- AI search does not behave like a classic ranked results page. It selects, summarizes and cites sources, often giving third-party pages a larger role than brand-owned content.
- Share of answer is becoming one of the more useful ways to think about AI search visibility, yet current research suggests only a small share of brands are tracking AI citation performance today.
AI Search Has Changed Where Visibility Comes From
When people search in ChatGPT, Perplexity or Google AI Mode, the experience no longer looks like a classic page of ten blue links.
The user often sees one generated answer first, with sources selected, compressed and summarized inside that response. A brand that is not part of that source set may not appear at all.
No ranking position. No visible result. No presence in the answer.
This is the gap that a post on X by Connor Gillivan, founder of TrioSEO and a 15-year SEO practitioner, brought into view on June 9.
My GEO Cheat Sheet: How to Rank on LLMs in 2026:
GEO isn’t “the new SEO.”
It’s SEO with a harder scoring system.
Most founders are optimizing for the wrong thing.
They want to “rank” in ChatGPT.
But LLMs don’t rank. They cite.
1/ Build brand authority first
– It’s the #1… pic.twitter.com/N4wWem53cK— Connor Gillivan (@ConnorGillivan) June 9, 2026
Gillivan described a six-point GEO framework and argued that brands need to think less about ranking pages and more about how AI systems select, trust and cite sources.
The post did not introduce a completely new idea. It put a practical name around something many SEO and content teams are already seeing in their work: AI search changes where visibility comes from.
The wider numbers explain why the discussion matters. DemandSage estimates that ChatGPT has reached 900 million weekly active users. Gartner has forecast that traditional search engine volume could fall 25% by 2026 as AI chatbots and virtual agents take over more information-seeking behavior.
Even if the exact forecasts change, the direction is clear enough: more product research, comparison queries and category discovery now happen inside AI-generated answers.
Third-Party Sources Are Becoming Part of the Visibility Layer
One figure from Gillivan’s post attracted particular attention: he said roughly 85% of brand mentions in AI-generated responses came from third-party pages rather than the brand’s own website.
That number should be treated carefully.
Gillivan did not publish a full methodology or sample size, so the 85% figure is best read as practitioner data rather than a universal benchmark. The exact share will vary by category, query type, brand strength and which AI system is being tested.
But the underlying point is still important.
Directories, review sites, comparison articles, list posts, community threads, analyst pages and aggregator sites are becoming part of how AI systems form a view of a brand.
the 85% stat on third party citations being where brand mentions actually come from is the part most seo strategies miss
— Tom Alder (@tomaldertweets) June 9, 2026
@tomaldertweets (Tom Alder): “the 85% stat on third party citations being where brand mentions actually come from is the part most SEO strategies miss”
That idea lines up with what many GEO researchers and AI visibility tools are now testing. AI answers often lean on sources that appear to carry category-level trust: review platforms, community discussions, developer forums, comparison pages, social platforms and established editorial sources.
For B2B and software queries, this can include places like LinkedIn, Reddit, GitHub, Stack Overflow, review aggregators and niche industry directories. For consumer categories, it may include forums, publishers, review sites, marketplaces and creator content.
The practical takeaway is simple: a brand’s own website still matters, but it is no longer the only place where AI systems build confidence.
Why a Comparison Table Is Now a Citation Decision
A brand that builds its content plan almost entirely around its own blog may be missing part of the AI search environment.
Gillivan also argued that comparison tables earn more citations from LLMs than standard text content. As with the 85% figure, that claim should not be treated as a settled benchmark without methodology. But it points toward a real pattern.
AI systems often need to answer comparative questions.
Users ask which product is better, which software fits a use case, which vendor is cheaper, which tool works for a specific team and which option has fewer tradeoffs.
Structured comparison content helps with that.
A table, list or clearly organized section gives an AI system cleaner material to extract. It also helps the user because the content already matches the decision they are trying to make.
Some AI visibility research suggests that search and AI systems evaluate content in smaller passages or chunks, which makes clearly structured comparison content easier to process, summarize and cite.
That means a comparison table is not only a formatting choice.
It can become a citation decision.
Most Brands Are Still Not Measuring the Right Signal
Gillivan’s framework points to share of answer as one way to measure GEO performance.
The idea is straightforward: how often does a brand appear in AI-generated answers when users ask questions that matter to that brand?
A brand might track how often it appears across prompts related to its category, competitors, use cases, pain points or buying decisions. Over time, that creates a visibility picture that looks different from classic rank tracking.
The problem is that most teams are not yet set up to measure this.
A GoodFirms 2026 survey found that only 14% of marketers currently track AI citation performance, even though 43% named AI search as a core priority for the year.
GEO didn’t replace SEO, it changed the rules so that you focused more on establishing authority over just relying on ranking.
— Nathan Hirsch (@itsnathanhirsch) June 9, 2026
@itsnathanhirsch (Nathan Hirsch): “GEO didn’t replace SEO, it changed the rules so that you focused more on establishing authority over just relying on ranking.”
The measurement gap matters because AI answers are already changing search behavior.
Ahrefs analyzed 300,000 keywords and found that the presence of an AI Overview was associated with a major drop in click-through rate for the top organic result. McKinsey has also reported that AI search is becoming an important source for product discovery and information, especially as users become more comfortable asking AI tools for recommendations.
This does not mean SEO rankings are irrelevant.
It means rankings no longer explain the whole discovery journey.
What the Data Says About Gillivan’s Claims
Two figures in the thread deserve careful handling: the 85% third-party citation figure and the claimed uplift from comparison tables.
Both appear to come from Gillivan’s own practitioner experience. No public sample size or methodology was included in the thread. That does not make the claims useless, but it does change how they should be used.
They are informed signals, not universal research findings.
Independent research does support the broader direction, even if it does not prove the exact numbers.
A 2025 AI visibility report from The Digital Bloom found that original statistics and direct quotations were associated with stronger AI visibility. The same report also identified brand search volume as an important predictor of LLM citations.
That fits the larger pattern: AI systems appear to reward sources that are easy to trust, easy to extract and easy to connect with a known entity.
GEO really just exposes what already mattered in SEO, authority and structure. The mechanics changed, but the fundamentals didn’t disappear.
— Kevin Box (@Fuel_YourGrowth) June 9, 2026
@Fuel_YourGrowth (Kevin Box): “GEO really just exposes what already mattered in SEO, authority and structure. The mechanics changed, but the fundamentals didn’t disappear.”
That is probably the most grounded way to read the GEO debate.
AI search has not erased SEO fundamentals. Authority still matters. Structure still matters. Original data still matters. Brand demand still matters. Third-party validation still matters.
What has changed is the interface.
When an AI system generates an answer, weak content has fewer places to hide. Thin pages, keyword stuffing and low-quality link tactics are less useful when the system is selecting a small group of sources to support one answer.
Where Content Investment Needs to Go
For content and marketing teams, the practical question is where to put effort next.
If third-party sources influence AI visibility, then the work cannot stop at publishing more blog posts on an owned domain.
Brands need to understand where their category is being described across the web.
That includes comparison articles, industry directories, review platforms, expert roundups, original research, community discussions and social posts from credible practitioners.
For some brands, Reddit and LinkedIn may matter. For others, GitHub, Stack Overflow, trade publications, analyst pages, YouTube or niche review sites may carry more weight.
The right source map depends on the category.
This is where GEO becomes less like a content calendar and more like digital reputation work. The goal is not only to publish answers. The goal is to make sure credible sources across the web describe the brand accurately, consistently and in the contexts where buyers are asking questions.
Expert Analysis
Gillivan’s thread captures where the GEO conversation is now moving.
The first phase was about naming the shift. The next phase is measurement.
Share of answer is a useful idea because it focuses on the outcome that matters: whether a brand appears in AI-generated responses when users ask commercially important questions.
But the infrastructure to measure this is still early.
Most marketing teams know how to report keyword rankings, impressions, clicks and conversions. Far fewer know how to track prompt sets, AI citations, brand mentions, source overlap, answer consistency or competitor share of answer across models.
That is the real gap.
The GoodFirms figure showing low adoption of AI citation tracking suggests the issue is not awareness. Marketers know AI search matters. They just do not yet have the reporting habits, workflows or confidence to treat it like a normal performance channel.
Until that changes, GEO budgets will move slowly.
The stronger takeaway is not that SEO is dead or that brands should abandon their own websites.
It is that visibility is becoming more distributed.
In classic SEO, a brand could often win by improving its own pages. In AI search, a brand may also need to win the surrounding web: the comparison pages, review sites, communities, data sources and trusted third-party references that AI systems use to decide what belongs in the answer.
That makes GEO harder to control.
It also makes it harder to fake.
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