Type one question into Google's AI Mode and you set off an invisible burst of activity. Before the single answer appears, the system has rewritten your question into a spread of related searches, fired them in parallel against the web and Google's own graphs, and stitched the most relevant passages from each result into one reply. That decomposition step is query fan-out: the model expands one human query into many machine queries, retrieves a different set of sources for each, then synthesises everything into a single response the user never has to click away from.
Query fan-out is now the core retrieval mechanism inside Google's AI Mode and AI Overviews, and a close variant runs inside Perplexity, ChatGPT Search and the deep research tools. It is the reason AI search reads far more of the open web than classic search ever did, and the reason a publisher can be read repeatedly without registering a single visit.
What is query fan-out?
Query fan-out is the process by which an AI search engine breaks one user query into multiple related sub-queries, runs them simultaneously, and merges the results into one synthesised answer. Traditional search maps one query to one ranked list of links. AI search maps one query to many parallel searches, each targeting a different facet of the question, and returns a single composed answer rather than a page of blue links.
Google described the technique by name when it launched AI Mode in May 2025, explaining that a custom version of Gemini 2.5 uses a "query fan-out technique" to issue multiple related searches across subtopics and then bring the results together. The user sees one tidy answer. Underneath it, the engine has consulted a far wider pool of pages than it would surface in a conventional results page.
How does query fan-out work?
When a query arrives, the model does not search for the literal phrase the user typed. It first interprets the intent, then generates a set of new queries that cover the obvious sub-questions, the implied ones, and adjacent angles the user did not think to ask. Those queries run in parallel against the live web and, in Google's case, against internal sources such as the Knowledge Graph, the Shopping Graph and Maps data. Each sub-query returns its own candidate passages. A reranking step then scores those passages and selects the strongest few to ground the final answer, with citations attached.
Independent analysis of Google's patents by iPullRank's Mike King traces this behaviour to filings including the "Search with stateful chat" patent and a patent on prompt-based query generation for diverse retrieval. The picture they paint is consistent: AI search is no longer deterministic. The same question can fan out into a different set of sub-queries from one session to the next, and the sources that end up cited depend on which passages best answer those generated sub-queries - not simply on who ranks first for the original term.
What are synthetic queries?
Synthetic queries are the new searches the model invents during fan-out that the user never typed. If someone asks for "the best running shoes for flat feet", the engine might synthesise sub-queries such as "running shoes with arch support", "overpronation running shoe reviews" and "stability vs motion control shoes". King's analysis identifies several distinct styles of synthetic query Google appears to generate, including reformulations, related queries, implicit follow-ups and comparison queries. Each style sends the engine looking at a different slice of the web, which is why content can be pulled into an answer for questions the publisher never explicitly targeted.
How many sub-queries does one search generate?
It varies with the complexity of the question and the mode in use. A standard AI Mode query typically fans out into roughly a dozen or more sub-queries. Google has said its Deep Search mode takes the same technique much further, issuing hundreds of searches for a single complex prompt before reasoning across the results to produce a fully cited report. Industry estimates for everyday queries tend to land in the region of eight to twenty sub-queries. The exact figure matters less than the direction of travel: every AI answer now represents many reads of the web, not one.
That multiplication is the heart of the publisher problem. One person asking one question can cause your server to be read several times over - once for each sub-query your content happens to satisfy - while producing, at most, one citation and very often no click at all.
Why does query fan-out matter for publishers?
Query fan-out widens the gap between how much of your content is consumed and how much traffic you receive. In classic search, a user who wanted to compare options ran several searches themselves, and each one was a chance for your page to be seen and clicked. Fan-out moves all of those searches inside the machine. The user runs one query; the engine runs the dozen that used to belong to the reader; and the answer is assembled without the user ever seeing, let alone visiting, the underlying pages.
The result is more reading and fewer clicks. Your content can inform an answer through several sub-queries simultaneously and still send you nobody, because the synthesised response satisfies the user in place. This is the mechanism beneath the zero-click trend publishers already feel: fan-out is one of the main reasons a single AI answer can quietly draw on, and substitute for, a dozen of your pages.
There is a regulatory wrinkle worth noting. Following new UK regulation, Google confirmed in June 2026 that it will let publishers opt out of being aggregated into its generative AI Search features through a toggle in Search Console, while remaining in standard Search results. That gives publishers a blunt on or off control over fan-out inclusion, but it is an all-or-nothing choice between AI visibility and AI invisibility, not a way to be compensated for the reads.
Does my page need to rank number one to be cited?
No, and this is the most important strategic shift fan-out introduces. Because the engine selects specific passages that answer specific sub-queries, the page that gets cited is frequently not the one ranking at the top for the original term. Industry analyses of AI Mode citations report that a large majority of cited pages sit outside the traditional organic top ten, because relevance to a synthesised sub-query, not headline ranking, is what earns the citation. A precise, well-structured passage on page two can be pulled into an answer ahead of the nominal number-one result.
For publishers and brands this changes the optimisation target. Visibility in AI search comes from comprehensively answering the sub-intents around a topic in clear, self-contained, well-sourced passages - the building blocks fan-out actually retrieves - rather than from winning a single head term.
What can publishers do about query fan-out?
Three responses are worth separating. First, structure content for passage-level retrieval: answer the obvious and implied sub-questions of a topic directly, in self-contained sections with clear headings, named statistics and their sources, so your passages are easy to extract and ground an answer. Tools that simulate fan-out, such as King's Qforia, can help map the sub-queries a topic is likely to generate so you can cover them deliberately.
Second, measure the reads, not just the rankings. Fan-out activity shows up as AI crawler and Live Search Agent requests in your server logs long before it shows up - if it ever does - as a referral in analytics. Counting those reads is the only way to see the true scale of consumption fan-out drives.
Third, recognise that comprehension and compensation are different problems. Optimising for fan-out helps you get cited; it does not pay you for the underlying reads. That gap is the layer blankspace works in: detecting Live Search Agent and AI traffic at the CDN edge, where the fan-out reads actually land, and turning that consumption into measurable, monetisable activity rather than invisible loss. Fan-out is what makes the read happen many times over; the open question for every publisher is whether those reads are counted and paid for, or simply given away.
Frequently asked questions
Is query fan-out the same as AI Mode?
No. AI Mode is the Google search surface; query fan-out is one of the techniques that powers it. AI Mode uses fan-out to gather sources before generating an answer, but fan-out as a method also appears, in different forms, in AI Overviews, Perplexity, ChatGPT Search and the various deep research tools. Fan-out is the retrieval behaviour; AI Mode is one product that relies on it.
Does query fan-out increase or decrease publisher traffic?
It tends to decrease referral traffic while increasing how often your content is read. Because the engine runs the many searches a user would previously have run themselves and answers in place, the clicks that those searches once produced largely disappear, even as your pages are consumed across multiple sub-queries to build the answer.
How is query fan-out different from deep research?
Deep research is fan-out scaled up. A standard AI Mode query fans out into roughly a dozen sub-queries; Google's Deep Search and similar agentic research tools apply the same technique to issue hundreds of searches for one complex prompt and compile a long, fully cited report. Deep research is the most read-intensive expression of fan-out.
Can I stop my content being used in query fan-out?
Partly. Following UK regulation, Google announced in June 2026 a Search Console toggle that lets publishers opt out of generative AI Search features such as AI Mode and AI Overviews while staying in standard Search. It is an all-or-nothing inclusion control rather than a compensation mechanism, and it applies to Google's surfaces, not to every AI engine that fans out queries.
How do I optimise for query fan-out?
Cover the sub-intents of a topic, not just the head term. Write clear, self-contained passages that answer the related and implied questions around your subject, support claims with named sources, and use descriptive headings so the reranker can isolate and cite individual sections. Simulating the likely sub-queries for a topic before writing helps ensure your content answers the questions the engine will actually generate.