Summary

Large language models (LLMs) have become the new arbiters of informational visibility. Brands, researchers, media outlets, individuals: any entity absent from their responses is, in effect, invisible to a growing share of users. This power of selection rests on documented structural biases (notoriety bias, gender bias, recency bias, AI-to-AI self-reference bias), an extreme concentration of cited sources, and a large-scale hallucination phenomenon that pollutes collective knowledge by attributing an “existence” to fictitious entities. Understanding these mechanisms is a first-order strategic imperative for any organization engaged in content production, reputation management, or scientific research.

The question “who appears in AI responses” is on the verge of supplanting “who appears on Google’s first page.” This shift is not incidental. ChatGPT now handles 2.5 billion daily queries from 883 million monthly users, and AI-driven referral traffic grew by 527% between early 2024 and early 2025. In this context, the act of citing, or not citing, is no longer the neutral gesture of a search engine listing URLs: it is a decision, algorithmic in nature, but whose consequences for the reputation, credibility, and perceived existence of an entity are now measurable. LLMs have become de facto publishers, without bearing the declared responsibilities that come with that role.

LLMs as the New Gatekeepers of the Informational Space

An Unprecedented Concentration of Sources in Web History

The open web theoretically allowed any indexed site to appear in search results. The architecture of LLMs follows a fundamentally different and far more restrictive logic. A generative engine cites an average of two to seven domains per response, compared to the ten traditional blue links served by Google. This radical compression of the source spectrum creates an unprecedented winner-takes-all effect at this scale. An analysis of 150,000 LLM citations conducted by Semrush in June 2025 reveals that Reddit accounts for 40.1% of cited sources, Wikipedia for 26.3%, and YouTube for 23.5%, with no other platform reaching 5%. Three platforms thus absorb nearly 90% of global visibility. For any company, researcher, or media outlet absent from these dominant ecosystems, the probability of appearing in an AI response is marginal, regardless of the intrinsic quality of their content.