Owned content provides accurate first-party information; earned sources add independent evidence and category authority. Their roles are complementary.
Are the 73%, 44%, and 11% figures universal benchmarks?+
No. They describe the cohorts and observation window in Kumar’s 2026 preprint. Use them as evidence of a brand-stature gap, not as fixed constants for every platform or market.
References
Sources are listed in APA 7 style. Preprints are identified as such and should not be treated as peer-reviewed findings unless separately published.
- Kumar, P. (2026). Generative engine optimization at scale: Measuring brand visibility across AI search engines [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2606.20065
- Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). Generative engine optimization: How to dominate AI search [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2509.08919
- Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14035