Query fan-out: the hidden searches behind every AI answer

When someone asks ChatGPT or Google AI Mode a question, the AI usually doesn't search for that exact question.

It splits it up.

This is called query fan-out: the AI turns one question into several related sub-searches, runs them in parallel and combines the results into one answer with sources. Google calls them "a set of concurrent, related queries."

That changes how brands get found in AI answers.

What Google officially confirms

• AI Mode issues "a multitude of queries simultaneously" (Google I/O 2025)

• Deep Search "can issue hundreds of searches"

• To be cited, a page just needs to be indexed and eligible for a snippet. No special markup needed

• Building pages only to catch fan-out queries counts as scaled content abuse

• Google Search ignores llms.txt

What Google doesn't publish: how many sub-queries it runs, how they are phrased, or how much a page that only matches a sub-query counts.

What outside measurements show

• 5 to 11 sub-queries per prompt, sometimes many more (Nectiv via Similarweb)

• 71% of fan-out queries have 7+ words. Only 4% have 1 to 3 (DEJAN, ~366k queries)

• OpenAI sub-queries are longer and contain about twice as many brand and product names as Google's

• 95% of fan-out queries have zero search volume in keyword tools (AirOps, 548k pages)

• 32.9% of cited pages appeared ONLY for a sub-query, not for the original prompt

That last number is the big one: a third of citations go to pages that would never rank for the question the user actually typed.

An example

Question: "Which CRM is best for a plumbing company with 15 employees?"

The AI might quietly search for:

1. Category: CRM for trade businesses

2. Comparison: CRM A vs CRM B for small teams

3. Price: CRM pricing for 15 users

4. Alternatives: alternatives to the market leader

5. Fit: CRM with job scheduling and invoicing

6. Freshness: best CRM 2026

7. Proof: reviews from plumbers

8. Concerns: data security, GDPR

Almost none of these show up in keyword tools. Every one of them decides which vendors end up in the answer.

What actually works

• One strong page that answers the main question AND the obvious follow-ups (price, fit, comparison, concerns) beats ten thin pages

• Lead every section with the answer, then explain

• Be concrete: audiences, features, numbers, years, prices. "Best-in-class solution" answers no sub-query

• Publish first-hand knowledge: your own data, tests, examples

• Get listed where AI looks: reviews, trade media, directories. Check which ones list your competitors but not you

• Keep the basics right: indexable, snippet-eligible, no robots.txt blocks on crawlers you want

The spam trap

A separate page for every possible sub-query is exactly what Google warns against. Simple test before publishing: would you create this page if AI search didn't exist, because your customers need it? 50 near-identical "CRM for [trade]" pages fail that test.

A 5-step process you can start this week

1. Collect 10 to 20 questions customers would ask an AI (sales, support, Search Console)

2. Find the sub-queries: with a tool that records them, the Gemini API field webSearchQueries, or the 8 types above

3. Check coverage: does one of your pages answer each sub-query concretely?

4. Check sources: which third-party pages does the AI cite, and are you on them?

5. Measure for several weeks. AI answers change daily, so single checks mislead

What nobody knows yet

The exact sub-query count in AI Mode, how much a sub-query-only page weighs, and whether targeted fan-out optimization measurably helps. Published tests are small. Use fan-out as a planning model, not as a formula.

Full article with all sources and a planning template: https://www.growresonance.ai/blog/query-fan-out

(Disclosure: I'm building growResonance, an open source AI visibility tool. It shows the sub-queries behind each ChatGPT answer, plus cited sources and competitors. All numbers above are from external studies, not ours.)

Question for you: Have you ever looked at the sub-queries behind an AI answer in your market? What surprised you most?