AI search does not run on a single citation model anymore. BrightEdge’s latest analysis of ChatGPT, Google AI Overviews, Google AI Mode, Gemini, and Perplexity shows that these engines often cite different websites while still ending up with many of the same brand recommendations.
That changes the SEO brief. Generative engine optimization, or GEO, is the practice of improving how a brand appears in AI-generated answers. In practice, this study suggests GEO is less about chasing one perfect source and more about building presence across the source layers AI systems trust: authoritative institutions, commercial and editorial coverage, and user-generated discussion.
The clearest finding is this: source overlap is low, brand overlap is much higher. BrightEdge found that pairwise overlap in top cited sources across the five AI surfaces ranged from 16% to 59%. Brand overlap stayed in a tighter band, from 36% to 55%.
That gap matters because it shows how AI engines can take different paths to a similar answer. Google AI Mode and Google AI Overviews were the closest pair on cited sources at 59%, but Gemini overlapped with AI Mode at only 27% and with AI Overviews at 34%. Even within Google’s own ecosystem, the citation logic is not uniform.
BrightEdge also found that the share of authoritative citations ranged from 10% to 26% by engine, while user-generated content ranged from 0.2% to 18%. That is not a small variation. It is a structural difference in how AI systems decide what kind of web evidence deserves a place in the answer.
Because each engine appears to have its own sourcing personality. The output looks similar to users, but the retrieval mix behind the answer is not.
BrightEdge found that Gemini had the highest reliance on authoritative sources, with 26% of citations coming from government, academic, and major institutional domains. Its UGC share was only 0.2%. That makes Gemini look less like a crowd-sourced recommender and more like a formal reference layer.
ChatGPT had the flattest distribution of cited sources in the study. Its top 10 domains accounted for just 18.5% of citations, lower than Gemini and Perplexity. That suggests a broader source mix, even though its UGC share remained low at 0.5%.
BrightEdge found that Perplexity concentrated heavily on institutional medical, government, encyclopedic, and medical publisher sources. Those categories made up about 30% of its citations. It also surfaced brands early, with 86% of brand mentions appearing in position five or earlier.
AI Mode spread citations across a wide catalog of domains and showed one of the lowest top-10 source concentrations at 19.4%. BrightEdge also found it had the strongest mix of review sites, finance data, and news media, with UGC at about 7%.
AI Overviews leaned harder into user-generated content than any other engine in the study. BrightEdge reported that about 18% of AIO citations came from UGC, while authoritative sources accounted for about 10%. It was the only engine where UGC outweighed authority, which is a major clue for brands that still treat AI visibility as a pure publisher or backlinks problem.
It means single-channel SEO is now a weak strategy. If a brand only invests in one source type, it will underperform anywhere that engine weights another layer more heavily.
BrightEdge frames the opportunity around three source layers:
The important nuance is that authority is category-relative. In other words, authoritative does not automatically mean .gov or .edu. For a software brand, an analyst firm or respected trade publication may matter more than a university site. BrightEdge’s own findings support that view, especially since .edu citations were limited across all five engines.
A concrete example from the study makes the point. A B2B SaaS company whose buyers rely on ChatGPT and Perplexity should prioritize authoritative coverage and strong commercial/editorial visibility, with UGC as a supporting layer. A consumer brand that depends on Google AI Overviews needs a heavier investment in creator, video, forum, and community visibility, because that is where AIO draws far more evidence.
The biggest mistake teams will make after reading this data is turning it into five separate playbooks. That is not what the pattern shows. The smarter move is to keep one brand strategy, but measure it engine by engine so you can see which source layer is carrying visibility and which one is missing.
This is exactly why BotRank’s AI Visibility feature matters. It lets teams run the same prompts across multiple AI engines, track how often the brand appears, compare competitors, and inspect which sources and pages are being surfaced over time. That matters because a brand can look strong in ChatGPT while staying weak in AI Overviews if it has solid editorial coverage but poor community presence. The reverse can happen too. When source overlap is loose but brand overlap is tighter, the real job is not guessing. It is measuring whether your brand associations, cited pages, and source footprint are strong enough to survive across different AI retrieval systems.
Start with buyer behavior, not platform hype. The right mix depends on which AI surfaces influence your category.
The practical next step is straightforward: map your category’s authoritative sources, commercial sources, and community sources, then check whether your brand is visible in each layer. That is a more durable plan than trying to reverse-engineer one model at a time.
AI citation overlap measures how often two AI engines cite the same websites in their answers. BrightEdge found that source overlap varied widely, which suggests each engine builds answers from a different slice of the web.
Because it shows that engines can reach similar brand recommendations through different evidence paths. For marketers, that means brand association and multi-layer visibility may matter more than winning on one exact source.
Not by default. BrightEdge’s findings suggest .edu domains were not heavily cited across these engines, which reinforces that authority depends on the category and query, not just the domain suffix.
No. BrightEdge found that AI Mode and AI Overviews were relatively similar to each other, but Gemini behaved quite differently, with a much stronger institutional bias.
Track brand mentions, cited sources, sentiment, and competitor presence by engine. If you only look at aggregate visibility, you will miss whether your weakness is authority coverage, editorial presence, or UGC.
AI search is not rewarding one type of website. It is rewarding brands that show up across the right evidence layers for their category. If you want to know which engines mention your brand, which pages support those mentions, and where competitors are winning, that is exactly the kind of gap BotRank helps surface.
