The 2003 Framework That Was Already Doing Query Fan Out

I was doing query fan out by hand for two decades before anyone called it that. I just called it something else: "Russian nesting dolls." In August, MJ Cachón published a dataset study.

She ran 189 branded prompts through ChatGPT, watched the model fire off 1,797 sub queries nobody typed. Reading it, I recognized the shape of something I'd been building into press releases since early 2003, long before "fan out" or "GEO" existed as terms.

The 15% Nobody Can Target Directly

Google first put a figure on it in its 2019 introduction to BERT, writing that 15% of the queries it sees on a given day are ones it has never encountered before. At Search Central Live NYC in March 2025, John Mueller revisited the number. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task.

What Cachón's Data Adds That My Old Trick Never Had

Here's where the nesting doll instinct gets a real upgrade. Cachón found that branded ChatGPT fan out doesn't behave randomly either. The first sub query in a run tends to be plain, conversational language. From there, the model. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The reporting question is whether this signal changes a decision. If it only creates another number in a dashboard, it adds noise. If it helps separate profile activity, website visits, calls, bookings, and direction requests, it can make local performance easier to understand.

My Take

I think the industry spent the 2010s optimizing for the wrong end of the doll. Head terms got all the strategy meetings and all the budget, while long tail phrasing got treated as an afterthought that Search Console would surface if you. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

How To Apply This To Your Content Strategy

You don't need Cachón's API access to use any of this. You need three habits. Find the nested phrase, not just the seed phrase. Whatever three word core term you're targeting, write down the two or three four word, and five word. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The 15% Nobody Can Target Directly in practice

Introduction I was doing query fan out by hand for two decades before anyone called it that. I just called it something else: "Russian nesting dolls." In August, MJ Cachón published a dataset study. She ran 189 branded prompts. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

What the visibility signal actually changes

What the visibility signal actually changes: the 2003 Framework That Was Already Doing Query Fan Out: the Practical Angle should be treated as a visibility signal, not a standalone headline. Introduction I was doing query fan out by hand for two decades before anyone called it that. I just called it something else: "Russian nesting dolls." In August, MJ Cachón published a dataset study. She ran 189 branded prompts through ChatGPT, watched. This connects with AI Overviews YouTube Gap when the same signal needs a clearer operating decision. A useful companion note is AI Search Optimization Isn’t the Hard Part, because it looks at a nearby part of the same system.

What the visibility signal actually changes: the practical question is whether the page, brand evidence, and surrounding content make the answer easier to trust. If that support is weak, search systems can still understand the topic but fail to connect it confidently to the brand. The same pattern also shows up in Questions That Reveal Your Real Search Performance, where the practical question is how the signal becomes visible.

What the visibility signal actually changes: that is why the response should begin with an audit of the evidence already on the site before creating a new asset. The fastest improvement is often a clearer page, a better internal link, or a stronger explanation of why the brand belongs in the answer.

Where the evidence needs to be tested

Where the evidence needs to be tested: a single study or ranking observation should not become a strategy by itself. It should become a diagnostic prompt: which source is being trusted, which query pattern is affected, and which part of the site would make that trust easier to earn?

Where the evidence needs to be tested: that keeps the response grounded. The goal is to improve the evidence chain around the topic rather than publish another summary that repeats what every other page already says.

Where the evidence needs to be tested: the important distinction is between a useful signal and a fashionable talking point. A useful signal changes the brief, the page structure, the linking plan, or the measurement view.

How to avoid overreacting to one data point

How to avoid overreacting to one data point: for content teams, the strongest move is to map the claim to existing assets before creating anything new. The right page may already exist, but it may need clearer headings, stronger internal links, fresher proof, or a better explanation of why the brand belongs in the answer.

How to avoid overreacting to one data point: this is also where title rewriting matters. A title should not copy the source headline; it should frame the practical implication so readers immediately know why the topic deserves attention.

How to avoid overreacting to one data point: the same standard should apply to every section. Each heading needs to earn its place by moving the reader through the evidence, not by repeating the outline in a more polished voice.

What this means for content and authority

What this means for content and authority: authority is becoming more contextual. It is not enough to be generally known in a category if the specific answer depends on a different source, a different index, or a different retrieval pattern.

What this means for content and authority: that means the content system should show consistent entities, related pages, credible references, and useful depth around the exact questions people and AI tools are asking.

What this means for content and authority: when the context is weak, AI systems can still mention the brand but describe it in the wrong frame. The fix is not more volume; it is cleaner evidence around the specific association.

Where internal links and entity clarity matter

Where internal links and entity clarity matter: internal links should do more than move crawlers around the site. They should explain relationships between topics, show which page owns which idea, and help both readers and search systems understand the next useful step.

Where internal links and entity clarity matter: the anchor text matters here. Vague links create weak context, while descriptive links can clarify the relationship between this post, related AI search analysis, and practical SEO execution.

Where internal links and entity clarity matter: this is especially important when the topic touches AI search because models and retrieval systems need clear relationships. A scattered cluster makes the site harder to interpret.