When you search for "best sneakers for walking," traditional search engines show you exactly that—results for walking sneakers. But AI search engines work differently. They quietly generate dozens of related queries behind the scenes: "best men's walking sneakers," "comfortable walking shoes for women," "walking sneakers for flat feet," and "affordable walking shoes." This process, called query fan-out, is silently reshaping how content gets discovered online.
Understanding fan-out queries isn't just academic—it's becoming critical for anyone who wants their content found in an AI-driven search landscape. As we've explored in our analysis of how LLMs heavily favor incumbents, the brands and publishers who master this shift will dominate visibility while others fade into digital obscurity.
How AI Search Engines Generate Sub-Queries
AI search engines don't simply match keywords anymore. When Google's AI Mode, Perplexity, or ChatGPT Search receives your query, they first decompose your intent into multiple dimensions. A search for "project management tools" triggers an internal analysis that considers company size, budget constraints, feature requirements, and industry specifics.
The AI then generates what researchers call "fan-out queries"—a constellation of related searches designed to capture every angle of your original intent. This happens in milliseconds, invisible to users but fundamental to how results get compiled.
Recent research by SurferSEO revealed that 84% of fan-out queries share at least one URL with the original query's top results, and 56% share five URLs. This clustering effect means that comprehensive content addressing multiple related topics has a significant advantage over narrow, single-focus pages.
The implications run deeper than simple ranking changes. Fan-out queries essentially create a content discovery mechanism that rewards depth over specificity.
The Content Clustering Revolution
Traditional SEO taught us to target specific keywords with laser focus. Fan-out queries turn this wisdom on its head. Content that covers a broad spectrum of related subtopics now outperforms hyper-targeted pages because it satisfies multiple fan-out queries simultaneously.
Take marketing automation software as an example. Instead of creating separate pages for "email marketing tools," "lead nurturing software," and "marketing automation platforms," successful brands now develop comprehensive guides that address all these angles within a unified framework.
This shift requires rethinking content architecture entirely. Rather than individual pages competing for single keywords, effective content strategies now involve creating semantic clusters—interconnected content that covers a topic from multiple perspectives while maintaining clear internal relationships.
The brands adapting fastest to this change are building what SEO experts call "content galaxies"—collections of related content that AI engines can draw from when constructing responses to various fan-out queries. These galleries establish topical authority while reducing the risk that algorithm changes will devastate individual page performance.
Why Content Cannibalization Became Deadly
In the fan-out era, content cannibalization—where multiple pages compete for similar keywords—has become more dangerous than ever. When AI engines encounter competing content from the same domain, they often exclude both pieces rather than choosing between them.
This happens because fan-out queries require clear, authoritative answers. If your domain provides conflicting or overlapping information, AI engines interpret this as uncertainty and look elsewhere for definitive sources.
Monitoring for cannibalization now requires understanding semantic relationships, not just keyword overlap. Two pages might use completely different keywords but address the same user intent from the AI's perspective. Advanced SEO tools are beginning to map these semantic conflicts, but many content creators remain blind to the problem.
The solution involves consolidating related content into comprehensive resources rather than maintaining separate pages. This approach aligns with how AI engines prefer to consume and reference information while establishing clearer topical authority.
Building Content for Fan-Out Success
Successful content in the fan-out era anticipates the questions users haven't asked yet. When someone searches for "customer relationship management," they might also need information about implementation timelines, integration capabilities, pricing models, and team training requirements.
Content that addresses these related angles within a logical structure performs better across multiple fan-out queries. This doesn't mean creating unfocused content, but rather developing comprehensive resources that serve as authoritative references for entire topic areas.
The most effective approach involves structuring content in layers. Start with the primary topic, then build out related sections that address common variations and follow-up questions. Use clear headings and semantic markup to help AI engines understand the relationships between different sections.
Monitoring AI search results in your industry reveals patterns in how engines generate fan-out queries for your topics. Tools like Perplexity often show the sub-queries they're considering, providing direct insight into how AI engines decompose user intent. Understanding these patterns becomes especially important when optimizing content for citation by AI search engines.
The Competitive Landscape Shift
Fan-out queries are creating winners and losers faster than most brands realize. Companies with comprehensive, well-structured content find themselves suddenly dominating searches they never specifically targeted. Meanwhile, brands with narrow, keyword-focused content watch their visibility evaporate as AI engines favor more complete resources.
This shift particularly affects how new brands can compete with established players. While traditional SEO often favored older domains with extensive backlink profiles, fan-out queries create opportunities for newer brands with superior content structure and coverage.
The brands emerging as AI search leaders share common characteristics: comprehensive topic coverage, clear content hierarchies, and semantic markup that helps AI engines understand content relationships. They've moved beyond thinking about individual keywords toward building content ecosystems that serve entire categories of related user needs.
As AI search engines continue evolving, fan-out queries will become more sophisticated. Early adaptation to this trend provides a significant competitive advantage that compounds over time. The content strategies that succeed today are laying the foundation for sustained visibility in an increasingly AI-driven search landscape.
For brands looking to understand what it takes to win in this environment, our analysis of best-in-class brands dominating AI search reveals the specific strategies industry leaders use to maintain consistent visibility across AI platforms.