A 2026 study of ChatGPT 5.5 by Seer Interactive found something worth watching: the model's hidden fan-out sub-queries are increasingly brand-specific. Instead of only searching generic phrases like "top GEO agencies," ChatGPT 5.5 also issues searches shaped like "[brand] GEO research." When a model's own sub-query is your brand name, your brand has stopped competing inside a generic list and started being part of the question. This piece lays out the data, attributed and hedged, and explains why it raises the stakes on entity strength.
It pairs with the mechanics in how query fan-out works and the week-to-week instability documented in why your AI share-of-voice score swings.
What did the ChatGPT 5.5 fan-out study find?
Seer Interactive (Wil Reynolds and Nick Haigler, 2026) examined the sub-queries ChatGPT 5.5 generates before it composes an answer. The fan-out step where a single prompt is silently expanded into many background searches. The headline finding is a shift in the shape of those sub-queries.
| Study detail | As reported |
|---|---|
| Source | Seer Interactive (2026), a digital-marketing agency |
| Prompt set | 617 prompts typical of growth VPs, CMOs, and marketing directors |
| Repeat test | One prompt asked 30 times ("As a VP of growth looking to future-proof our search strategy…") |
| Key shift | Fan-out moved from generic ("top GEO agencies") to brand-specific ("[brand] GEO research") sub-queries |
| Brand presence | Specific brand names appeared in roughly half of the 30 answers |
Source: Seer Interactive, 2026. This is a single-vendor, self-reported analysis of one model, shared as practitioner research, so read the direction as the signal and treat the specific proportions as directional, not precise. Seer's own framing is that human-validated work, not scalable content, is what gets a brand "hardwired" into the model.
Why does a brand-name sub-query matter so much?
Because it changes what game you are playing. In a generic fan-out. The model searches "best CRM for startups". Your brand is one candidate the model has to discover and select from a retrieved list. In a brand-specific fan-out. The model searches "your brand pricing" or "your brand review". The model has already decided your brand is relevant and is now gathering detail on it.
When the AI's own sub-query is your brand name, you are no longer an option inside the answer. You have become part of the question. That only happens when the model has strongly associated your brand with the topic.
This is the difference between being found and being assumed. A brand-specific fan-out is downstream evidence of a strong entity association. The model reaches for your name unprompted because, across the corpus it learned from, your brand and the topic co-occur again and again. It is the same mechanism behind why AI keeps citing one brand and how AI engines choose which brands to name.
Is this consistent with what other engines do?
Directionally, yes. The trend line across independent work points the same way, even though methods differ. Newer, more capable models tend to fan out into more, longer-tail, and more specific sub-queries rather than a single broad search. That aligns with:
- The reasoning-model pattern documented elsewhere. A "thinking" model can spawn dozens of sub-queries per prompt, some of which name specific entities, as covered in how ChatGPT picks the sources it cites.
- The primacy of brand-level signals: correlation studies repeatedly rank branded mentions and branded search among the strongest predictors of AI citation, above raw backlinks (see the three citation levers).
The caution: this is one study of one model version, and model behaviour changes between releases. The durable claim. Capable models increasingly resolve topics down to named entities. Is firmer than any single number, and it survives whichever exact fan-out ChatGPT does next quarter.
What should you actually do about it?
You cannot make a model fan out to your brand directly. There is no setting for it. You earn it indirectly, by building the association the model reads:
- Publish work worth naming. Original research, specific data, and genuine thought leadership get discussed and repeated across independent sources. The corroboration that teaches a model to link your brand to the topic. Scalable, me-too content does not.
- Be a recognised entity, consistently. Use one consistent brand name, keep your Organization identity clean and machine-legible, and build presence where your buyers and the models both look. Including communities, per using Reddit for AI-search visibility.
- Go deep on a focused topic. Singular-topic depth builds a stronger association than broad, shallow coverage. The same principle behind earning a place in AI-cited best-lists through earned authority rather than self-promotion.
- Measure the right thing. Being fanned out to is upstream of being cited, which is upstream of being recommended. Track share of voice as a smoothed trend across many prompts and every engine, not a single reading.
The practical read for 2026: as models get better at resolving a question down to named brands, the payoff shifts from ranking a page to being a known entity. Entity strength compounds slowly and is hard to fake, which is exactly why it is worth building. Tracking whether the engines have started reaching for your brand by name. Across ChatGPT, Gemini, Claude, and Perplexity, over time. Is exactly what Buffy Intel is built to measure.