ChatGPT query fan-out: why one search is now ten

The “Long Tail” is dead. Long live the “Wide Fan.”

In traditional SEO, we optimized for the long-tail keyword: “Best project management software for small creative agencies under $50.”

We wrote massive, monolithic articles trying to rank for that exact phrase.

But when a user types that into ChatGPT or Perplexity today, the engine doesn’t search for that string. It does something smarter, faster, and infinitely more complex.

It performs Query Fanout (also known as Query Decomposition).

It takes that single complex prompt and explodes it into 5, 10, or even 20 simultaneous “atomic” searches. If your content answers one of those atomic questions perfectly, you win. If you only tried to rank for the long tail, you lose.

Table of contents

• What is query fanout?
• The mechanics of decomposition
• Why this breaks traditional SEO
• Optimizing for atomic intent
• The “Frankenstein” answer
• Tracking your fanout performance

What is query fanout?

Query Fanout is the process by which an AI agent breaks a multi-part user request into discrete, solvable sub-tasks, executes them in parallel, and synthesizes the results.

The Old Way (Google Classic): User: “Compare Slack vs Teams for developers” Engine: Looks for pages containing “Slack vs Teams for developers”

The New Way (AI Search): User: “Compare Slack vs Teams for developers” AI Agent: I need to know:

1. What are the developer-specific features of Slack? (Search A)
2. What are the developer-specific features of Teams? (Search B)
3. What is the API rate limit for Slack? (Search C)
4. How does Teams integrate with GitHub? (Search D)
5. What is the pricing difference? (Search E)

The AI runs all five searches at once. It reads five different pages (potentially from five different websites), extracts the facts, and writes a single answer.

The mechanics of decomposition

This isn’t magic; it’s Chain of Thought (CoT) reasoning applied to search.

When models like OpenAI’s o1 or GPT-4o receive a prompt, they first engage in a “planning” phase. They identify missing information.

The Fanout Workflow:

1. Ingest: Receive complex user prompt.
2. Decompose: Identify independent variables.
3. Execute: Fire off parallel AI Crawlers to fetch data.
4. Read: Parse the content (this is where llms.txt helps massively).
5. Synthesize: Combine the disparate facts into a coherent narrative.

The result? A user gets a comprehensive answer without ever clicking a link. The AI has done the “tab surfing” for them.

Why this breaks traditional SEO

For 20 years, SEOs have been taught to write “The Ultimate Guide.” We stuff every possible sub-topic into one 5,000-word URL to maximize topical authority.

Query Fanout penalizes the “Ultimate Guide.”

Why? Because “Ultimate Guides” are often:

• Hard to parse (too much fluff).
• Broad but shallow.
• Slow to load.

AI agents prefer Atomic Content—content that answers one specific thing with extreme depth and authority.

If the AI is looking for “Slack API rate limits,” it prefers a developer documentation page that answers exactly that over a “Top 10 Chat Tools” blog post that mentions it in passing.

Optimizing for atomic intent

To win in a Query Fanout world, you need to shift your content strategy from “Keywords” to “Facts.”

1. Fragment your content

Instead of one giant page, create a hub-and-spoke model where specific questions get specific pages.

• Bad: One page on “All about our Pricing.”
• Good: Separate URLs or clearly defined H2 sections for “Enterprise Pricing,” “Startup Discounts,” and “Non-Profit Tiers.”

2. Be the “Fact Supplier”

AI engines trust data. If you conduct a survey or publish a benchmark report, you become the primary source for that data point. When the AI fans out to find “average churn rate in SaaS,” it will cite your report.

3. Structured Data is King

Use Schema.org markup to label your atomic facts. If you have a pricing table, wrap it in Product schema. If you have a Q&A, use FAQPage schema. This helps the bot extract the specific “shard” of information it needs during the fanout process.

The “Frankenstein” answer

The final answer the user sees is a monster stitched together from different body parts.

• The Introduction might come from Wikipedia.
• The Pricing Comparison might come from your pricing page.
• The Pros/Cons might come from a Reddit thread (or a competitor’s comparison page).

Your goal: Own as many “body parts” as possible.

You want to be the source for the pricing and the features and the security compliance. This requires a holistic GEO strategy.

Tracking your fanout performance

This is the trickiest part. In Google Search Console, you might see impressions for queries you didn’t explicitly target, or you might see a drop in clicks despite high visibility (because the AI took the fact and ran).

How to measure success:

1. Citation Density: Use tools to check how often your brand is cited as a source in these complex answers.
2. Fact Retrieval: Monitor if the specific unique data points you publish (e.g., “our uptime is 99.99%”) are being surfaced in AI answers.

The cloro Advantage: You can’t manually test every variation of a complex query. cloro automates this. It can simulate complex, multi-step prompts to see how engines like ChatGPT and Perplexity decompose them, acting as a powerful ChatGPT visibility tracker.

cloro shows you:

• Which sub-queries are being generated.
• Which of your pages are being fetched for those sub-queries.
• Whether the final synthesized answer is accurate.

The future of search isn’t about ranking for the question. It’s about ranking for the answer to the sub-question you didn’t even know the AI was asking.