ChatGPT Fan-Out Queries Reward Authority: Act Now

TL;DR: ChatGPT now fires up to 7–29 fan-out queries per user prompt, increasingly scoped with the site: operator to pull from trusted domains — .gov, Reddit, and established brands. Operators in high-CAC verticals are being filtered out before citations are even assigned. This is a structural SEO problem, and it needs a structural fix.

What ChatGPT Fan-Out Queries Actually Are

When a user asks ChatGPT something that requires current information, the model does not reach into its training data. It fires a set of background web searches — fan-out queries — runs them in parallel, and synthesizes an answer from whatever it retrieves. These queries are the instructions ChatGPT gives itself before writing a response. Every operator it uses, every site: scope, every word choice tells you what the model considers a credible source.

Two terms matter here: retrieved and cited. Retrieved means ChatGPT fetched the page during its search process. Cited means the page was actually linked in the final answer. Right now those two metrics are moving in opposite directions. More pages are being retrieved — average source counts roughly doubled from about 12 to 24 with the ChatGPT 5.6 rollout — but fewer unique domains are being cited per response. ChatGPT is casting a wider net and selecting a smaller, more curated catch.

Tools like Peec AI, Profound, the Resoneo Chrome plugin, and FanoutFox let SEOs watch this process in real time. If you haven’t looked at what queries the model is firing for your tracked prompts, you’re operating blind.

The Numbers From Independent Research

Several researchers have measured fan-out behavior independently, with converging results that are hard to ignore.

David Konitzny at Peec AI captured the shift the day ChatGPT 5.6 became the default model. The share of prompts generating only a single fan-out query dropped from 94% to 43.5%. Average retrieved sources doubled. Prompts requiring a second fan-out iteration jumped from 5% to 33.5%. The site: operator went from appearing in roughly 0.3% of fan-out queries to about 23%.

Chris Long at Nectiv, comparing around 4,000 prompts on 5.6 Sol against a 2025 baseline, found average fan-out queries per prompt went from 2.17 to 7.61. The longest query chain grew from 4 searches to 29. The terms “site:,” “official,” and “gov” ranked as top unigrams, with site: appearing in 64% of queries.

There is a gap between those two site: figures — 23% versus 64% — and it likely comes down to methodology. Long’s team pulls fan-outs from OpenAI’s API, which surfaces more structured query data, while other tools extract from the consumer interface. Both methodologies point in the same direction: site: operator use is growing fast, and it is changing which domains get retrieved.

One more figure worth anchoring: over 90% of ChatGPT’s weekly users are on the free plan, according to reporting in Search Engine Land. Whatever the free default model does when it searches is what the overwhelming majority of ChatGPT interactions look like. Right now, that default is Luna, the cheaper variant of 5.6. What Luna retrieves is what most users see.

How the Model Decides Which Sites to Scope

The site: operator behavior is not random. Lily Ray’s observation across roughly 20 legal-space prompts found that every single one returned citations exclusively from .gov domains. For product specs and pricing, the model goes directly to brand domains — site:sephora.com, site:costco.com — often without the user naming those brands in the original prompt. For opinions and community feedback, it scopes to Reddit, including specific subreddits.

The pattern is consistent: ChatGPT appears to be using site: to pre-filter for domain authority before it ever scores individual page relevance. If your domain is not in the model’s mental map of trusted sources for your category, you are not being retrieved. You are not even losing the citation — you are being skipped at the query construction stage.

The model’s higher-tier versions search harder. ChatGPT 5.4 Thinking fired 10-plus searches for a single prompt, including multiple site: queries. The cheaper 5.3 Instant version ran two to three fan-outs. Better models are more aggressive in scoping their sources, which means authority gaps get amplified as users upgrade.

What This Means for High-CAC Vertical Operators

Forex brokers, iGaming platforms, crypto exchanges, and personal injury law firms all operate in categories where a single converted lead can be worth hundreds or thousands of dollars. AI search visibility in those verticals is not an abstract concern — it is a lead volume problem.

Consider a prospective trader asking ChatGPT to compare forex brokers. If the model fires site: queries against two or three well-established broker domains and synthesizes its answer from those pages, smaller or newer operators do not appear. The user never knows they were an option. Solid forex lead generation strategy now has to account for this retrieval layer, not just organic rankings.

The same logic applies to iGaming. A user asking “what are the safest online casinos in [state]” may trigger site: queries against established review aggregators or licensed casino domains with strong authority signals. New operators or those without citation history in those aggregators get filtered before the conversation starts. iGaming acquisition strategy needs a layer built for AI retrieval, not just paid media and affiliate placements.

Legal is perhaps the most urgent case. Every tested legal prompt returned .gov citations. Law firms that have built their content strategy around long-tail informational queries may find that AI retrieval completely bypasses their pages in favor of government sources. Law firm marketing needs to pivot toward content that complements .gov sources rather than competing with them head-on — mass tort explanations, process walkthroughs, jurisdiction-specific analysis that .gov pages do not provide.

Crypto operators face the brand-scoping problem: if ChatGPT associates a query with established exchange brands, it goes directly to those domains. Crypto lead acquisition for mid-tier platforms requires building enough brand authority that the model includes your domain in its category map — not just your keyword rankings.

Three Structural Fixes Operators Should Run Now

This is not a content refresh problem. It is a domain authority and entity-recognition problem. Here is where to focus effort.

Build entity authority, not just keyword coverage. ChatGPT appears to use a mental map of credible domains per topic category. The path onto that map runs through citations in authoritative third-party sources — major trade publications, regulated review sites, government-adjacent content. Operators should audit where their brand appears in those sources and what signals the model would use to recognize them as authoritative in their category. A full marketing audit that includes AI visibility — not just organic rankings — is the starting point.

Structure content to survive retrieval filtering. If your category triggers .gov or Reddit scoping, informational pages competing with those sources are fighting uphill. Reorient content toward what those high-authority sources cannot provide: practitioner-level specificity, proprietary data, regional or regulatory nuance. This is where performance-driven content operations matter — not volume, but precision placement in the gaps ChatGPT’s preferred sources leave open.

Treat fan-out query monitoring as a channel metric. Tools exist to track which fan-out queries your tracked prompts are generating and whether your domain appears in retrieval. If you are not running this data monthly, you are not managing your AI search exposure. Pair that monitoring with precision targeting across paid channels to maintain visibility while you build organic AI retrieval presence. These are not separate strategies — they are coverage layers in the same acquisition system.

The Trajectory

OpenAI is doing with fan-out queries something analogous to what Google did with E-E-A-T: building signals into the retrieval process that penalize low-authority, low-trust content before it reaches the user. The difference is speed. Google rolled out E-E-A-T concepts over years. ChatGPT’s site: operator usage went from 0.3% to 23% in a single model update cycle.

Operators who treat AI search as a secondary concern while focusing on traditional paid and organic channels are taking on accumulating risk. The model is getting better at recognizing authority and worse at surfacing anyone outside its curated domain map. Building AI retrieval presence now, while the model is still learning which domains belong in which categories, is materially easier than trying to break in after those patterns are established.

If your vertical competes on cost-per-acquisition and your brand is not being retrieved by ChatGPT, your CAC from AI-referred traffic is effectively infinite. That is the number worth fixing. AI-driven lead qualification systems can help capture and convert the traffic you do earn — but first, your domain has to make it past the fan-out filter.

Originally reported by Search Engine Journal, August 2026.