Ask an AI engine a buying question like "best AI SEO service" or "affordable monthly SEO" and it answers in a paragraph, then names a few sources underneath. Those named sources are the new front page. We wanted to know, from real data rather than opinion, which pages actually earn that spot for SEO and GEO queries in 2026. So we started logging it every week across the sites we run. This is what we are seeing so far.

One thing up front, because it decides how much weight to give any of this: it is a small first-party sample, gathered by our own automated tracking, and it is still running. We say more about that in the method section below and again in the limitations note at the end. Read the magnitudes here as directional, not as a poll of the whole web.

Method: what we measured and how

ConceptSEO runs weekly citation tracking across the domains it manages (hyperliquidguide.com, carsnipe.com, drcritter.com, immlawcenter.com) and its own site. For this benchmark we pulled the SEO and GEO slice of that data: a set of commercial buyer queries ("done-for-you SEO", "affordable monthly SEO", "best AI SEO service", "best GEO agency") and a smaller set of definitional queries ("what is GEO", "what is generative engine optimization").

For each query we recorded which sources three engines named in their answers: ChatGPT search, Perplexity, and Google AI Mode (the same surface that feeds AI Overviews). The tracking is automated and AI-assisted, which is how we can run it weekly instead of by hand, and it is the same pipeline described in our write-up on GEO citation tracking. What follows is counts of cited-source types, not rankings and not traffic.

The headline finding: directories and roundups win the commercial queries

For the buyer queries, the answers rarely cited the vendors themselves. The sources the engines named were overwhelmingly third-party aggregators: review directories that carry a dedicated GEO category, and "best tools" or "best agencies 2026" roundup listicles. In the results we logged, the two review directories that kept surfacing were G2's Generative Engine Optimization category and Clutch's GEO agencies directory. The roundups came from a rotating cast of publishers, among them theStacc, Nightwatch, Thrive, SE Ranking, Yotpo, Semrush, and OGTool.

Put plainly: across the domains and queries we track weekly, roughly two-thirds of the cited sources we logged for commercial SEO queries were directories or roundup listicles rather than a vendor's own site. That is a directional figure from a small sample, not a precise measurement, but the lean was consistent enough week to week that we are comfortable stating it.

The definitional queries broke the other way. "What is GEO" and its variants pulled far more from high-authority editorial publications: Search Engine Land, Backlinko, and Inc. showed up where the vendor directories had dominated the buyer queries. So the source mix is not fixed. It tracks intent.

Was the cited source the ranking page, or a third party?

This is the part most site owners get wrong, so it is worth stating directly. For the commercial queries, the primary-site citation share was low. When an engine wanted to recommend a service, it mostly did not quote that service's own page. It quoted a directory or a roundup that listed the service. The vendor's homepage and pricing page were usually not the cited source, even when the vendor was arguably the best fit for the question.

We watched one case that makes the mechanism obvious. A vendor whose own product model matched the AI's framing of the ideal answer almost exactly was still left out of the answer. Not because it was irrelevant, but because there was no self-contained, retrievable page stating who the product is for, what it costs, and what you get. The engine had nothing clean to lift, so it reached for a source that did. Recommendation followed retrievable fit-facts, not relevance.

Informational queries were friendlier to primary sources. Original pages, especially ones publishing their own data, earned citations at a noticeably higher rate than they did on the commercial side. If you publish something first-hand and specific, an engine is more willing to name you as the origin of it.

Cited-source types by query intent

Here is the pattern in one table. The labels are directional (dominant, common, rare) and come from the source types we logged, not from invented percentages.

Cited source type Commercial buyer queries Definitional queries
Review directories (G2, Clutch GEO categories) Dominant Rare
"Best tools / agencies 2026" roundups Dominant Common
High-authority editorial publications Common Dominant
Vendor's own site (primary source) Rare Common
Original-data / first-party research pages Rare Common

How ChatGPT, Perplexity, and Google AI Mode differ

The three engines agreed on the broad shape but weighted their sources differently, and the differences are practical.

Perplexity leaned hardest on roundup listicles and, usefully, showed its sources openly. When a "best X 2026" article existed, Perplexity was the most likely of the three to pull from it and cite it by name. If you are trying to see the citation pattern for yourself, it is the easiest engine to read.

ChatGPT search blended the two aggregator types, mixing directories and roundups in a single answer. It was less predictable than Perplexity about which one it would favor for a given query, but the sources still came from the aggregator layer far more often than from vendor sites.

Google AI Mode leaned on the passage that best answered the sub-question, even when that passage lived on a page ranking below position one. This is the one to internalize: AI Mode was the most willing to reach past the top-ranked result for a cleaner answer chunk lower down. A page that is not winning the blue-link race can still win the citation if it states the answer better.

What the cited pages had in common

Different engines, same traits. The sources that kept getting named, whether directory, roundup, or editorial, shared a short list of properties, and they line up with what we already argue in our guide to earning citations in AI search.

  • Self-contained passages. A two to five sentence chunk that answers the sub-question on its own, without needing the rest of the page for context. This was the single most consistent trait.
  • High information density. Named entities and concrete numbers packed into each sentence, rather than hedged or general copy. Specific text gives an engine something to trust and lift.
  • Schema with a real graph. Organization or Article structured data with a populated sameAs that ties the page to a known entity, not an empty markup shell.
  • Freshness that agrees with itself. Recent dates where datePublished, the sitemap entry, and the visible date on the page all match. Contradictory or stale dates correlated with pages that got skipped.

None of these is exotic. The reason directories and roundups win is not that they are more authoritative in some abstract sense. It is that a "best 10 tools" listicle is built entirely out of self-contained, dense, dated passages. It is the ideal shape for retrieval, by accident of its format.

What this means for a small site trying to get cited

Three takeaways come straight out of the data, and none of them require being a big brand.

First, for commercial intent, being in the answer often means being in the aggregators the answer cites, not just fixing your own pages. If your category has a G2 or Clutch listing and a set of "best of 2026" roundups, your presence in those is part of the citation surface. This is not a licence to chase spammy link placements. It is a reason to make sure the legitimate directories and roundups in your space have you listed accurately.

Second, build the retrievable fit-facts page you are missing. The vendor that got left out despite being a perfect match lost the citation to a missing page, not to a stronger competitor. A clean, self-contained page stating who your product is for, what it costs, and what a buyer gets is the single highest-leverage fix for commercial-query visibility. Write the passage an engine would want to lift, and lift it yourself first.

Third, on the informational side, original data is your opening. Primary and first-party pages did better for definitional queries, and pages publishing their own numbers did best of all. A small site cannot outrank an encyclopedia on "what is GEO", but it can own the citation for a specific finding nobody else has published. That is a large part of why we built this benchmark in the first place, and it is the strategy behind the rest of our GEO work.

Methodology and limitations

Treat this as a first-party, ongoing benchmark, not a definitive study. A few things you should know before you cite it:

  • The sample is small. It covers a handful of domains we manage plus our own site, and a focused set of SEO and GEO queries, not a broad cross-section of industries.
  • The tracking is automated and AI-assisted. That is what lets us run it every week, and it also means the source classification is machine-produced and imperfect, not hand-audited line by line.
  • The magnitudes here (dominant, common, rare, "roughly two-thirds") are directional summaries of what we logged, not precise statistics. We have deliberately avoided quoting decimal percentages we cannot stand behind.
  • AI answers change. Engines rerank sources, roundups get republished, and directories update. A pattern that held this cycle can shift, which is exactly why we track it on a weekly loop rather than publishing once and walking away.

We will keep updating the picture as the sample grows. If you want to see where your own site currently stands in AI answers for your buyer queries, a free audit is the simplest place to start. It will show you which of the traits above your important pages already have, and which ones are keeping you out of the answer.