Query Fan-Out: How AI Search Turns One Question Into 10 (and How to Get Cited in All of Them)

The short version: when you ask Google's AI Mode or AI Overviews a question, it does not run one search. It runs many. Google calls this "query fan-out," and it issues multiple related searches across subtopics at once, then synthesizes one answer. Seer Interactive measured an average of 10.7 of these fan-out queries per prompt on Gemini 3, and found that 95 percent of them had zero monthly search volume. That last number is the whole story: to get cited, you have to answer the sub-questions the AI invents, and a keyword tool will never show you most of them.

Here is how fan-out works, a real worked example, and how to structure content so you show up across the whole fan, not just the headline query.

What is query fan-out?

Query fan-out is Google's term for how its AI search decomposes one question into many. In Google's own words, AI Mode "uses a query fan-out technique, issuing multiple related searches concurrently across subtopics and multiple data sources," then brings the results together into one answer (Google, The Keyword blog, March 2025). Google's developer documentation confirms the same technique runs in both AI Overviews and AI Mode, and notes it casts a wider net that can surface a more diverse set of supporting pages than a traditional results list (Google Search Central, updated December 2025).

The practical shift: classic search matched your page to one query. AI search matches passages of your content to a spray of related sub-queries you never see. You are no longer competing for a keyword. You are competing for coverage of a question's whole neighborhood.

How many searches does one question become?

The best-measured answer comes from Seer Interactive. Analyst Nick Haigler ran 501 prompts through the Gemini 3 API with grounding forced on, which exposes the fan-out queries the model issues, and found an average of 10.7 fan-out queries per prompt, ranging from 3 to 28 (Seer Interactive, November 2025). That was up 78 percent from an average of 6.01 on the previous Gemini 2.5.

Two caveats we will state plainly, because a statistic is only useful if it is honest. This is Seer's own analysis of the Gemini API with forced grounding, which is a strong proxy for what AI Mode does internally, not a direct read of production Google AI Mode. And it is vendor research, not a peer-reviewed study. We cite it because the methodology is disclosed and the sample is real, but attribute it as Seer's finding, not as something Google published.

The figure that matters most for your content is this one: Seer found 95 percent of those fan-out queries had zero monthly search volume, with an average length of 6.7 words. The AI is generating long, specific, conversational sub-questions that no keyword tool tracks, because almost nobody types them verbatim into a search box.

Why this breaks traditional keyword research

If 95 percent of the queries deciding your visibility do not appear in any keyword tool, then keyword-first content planning is aiming at the wrong target. Ranking a page for the head term is necessary but no longer sufficient. The AI is also searching for the definitions, comparisons, costs, objections, and adjacent decisions wrapped around that term, and it assembles its answer from whichever sources cover them best.

This rewards a specific kind of page: one that covers a topic's real conceptual neighborhood in self-contained, clearly-labeled passages, rather than one thin page per keyword. It is the same answer-first structure that wins AI citations generally, applied at the scale of a whole question cluster.

A real fan-out map

To make this concrete, here is the fan-out neighborhood around a query we care about: "best digital marketing agency in Toronto." The sub-queries below are real Google autocomplete suggestions retrieved on September 21, 2026 (Canada region). Autocomplete is a documented proxy for the demand the fan-out draws on; we did not scrape the People Also Ask box, which Google walls off. Grouped by the intent a fan-out would cover:

Selection and comparison

• which is the best digital marketing agency
• how to choose a digital marketing agency
• how to choose the right marketing agency for your business
• what to look for in a digital marketing agency
• top digital marketing agencies in toronto
• do i need a digital marketing agency

Trust and objection

• are digital marketing agencies worth it
• is a digital marketing agency worth it
• should i hire a digital marketing agency
• why hire a digital marketing agency

Definitional and entity

• what does a digital marketing agency do
• what is a ppc agency
• what is a b2b marketing agency

Cost and pricing

• how much does a marketing agency cost per month
• how do marketing agencies charge
• average cost of digital marketing agency
• how much should i pay a marketing agency
• marketing agency retainer

Adjacent services (Toronto)

• seo agency toronto
• ppc agency toronto
• social media marketing agency toronto
• b2b marketing agency toronto

One head query, five distinct intents, roughly twenty real sub-queries, and that is before the model reformulates any of them. A page that only says "we are the best digital marketing agency in Toronto" answers exactly one node in that map. A page that explains how to choose an agency, what one costs, what the services are, and when you do not need one answers most of it.

The cost query makes the point even more sharply. "How much does a marketing agency cost in Toronto" returns no autocomplete at all, because the full local string is too specific for anyone to type. Yet the cost stem fans out cleanly into real sub-queries: how much per month, how do agencies charge, average cost, retainer versus hourly, how much should I pay. The fan-out reformulates a question nobody types into a dozen it can actually search.

How to get cited across the fan-out

The optimization consensus across credible sources is consistent, and it is not exotic.

• Cover the whole cluster, not one keyword. Map the sub-questions around your head term (autocomplete, People Also Ask, related searches, and your own Search Console queries are the cheapest sources) and make sure your content answers each one.
• Write self-contained passages. Each sub-question should be answerable from a single chunk of your page without the reader needing the rest of the article. That is what the model lifts.
• Make entities and relationships explicit. State who, what, where, and how things relate in plain language, so a passage matches a specific sub-query instead of gesturing at the topic.
• Choose semantic depth over repetition. Expanding into related concepts helps more than repeating the head phrase, because the fan-out is searching concepts, not matching strings.
• Keep the structure machine-readable. Clear headings, a direct answer near the top of each section, and standard structured data all make passages easy to extract.

If you have read our other work, this is the same answer-first playbook we apply to every AI-search page, now aimed at a cluster of questions instead of one. See the 8 things AI engines check before they cite you and how to get recommended by ChatGPT.

What fan-out does not mean

• It does not mean publish ten thin pages per query. It means cover a topic's real neighborhood well on strong pages.
• It does not mean chase a fixed list. Fan-out sub-queries are model-generated and shift between versions and prompts, so you optimize for coverage and clarity, not a static keyword set.
• It does not mean Google publishes the numbers. The 10.7 average is Seer's measurement of the Gemini 3 API, and we label it that way.

How to find your own fan-out

You cannot see the exact queries a model fans out, but you can approximate the neighborhood cheaply. Take your head query, then pull Google autocomplete, the People Also Ask box, the related-searches strip, and your own Search Console queries for that topic. Cluster them by intent, the way we did above, and you have a working map of what an AI answer will look for. Cover the gaps, write each answer to stand on its own, and you move from competing for one query to being citable across the fan. That measurement and mapping is part of what we do in our AEO work.

FAQ

What is query fan-out? Query fan-out is Google's term for how AI Mode and AI Overviews answer a question by issuing multiple related searches across subtopics at once, then synthesizing the results into one response (Google, 2025). It means AI search decomposes one query into many.

How many sub-queries does AI search generate per question? Seer Interactive measured an average of 10.7 fan-out queries per prompt on the Gemini 3 API, ranging from 3 to 28, up from about 6 on Gemini 2.5 (November 2025). It is a vendor measurement of the API, not an official Google figure, but the methodology is disclosed.

Does query fan-out change SEO and keyword research? Yes. Seer found 95 percent of fan-out queries had zero monthly search volume, so keyword tools cannot show you most of the queries deciding your AI visibility. The winning move is comprehensive topic coverage in self-contained passages, not one page per keyword.

How do I optimize for query fan-out? Map the sub-questions around your head term using autocomplete, People Also Ask, related searches, and Search Console, then answer each as a self-contained passage with explicit entities and clear structure. Cover the cluster, not a single keyword.

Which engines use query fan-out? Google confirms both AI Overviews and AI Mode use the fan-out technique. More broadly, AI assistants that decompose a complex prompt into sub-searches behave the same way, so the coverage principle applies across AI search.

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