Query fan-out is an AI search process that splits a user's query into multiple subqueries, collects information for each, and merges the relevant parts into a single response. It matters because one question can trigger an estimated eight to 20 or more background searches, and your page can be cited for any of them.
Where the term comes from
Google popularized the term when it introduced AI Mode. In her Google I/O 2025 keynote, Head of Search Elizabeth Reid said Search breaks questions into subtopics and issues a multitude of queries at the same time.
It marks a change in how search engines handle a query. They moved from one-to-one (one query, one set of results) to many-to-one (similar queries return the same results) to one-to-many (one search expands into many).
How query fan-out works
Semrush breaks the process into five parts: analysing the prompt, decomposing it into subqueries, retrieval, scoring the retrieved content, and synthesis. The subqueries run in parallel across several data sources, including web indexes, knowledge graphs, product databases and social media.
How many searches run depends on the system and the question. Research from Seer Interactive and Nectiv found an average of 9 to 11 fan-out queries per prompt, with 59% of prompts triggering 5 to 11 searches, and 24% triggering 12 to 19, reaching as high as 28. Deep research tools go much further. For a red phone case, AI Mode typically made 5 to 11 searches while ChatGPT Deep Research made 420.
An example
Ask an AI system "how to start a podcast" and it does not search that phrase alone. It splits the question into sub-queries about structure, branding, technical setup, hosting, finding guests, content planning, promotion and audience engagement. A page that answers "how much does podcast hosting cost" clearly can be cited in that answer, even though it never mentions starting a podcast in its title.
Types of fan-out queries
The common types are reformulation, implicit, comparative, recency, contextual variation and next-step. Ahrefs lists related topics as well. In practice that means the system rephrases your question, asks what you did not say out loud, compares options, looks for the latest information, adjusts for your situation, and anticipates what you will ask next.
Can you predict the fan-out queries?
Only roughly. Fan-out queries are synthetic and probabilistic: generated on the fly, shifting from one run to the next, and most carry little or no search volume of their own. Keyword tools will not list them.
What you can predict is the shape. For any buyer question, write down the related questions a person would need answered to act on it: the cost, the alternatives, the setup, what can go wrong. Those are the sub-questions an AI system is likely to search, and each deserves a clear answer somewhere on your site.
Why it matters for your pages
Coverage beats one perfect keyword. Brands whose pages address users' intents more thoroughly may gain more search visibility and more AI mentions and citations. A section headed by a real sub-question, answered in its first sentence, gives the system a passage it can lift for that subquery. More on how Google picks those passages in AI Overviews ranking factors.
Common mistakes
Writing one page per phrasing is the main one. Fan-out queries shift between runs, so chasing each wording produces thin pages. Cover the intent once, thoroughly. The other is checking a single answer. Because the subqueries change, ask the same question several times before deciding whether you are cited.
Frequently asked questions
What does fan-out mean in SQL?
Something different. In databases, fan-out usually describes a join where one row matches many rows, so the result multiplies. In AI search it means one question spreading into many searches.
What does fan-out mean for logic gates?
In electronics, fan-out is how many inputs a single gate's output can drive. The AI search term borrows the picture of one thing spreading out to many.
How do I find fan-out queries?
There is no complete list, because they are generated on the fly and change from run to run. Map the sub-questions behind each buyer question yourself, then check whether your pages answer them.