What Is Query Fan-Out and Why It Matters for Your SEO in 2026

When someone asks ChatGPT, Google AI Mode or Perplexity a detailed question, the system does not simply search for that exact sentence. It breaks the question into several smaller sub-questions, searches for each one separately, and then combines everything into a single answer. This process is called query fan-out, and it is quietly changing which pages get cited in AI-generated answers and which ones get skipped entirely.

What Query Fan-Out Actually Means

Query fan-out is the process AI search systems use to split one user question into multiple parallel sub-queries before generating a response. Google's Head of Search, Elizabeth Reid, introduced the term at Google I/O 2025 while explaining how AI Mode works internally.

Here is a simple example. If someone asks "best accounting software for a small business in Australia," the AI does not search for that exact phrase. It might generate sub-queries such as "top accounting software 2026," "cloud accounting pricing comparison Australia," "accounting software for sole traders," and "software with BAS integration." Each sub-query pulls results from different sources, and the AI stitches the best answers together into one response.

This matters because your content is now competing at the level of these smaller sub-questions, not just the original broad search term.

Why Ranking First Is No Longer Enough

A widely cited Surfer SEO study, which analysed more than 173,000 URLs, found that 68 percent of pages cited in AI Overviews did not rank in the top 10 organic results for the original query. Query fan-out explains why. The AI is not necessarily pulling from the page ranked first for the broad topic. It is pulling from whichever page gives the clearest, most specific answer to each individual sub-question it generated.

In practice, this means a page ranked seventh that directly and precisely answers one narrow sub-question can earn an AI citation over a page ranked first that only covers the topic in general terms.

How the Big Three Handle Fan-Out Differently

Google AI Mode uses a version of Gemini to break down complex questions, drawing on Google's extensive search index, which gives it the largest retrieval pool of any AI search system. ChatGPT tends to generate fewer sub-queries but pulls from a wider mix of source types, including forums and niche publications that Google sometimes ranks lower. Perplexity is the most transparent of the three, showing users the exact sub-queries it generated, and it leans more heavily on recently published content than the other platforms.

The common thread across all three is the same: content needs to satisfy the sub-questions behind a search, not just the headline query.

What This Means for Your Content Strategy

Three shifts matter most here.

Topical depth now carries more weight than a single well-optimised page. A website with one article on a subject sends a weaker signal than a website covering the same subject from multiple angles, such as pricing, comparisons, common mistakes and implementation guides, all linked together internally. AI systems appear to favour sites that demonstrate this kind of coverage when deciding what to cite.

Writing needs to shift from exact-match keywords toward natural, conversational language. Sub-queries generated during fan-out use everyday phrasing rather than rigid keyword syntax, so content built purely around exact-match terms can miss the semantic variations AI systems actually search for.

Freshness plays a bigger role than many businesses assume, particularly for Perplexity and Google AI Mode. Pages that have not been updated in over a year tend to lose ground to newer competing content answering the same sub-questions.

Practical Steps to Optimise for Query Fan-Out

Start by mapping the sub-questions behind your main topics rather than relying on a flat list of keywords. Typing your target question into ChatGPT, Perplexity and Google AI Mode and studying which sub-questions and sources appear is a useful way to reverse-engineer what these systems are actually looking for.

From there, structure content around question-based headings that mirror likely sub-queries, cover the realistic follow-up questions either on the same page or across a connected cluster of pages, and add structured data such as FAQ or HowTo schema to help AI systems map your content to specific questions more accurately.

The Bigger Shift Behind This

Query fan-out is one of the main reasons Generative Engine Optimisation has become a distinct discipline rather than an extension of traditional SEO. A brand that ranks well for one broad term but has no supporting content around the related sub-topics will increasingly lose visibility to competitors with deeper, more connected coverage, even if that competitor's overall domain authority is lower.

For Australian businesses, this means the traditional approach of targeting one keyword per page is becoming less effective on its own. The websites earning consistent AI citations tend to be the ones treating a topic as an ecosystem of related questions rather than a single search term to rank for.

Want to see how your content holds up under query fan-out? Our team builds SEO and content strategies around topical depth, mapping the sub-questions your audience is actually asking rather than optimising for a single keyword in isolation. Get in touch with EG Digital and we'll walk you through where the gaps are.