{"id":1527,"date":"2026-07-21T07:33:13","date_gmt":"2026-07-21T07:33:13","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/ai-mode-content-strategy\/"},"modified":"2026-07-21T07:33:13","modified_gmt":"2026-07-21T07:33:13","slug":"ai-mode-content-strategy","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/ai-mode-content-strategy\/","title":{"rendered":"Content Strategy for AI Mode: Which Pages Survive the Fan-Out"},"content":{"rendered":"

A content strategy for AI Mode is a plan for covering the hidden sub-queries Google generates from a single prompt, using enough separately answerable pages and sections that your domain can fill several of those branches at once.<\/strong> It is not a longer-content strategy and not a more-pages strategy \u2014 both fail for the same reason.<\/p>\n

We tracked 19,412 AI Mode citations across 60 prompts over eight weeks to settle the argument. Neither camp won. The variable that predicted citations had almost nothing to do with word count.<\/p>\n

This matters because the two camps give opposite instructions with real cost attached. Consolidate everything into definitive guides, says one. Publish a page per sub-question, says the other. Until now the evidence on either side has been anecdotal.<\/p>\n

What the query fan-out does to a page<\/h2>\n

Query fan-out is the process where an AI search system converts one user prompt into several concurrent sub-queries, retrieves results for each, then synthesises a single answer.<\/strong> Google describes it in its guidance on AI features in Search<\/a> as issuing "a set of related searches" across its index to assemble one response.<\/p>\n

Your page is no longer competing for one query. It is competing separately for each branch of a tree it never sees. In our sample the median parent prompt produced 9.3 distinguishable facets, ranging from 4 to 21.<\/p>\n

That creates more citation slots than a classic SERP has ranking positions \u2014 and it changes what "covering a topic" means. A page can be excellent on the parent question and invisible to seven of the nine branches. For the retrieval mechanics behind those branches, see our breakdown of how one prompt becomes dozens of hidden searches<\/a>.<\/p>\n

\"Diagram<\/figure>\n

Why existing advice doesn't answer the question<\/h2>\n

Guides on depth versus breadth converge on the same conclusion: build topic clusters, balance both, link them semantically. Not wrong. Just untested at the level where the decision gets made \u2014 the individual URL.<\/p>\n

None of them publish citation counts per URL. Meanwhile the strongest public datasets measure URL attributes rather than content architecture. Otterly's study of 1,028,959 cited URLs found path depth correlated with citation volume at r = +0.002 and URL length at r = \u22120.025 \u2014 effectively zero. The shape of the address tells you nothing.<\/strong><\/p>\n

So the honest state of the evidence was: URL formatting doesn't matter, clusters are a good idea in the abstract, and nobody had measured whether one deep page or a set of narrow pages captures more of the fan-out.<\/p>\n

How we tested depth against breadth<\/h2>\n

We tracked 60 parent prompts daily in Google AI Mode for eight weeks, logged every cited URL, and isolated the 41 cases where one domain ran both a comprehensive hub and three or more narrow pages on the same topic.<\/strong> Same domain, same topic, same authority, different architecture \u2014 that matched-pair design is what makes the comparison usable.<\/p>\n