Topical Authority Injection in AI Search

Ask an AI assistant a broad recommendation question, something like the best hotel in a city or the best car in a category, and something happens before it ever runs a web search. The model does not send your question out as you typed it. It quietly rewrites the question into a fan-out query, sometimes breaking a single question into several related sub-queries, and adding things like the current year, comparison words like best or which, and category specific terms that were never part of your original wording. This rewritten version, or set of versions, is what we refer to as the fan query, the actual search or searches the model sends out on your behalf, as opposed to the question you originally asked.

At Visby, we ran an analysis of over 11,000 prompts to look closely at how these fan queries were built, and one pattern stood out enough that it deserves its own name: Topical Authority Injection, the tendency for the rewritten query to already lean toward the brands the model considers authoritative in that category.

This is not a one-model quirk. It shows up across generative AI assistants whenever a query touches a category with recognizable brand leaders. This article walks through what the behavior looks like in practice, why it is driven by authority perception rather than random expansion, and why it creates a real disadvantage for brands that have not yet entered a model's internal authority picture.

What Is a Fan-Out Query, and How Does It Expand?

A fan-out query is the actual search an AI assistant runs behind the scenes after you ask it a question, as opposed to the question you typed. When you ask something like "best running shoes for beginners," the model rarely sends that exact phrase to a search engine. It first turns your question into one or more fan-out queries, versions of the question rewritten and sometimes split into several related searches, and it is those fan-out queries that go out to retrieve information.

LLM Models do this because a single short question is often not specific enough to return a strong set of results on its own. Search engines reward specificity, so the model tries to fill in the gaps your question left open: what time frame you probably care about, what related terms might match the same intent, and what kind of source is likely to have the answer. In effect, the model is trying to search the way an experienced researcher would, rather than searching your words literally.

Not every fan-out query expands in the same way, though. Looking closely at how these expansions were built, two distinct types stood out.

The first is query enrichment, the neutral kind of expansion. It is what happens when the model adds a current year, a question word like which or how, a synonym, or a language variation to help the search return better results. A question like "fuel prices" becoming "fuel prices 2026 current" is enrichment. The intent behind the question has not changed. The model is just trying to make sure the results are up to date and well matched, not steering the answer toward anything in particular.

The second type is structurally different. Instead of just making the question more searchable, the model sometimes adds specific brand or institution names that the user never mentioned at all. Someone asking about trustworthy online fashion retailers gets a query fan-out that already assumes the answer probably includes a handful of specific retailer names, and those names get written directly into the search before a single result is fetched. We call this Topical Authority Injection, and it is the focus of the rest of this guide.

What Is Topical Authority Injection?

Topical Authority Injection is the pattern where an AI assistant expands a user's question into a longer fan query by adding category defining brand names, recency markers, and references to recognized data sources, before it retrieves a single result. The assistant is not neutrally searching for an answer. It is using its existing knowledge of who the established players are in that topic as a reference point, an internal authority source it leans on while building the search.

Generative models carry an internal sense of which brands or institutions are authoritative for a given topic or industry, and that sense can surface in the fan query even when the user's original question never named a single brand.

In our analysis, this showed up clearly. A simple, brand-agnostic question about a product category consistently turned into a query fan-out carrying several additional words, and a meaningful share of those words were brand names the user never typed, current year references, or terms tied to market data and rankings. The original question was rarely searched as written. It was expanded first, and in many cases, it was expanded with an opinion already baked in.

Measuring Topical Authority Injection Across 11,660 Prompts

We ran this analysis on a dataset of 11,660 prompts collected through Visby's AI visibility tracking infrastructure, comparing fan queries across three engines: ChatGPT, Google AI Overview, and Claude. For every prompt-engine pair, we compared the generated search query against the original prompt word by word, checking whether any brand or institution name appeared that the user never typed.

The gap between engines turned out to be far larger than we expected. ChatGPT injected a brand or institution name the user never mentioned into 53.8 percent of prompts. Google AI Overview did this in 4.2 percent of prompts, and Claude in 4.6 percent. In other words, ChatGPT injects brand names roughly 12 times more often than the other two engines.

A few concrete examples make the scale of this clearer. For the prompt "trustworthy online fashion shopping sites," ChatGPT's query fan-out added ten specific retailer names that never appeared in the original question. For a prompt asking which cars stand out for their personalization features, ChatGPT added 23 brand names on its own, from Audi and BMW to Bentley, Ferrari, and Rolls Royce, while Google and Claude added none for the same prompt. In finance related prompts, regulatory bodies and major banks were among the names most frequently injected.

What This Means for Brand Visibility in AI Search

For brands with strong existing recognition, this behavior reinforces an advantage that likely already exists in traditional search and media coverage. For brands still building that recognition, it introduces a structural gap that content alone cannot fully close, because the search query behind an AI answer was never neutral to begin with.

This changes what "ranking well" means in AI search. It is no longer only a question of what a page says or how well it is optimized. It also depends on whether a brand has entered the model's internal authority picture for its category, since that picture directly shapes what the model searches for on a user's behalf before your content gets a chance to compete.

How Brands Can Work Toward Being Included

Closing this gap takes time, but the direction is clear from what we observed. Brands that appeared among the injected names consistently had strong, current, and frequently cited presence tied to the specific segment they compete in, not just their category in general. Association with recognized data sources and industry reporting also correlated with stronger odds of being one of the names the model injects.

This is also where classic SEO alone starts to fall short. Ranking well on Google has never guaranteed a spot in a model's internal shortlist, since that shortlist is formed before a search even runs. Publishing keyword optimized pages is still necessary, but it is no longer sufficient on its own. What actually shifts a model's perception is a pattern of authority signals across a category: consistent segment specific content, mentions alongside recognized data sources, and a presence that keeps showing up wherever that category gets discussed.

None of this guarantees a brand enters the fan query immediately. But steady, segment specific authority building, backed by recent and verifiable information, is what shifts a model's perception over time. Our projection, based on how injection behaved in the data, is that a brand's realistic goal is to move a category from generic enrichment toward being one of the names the model injects by default, rather than expecting to displace an already established set overnight. Brands that wait for search results alone to carry them are working against a bias that was already set before the search began.

This is exactly the gap Visby was built to close. Instead of guessing which fan queries your brand is or is not part of, Visby tracks how your brand actually shows up across ChatGPT, Claude, Gemini, and Google AI Overview, and turns that into specific, prioritized actions for building the kind of authority these models already reward. Today, more than 2,500 brands worldwide use Visby to track their AI visibility, Visby earned 13 badges in G2's Summer 2026 Answer Engine Optimization category, and Visby is an OpenAI Select Partner.

FAQ

What is Topical Authority Injection in AI search?

It is when an AI assistant adds brand names and authority signals into its own search query, based on which brands it already perceives as category leaders, rather than searching a user's original question exactly as written.

Does this happen with every type of query?

No. We saw two distinct behaviors in our data. Query enrichment happens on almost every query, adding neutral things like the current year or a question word. Topical authority injection is different and more selective: it happens mainly in categories where the model already has a confident sense of a few dominant brands, and those specific names get written into the fan query. In broader or less defined categories, the fan query tends to stay generic instead.

Is this specific to one AI assistant?

No, but the intensity varies a lot by engine. In our data, ChatGPT injected brand or institution names into 53.8 percent of prompts, compared to 4.2 percent for Google AI Overview and 4.6 percent for Claude, roughly a 12x difference. All three engines showed the behavior, but ChatGPT relied on it far more heavily than the other two.

Can a brand fix this through website content alone?

Content plays a role, but it is not the full answer. Since the bias is introduced before search even happens, brands also need consistent, segment specific authority and presence across recognized sources to shift how a model perceives them over time.