You are not competing for the question your customer asked
The model rewrites it into several questions you never see, then answers those. Here is what it adds.
April 23, 2026 · 3 min read
This is the most useful thing we know about how AI answers get built, and almost nobody outside the industry has been told it.
When someone types a question into Google’s AI Mode, Google does not go and search for that question. In Google’s own words:
“Under the hood, AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf.”
Deep Search, they add, “can issue hundreds of searches.”
So there is a layer between your customer and the web that nobody optimises for, because until recently nobody could see it.
In April 2026, Peec AI collected five million of these fan-outs across ChatGPT, Perplexity and Grok. The average number of sub-queries generated from a single user question: Perplexity 1.4, ChatGPT 2.1, Grok 6.8.
But the interesting data is not the count. It is what the models add. These are words that appeared in the machine’s sub-queries and not in the human’s question:
"best" 15.33% rising to 24.3% on advisory-style questions
"what" 8.72%
"review(s)" 6.84%
"2026" 5.44%
"top" 5.24%
"comparison" 4.48%
"vs" 4.27%
"company/companies" 4.02%
The machine asks “best X” on your customer’s behalf about one time in six, even when they never said the word. On questions phrased as asking for advice, closer to one in four.
Now the consequence, and it is measurable. In March 2026 Ahrefs analysed 863,000 keyword result pages and four million AI Overview citations. Only 38% of AI Overview citations now come from Google’s top ten — down from 76% eight months earlier. About a third come from positions 11 to 100. About a third from beyond position 100 entirely.
Their attribution is that Gemini 3’s arrival in January 2026 expanded the reliance on fan-out: AI Overviews now lean “less on the direct search results and more on the sources showing up in fan out query SERPs.”
And in a separate study of 1.4 million ChatGPT prompts, the semantic similarity of a page’s title to the fan-out query (0.656) predicted citation better than its similarity to the user’s actual prompt (0.602). The machine’s rephrasing is a better predictor than the human’s phrasing.
What this changes, practically.
Ranking first for the phrase your customer types is no longer the game. The pages pulled into an answer are the ones that match a machine’s rephrasing — comparative, superlative, dated, “X vs Y”, “best of”. That is why one page type dominates: in December 2025 Ahrefs found 43.8% of all cited page types were “best X” lists, by a wide margin the largest single category.
Which leads to an uncomfortable strategic conclusion. You cannot write every “best plumber in Columbus” article, and if you write your own it competes badly against third-party ones. What you can do is be in them. The fan-out data is the clearest argument we know of for why the off-site work — getting into guides, local media, trade directories and other people’s round-ups — is not a nice-to-have bolted onto a website project. It is where a large share of the citations actually live.
One caveat on the listicle finding, because the same study contains it: 35% of the cited lists came from low-authority domains, “many of which were highly questionable.” Ahrefs themselves declined to adopt the tactic, choosing original research and awards instead. Being in a bad list is not the same as being in a good one, and a model reading a spam round-up is a weaker signal than a model reading a local newspaper’s guide.
And a caveat on the whole picture. Fan-out behaviour is changing quickly — Ahrefs notes AI Overviews change their citations, content and fan-outs roughly every two days. The percentages above will move. The mechanism won’t.
