THE query fan out modifies a fundamental mechanics of online search: a question can now trigger several parallel searches before an answer is produced. A development which also forces publishers and SEO professionals to review their approach.
The search engine no longer responds only to the query entered
For a long time, the operation of a search engine could be summarized quite simply: the Internet user entered a query, then an algorithm classified the documents deemed most relevant. The arrival of generative engines introduces an additional step between the question and the results: the decomposition of the need.
This is precisely the role of the fan out query. For understand everything about the fan out queryWe Growth describes this initial query as a “starting point” from which the system explores different explicit or implicit intentions.
Google now gives an official definition of the mechanism: its model simultaneously generates several related queries in order to obtain the information necessary for the response. A question about rehabilitating a lawn overgrown with weeds may, for example, trigger separate searches about herbicides, chemical-free methods or preventing their reappearance.
This mechanism becomes more important as uses change. In 2026, Google indicates that queries made in AI Mode are on average three times longer than traditional searches. Nearly one in six queries now uses voice or image. Search therefore becomes less telegraphic and more conversational.
A question can now provoke ten searches
The term “fan out” describes the phenomenon quite well: from a single point a range of research unfolds. Google publicly mentions a mechanism capable of performing about a dozen searches in the time previously needed to complete a single one.
Let’s take a question like “Which laptop should I choose for working on the go?” “. A generative engine can separately search for the autonomy of recent models, their weight, their power, their connectivity, their price, the needs associated with certain professions or even recent comparisons. The final answer then results from the synthesis of several of this research.
The difference with a simple reformulation of keywords is important. The system does not only search for synonyms. It attempts to discover dimensions that the user has not formulated.
We Growth thus distinguishes between the information explicitly provided and the implicit variables that the system can project. A tourist request mentioning Florence and a three-day stay does not lead to the same searches as a simple “What to do in Florence?” “. Duration becomes a constraint that can direct information retrieval towards itineraries rather than simple lists of places.
The fan out query moves the competition from the page to the information fragment
This architecture has a less visible consequence for SEO: a site no longer necessarily has to be the best answer to the entirety of a question to participate in the generated answer.
A sub-query devoted to price can bring up one source, while another relating to a technical characteristic can mobilize a second. Google also specifies that AI Mode and AI Overviews can identify a larger and more diverse set of pages than a traditional search.
This logic brings referencing closer to the information retrieval mechanisms used in RAG architectures, or Retrieval-Augmented Generation. The system retrieves relevant information before using it to construct a response. The granularity of the content then takes on more importance.
We Growth’s analysis emphasizes in particular the capacity of a fragment to be autonomous: a precise definition, a piece of data accompanied by its context or a clearly delimited technical explanation can be more easily recovered than a passage whose meaning depends on the five preceding paragraphs.
This does not mean that you should turn each article into a succession of short answers. Rather, the challenge is to maintain an editorial narrative while creating independently understandable units of information.
The old “one keyword, one page” reflex reaches its limits
Query fan out also poses a problem for editorial strategies built exclusively around keyword volumes. A single question can now lead the engine to queries that no user has directly entered.
For a publisher, analyzing only the main keyword therefore amounts to observing the input to the system without seeing the paths taken afterwards.
A more suitable approach consists of mapping the needs likely to surround the question: comparison criteria, exceptions, costs, constraints, recent data, definitions or usage scenarios. These are potentially all branches of the fan out.
This development does not, however, make classic SEO obsolete. Google explicitly states in 2026 that optimization for its generative experiences remains, from its point of view, SEO. The fundamentals remain: pages accessible for exploration, useful content, understandable internal links, reliable information and a satisfactory user experience. GEO, for Generative Engine Optimizationabove all adds a new way of thinking about the recovery and reuse of information.
Being cited by an AI does not guarantee more traffic
The transformation is particularly sensitive for publishers because visibility and clicks tend to dissociate.
A Pew Research Center study on 68,879 Google searches carried out in the United States in 2025 provides a first order of magnitude. When an AI-generated summary appeared, users clicked on a traditional result in 8% of visitsagainst 15% when this summary was absent. Links directly cited in the summary were only followed in 1% of visits.
The study also noted that 88% of AI Overviews observed cited at least three sources. In other words, visibility is distributed more between several documents while the propensity to click may decrease.
This change is happening on a large scale. Google announced by 2025 more than 1.5 billion monthly users for AI Overviews. In February 2026, a Pew Research Center survey also indicated that 60% of American adults said they read AI summaries at least occasionally displayed in search engines.
For brands and media, measuring only average position or organic traffic therefore becomes less revealing of real visibility. A source can participate in a generated response without immediately producing a visit.
Optimizing all possible subqueries would be a bad idea
A temptation naturally appears: anticipate each branch of the fan out query and produce content for each. However, this strategy risks recreating the historical excesses of SEO in a new form.
The engines do not publish the exact list of queries generated for each user. These can also evolve depending on the wording, the context of the conversation, the necessary freshness of the data or the nature of the task.
We Growth also highlights the probabilistic nature of the process: two similar questions do not necessarily produce the same recovery path. It would therefore be risky to consider the fan out as a fixed tree structure that an SEO tool could reconstruct once and for all.
The editorial gain is rather found in the reasoned coverage of a subject. A page that can accurately answer important questions, clearly identify its sources, and distinguish facts, explanations, and limitations has more potential entry points for retrieval systems.
The query fan out above all announces a less linear search
The next step is already beyond generating text responses. Google applies fan out to visual searches and also uses it for some agentic functions: a search for tickets can lead the system to review hundreds of optionswith prices and availability, before presenting a selection. Its Shopping Graph also claims more than 45 billion product sheetsof which more than two billion are updated every hour.
Search is thus evolving from a “query, results, click” model towards paths where an agent breaks down a task, queries several sources and synthesizes part of the work itself. The query fan out is therefore probably not a new SEO technique to add to a checklist. Above all, it reveals a deeper transformation: tomorrow, an increasing part of the visibility of content will depend on its ability to answer a question that the user has sometimes never formulated.
