The short version
The sharpest people in SEO are working out how their hard-won craft applies to AI answers, and one idea keeps surfacing: the query fan-out. You can watch them hash it out on threads like this r/SEO discussion.
- Growing evidence says the query fan-out is fundamental to measuring your AI visibility, what marketers call answer engine optimization, or AEO. Miss it and you're measuring the wrong thing.
- A fan-out is the several different searches an AI runs behind a single question. It's your customer's real intent, split into the ways real people actually ask.
- You can test it by hand, but it drifts and it bends toward you instead of your customer. Adacity was engineered with fan-out as its backbone, across both the questions and the buying journey.
How do AI assistants use a query fan-out?
AI assistants use a query fan-out to turn one question into several parallel searches, run them at once, and blend the results into a single answer. Google describes it plainly in its own developer documentation: AI Overviews and AI Mode may issue multiple related searches across subtopics and data sources to build a response. Ask its AI Mode about things to do in Nashville with a group and it writes its own follow-up questions, about great restaurants, good bars, and options if you're bringing kids, then searches each one.
So a top Google ranking no longer decides whether AI names you. Say a shopper asks ChatGPT for the best standing desk under $500. You might hold the top organic result for "standing desk," but the assistant never searches "standing desk." It breaks the ask into narrower searches and reads the results for each. In one Semrush example, a single prompt fanned out into eight. If you rank for the phrase the customer typed but not for the ones the model wrote, you're not in the room where the answer gets built. FlexiSpot and Uplift show up across those narrower searches, and the shopper hears their names instead of yours.
How can you test a query fan-out yourself?
You can test a query fan-out yourself by going past keywords to the intent underneath them. That is the whole shift the r/SEO thread is circling: the several searches aren't noise, they're the several real ways real people ask for one thing, so you have to think like the buyer, not the keyword planner.
Here is the manual version. Start from one real customer question, then expand it the way the model would. Write out the many ways buyers actually phrase it, in their words. Ask the real questions, not your marketing lines. Then walk the purchase journey, because each stage fans differently. Someone learning the category asks whether a standing desk is even worth it and what to look for. Someone comparing asks how the FlexiSpot E7 holds up against the Uplift V2 in a small office. Someone ready to buy asks about return windows and delivery. Run that whole set across ChatGPT, Gemini, and Claude, and note who gets named at each step.
It's real work, and it beats counting keywords. Monitor two things, though, so your data stays accurate. First, the models tailor answers to you. Run the test from your own logged-in account and your history, your location, and your past chats bend the results toward what is relevant to you, not to the customer persona you are trying to stand in for. The more you test, the more the model learns you, and the further it drifts from what a fresh prospect would see. Second, the fan itself moves. One Profound analysis of roughly 7 million ChatGPT prompts tracked how often ChatGPT added the word "reddit" to its behind-the-scenes searches, and that share climbed from about 0.15% to 3.68% in a matter of months, a 24-fold jump. A snapshot you took last month is already stale.
How is Adacity built to get this right?
Adacity is built to run the fan-out the way an AI does, without the bias, across both the questions and the journey. Three capabilities make that work.
First, question discovery. We start from real buyer language, the way customers phrase things in their own words, not a keyword list you wrote, and turn it into the real questions a shopper asks. Second, the fan itself. We run 150 to 400 or more of those questions across ChatGPT, Gemini, and Claude with live search on, from a neutral stance rather than your personalized account, and trace where each answer came from. Third, journey discovery. We fan out across the purchase journey too, from awareness (learning the category) to consideration (comparing options) to decision (ready to buy), so you see where you show up and where you disappear at each step.
The tracing matters because most of the answer isn't built from your site. About 85% of what AI repeats about a brand comes from third-party sources, not the company's own pages, so a fan-out search often gets answered on a review, a forum thread, or an article you don't control. Real questions, fanned across intent and across the journey, traced back to their sources, in one report. Adacity has you covered.
See the questions AI is really asking about you
The argument on r/SEO will keep going. You don't have to settle it to move. AI is already fanning your customers' questions into searches you can't see, at every stage of the journey, naming some brands over others right now. Want to see the whole fan, the real questions and where you stand at each step? Run the $5 check. You get the questions AI gets about you, the brands it picks, and where it's pulling its answers, in your inbox within the hour.