Query fan-out: answer the hidden queries

Query fan-out is the technique Google's AI Mode uses to turn a single question into several related searches that run at the same time, then merges those results into one answer. Google describes it plainly: AI Mode "uses a 'query fan-out' technique, issuing multiple related searches concurrently across subtopics and multiple data sources and then brings those results together" (blog.google). The person types one question. The engine runs many.

That changes what it means to rank. In classic search, your page competed for one query and one results page. Under fan-out, your page is retrieved (or ignored) across a whole cluster of sub-queries the user never typed and never sees. If your content answers the headline question but skips the adjacent ones, another site fills those gaps and collects the citation.

#What Google actually does

Google's own description credits a custom version of Gemini 2.0 that is "particularly helpful for questions that need further exploration, comparisons and reasoning" (blog.google). The model reads the original question, works out the entities and intents behind it, and generates its own set of searches to cover them: comparisons, specifications, prices, steps, alternatives, and related entities. Those searches run in parallel and the answer is synthesized from what comes back.

The count is higher than most people expect. Ahrefs reports an average of 9 to 11 fan-out queries per prompt, with 59% of prompts triggering 5 to 11 searches, 24% triggering 12 to 19, and some reaching as high as 28 (Ahrefs). A single buying-intent question can quietly become a dozen retrievals across a dozen possible source pages.

#Why keyword research stops working here

The sub-queries an engine invents are not the phrases in your keyword tool. Ahrefs found that more than 95% of fan-out queries receive no recurring search volume at all (Ahrefs). They are synthetic. They exist for a few milliseconds inside one reasoning chain and then disappear. You cannot look them up in a volume database because, for practical purposes, no human ever searched them.

So the old workflow (find a keyword with volume, write a page for it, track its rank) covers a shrinking slice of how AI search retrieves. The unit of optimization is no longer the keyword. It is the set of specific, answerable claims a reasoning engine would need to satisfy the fan-out for your topic.

#Coverage beats position

Under fan-out, a page can rank well for the main query and still earn zero citations if it fails the sub-queries the engine cares about. The engine retrieves passages, not whole pages. It pulls the specific paragraph that answers each sub-query, from whichever source answers it most directly. Ten strong paragraphs on one page, each answering a distinct sub-question cleanly, will out-cite one long page that buries the same facts in prose.

This is why passage-level structure matters more than word count. Each sub-answer should be findable on its own: a clear question or claim, followed by a tight, specific paragraph that resolves it, with the concrete detail (the number, the definition, the comparison) stated in plain terms rather than implied.

#How to map the fan-out for a topic

You can approximate the fan-out before you write. The method is straightforward.

Start with the primary question your page targets. Then ask what a careful researcher would need to check before trusting an answer: definitions, how it compares to the obvious alternative, what it costs, how to do it, what the common mistakes are, and which entities are involved. Those categories mirror the comparison, specification, and how-to angles Google says the model explores. Write each one as a plain sub-question.

For a page on choosing a project management tool, the fan-out is not one query. It is "what is the difference between tool A and tool B," "how much does tool A cost," "does tool A integrate with tool C," "is tool A good for small teams," and so on. Each of those deserves its own labeled, self-contained passage on the page.

Then audit your draft against the list. Any sub-question your page does not answer directly is a citation you have handed to a competitor.

#Does covering the fan-out actually move citations

There is early public evidence that it does. Semrush ran an experiment across four of its own articles, mapping 10 to 20 fan-out queries per article and rewriting to cover them. Over one month the four articles went from being cited twice to five times, a 150% increase in citations (Semrush). The same team was candid about the volatility: their broader brand-visibility and share-of-voice numbers fell during the test as ChatGPT cut citations platform-wide, and they concluded that fan-out coverage can raise citations even though predictable growth is hard while the platforms keep shifting (Semrush).

Treat that as directional, not a guarantee. One month, four pages, one company. The mechanism is sound, though, and it matches what Google has told us about how the retrieval works.

#What we see in audits

In our own Crawlmind audits, the pages that lose ground in AI answers tend to share a pattern. They answer the headline question competently and then trail off, leaving the adjacent sub-questions to whatever else the engine can find. The pages that hold citations do the opposite. They resolve the main question in the first paragraph and then work through the predictable follow-ups, each in its own clearly marked section, each with the specific detail stated rather than gestured at.

None of this requires guessing at a secret algorithm. Google published the shape of it. A question fans out into many. Your job is to make sure that when it does, your page is the cleanest available answer to as many of those hidden queries as you can honestly cover.