Title: Unpacking AI SEO: Why Query Fan Out and Query Drift are Changing the Game — and Why LLMs Aren’t Search Engines
As an AI SEO and GEO (Generative Engine Optimization) researcher, I spend my days dissecting—and, at times, demystifying—the profound shifts AI has brought to search and digital visibility. Much of the recent industry buzz centers around how Large Language Models (LLMs) like Perplexity, Gemini, and ChatGPT are revolutionizing information access. Yet there’s a crucial misunderstanding: these LLMs are not new, independent search engines. Instead, they’re complex synthesizers—interfacing with and evolving on top of existing web infrastructure, while introducing radical changes to how content creators must approach search optimization.
Let’s dive into the mechanics underneath the LLM “answer surface,” focusing on Query Fan Out and Query Drift, and why these concepts are now central to winning at AI SEO and GEO.
LLMs Aren’t Search Engines—They’re “Synthesizer Wrappers”
First, it’s critical to address the misconception: Perplexity, Gemini, ChatGPT (and their peers) do not crawl the web independently in the way Google or Bing does. Most LLM-based answer engines don’t have their own fresh, proprietary, at-scale index of every page online. Instead, they either:
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“Wrap” around existing web search engines, sending queries to Bing or Google, then ingesting and interpreting those results,
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Combine this live querying with data from their own model’s training corpus, cached SERPs, or a handful of crawling partnerships.
This reality shapes every aspect of AI SEO/GEO strategy. Unlike classic search engines, where ranking high for a single query might consistently deliver you the bulk of traffic, LLMs fundamentally remix how, what, and from where they cite. Here’s why.
The Power of Query Fan Out: A New Content Game
When you type a question into Perplexity, Gemini, or ChatGPT, you’re not simply running a single search. Instead, these LLMs execute “Query Fan Out”: breaking your query down into dozens of micro-queries that explore adjacent intents, supporting facts, specific subtopics, comparisons, entity attributes, and more.
For example: Imagine a user asks, “What is the best EV for snowy climate under $60,000?” Instead of forwarding just that string, an LLM may simultaneously conduct related micro-searches such as:
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“Best EVs for winter driving”
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“EVs with AWD under $60,000”
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“EV battery cold performance”
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“EV safety ratings snowy conditions”
It then synthesizes snippets, facts, tables, and user experiences gathered from across these fanned-out results, curating the answer you see. Critically, this method levels the playing field: a site may not be the top-ranked for the main query, but if it answers one of these “fan-out” sub-topics exceptionally well, it stands a strong chance of being cited in the synthesized reply.
Implication for AI SEO/GEO:
No longer can you simply target a single “money keyword.” Instead, successful content must anticipate and address the breadth of possible micro-intents the LLM will deploy in its fan-out. Single-topic, thin posts or those optimized for a singular phrase will get bypassed unless they also serve as the web’s best answer for a related, contextually relevant sub-query.
The Challenge of Query Drift: Moving Targets in AI Visibility
As if optimizing for a swarm of queries isn’t enough, the concept of Query Drift means the target is continually moving. Query Drift refers to how LLMs subtly—sometimes significantly—shift and reinterpret the user’s original query, both over time and even within a single session.
This drift can happen for several reasons:
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LLMs may paraphrase or broaden/narrow a query to maximize diversity, coverage, or freshness.
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Their underlying search APIs or cached corpora change, resulting in different input data.
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Newer data may capture trending topics, while older queries lean on training set “memory.”
So, for our EV example, tomorrow the LLM might drift toward “top electric crossovers for ice and snow 2025” or “affordable EVs with heated tires and traction control,” pulling in an entirely different web of sources.
Why should GEO/SEO teams care?
Because your page might rank for a fan-out variant one day yet become invisible with the next algorithmic or drift iteration. If the information on your site isn’t kept up-to-date, broad enough to answer multiple reformulations, or robust in semantic coverage, your AI visibility may appear inconsistent or short-lived.
Practical Strategies for the New AI SEO/GEO Frontier
1. Build Authority First, Answer Sub-Queries Second
The AI fan-out process spins micro-searches for every user question, but if your site isn’t already trusted—meaning, well-ranked and well-linked—it’s far less likely to be included in the pool of sources the LLM synthesizes from. Invest relentlessly in boosting your core site’s authority:
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Secure quality backlinks from industry leaders, major media, and knowledge graph sources.
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Strengthen your domain by interlinking high-performing pages, ensuring each receives and passes PageRank.
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Aim for presence in trusted citation hubs (Wikipedia, data aggregators, government/educational domains).
2. Dominate Link-Worthy Verticals To Win in Fan-Out
Because LLMs “fan out” queries into adjacent topics, they often pull supporting facts from the highest-ranking or most-linked pages in each vertical. Instead of scattering thin content across hundreds of sub-topics, focus on building a web of link-worthy pages in a strategic cluster:
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Own the top search positions for each relevant sub-query. It’s not about answering the most sub-questions on a page—it’s about being the strongest source LLMs find for as many fanned-out searches as possible.
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Concentrate your efforts on a cluster of related terms by making each page an authoritative, link-gathering resource.
3. Prioritize Backlink Velocity and Fresh Mentions
LLMs and AI search wrappers are tuned to trust what other reputable sites reference—especially for queries with fan-out variants. PageRank isn’t static: new, relevant, and high-authority backlinks signal ongoing trust and influence:
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Launch regular campaigns to attract new inbound links to core and emerging pages.
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Seek coverage in current news, top blogs, and dynamically-updated web properties
4. Monitor and Reinforce Winning Pages
Query Drift and Fan Out mean the winning sub-queries shift over time, but the underlying Google/Bing index is still the root substrate for LLMs. Protect and reinforce top-performing pages:
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Use link reclamation and internal links to consolidate PageRank toward pages that frequently appear in fan-out queries.
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Respond aggressively if a competitor overtakes you for a high-fan-out search, both in content and in link building.
In Closing: The Age of Synthesis, Not Search
To succeed in the modern online landscape, championed by LLMs like Perplexity, Gemini, and ChatGPT, content creators and SEO strategists must fundamentally rethink their playbooks. These tools aren’t search engines—they are synthesizers, wiring together the best web information in response to clouds of evolving queries. Only by designing your content for the realities of Query Fan Out and Query Drift, and by understanding that SERP-first is no longer enough, can you ensure your message echoes in this generative age.
Stay curious, stay agile, and recognize that AI visibility is now about anticipating the next question—before either user or machine asks it.
