Query Fan-Out

Why it matters: fan-out decides which passages get retrieved, so each self-contained answer to a sub-question is another chance to be retrieved.

Query fan-out is Google’s technique of breaking one search into subtopics and issuing multiple related searches simultaneously across data sources, then synthesizing the retrieved passages into a single cited answer. Google says AI Mode uses it, Google’s docs say AI Overviews may too — and it’s the main reason AI search can cite your page for questions nobody actually typed. Not hypothetical: in our site’s first two Search Console weeks, 126 of 138 query-attributed impressions — 91% — came from one query cluster splintered into misspellings and Danish and Dutch variants (our data).

Where does the term “query fan-out” come from?

“Query fan-out” is Google’s own term, first used publicly on March 5, 2025, in the post introducing AI Mode. Robby Stein, VP of Product for Google Search, wrote: “It uses a ‘query fan-out’ technique, issuing multiple related searches concurrently across subtopics and multiple data sources and then brings those results together to provide an easy-to-understand response” (Google, March 2025).

Elizabeth Reid (VP, Head of Search) made it the centerpiece of the I/O 2025 announcement: “AI Mode uses our query fan-out technique, breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf. This enables Search to dive deeper into the web than a traditional search on Google, helping you discover even more of what the web has to offer and find incredible, hyper-relevant content that matches your question” (Google, May 2025).

The name is borrowed engineering, not marketing: in distributed systems, “fan-out” means “the delivery (or spreading) of a message to one or multiple destinations possibly in parallel” (Wikipedia).

How does query fan-out work?

Mechanically, fan-out is a four-step pipeline: one query in, many sub-queries out, passages retrieved for each, one synthesized answer back.

1. You ask one question — often long and multi-intent. Google reports the average AI Mode search is “triple the length of a traditional Search query” (Google, May 2026).
2. A custom Gemini model decomposes it into related sub-queries across subtopics, issued concurrently “on your behalf.”
3. Each sub-query retrieves candidates from multiple data sources. Mike King’s patent analysis suggests selection then happens at passage level, not page level — dense retrieval plus pairwise LLM ranking (iPullRank); a well-argued reading of real patents, not confirmed Google mechanics.
4. The model synthesizes one response with links to sources.

In other words, fan-out is the retrieval-expansion step of a retrieval-augmented generation pipeline — it widens what gets fetched before the model writes. And it keeps escalating: since Gemini 3 reached AI Mode (November 2025), fan-out can “perform even more searches to uncover relevant web content” (Google, Nov 2025).

Which Google surfaces use query fan-out?

All of Google’s generative search surfaces fan out, but the official wording differs by surface. The March and May 2025 posts attributed fan-out to AI Mode only. By late 2025, Google’s Search Central documentation extended it to both surfaces, hedged with a “may”: “Both AI Overviews and AI Mode may use a ‘query fan-out’ technique — issuing multiple related searches across subtopics and data sources — to develop a response,” which lets them “display a wider and more diverse set of helpful links” than classic search (Google Search Central).

Deep Search is the same idea at higher intensity — per Reid, it “can issue hundreds of searches, reason across disparate pieces of information, and create an expert-level fully-cited report in just minutes” (Google, May 2025).

How many sub-queries does Google actually run?

Nobody outside Google knows — Google has never published a sub-query count for standard AI Mode or AI Overviews. The “8–12 sub-queries” figure circulating in SEO blogs is not from Google; it generalizes from a handful of observed interface examples — Semrush captured one AI Mode response kicking off 8 searches (Semrush), and Google’s own I/O demo showed roughly a dozen (Aleyda Solis) — so treat any fixed range as folklore. The only official volume language is “a multitude of queries” for AI Mode and “hundreds of searches” for Deep Search.

You can’t count them yourself, either. The generative-AI performance reports Google added to Search Console on June 3, 2026 show impressions within AI Overviews and AI Mode by page, country, device, and date — but list no query metrics at all (Google Search Central Blog), and the rollout began with a small subset of UK site owners per Search Engine Roundtable. Our own non-enrolled property returned zero rows from the searchAppearance API on July 2, 2026 (our data). The sub-queries Google runs on your behalf stay invisible — I’d rather flag that gap than fake precision.

Why does query fan-out matter for SEO?

Because retrieval happens per sub-query — and, per the patent analysis, selection likely happens per passage — fan-out multiplies the ways a page can enter an AI answer. Three practical consequences:

• You can be retrieved for sub-questions you never targeted. In our site’s first two GSC weeks, 126 of the 138 query-attributed impressions — 91% — came from one AI Mode / AI Overviews query cluster, a fragmented long tail including misspellings (“ai mod3”, “a1 mode”) and Danish and Dutch variants (our data). One new site’s experience, not a universal law — but that’s the splintered query space fan-out operates over.
• Write passages that stand alone. If passages are judged individually — as the patent analysis suggests — each section of a page needs to answer its question completely on its own.
• Topical coverage gives fan-out more hooks. Aleyda Solis frames this as answering “a facet”: in-depth answers per subtopic, organized into clusters (Aleyda Solis).

Google’s own baseline is more boring: pages must be indexed and snippet-eligible, and “There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary” (Google Search Central).

Do AI Mode and AI Overviews cite the same pages?

Rarely — and fan-out plus passage-level selection is the likely reason. Ahrefs analyzed 540,000 query pairs for citations and found AI Mode and AI Overviews cite the same URLs only 13.7% of the time (16.3% comparing only top-3 citations), even though the answers were semantically near-identical — 89.7% of pairs above 0.8 similarity across 730,000 query pairs analyzed for content (Ahrefs). As Ahrefs put it: “Put simply: 9 out of 10 times, AI Mode and AI Overview agreed on what to say. They just said it differently and cited different sources.”

When one question fans out into many sub-queries and passages compete individually, the same fact gets sourced from different pages on different surfaces. You can’t lock in a citation — but every fan-out is a fresh draw you can be in.

Sources: Google — AI Mode I/O 2025 update · Google Search Central — AI features and your website · Ahrefs — AI Overviews vs AI Mode · iPullRank — How AI Mode works · Aleyda Solis — Google’s query fan-out technique