YouTube’s Algorithm Doesn’t Push Videos Anymore—And That Changes Everything About How You Build Reach
I’ve spent two decades watching platforms manipulate creator success through opaque distribution systems. YouTube just broke that pattern.
The platform’s 20th anniversary report reveals something I’ve been tracking for months but couldn’t fully articulate until now. YouTube shifted from a subscriber-broadcast model to an interest-based discovery system. The algorithm doesn’t push content to audiences anymore. Viewers control what gets recommended to them through their watch history and engagement patterns.
This isn’t a minor adjustment to how content gets distributed. This is a structural recalibration that eliminates the artificial barrier between new creators and established channels.
The Subscriber Count Myth Just Collapsed
Small channels have a real shot at wide reach now. The algorithm cares more about viewer response than subscriber counts or upload history. If a video hooks the right audience, it gets recommended regardless of who made it.
I tested this with a client last quarter. We launched a new channel with zero subscribers. Within three weeks, one video hit 47,000 views. The channel had 12 subscribers at the time.
That doesn’t happen in a subscriber-dependent system.
YouTube’s Director of Growth confirmed this through built-in viewer surveys that collect feedback on how people feel about what they watched. The platform optimizes for satisfaction over watch time. This means quality of engagement beats quantity of followers.
The implication for mid-growth companies is significant:
- You can rebuild reach quickly if you lose platform access.
- Your audience isn’t trapped behind a subscriber wall anymore.
- Content quality and viewer response determine distribution, not historical follower count.
TV Screens Now Dominate YouTube Consumption
YouTube amassed 45.1 billion viewer hours between January and June 2025. TV screens accounted for 36% of total viewer hours—16.3 billion hours. That’s more than mobile devices at 29% or 13.2 billion hours.
People over 50 represent about 36% of all time spent watching YouTube on TV screens, more than the combined 28% for teenagers and adults 18–34.
I didn’t expect that demographic split.
This shift to TV-based consumption changes content strategy fundamentally. Viewers watch YouTube on TV while cooking dinner, working from home, or doing household tasks. They want longer-form content suitable for multitasking, not just quick hits between meetings.
Your content now needs to:
- Function as background-capable media.
- Remain engaging enough to hold attention when viewers look up.
- Be structured for longer sessions rather than 30–60 second bursts.
That’s a different production requirement than optimizing for mobile-first consumption.
The Shorts Monetization Gap Reveals Platform Economics
YouTube Shorts now averages over 70 billion daily views globally. The format exploded in growth, but monetization tells a different story.
-
- Typical Shorts ad rates: roughly $0.01–$0.30 per 1,000 views in many niches.
-
- Long-form with multiple ad breaks: often $5–$25+ per 1,000 views in premium markets.
A viral Short commonly delivers only a small number of new subscribers relative to views, and the majority of those views come from non‑subscribers. Channels that combine Shorts with long-form content tend to grow significantly faster, but it’s the long-form content that keeps viewers on the channel and watching more videos.
In client accounts, the pattern is consistent:
- Shorts drive discovery.
- Long-form drives revenue and relationship.
The recommendation system often tests new Shorts with a small audience first; if performance is strong, the video gets shown to wider audiences over time, which means Shorts can take off weeks or months after posting.
This creates a clear strategic split:
- Use Shorts for audience acquisition and brand awareness.
- Use long-form for monetization and relationship depth.
Trying to force Shorts into a primary revenue role creates frustration because the platform economics don’t support it at scale.
Most Creators Still Earn Under $15,000 Annually
The creator economy has been estimated at around $250 billion in recent years, yet more than half of individual creators report earning under $15,000 a year, while only a small single‑digit percentage clear $100,000+ annually.
Top earners typically maintain multiple revenue streams—often around three on average—compared to roughly two for lower‑earning creators. Their income mix tends to include:
- Brand sponsorships.
- Digital products.
- Affiliate and ad revenue.
- Services.
- Paid subscriptions.
The pattern is clear: diversification determines financial viability.
Audience ownership is even more revealing. A majority of professional creators report owning their audience directly via email, and those with strong email lists are several times more likely to earn over $30,000 per year.
Across client engagements, the same dynamics show up:
- Platform reach fluctuates.
- Email lists remain relatively stable.
- Direct communication channels create resilience against distribution volatility.
Professional Infrastructure Becomes Mandatory
Among top‑earning creators, a large majority work on their creator business as their primary job, and most collaborate with at least one other person, compared to much lower figures among the general creator population.
As YouTube becomes more financially viable, amateur creators face pressure to professionalize. Content is increasingly viewed as infrastructure requiring dedicated resources.
After watching dozens of mid‑growth companies attempt to “wing it” with spare time and enthusiasm, one conclusion holds:
- The production quality threshold keeps rising.
- The consistency requirement keeps intensifying.
- The strategic complexity keeps expanding.
You need dedicated capacity to maintain competitive positioning. That doesn’t mean a full production team on day one, but it does mean treating content as core business infrastructure rather than marketing decoration.
Budget accordingly. Staff accordingly. Measure accordingly.
Early Monetization Signals Long-Term Success
Survey data on creators shows that nearly half of top earners made their first dollar within the first few months of starting, versus a smaller fraction among the broader creator pool.
This supports a simple principle: test small before you invest big.
- Start with one revenue stream and prove it works.
- Get your first paying customer.
- Optimize that conversion path.
- Only then add a second revenue stream.
Many creators spread effort across several income sources that each generate tiny amounts instead of focusing long enough on a single, higher‑leverage stream.
Platform Selection Determines Commercial Viability
Different platforms monetize attention in radically different ways. For example, creator surveys and platform reports suggest that LinkedIn and certain podcast ecosystems produce a higher proportion of creators earning $30,000+ compared with short‑form‑only platforms, especially in B2B and finance niches.
When asked for their primary platform, respondents often report a split along these lines:
- Podcasts as a primary platform for a significant minority.
- YouTube for another large segment.
- Newsletters, live streaming, and short‑form platforms making up the remainder.
Podcasters and B2B‑focused creators tend to outperform short‑form‑only creators in average income, largely because their audiences have budgets and buying authority.
The takeaway: platform economics matter more than pure content quality.
- If you sell to enterprise buyers, LinkedIn often beats consumer‑focused platforms regardless of follower count.
- If you need deep relationship development, podcasts and long‑form often beat Shorts regardless of production budget.
Companies that chase reach on platforms where their ideal customers lack purchasing power often build large but low‑value audiences.
Choose platforms where your audience has both attention and transaction capability.
What This Means For Your Content Strategy
YouTube’s transformation from subscriber‑dependent distribution to interest‑based discovery creates three immediate opportunities for mid‑growth companies.
- Enter without existing audience infrastructure.
The algorithm evaluates content performance independently of channel history. This lowers the barrier to building new distribution channels when you need to diversify platform risk. - Optimize for satisfaction, not vanity metrics.
Viewer response and satisfaction signals determine reach more than subscriber counts or raw view totals. This shifts focus from audience size to audience quality, which aligns better with B2B and high‑ticket models.
Hi All My Marcom Peeps,
This isn’t the story of an agency — it’s the story of an innovator that’s been proving what real marketing can do for decades.
Hi, I’m Daniel Elliott.
I Build Brands That Sell.I’ve spent four decades building marketing campaigns that make businesses more money, predictably, profitably, & without the agency BS and egos.
I’m Daniel Elliott, founder and strategist at Appture Digital Media. I’m reaching out because, after decades in this industry, I’ve seen the same frustration: good businesses waste serious money on marketing that generates “awareness” and “clicks,” but never consistent income.
Let’s be honest—most marketing isn’t strategy, it’s noise. And noise doesn’t pay the bills.
At Appture Digital, we don’t “do marketing.” We build systems that sell.
We specialize in taking the chaos out of your growth with a proven 3-step approach:
- Strategy First: We uncover the exact triggers and motivations that make your customers buy.
- System Build: We engineer funnels, automation, and follow-up that convert attention into revenue—predictably.
- Scale & Optimize: We turn up the volume, allowing your ROI to compound while your effort stays the same.
We achieve this with a curated offering of advanced services that include: Web Development, Marketing Strategy, Video Marketing, Social Media Marketing, Website Makeovers, Hybrid & Virtual Events, Email Marketing, Podcast and Live Stream Studio
If you’re ready to move past the hype and start building a machine that generates consistent, measurable profit, let’s talk.
I’m not interested in a pitch deck; just a real conversation about how this system would work for your business.
Ready to build a system that sells?
Best regards,
Daniel Elliott
Founder, Appture Digital Media
Search engine updates have become a regular source of anxiety for marketers. Nearly every month, a new Google update rolls out, often labeled a “core update,” and the reaction is predictable: speculation, analysis, and a scramble to identify what changed. The challenge is that Google rarely provides specific, actionable details. Unlike older updates that targeted clear issues like spammy backlinks or thin content, today’s updates are far less transparent.
The reality is that SEO is no longer a simple, rules-based system.
For years, ranking strategies revolved around measurable inputs: keyword density, backlink counts, word count, and exact-match phrases. These metrics created the illusion that SEO was a formula you could reverse-engineer. That model no longer reflects how search works today. Google’s algorithm has evolved to behave less like a rigid equation and more like a system designed to mimic human judgment.
Chasing Google vs. Understanding Users
Many marketers are still trying to “solve” Google’s algorithm, aiming to match what works right now. But Google is constantly advancing, prioritizing where search is going rather than where it has been. The shift is clear: success is no longer about gaming metrics—it’s about satisfying user intent.
As Google’s Search Liaison has explained, focusing on the algorithm puts you behind. Focusing on what users actually want puts you ahead.
Traditional metrics still have value. Search volume helps identify demand, and high-quality backlinks remain a trust signal. But they are no longer the primary drivers of success. Google has made it clear that content should be created for people first, not search engines.
This shift calls for a new mindset: human-centered optimization.
Metrics vs. Meaningful Content
When rankings drop after an update, the initial reaction is often frustration. Site owners compare their pages to competitors and point out differences in metrics: longer content, higher authority scores, or better keyword usage. On paper, their page appears stronger.
However, a closer review often reveals a different story.
Pages that perform well tend to offer clearer, more useful, and more original content. They demonstrate real expertise, avoid unnecessary filler, and provide a better overall experience. Meanwhile, underperforming pages often rely too heavily on optimization tactics while neglecting actual value.
The disconnect comes down to perspective. Metrics provide data, but users respond to clarity, relevance, and usefulness.
The Shift to Human-Centered Optimization
Improving performance today requires stepping outside of analytics tools and evaluating your site as a real visitor would.
Start by identifying your target audience. Consider what they are searching for, what problems they are trying to solve, and what outcome they expect. Then, navigate your site as if you were encountering it for the first time.
Ask yourself:
- Can you quickly find the information you need?
- Is the content clear and helpful, or overly long and unfocused?
- Are there distractions like intrusive popups or excessive ads?
- Is it obvious what to do next?
Document any friction points you encounter. These insights are often more valuable than any SEO tool.
For an even clearer perspective, compare your experience with a competitor’s site. If their page feels more helpful or easier to use, identify exactly why. Those differences often highlight what Google is rewarding.
If it’s difficult to evaluate your own site objectively, bringing in an outside perspective—such as a UX audit—can uncover blind spots you might miss.
The Real Competitive Advantage
The most effective SEO strategy today is not based on chasing updates or decoding ranking factors. It is built on understanding people.
Search engines are increasingly aligned with user expectations. That means the sites that win are the ones that genuinely help, inform, and guide their audience.
The most powerful SEO tool available is not a platform or a dataset. It is the ability to think like your user and create an experience that meets their needs better than anyone else.
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It’s been a turbulent stretch for Google. Concerns about declining search quality have been bubbling up since at least last fall’s Helpful Content Update, and documents from Google’s antitrust trial added fuel to the fire by suggesting that executives pushed to grow ad revenue and share price by increasing search queries—essentially, making it harder for users to get straight to what they need.
At the same time, well-known voices connecting Google and the SEO world, like Danny Sullivan and John Mueller, continue to say that Google’s priority is improving search results and helping people find better information, not worse.
From my experience in the newspaper industry, I don’t see these positions as mutually exclusive. Both can be true.
Just like Google, a newspaper has two core engines: one that creates something readers genuinely want, and one that sells ads. The newsroom works to uncover and explain the news, giving people what they need to know. But as subscription revenue shrinks and ad revenue becomes the main lifeline, it’s easy for the ad side to start driving decisions. When that happens, coverage tends to get shorter and more superficial, while more space and budget are devoted to advertising instead of reporting.
I don’t doubt that Google’s Search teams are genuinely focused on improving the SERPs and helping users find what they’re looking for. But Google is still a business, and it has to sustain itself. With AI development now front and center in its fight to maintain dominance, there’s a new layer of financial pressure in the mix as well—building and running large‑scale AI systems is expensive.
On top of that, we now have a major antitrust ruling against Google, and that’s likely to reshape the landscape in meaningful ways.
So where does all of this leave Google—and where does it leave us? One way to think about the future is as a two‑track path: one for today’s free, ad‑supported Google, and another for a more advanced, subscription‑driven tier of AI search, like what’s currently called Gemini Advanced.
Free vs. AI: The Roads Forward
Most people don’t love paying for something they can get for free. When news moved online, many readers lost interest in paying for newspaper subscriptions. The result has been the closure of many newsrooms, and among those that survived, the ones that managed to convince readers that their deeper coverage was worth an online subscription fee.
Similarly, you probably won’t find a crowds of people eager to pay specifically for “Google Search.” But what if the paid product isn’t just search as we know it, but the version we’ve long been promised: something that reliably surfaces exactly what you need, with minimal ads and noise? Google keeps saying that its goal is a more human‑centered search experience that helps people find genuinely helpful content.
Given the Justice Department’s ruling, it’s not hard to imagine a future where we effectively have two Googles: a free, ad‑funded search engine that looks a lot like the Google of a few years ago, and a subscription‑based, AI‑powered assistant—a kind of modern digital Jeeves—that guides you to high‑quality, user‑focused, trustworthy information.
If that’s the direction we’re heading, then success online will mean being discoverable in both worlds. We’ll need to optimize for the free search engine and for the more advanced AI‑driven tools that people will use alongside it.
SEO for Today and Tomorrow
What doesn’t change is the need to keep raising the quality bar for our content. We still have to focus on meeting user intent for the queries we hope to rank for. Google has repeated this message over and over and over again: fill your site with content designed to help real readers, not keyword‑stuffed fluff written purely for an algorithm.
That’s the kind of material Google says it wants to reward, and it’s also the kind of material AI systems are more likely to rely on and reference when building answers for users.
At the same time, it’s obvious that backlinks are very much alive. In fact, signals like backlinks, clicks, and Chrome data surfaced clearly in the May Google document leak, and Jim Boykin’s recent research shows that strong links remain one of the key levers if you want to rank in Google’s results.
There’s little reason to believe that would suddenly stop being true if search results become more separated from other products like Gemini and Chrome. Links are still how the web signals relationships and recommendations.
Put simply, both high‑quality on‑page content and off‑page signals are likely to remain essential to your visibility, regardless of whether users are relying on classic SERPs, AI answer boxes, or some combination of tools we haven’t fully seen yet. Making space for both in your strategy is likely to be the difference between a site that keeps performing and one that slowly fades.
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Hi everyone, Daniel Elliott here. If you know me, you know I spend a lot of time trying to understand how Google really works—testing, digging into docs, and watching how the algorithm changes over time. Today I want to walk through one of the more interesting angles from the leaked Google documentation: how Google thinks about authorship.
Specifically, we’ll look at how Google identifies and evaluates the people behind content—both on your own site and on the sites that link to you. If you’ve been treating authors as an afterthought, this is a good time to reconsider. The way Google processes author data now can have a serious impact on your SEO performance.
In this post, I’ll break down one core area from the leak, show how author-related signals show up in different API calls, and talk through how you can use that understanding to strengthen your on‑page SEO and your backlink strategy.
What the Leak Revealed About Authors and Google’s Algorithm
How Google’s APIs Pull Author Data
To see how authorship fits into the bigger picture, it helps to look at the API calls mentioned in the documentation. These calls give Google structured ways to pull information about authors, pages, links, and relationships across the web. Understanding what they do doesn’t mean you can “control” them, but it does give you useful clues about what to expose clearly on your site.
Here are some of the calls that matter most from an SEO and author‑signal perspective:
GET page_meta_info- Definition: Retrieves metadata from a webpage, including an author name from HTML
<meta>tags when that’s present. - SEO Insight: Make sure your pages have reliable meta information, including author tags where it makes sense. It helps Google connect content to specific people more efficiently.
- Definition: Retrieves metadata from a webpage, including an author name from HTML
GET /user_profiles/{user_id}- Definition: Pulls detailed author or user profile data from a CMS that stores person‑level information.
- SEO Insight: If you run a multi‑author site, treat profiles as real assets. Rich, well‑maintained profiles support trust and clarity around who is behind your content.
GET /author_info?user_id={user_id}- Definition: Retrieves structured information about an author—bios, roles, and what they’ve contributed.
- SEO Insight: Aim for systems that can expose this kind of data cleanly. The more coherent your author information is, the stronger your author signals can be.
GET /link_meta_info?url={page_url}- Definition: Looks at links on a page and can associate them with authors of the referenced content.
- SEO Insight: Backlinks from content with visible, credible authorship are more useful than links from anonymous or unclear sources.
GET external_meta_info?external_url={link_url}- Definition: Fetches metadata from other sites to extract authorship details for linked content.
- SEO Insight: Earning links from sites that treat authorship seriously—clear bylines, bios, and structured data—helps reinforce the trust around your own content.
ScienceCitationAuthor- Definition: A structured model for detailed author metadata, such as name, role, and institutional affiliation.
- SEO Insight: If you operate in scientific or academic spaces, use appropriate schemas and detailed author data. It fits directly into how Google can model expertise in those domains.
ScienceCitationTranslatedAuthor- Definition: Similar to
ScienceCitationAuthor, but allows for translated author names in multilingual settings. - SEO Insight: For multi‑language publications, consistent, well‑translated author information helps keep identity and attribution coherent across locales.
- Definition: Similar to
NlpSciencelitCitationData- Definition: Contains author data connected to citations or articles, tying people to the research they publish.
- SEO Insight: In research‑heavy topics, good citation practices and clear author links make it easier for Google to read your work as part of a credible body of knowledge.
NlpSciencelitAuthor- Definition: Holds basic author information such as first name and last name.
- SEO Insight: Simple details still matter. Consistent, correctly formatted names across platforms and content help Google match authors to their wider footprint.
NlpSciencelitArticleMetadata- Definition: Deals with article‑level metadata, including authorship.
- SEO Insight: Clean, complete article metadata is part of how your work becomes easier to discover and trust—especially for long‑form or specialist content.
VideoVideoClipInfo- Definition: Includes author‑related attributes for video content.
- SEO Insight: For video SEO, treat creator details as part of your optimization stack. Clear attribution helps connect channels, videos, and topics back to real people.
NlpSemanticParsingModelsShoppingAssistantProductMediaProduct- Definition: Relates to media products tied to shopping, including information about the people behind those products.
- SEO Insight: If you sell media or content‑based products, solid authorship data can support recommendations and discovery in commerce‑related surfaces.
ScienceIndexSignal- Definition: Connects author information to scholarly articles and indexing signals.
- SEO Insight: For academic SEO, precise, standardized author data helps you benefit from indexing systems and signal aggregation.
NlpSaftDocument- Definition: Mentions authors inside broader document contexts using NLP.
- SEO Insight: Even if you don’t rely heavily on explicit tags, Google can infer authorship from the way you write and reference people, so clarity in your content still matters.
OceanDataDocinfoWoodwingItemMetadata- Definition: Handles authorship within item metadata in document management systems.
- SEO Insight: If you have complex editorial workflows, treat author metadata as a first‑class field, not an afterthought. It will pay off in consistency.
NlxDataSchemaDocument- Definition: Includes author‑related data from a document analysis point of view.
- SEO Insight: The more your CMS can expose detailed document and author information, the easier it is for systems like Google to interpret and trust your content.
Taken together, these and other author‑centric calls touch both what happens on your own pages and what happens in your backlink profile.
On‑page signals are about the content you publish: clear bylines, structured data, complete profiles, and evidence that real, qualified people stand behind what’s on your site.
Backlink signals are about who talks
Your content can rank on the first page of Google and still never be cited or mentioned by LLMs.
This makes sense once you understand query fan-out, a background process AI systems use to build answers.
When someone asks ChatGPT or Perplexity a question, it doesn’t default to the best-ranking page.
Instead, it runs related searches behind the scenes, pulling from the most relevant and reliable sources, regardless of position.
If your brand doesn’t show up in those searches (whether through your own content or third parties), you’re unlikely to make it into the answer.
High rankings don’t hurt, of course.
But in AI search, coverage and retrievability are king.
In this guide, I’ll teach you how to optimize your content strategy for query fan-out to help increase your AI visibility.
You’ll learn:
- Why LLMs use query fan-out
- How it behaves differently across major AI platforms
- Why it changes how you create and structure content
- A 6-step workflow for earning more citations in AI search
Free template: Our Query Fan-Out Audit Template includes ready-to-use spreadsheets for logging money prompts, sub-queries, and content gaps — plus a checklist to keep you on track. Download it now to follow along.
First, I’ll dive deeper into how query fan-out works.
What Is Query Fan-Out?
Query fan-out is a process AI search systems use to break a single user query into multiple sub-queries to create the most helpful response.
In other words, the AI “fans” the query out into a series of related sub-questions to build a more complete picture of the topic.
It then pulls information from multiple sources — editorial sites, Reddit threads, comparison and product pages — and synthesizes it into a single comprehensive answer.
AI systems use query fan-out for a few reasons:
- Confirm information: A single source might be wrong or biased. Running parallel sub-queries allows the system to cross-reference multiple sources and find consensus before committing to an answer.
- Handle complex, specific queries: When a question has multiple layers, like comparing two products across price, reliability, and long-term value, fan-out breaks it into manageable pieces that the system can research independently.
- Answer the real question: Someone searching “best toothbrush” probably also wants to know about price, battery life, and durability, even if they didn’t say so. Fan-out anticipates those needs and gathers evidence upfront.
For example, a search for “best toothbrush” might trigger sub-queries like “best electric toothbrushes [year]” and “best toothbrushes for sensitive gums.”
This helps the AI build a more complete and useful answer:
Sub-Query What It Contributes to the AI Response Best electric toothbrushes Top-rated picks and editorial consensus Best toothbrushes for sensitive gums Use-case recommendations Oral-B vs. Philips Sonicare Head-to-head comparison data Best eco-friendly toothbrushes Value picks and pricing information The AI then synthesizes those findings into a single answer that covers everything the user might want to know: top picks, price ranges, use-case breakdowns, and comparisons.
In this way, it anticipates the user’s needs, even though the original prompt (best toothbrush) was just two words.
What Query Fan-Out Is NOT
Now that we’ve covered what query fan-out is, let’s clear up a few common misconceptions.
Query fan-out is not:
- Keyword research: This is the process of finding terms your audience searches for. Query fan-out is something AI systems do automatically, behind the scenes, every time someone asks a question.
- People Also Ask: PAA is a visible SERP feature that shows users what else they might want to search. Fan-out happens in the background whether you can see it or not.
- A fixed set of queries: Only 27% of fan-out sub-queries remain consistent across repeated searches, according to a SurferSEO study. Sub-queries vary by phrasing, user context, and platform.
Further reading: AI SEO Myths, Debunked: A No-BS Guide for Marketers
Why Query Fan-Out Matters for AI Visibility
Understanding what query fan-out is only gets you so far. The real question is: What does it mean for your content strategy?
Here are four shifts that should make you rethink how you approach content.
You Don’t Need Top Rankings to Get AI Citations
Top rankings don’t automatically translate to AI citations.
When AI breaks a query into sub-queries, it pulls the most relevant and complete source for each one, regardless of where it ranks.
ChatGPT cites pages in position 21+ almost 90% of the time, according to a Semrush study.
Perplexity and Google show the same pattern.
AI Retrieves Passages, Not Pages
Rather than directing users to a page, AI systems scan your content and synthesize the exact passage that resolves a query.
This means that the earlier you answer a question, the better your chances of being extracted.
The data backs this up.
44.2% of citations in ChatGPT responses come from the first 30% of a page, while 31.1% come from the middle, and 24.7% from the final third, according to growth advisor Kevin Indig’s analysis of 1.2 million ChatGPT responses.
You’re Competing Across a Whole Topic, Not Individual Keywords
SEO often revolves around individual keywords. Query fan-out revolves around comprehensive coverage.
That’s why broad, well-connected coverage across a topic (think pillar pages and topic clusters) can help you earn more AI visibility.
Pro tip: Pages that rank for fan-out queries (not just the main query) are 161% more likely to get cited, according to a SurferSEO AI Overviews study.
Query Fan-Out Collapses the Buying Journey
We were taught that buyers move linearly — awareness, consideration, decision — and have long optimized content for each stage.
With AI, those stages collapse into one.
A single high-intent question triggers the system to fan out.
It pulls awareness-level context, consideration-level comparisons, and decision-level specifics into one answer.
The entire buying journey can now happen in a single interaction. So your content needs to work across the full funnel, not just the stage you’re targeting.
Pro tip: Want to work through these steps as you read? Our free Query Fan-Out Audit Template has spreadsheets for tracking your money prompts, sub-queries, intent buckets, and content gaps — plus a checklist to keep the full workflow on track.
The Query Fan-Out Workflow: 6 Steps to Earn More AI Citations
This six-step workflow shows you how to earn more AI citations by identifying and targeting high-impact sub-queries.
It’s repeatable, so you can follow these steps for every topic that matters to your business.
Note: Each AI platform handles fan-out differently, from the number of sub-queries it runs to how it cites sources. We cover the platform differences in depth after the workflow.
Step 1: Find Your Money Prompts
Money prompts are the conversational phrases or questions your ideal customer would ask an AI tool when trying to solve the problem your product or service addresses.
Money prompts are:
- Typically long-tail and highly specific
- Tied to a real use case or constraint
- Close to a decision, not just browsing
Think of money prompts as the AI SEO equivalent of money keywords: high-commercial-intent keywords designed to drive sales.
For example, “noise-canceling headphones ” is a keyword.
“What noise-canceling headphones are best for working from home with kids around, and cost under $300?” is a money prompt.
Look for money prompts where your audience asks questions:
- Customer support tickets
- Community forums
- Sales call transcripts
- Internal chat logs
- Google Search Console queries
For example, when I searched for noise-canceling headphones on Reddit, I found multiple money prompts in real users’ posts.
Like this one that asks for the best noise-canceling headphones for telehealth:
And this one asking for durable headphones that will last longer than 2 years:
Forums and transcripts are a good starting point. But you’ll need a dedicated tool to find money prompts using real AI search data.
Semrush’s AI Visibility Toolkit tells you exactly what users type into AI tools, along with the AI’s response.
To show you how it works, I’ll use Bose, a well-known headphone brand, as an example.
Note: I’ll be using Semrush to show you how to complete the query fan-out workflow. If you don’t have a subscription, sign up for a free trial of Semrush One, which includes the AI Visibility Toolkit and Semrush Pro.
First, I searched Bose’s domain in the Visibility Overview tool.
The “Topics & Sources” report revealed over 123.7K prompts where the brand already appears in AI answers.
Filtering by “noise canceling” let me dig deeper into topic-specific money prompts like “noise-canceling headphones for sensory issues.”
Clicking the prompt provides a full breakdown: the AI’s response, every brand mentioned alongside yours, and the exact sources it cited.
Follow the same process for your own domain.
These prompts are your highest-priority money prompts — your audience is already searching them, and AI is already answering them.
Don’t have AI visibility yet? Use the Prompt Research tool.
Enter a broad topic to see the prompts that generate the most AI results in your industry.
As you find relevant prompts, add them to your spreadsheet.
Even a few money prompts give you enough to work with for the next step.
Step 2: Generate Your Fan-Out Set
There are two ways to generate fan-out sets: manually or with a dedicated fan-out tool.
The manual approach is free and helps you understand how fan-out behaves, while tools are faster and better suited to working at scale.
I’ll start with the manual method.
Paste this prompt template into any AI platform to get a fan-out set:
Expand this question into the sub-queries an AI system might search to answer it: [your money prompt].
When I ran my Reddit money prompt through ChatGPT, it returned sub-queries grouped into categories:
- “Core Product Category”
- “Durability & Longevity”
- “Battery & Hardware Lifespan”
- “Reliability & Failure Rates”
Each category is a potential content gap you’ll address in Step 4.
Run your money prompt through multiple AI tools to get a more complete picture, since each platform tends to expand prompts differently.
Pro tip: Manual research is a solid starting point, but outputs can contain inaccuracies or hallucinations. A dedicated fan-out tool simulates how different AI platforms expand your query and returns an organized list of sub-queries you can act on immediately.
For a faster option, Backlinko’s free ChatGPT Query Fan-Out Tool is worth trying.
Install the Chrome extension, open ChatGPT, and ask your money prompt. The extension captures the response in real time and breaks down every sub-query ChatGPT ran behind the scenes.
When I ran a prompt through it, the panel showed:
- Each sub-query the model generated
- The metadata behind the response, including model version
- Every URL cited, categorized by type: sources, products, images, and news
As you gather sub-queries, assign a query type to each — this tells you what kind of content you’ll need to create in the next step.
Use these definitions to categorize them.
Query Type What It Means Reformulation A reworded version of the original prompt Comparative Weighs two or more options against each other Implicit Addresses a need the user didn’t explicitly state Personalized Tailored to a specific situation, constraint, or preference Entity expansion Drills into a specific brand, product, or person mentioned Related A connected topic the AI anticipates the user might want next Step 3: Bucket Sub-Queries by Intent Type
Bucketing by intent tells you what types of content to create and the ideal format for each.
To categorize a sub-query, answer this question: What does the person actually want to do after getting an answer?
Consider an example from the noise-canceling headphones query fan-out set: “Sony vs Bose Noise Canceling Headphones.”
Someone asking this is weighing two specific products against each other, so it’s a “comparison” query.
The right format for this query is a head-to-head comparison page or table, not a general buying guide or listicle.
The intent isn’t always this obvious, and some sub-queries may fit more than one bucket.
When that happens, place it where the strongest intent lies.
Here’s a general guide to the main intent buckets and what each one calls for:
Bucket Description Example Sub-Query Content Format Definitions / Basics What is X? How does X work? “how do noise canceling headphones work” Explainer article, glossary section Comparisons / Alternatives X vs Y, alternatives to X “apple airpods max vs sony wh 1000xm4” Comparison page, head-to-head section Best for X / Recommendations Best option for a specific use case “best noise canceling headphones for working from home” Listicle, buying guide Problems / Troubleshooting How to fix X, why does X happen “how to get rid of background noise in audio” How-to guide, FAQ section Pricing / Value How much does X cost, is X worth it “are there any good wireless headphones with noise cancellation under $150?” Pricing page, value comparison section Social Proof / Discussions Reviews, Reddit opinions, user experience “best earbuds for calls in noisy environment reddit” Review roundup, user feedback section Step 4: Audit Your Existing Content for Gaps
Once you’ve bucketed your sub-queries by intent and format, check which ones your site already covers and which ones it doesn’t (aka content gaps).
Start by searching your own site.
Type “site:yourdomain.com [sub-query topic]” into Google.
For example, running “site:bose.com noise canceling headphones” surfaces all their pages on that topic.
From here, evaluate each page against the sub-query it should cover:
- Coverage: Does it directly answer the sub-query, or just mention the topic in passing?
- Format: Is it the right content format for the intent?
- Self-contained answers: Can the answer stand on its own, without the reader needing to look anywhere else?
Categorize each page by its coverage level:
Coverage Level What It Looks Like What to Do Not covered No page on your site addresses this sub-query at all Create new content targeting this sub-query directly Partially covered A page mentions the topic in passing but doesn’t resolve the sub-query directly Add a dedicated section to the existing page that fully answers the sub-query Fully covered A dedicated section or page answers the sub-query completely and can be extracted and cited by AI without needing surrounding context Monitor for AI citations and update regularly to stay current For each sub-query, you’ll also want to know which competitors are showing up for your money prompts.
Run your money prompts through AI platforms to gather this information manually. Or refer back to your research from the AI Visibility Toolkit in Step 1.
Click any prompt to see which brands were mentioned and the exact sources the AI cited.
Already showing up alongside competitors? That’s a prompt worth protecting — focus on strengthening your coverage so you stay in the answer.
If competitors are showing up and you’re not, that’s a gap worth closing before they own it.
Step 5: Structure Your Content So AI Can Extract It
Creating the right content is only half the job. The other half is making it easy for AI to find, parse, and use.
Start by filling the gaps you identified in Step 4.
For sub-queries with no coverage, create dedicated pages or sections that target them directly.
For partial coverage, add self-contained answers to existing pages that resolve the sub-query without needing surrounding context.
Then, structure everything so AI can extract it cleanly:
- Address specific questions directly — lead with the answer, not background context
- Use content chunking: Break content into focused sections with clear headings, short paragraphs, and bullet points
- Front-load key information early in the page or section
- Use clear, precise language, including specific product names, figures, and use-case-specific wording
- Add FAQ sections
Here’s what this looks like in action.
Bose has over 63.9K mentions across AI platforms in the U.S. alone:
It helps that they’re a household name. But their content is also built to be extracted.
Their product pages front-load specific claims as scannable elements — “24 hours of battery life” and “legendary noise cancelation” — rather than burying them in copy.
Key specs are organized into structured comparison tables:
And they build dedicated landing pages for use cases like flying, using descriptive, scenario-specific language.
This matters because AI fans out into use-case-specific sub-queries.
When I searched “best noise-canceling headphones for flight anxiety,” AI Mode recommended Bose, using nearly identical language from Bose’s flight landing page.
When a user’s prompt matches the scenario your page was built for, AI systems may be more likely to pull from it.
This is a clear example of that in action.
You don’t need a complete site overhaul to make this work.
Even restructuring a few high-priority pages to address your fan-out gaps can improve your chances of being extracted and cited.
Step 6: Measure Your Performance in AI Search
Once your content is structured and live, track your performance in LLMs.
Start with the money prompts you identified in Step 1.
For each one, you want to know:
- Are you showing up? Is your brand mentioned or recommended in the response?
- Is what it says accurate? Are the claims the AI makes about your brand correct, or is it pulling outdated or wrong information?
- How do you compare? Which competitors appear in the same response, and how are they positioned relative to you?
If you’re tracking manually, run them through multiple LLMs (in a private or incognito window) and record what you find.
But once you’re tracking dozens of sub-queries across platforms, manually tracking gets messy (and time-consuming).
I use Semrush’s Prompt Tracker to automate the process.
It alerts you to changes in mentions for your money prompts, so you don’t have to keep re-running them yourself.
Another helpful tool is the Visibility Overview.
It provides an AI visibility score that tracks how often you’re showing up in AI answers compared to competitors.
The Perception tool tracks sentiment so you know how LLMs describe your brand — and if they mention competitors more favorably.
It also breaks down the factors driving that sentiment.
For Bose, “industry-leading noise cancellation” shows up as a strength, while “over-the-ear models not sweatproof” flags a use-case they could address with targeted content.
Tracking should be an ongoing process.
Revisit your money prompts regularly and update your content as new sub-queries emerge or competitors gain ground.
How Query Fan-Out Works Across Different Platforms
How content surfaces in an AI answer depends on several factors:
- Whether the system searches the live web or draws from its training knowledge
- How many sub-queries it runs
- Which sources it favors, and how it cites them
Understanding those patterns helps you make smarter decisions about content structure, format, and where to focus your optimization effort.
Plus, if a competitor outperforms you in a specific LLM, understanding how that platform handles fan-out can help you figure out why.
Platform How Fan-Out Works ChatGPT Reasons internally, then runs live web searches when a question requires fresh data, comparisons, or current information Perplexity Combines conversation context with real-time web search Claude Clarifies intent first; relies mostly on training data Google AI Overviews Synthesizes Google’s index into condensed, featured-snippet-style summaries Google AI Mode Breaks complex prompts into multiple searches across Google’s index Note: Some of the behavior described below is based on how each system describes its own reasoning when prompted. LLMs aren’t always reliable narrators of their own processes, so treat these observations as directional rather than definitive.
ChatGPT
For simple, informational queries, ChatGPT usually responds from its training data without running a live search.
But that changes when the question requires fresh information, comparisons, or real-world data.
When I asked which car I should buy (Toyota vs. Honda) in Thinking mode, ChatGPT spent about 22 seconds reasoning through the question.
Then, it produced an answer drawn from 41 cited sources
That’s query fan-out in action: one prompt, varied sources, and multiple sub-queries running behind the scenes.
By default, you can’t see the sub-queries ChatGPT runs. But I’ll show you how to find them (don’t worry — it’s easier than it looks).
Note: This DevTools method only works in the web version of ChatGPT. You can’t access sub-query data on mobile or in the desktop app.
First, search a money prompt in ChatGPT.
Then, look at your browser’s address bar and copy the slug that appears after chatgpt.com/c/ — that’s the unique ID for your conversation
Next, right-click anywhere on the page and select “Inspect.”
A developer panel will open on the side of your screen:
- Click “Network” at the top of that panel
- Paste the slug you copied into the filter bar
- Refresh the page
Click on the fetch version of the slug (here, it’s the second option under the Name column).
Then, open the Response tab.
Once it loads, press Ctrl+F (or Cmd+F on Mac) and search for the word “queries.”
What appears is the exact set of internal searches ChatGPT ran before producing its answer.
For the Toyota vs Honda prompt, ChatGPT generated queries around:
- Vehicle specifications
- Fuel economy
- Reliability
- Safety ratings
- Long-term ownership costs
Once you have the sub-queries, cross-reference them against your content.
Are you targeting each one? Do your pages use the same language ChatGPT is searching for — “long-term ownership costs” rather than just “value”?
ChatGPT often pulls from third-party sources like Reddit threads, review sites, and comparison pages.
So topical authority matters here — not just what’s on your site, but whether your brand shows up across the sources ChatGPT is likely to retrieve.
Perplexity
Perplexity runs two types of fan-out simultaneously:
- Internal fan-out — scans your prior conversation history for relevant context
- External fan-out — searches the external web for relevant information
The final answer draws on both layers, which means your content needs to work for a range of user situations, not just one.
For the Toyota vs. Honda question, Perplexity’s first batch of sub-queries had nothing to do with the cars.
Instead, it checked whether I’d previously mentioned anything that could shape its recommendation.
Like budget constraints, driving habits, or past questions about either brand.
Only after that internal scan did it launch external searches about reliability, ownership cost, and safety ratings.
What this means for your content: Perplexity may pair your page with context you can’t predict: a user’s past questions, constraints, or preferences.
Your content needs to be specific and self-contained enough to remain accurate and useful no matter the surrounding context.
Claude
Claude takes a different approach.
Rather than immediately running sub-queries, it asks clarifying questions first. Then, it generates a response tailored to your answers.
When I asked the Toyota vs. Honda question, Claude presented a preference widget before producing an answer.
Once I responded, it generated a recommendation tailored to my priorities.
Because it clarifies intent before searching, Claude tends to generate fewer, more targeted fan-out sub-queries than other platforms.
The implication for your content: Answer specific, well-defined use cases directly rather than trying to cover every angle on a single page.
Google AI Overviews and AI Mode
AI Overviews appear as concise, AI-generated summaries with sources listed in a clickable sidebar.
They work by synthesizing Google’s existing web index into a tighter, more contained summary.
AI Mode, by contrast, is a dedicated conversational search tab designed for complex, multi‑part questions.
Like AI Overviews, it draws on Google’s index to generate answers, but it offers more interaction and depth.
Neither platform exposes the sub-queries it runs.
But SEOs have found a way to extract Google’s fan-outs using Screaming Frog configured with a Gemini API. Watch Dan Hinckley’s tutorial for a full walkthrough.
For both, the optimization focus is the same: Front-load your answers, use descriptive subheadings, and structure content so individual passages stand on their own.
AI Search Runs on Query Fan-Out — Your Content Strategy Should Too
High rankings alone won’t earn AI mentions.
The brands showing up are the ones covering the questions their audience is actually asking and making that content easy for AI to extract and cite.
You’ve got the query fan-out framework. Now it’s about execution.
Start with one money prompt, map the sub-queries, and audit where your content stands.
Then work through the gaps, one topic at a time.
Next, dive deeper into how to get your brand seen and trusted across AI platforms with our AI search strategy guide.
The post Query Fan-Out: What It Is and How It Affects AI Visibility appeared first on Backlinko.
- Build compounding content assets, not disposable posts.
YouTube videos can keep generating views months after publication. Your output becomes an asset base, not just a stream of quickly‑expiring content.
Hi All My Marcom Peeps,
This isn’t the story of an agency — it’s the story of an innovator that’s been proving what real marketing can do for decades.
Hi, I’m Daniel Elliott.
I Build Brands That Sell.I’ve spent four decades building marketing campaigns that make businesses more money, predictably, profitably, & without the agency BS and egos.
I’m Daniel Elliott, founder and strategist at Appture Digital Media. I’m reaching out because, after decades in this industry, I’ve seen the same frustration: good businesses waste serious money on marketing that generates “awareness” and “clicks,” but never consistent income.
Let’s be honest—most marketing isn’t strategy, it’s noise. And noise doesn’t pay the bills.
At Appture Digital, we don’t “do marketing.” We build systems that sell.
We specialize in taking the chaos out of your growth with a proven 3-step approach:
- Strategy First: We uncover the exact triggers and motivations that make your customers buy.
- System Build: We engineer funnels, automation, and follow-up that convert attention into revenue—predictably.
- Scale & Optimize: We turn up the volume, allowing your ROI to compound while your effort stays the same.
We achieve this with a curated offering of advanced services that include: Web Development, Marketing Strategy, Video Marketing, Social Media Marketing, Website Makeovers, Hybrid & Virtual Events, Email Marketing, Podcast and Live Stream Studio
If you’re ready to move past the hype and start building a machine that generates consistent, measurable profit, let’s talk.
I’m not interested in a pitch deck; just a real conversation about how this system would work for your business.
Ready to build a system that sells?
Best regards,
Daniel Elliott
Founder, Appture Digital Media
Search engine updates have become a regular source of anxiety for marketers. Nearly every month, a new Google update rolls out, often labeled a “core update,” and the reaction is predictable: speculation, analysis, and a scramble to identify what changed. The challenge is that Google rarely provides specific, actionable details. Unlike older updates that targeted clear issues like spammy backlinks or thin content, today’s updates are far less transparent.
The reality is that SEO is no longer a simple, rules-based system.
For years, ranking strategies revolved around measurable inputs: keyword density, backlink counts, word count, and exact-match phrases. These metrics created the illusion that SEO was a formula you could reverse-engineer. That model no longer reflects how search works today. Google’s algorithm has evolved to behave less like a rigid equation and more like a system designed to mimic human judgment.
Chasing Google vs. Understanding Users
Many marketers are still trying to “solve” Google’s algorithm, aiming to match what works right now. But Google is constantly advancing, prioritizing where search is going rather than where it has been. The shift is clear: success is no longer about gaming metrics—it’s about satisfying user intent.
As Google’s Search Liaison has explained, focusing on the algorithm puts you behind. Focusing on what users actually want puts you ahead.
Traditional metrics still have value. Search volume helps identify demand, and high-quality backlinks remain a trust signal. But they are no longer the primary drivers of success. Google has made it clear that content should be created for people first, not search engines.
This shift calls for a new mindset: human-centered optimization.
Metrics vs. Meaningful Content
When rankings drop after an update, the initial reaction is often frustration. Site owners compare their pages to competitors and point out differences in metrics: longer content, higher authority scores, or better keyword usage. On paper, their page appears stronger.
However, a closer review often reveals a different story.
Pages that perform well tend to offer clearer, more useful, and more original content. They demonstrate real expertise, avoid unnecessary filler, and provide a better overall experience. Meanwhile, underperforming pages often rely too heavily on optimization tactics while neglecting actual value.
The disconnect comes down to perspective. Metrics provide data, but users respond to clarity, relevance, and usefulness.
The Shift to Human-Centered Optimization
Improving performance today requires stepping outside of analytics tools and evaluating your site as a real visitor would.
Start by identifying your target audience. Consider what they are searching for, what problems they are trying to solve, and what outcome they expect. Then, navigate your site as if you were encountering it for the first time.
Ask yourself:
- Can you quickly find the information you need?
- Is the content clear and helpful, or overly long and unfocused?
- Are there distractions like intrusive popups or excessive ads?
- Is it obvious what to do next?
Document any friction points you encounter. These insights are often more valuable than any SEO tool.
For an even clearer perspective, compare your experience with a competitor’s site. If their page feels more helpful or easier to use, identify exactly why. Those differences often highlight what Google is rewarding.
If it’s difficult to evaluate your own site objectively, bringing in an outside perspective—such as a UX audit—can uncover blind spots you might miss.
The Real Competitive Advantage
The most effective SEO strategy today is not based on chasing updates or decoding ranking factors. It is built on understanding people.
Search engines are increasingly aligned with user expectations. That means the sites that win are the ones that genuinely help, inform, and guide their audience.
The most powerful SEO tool available is not a platform or a dataset. It is the ability to think like your user and create an experience that meets their needs better than anyone else.
Canva made this painfully clear: they gave 5,000 employees a full week to explore AI, and most couldn’t figure out how to start. No surprise there, most vibe-coding tools expect you to manage files, wrangle a terminal, and deploy to…Read more ›
On the one hand, Google says they don’t care whether AI is used for content creation, as long as the content isn’t designed “with the primary purpose of manipulating ranking in search results.” On the other hand, people like Lily…Read more ›
It’s been a turbulent stretch for Google. Concerns about declining search quality have been bubbling up since at least last fall’s Helpful Content Update, and documents from Google’s antitrust trial added fuel to the fire by suggesting that executives pushed to grow ad revenue and share price by increasing search queries—essentially, making it harder for users to get straight to what they need.
At the same time, well-known voices connecting Google and the SEO world, like Danny Sullivan and John Mueller, continue to say that Google’s priority is improving search results and helping people find better information, not worse.
From my experience in the newspaper industry, I don’t see these positions as mutually exclusive. Both can be true.
Just like Google, a newspaper has two core engines: one that creates something readers genuinely want, and one that sells ads. The newsroom works to uncover and explain the news, giving people what they need to know. But as subscription revenue shrinks and ad revenue becomes the main lifeline, it’s easy for the ad side to start driving decisions. When that happens, coverage tends to get shorter and more superficial, while more space and budget are devoted to advertising instead of reporting.
I don’t doubt that Google’s Search teams are genuinely focused on improving the SERPs and helping users find what they’re looking for. But Google is still a business, and it has to sustain itself. With AI development now front and center in its fight to maintain dominance, there’s a new layer of financial pressure in the mix as well—building and running large‑scale AI systems is expensive.
On top of that, we now have a major antitrust ruling against Google, and that’s likely to reshape the landscape in meaningful ways.
So where does all of this leave Google—and where does it leave us? One way to think about the future is as a two‑track path: one for today’s free, ad‑supported Google, and another for a more advanced, subscription‑driven tier of AI search, like what’s currently called Gemini Advanced.
Free vs. AI: The Roads Forward
Most people don’t love paying for something they can get for free. When news moved online, many readers lost interest in paying for newspaper subscriptions. The result has been the closure of many newsrooms, and among those that survived, the ones that managed to convince readers that their deeper coverage was worth an online subscription fee.
Similarly, you probably won’t find a crowds of people eager to pay specifically for “Google Search.” But what if the paid product isn’t just search as we know it, but the version we’ve long been promised: something that reliably surfaces exactly what you need, with minimal ads and noise? Google keeps saying that its goal is a more human‑centered search experience that helps people find genuinely helpful content.
Given the Justice Department’s ruling, it’s not hard to imagine a future where we effectively have two Googles: a free, ad‑funded search engine that looks a lot like the Google of a few years ago, and a subscription‑based, AI‑powered assistant—a kind of modern digital Jeeves—that guides you to high‑quality, user‑focused, trustworthy information.
If that’s the direction we’re heading, then success online will mean being discoverable in both worlds. We’ll need to optimize for the free search engine and for the more advanced AI‑driven tools that people will use alongside it.
SEO for Today and Tomorrow
What doesn’t change is the need to keep raising the quality bar for our content. We still have to focus on meeting user intent for the queries we hope to rank for. Google has repeated this message over and over and over again: fill your site with content designed to help real readers, not keyword‑stuffed fluff written purely for an algorithm.
That’s the kind of material Google says it wants to reward, and it’s also the kind of material AI systems are more likely to rely on and reference when building answers for users.
At the same time, it’s obvious that backlinks are very much alive. In fact, signals like backlinks, clicks, and Chrome data surfaced clearly in the May Google document leak, and Jim Boykin’s recent research shows that strong links remain one of the key levers if you want to rank in Google’s results.
There’s little reason to believe that would suddenly stop being true if search results become more separated from other products like Gemini and Chrome. Links are still how the web signals relationships and recommendations.
Put simply, both high‑quality on‑page content and off‑page signals are likely to remain essential to your visibility, regardless of whether users are relying on classic SERPs, AI answer boxes, or some combination of tools we haven’t fully seen yet. Making space for both in your strategy is likely to be the difference between a site that keeps performing and one that slowly fades.
Every week brings a new acronym, a new era, a new thing you are supposedly already too late for. Some of it changes how we work, but a lot of it is—let’s be honest—noise. Well, fear not. I’ve curated some…Read more ›
Our free llms.txt generator is based on detailed analysis of 137,000 domains. Share this free tool with your boss, your colleagues, your clients, your GEO agency, or anyone who recommends you use an llms.txt file.
Before someone ever lands on your website, an AI assistant may have already explained what your product does, compared it with competitors, and helped them decide whether you’re worth considering. By the time someone clicks through to your site after…Read more ›
Hi everyone, Daniel Elliott here. If you know me, you know I spend a lot of time trying to understand how Google really works—testing, digging into docs, and watching how the algorithm changes over time. Today I want to walk through one of the more interesting angles from the leaked Google documentation: how Google thinks about authorship.
Specifically, we’ll look at how Google identifies and evaluates the people behind content—both on your own site and on the sites that link to you. If you’ve been treating authors as an afterthought, this is a good time to reconsider. The way Google processes author data now can have a serious impact on your SEO performance.
In this post, I’ll break down one core area from the leak, show how author-related signals show up in different API calls, and talk through how you can use that understanding to strengthen your on‑page SEO and your backlink strategy.
What the Leak Revealed About Authors and Google’s Algorithm
How Google’s APIs Pull Author Data
To see how authorship fits into the bigger picture, it helps to look at the API calls mentioned in the documentation. These calls give Google structured ways to pull information about authors, pages, links, and relationships across the web. Understanding what they do doesn’t mean you can “control” them, but it does give you useful clues about what to expose clearly on your site.
Here are some of the calls that matter most from an SEO and author‑signal perspective:
GET page_meta_info- Definition: Retrieves metadata from a webpage, including an author name from HTML
<meta>tags when that’s present. - SEO Insight: Make sure your pages have reliable meta information, including author tags where it makes sense. It helps Google connect content to specific people more efficiently.
- Definition: Retrieves metadata from a webpage, including an author name from HTML
GET /user_profiles/{user_id}- Definition: Pulls detailed author or user profile data from a CMS that stores person‑level information.
- SEO Insight: If you run a multi‑author site, treat profiles as real assets. Rich, well‑maintained profiles support trust and clarity around who is behind your content.
GET /author_info?user_id={user_id}- Definition: Retrieves structured information about an author—bios, roles, and what they’ve contributed.
- SEO Insight: Aim for systems that can expose this kind of data cleanly. The more coherent your author information is, the stronger your author signals can be.
GET /link_meta_info?url={page_url}- Definition: Looks at links on a page and can associate them with authors of the referenced content.
- SEO Insight: Backlinks from content with visible, credible authorship are more useful than links from anonymous or unclear sources.
GET external_meta_info?external_url={link_url}- Definition: Fetches metadata from other sites to extract authorship details for linked content.
- SEO Insight: Earning links from sites that treat authorship seriously—clear bylines, bios, and structured data—helps reinforce the trust around your own content.
ScienceCitationAuthor- Definition: A structured model for detailed author metadata, such as name, role, and institutional affiliation.
- SEO Insight: If you operate in scientific or academic spaces, use appropriate schemas and detailed author data. It fits directly into how Google can model expertise in those domains.
ScienceCitationTranslatedAuthor- Definition: Similar to
ScienceCitationAuthor, but allows for translated author names in multilingual settings. - SEO Insight: For multi‑language publications, consistent, well‑translated author information helps keep identity and attribution coherent across locales.
- Definition: Similar to
NlpSciencelitCitationData- Definition: Contains author data connected to citations or articles, tying people to the research they publish.
- SEO Insight: In research‑heavy topics, good citation practices and clear author links make it easier for Google to read your work as part of a credible body of knowledge.
NlpSciencelitAuthor- Definition: Holds basic author information such as first name and last name.
- SEO Insight: Simple details still matter. Consistent, correctly formatted names across platforms and content help Google match authors to their wider footprint.
NlpSciencelitArticleMetadata- Definition: Deals with article‑level metadata, including authorship.
- SEO Insight: Clean, complete article metadata is part of how your work becomes easier to discover and trust—especially for long‑form or specialist content.
VideoVideoClipInfo- Definition: Includes author‑related attributes for video content.
- SEO Insight: For video SEO, treat creator details as part of your optimization stack. Clear attribution helps connect channels, videos, and topics back to real people.
NlpSemanticParsingModelsShoppingAssistantProductMediaProduct- Definition: Relates to media products tied to shopping, including information about the people behind those products.
- SEO Insight: If you sell media or content‑based products, solid authorship data can support recommendations and discovery in commerce‑related surfaces.
ScienceIndexSignal- Definition: Connects author information to scholarly articles and indexing signals.
- SEO Insight: For academic SEO, precise, standardized author data helps you benefit from indexing systems and signal aggregation.
NlpSaftDocument- Definition: Mentions authors inside broader document contexts using NLP.
- SEO Insight: Even if you don’t rely heavily on explicit tags, Google can infer authorship from the way you write and reference people, so clarity in your content still matters.
OceanDataDocinfoWoodwingItemMetadata- Definition: Handles authorship within item metadata in document management systems.
- SEO Insight: If you have complex editorial workflows, treat author metadata as a first‑class field, not an afterthought. It will pay off in consistency.
NlxDataSchemaDocument- Definition: Includes author‑related data from a document analysis point of view.
- SEO Insight: The more your CMS can expose detailed document and author information, the easier it is for systems like Google to interpret and trust your content.
Taken together, these and other author‑centric calls touch both what happens on your own pages and what happens in your backlink profile.
On‑page signals are about the content you publish: clear bylines, structured data, complete profiles, and evidence that real, qualified people stand behind what’s on your site.
Backlink signals are about who talks
Your content can rank on the first page of Google and still never be cited or mentioned by LLMs.
This makes sense once you understand query fan-out, a background process AI systems use to build answers.
When someone asks ChatGPT or Perplexity a question, it doesn’t default to the best-ranking page.
Instead, it runs related searches behind the scenes, pulling from the most relevant and reliable sources, regardless of position.
If your brand doesn’t show up in those searches (whether through your own content or third parties), you’re unlikely to make it into the answer.
High rankings don’t hurt, of course.
But in AI search, coverage and retrievability are king.
In this guide, I’ll teach you how to optimize your content strategy for query fan-out to help increase your AI visibility.
You’ll learn:
- Why LLMs use query fan-out
- How it behaves differently across major AI platforms
- Why it changes how you create and structure content
- A 6-step workflow for earning more citations in AI search
Free template: Our Query Fan-Out Audit Template includes ready-to-use spreadsheets for logging money prompts, sub-queries, and content gaps — plus a checklist to keep you on track. Download it now to follow along.
First, I’ll dive deeper into how query fan-out works.
What Is Query Fan-Out?
Query fan-out is a process AI search systems use to break a single user query into multiple sub-queries to create the most helpful response.
In other words, the AI “fans” the query out into a series of related sub-questions to build a more complete picture of the topic.
It then pulls information from multiple sources — editorial sites, Reddit threads, comparison and product pages — and synthesizes it into a single comprehensive answer.
AI systems use query fan-out for a few reasons:
- Confirm information: A single source might be wrong or biased. Running parallel sub-queries allows the system to cross-reference multiple sources and find consensus before committing to an answer.
- Handle complex, specific queries: When a question has multiple layers, like comparing two products across price, reliability, and long-term value, fan-out breaks it into manageable pieces that the system can research independently.
- Answer the real question: Someone searching “best toothbrush” probably also wants to know about price, battery life, and durability, even if they didn’t say so. Fan-out anticipates those needs and gathers evidence upfront.
For example, a search for “best toothbrush” might trigger sub-queries like “best electric toothbrushes [year]” and “best toothbrushes for sensitive gums.”
This helps the AI build a more complete and useful answer:
Sub-Query What It Contributes to the AI Response Best electric toothbrushes Top-rated picks and editorial consensus Best toothbrushes for sensitive gums Use-case recommendations Oral-B vs. Philips Sonicare Head-to-head comparison data Best eco-friendly toothbrushes Value picks and pricing information The AI then synthesizes those findings into a single answer that covers everything the user might want to know: top picks, price ranges, use-case breakdowns, and comparisons.
In this way, it anticipates the user’s needs, even though the original prompt (best toothbrush) was just two words.
What Query Fan-Out Is NOT
Now that we’ve covered what query fan-out is, let’s clear up a few common misconceptions.
Query fan-out is not:
- Keyword research: This is the process of finding terms your audience searches for. Query fan-out is something AI systems do automatically, behind the scenes, every time someone asks a question.
- People Also Ask: PAA is a visible SERP feature that shows users what else they might want to search. Fan-out happens in the background whether you can see it or not.
- A fixed set of queries: Only 27% of fan-out sub-queries remain consistent across repeated searches, according to a SurferSEO study. Sub-queries vary by phrasing, user context, and platform.
Further reading: AI SEO Myths, Debunked: A No-BS Guide for Marketers
Why Query Fan-Out Matters for AI Visibility
Understanding what query fan-out is only gets you so far. The real question is: What does it mean for your content strategy?
Here are four shifts that should make you rethink how you approach content.
You Don’t Need Top Rankings to Get AI Citations
Top rankings don’t automatically translate to AI citations.
When AI breaks a query into sub-queries, it pulls the most relevant and complete source for each one, regardless of where it ranks.
ChatGPT cites pages in position 21+ almost 90% of the time, according to a Semrush study.
Perplexity and Google show the same pattern.
AI Retrieves Passages, Not Pages
Rather than directing users to a page, AI systems scan your content and synthesize the exact passage that resolves a query.
This means that the earlier you answer a question, the better your chances of being extracted.
The data backs this up.
44.2% of citations in ChatGPT responses come from the first 30% of a page, while 31.1% come from the middle, and 24.7% from the final third, according to growth advisor Kevin Indig’s analysis of 1.2 million ChatGPT responses.
You’re Competing Across a Whole Topic, Not Individual Keywords
SEO often revolves around individual keywords. Query fan-out revolves around comprehensive coverage.
That’s why broad, well-connected coverage across a topic (think pillar pages and topic clusters) can help you earn more AI visibility.
Pro tip: Pages that rank for fan-out queries (not just the main query) are 161% more likely to get cited, according to a SurferSEO AI Overviews study.
Query Fan-Out Collapses the Buying Journey
We were taught that buyers move linearly — awareness, consideration, decision — and have long optimized content for each stage.
With AI, those stages collapse into one.
A single high-intent question triggers the system to fan out.
It pulls awareness-level context, consideration-level comparisons, and decision-level specifics into one answer.
The entire buying journey can now happen in a single interaction. So your content needs to work across the full funnel, not just the stage you’re targeting.
Pro tip: Want to work through these steps as you read? Our free Query Fan-Out Audit Template has spreadsheets for tracking your money prompts, sub-queries, intent buckets, and content gaps — plus a checklist to keep the full workflow on track.
The Query Fan-Out Workflow: 6 Steps to Earn More AI Citations
This six-step workflow shows you how to earn more AI citations by identifying and targeting high-impact sub-queries.
It’s repeatable, so you can follow these steps for every topic that matters to your business.
Note: Each AI platform handles fan-out differently, from the number of sub-queries it runs to how it cites sources. We cover the platform differences in depth after the workflow.
Step 1: Find Your Money Prompts
Money prompts are the conversational phrases or questions your ideal customer would ask an AI tool when trying to solve the problem your product or service addresses.
Money prompts are:
- Typically long-tail and highly specific
- Tied to a real use case or constraint
- Close to a decision, not just browsing
Think of money prompts as the AI SEO equivalent of money keywords: high-commercial-intent keywords designed to drive sales.
For example, “noise-canceling headphones ” is a keyword.
“What noise-canceling headphones are best for working from home with kids around, and cost under $300?” is a money prompt.
Look for money prompts where your audience asks questions:
- Customer support tickets
- Community forums
- Sales call transcripts
- Internal chat logs
- Google Search Console queries
For example, when I searched for noise-canceling headphones on Reddit, I found multiple money prompts in real users’ posts.
Like this one that asks for the best noise-canceling headphones for telehealth:
And this one asking for durable headphones that will last longer than 2 years:
Forums and transcripts are a good starting point. But you’ll need a dedicated tool to find money prompts using real AI search data.
Semrush’s AI Visibility Toolkit tells you exactly what users type into AI tools, along with the AI’s response.
To show you how it works, I’ll use Bose, a well-known headphone brand, as an example.
Note: I’ll be using Semrush to show you how to complete the query fan-out workflow. If you don’t have a subscription, sign up for a free trial of Semrush One, which includes the AI Visibility Toolkit and Semrush Pro.
First, I searched Bose’s domain in the Visibility Overview tool.
The “Topics & Sources” report revealed over 123.7K prompts where the brand already appears in AI answers.
Filtering by “noise canceling” let me dig deeper into topic-specific money prompts like “noise-canceling headphones for sensory issues.”
Clicking the prompt provides a full breakdown: the AI’s response, every brand mentioned alongside yours, and the exact sources it cited.
Follow the same process for your own domain.
These prompts are your highest-priority money prompts — your audience is already searching them, and AI is already answering them.
Don’t have AI visibility yet? Use the Prompt Research tool.
Enter a broad topic to see the prompts that generate the most AI results in your industry.
As you find relevant prompts, add them to your spreadsheet.
Even a few money prompts give you enough to work with for the next step.
Step 2: Generate Your Fan-Out Set
There are two ways to generate fan-out sets: manually or with a dedicated fan-out tool.
The manual approach is free and helps you understand how fan-out behaves, while tools are faster and better suited to working at scale.
I’ll start with the manual method.
Paste this prompt template into any AI platform to get a fan-out set:
Expand this question into the sub-queries an AI system might search to answer it: [your money prompt].
When I ran my Reddit money prompt through ChatGPT, it returned sub-queries grouped into categories:
- “Core Product Category”
- “Durability & Longevity”
- “Battery & Hardware Lifespan”
- “Reliability & Failure Rates”
Each category is a potential content gap you’ll address in Step 4.
Run your money prompt through multiple AI tools to get a more complete picture, since each platform tends to expand prompts differently.
Pro tip: Manual research is a solid starting point, but outputs can contain inaccuracies or hallucinations. A dedicated fan-out tool simulates how different AI platforms expand your query and returns an organized list of sub-queries you can act on immediately.
For a faster option, Backlinko’s free ChatGPT Query Fan-Out Tool is worth trying.
Install the Chrome extension, open ChatGPT, and ask your money prompt. The extension captures the response in real time and breaks down every sub-query ChatGPT ran behind the scenes.
When I ran a prompt through it, the panel showed:
- Each sub-query the model generated
- The metadata behind the response, including model version
- Every URL cited, categorized by type: sources, products, images, and news
As you gather sub-queries, assign a query type to each — this tells you what kind of content you’ll need to create in the next step.
Use these definitions to categorize them.
Query Type What It Means Reformulation A reworded version of the original prompt Comparative Weighs two or more options against each other Implicit Addresses a need the user didn’t explicitly state Personalized Tailored to a specific situation, constraint, or preference Entity expansion Drills into a specific brand, product, or person mentioned Related A connected topic the AI anticipates the user might want next Step 3: Bucket Sub-Queries by Intent Type
Bucketing by intent tells you what types of content to create and the ideal format for each.
To categorize a sub-query, answer this question: What does the person actually want to do after getting an answer?
Consider an example from the noise-canceling headphones query fan-out set: “Sony vs Bose Noise Canceling Headphones.”
Someone asking this is weighing two specific products against each other, so it’s a “comparison” query.
The right format for this query is a head-to-head comparison page or table, not a general buying guide or listicle.
The intent isn’t always this obvious, and some sub-queries may fit more than one bucket.
When that happens, place it where the strongest intent lies.
Here’s a general guide to the main intent buckets and what each one calls for:
Bucket Description Example Sub-Query Content Format Definitions / Basics What is X? How does X work? “how do noise canceling headphones work” Explainer article, glossary section Comparisons / Alternatives X vs Y, alternatives to X “apple airpods max vs sony wh 1000xm4” Comparison page, head-to-head section Best for X / Recommendations Best option for a specific use case “best noise canceling headphones for working from home” Listicle, buying guide Problems / Troubleshooting How to fix X, why does X happen “how to get rid of background noise in audio” How-to guide, FAQ section Pricing / Value How much does X cost, is X worth it “are there any good wireless headphones with noise cancellation under $150?” Pricing page, value comparison section Social Proof / Discussions Reviews, Reddit opinions, user experience “best earbuds for calls in noisy environment reddit” Review roundup, user feedback section Step 4: Audit Your Existing Content for Gaps
Once you’ve bucketed your sub-queries by intent and format, check which ones your site already covers and which ones it doesn’t (aka content gaps).
Start by searching your own site.
Type “site:yourdomain.com [sub-query topic]” into Google.
For example, running “site:bose.com noise canceling headphones” surfaces all their pages on that topic.
From here, evaluate each page against the sub-query it should cover:
- Coverage: Does it directly answer the sub-query, or just mention the topic in passing?
- Format: Is it the right content format for the intent?
- Self-contained answers: Can the answer stand on its own, without the reader needing to look anywhere else?
Categorize each page by its coverage level:
Coverage Level What It Looks Like What to Do Not covered No page on your site addresses this sub-query at all Create new content targeting this sub-query directly Partially covered A page mentions the topic in passing but doesn’t resolve the sub-query directly Add a dedicated section to the existing page that fully answers the sub-query Fully covered A dedicated section or page answers the sub-query completely and can be extracted and cited by AI without needing surrounding context Monitor for AI citations and update regularly to stay current For each sub-query, you’ll also want to know which competitors are showing up for your money prompts.
Run your money prompts through AI platforms to gather this information manually. Or refer back to your research from the AI Visibility Toolkit in Step 1.
Click any prompt to see which brands were mentioned and the exact sources the AI cited.
Already showing up alongside competitors? That’s a prompt worth protecting — focus on strengthening your coverage so you stay in the answer.
If competitors are showing up and you’re not, that’s a gap worth closing before they own it.
Step 5: Structure Your Content So AI Can Extract It
Creating the right content is only half the job. The other half is making it easy for AI to find, parse, and use.
Start by filling the gaps you identified in Step 4.
For sub-queries with no coverage, create dedicated pages or sections that target them directly.
For partial coverage, add self-contained answers to existing pages that resolve the sub-query without needing surrounding context.
Then, structure everything so AI can extract it cleanly:
- Address specific questions directly — lead with the answer, not background context
- Use content chunking: Break content into focused sections with clear headings, short paragraphs, and bullet points
- Front-load key information early in the page or section
- Use clear, precise language, including specific product names, figures, and use-case-specific wording
- Add FAQ sections
Here’s what this looks like in action.
Bose has over 63.9K mentions across AI platforms in the U.S. alone:
It helps that they’re a household name. But their content is also built to be extracted.
Their product pages front-load specific claims as scannable elements — “24 hours of battery life” and “legendary noise cancelation” — rather than burying them in copy.
Key specs are organized into structured comparison tables:
And they build dedicated landing pages for use cases like flying, using descriptive, scenario-specific language.
This matters because AI fans out into use-case-specific sub-queries.
When I searched “best noise-canceling headphones for flight anxiety,” AI Mode recommended Bose, using nearly identical language from Bose’s flight landing page.
When a user’s prompt matches the scenario your page was built for, AI systems may be more likely to pull from it.
This is a clear example of that in action.
You don’t need a complete site overhaul to make this work.
Even restructuring a few high-priority pages to address your fan-out gaps can improve your chances of being extracted and cited.
Step 6: Measure Your Performance in AI Search
Once your content is structured and live, track your performance in LLMs.
Start with the money prompts you identified in Step 1.
For each one, you want to know:
- Are you showing up? Is your brand mentioned or recommended in the response?
- Is what it says accurate? Are the claims the AI makes about your brand correct, or is it pulling outdated or wrong information?
- How do you compare? Which competitors appear in the same response, and how are they positioned relative to you?
If you’re tracking manually, run them through multiple LLMs (in a private or incognito window) and record what you find.
But once you’re tracking dozens of sub-queries across platforms, manually tracking gets messy (and time-consuming).
I use Semrush’s Prompt Tracker to automate the process.
It alerts you to changes in mentions for your money prompts, so you don’t have to keep re-running them yourself.
Another helpful tool is the Visibility Overview.
It provides an AI visibility score that tracks how often you’re showing up in AI answers compared to competitors.
The Perception tool tracks sentiment so you know how LLMs describe your brand — and if they mention competitors more favorably.
It also breaks down the factors driving that sentiment.
For Bose, “industry-leading noise cancellation” shows up as a strength, while “over-the-ear models not sweatproof” flags a use-case they could address with targeted content.
Tracking should be an ongoing process.
Revisit your money prompts regularly and update your content as new sub-queries emerge or competitors gain ground.
How Query Fan-Out Works Across Different Platforms
How content surfaces in an AI answer depends on several factors:
- Whether the system searches the live web or draws from its training knowledge
- How many sub-queries it runs
- Which sources it favors, and how it cites them
Understanding those patterns helps you make smarter decisions about content structure, format, and where to focus your optimization effort.
Plus, if a competitor outperforms you in a specific LLM, understanding how that platform handles fan-out can help you figure out why.
Platform How Fan-Out Works ChatGPT Reasons internally, then runs live web searches when a question requires fresh data, comparisons, or current information Perplexity Combines conversation context with real-time web search Claude Clarifies intent first; relies mostly on training data Google AI Overviews Synthesizes Google’s index into condensed, featured-snippet-style summaries Google AI Mode Breaks complex prompts into multiple searches across Google’s index Note: Some of the behavior described below is based on how each system describes its own reasoning when prompted. LLMs aren’t always reliable narrators of their own processes, so treat these observations as directional rather than definitive.
ChatGPT
For simple, informational queries, ChatGPT usually responds from its training data without running a live search.
But that changes when the question requires fresh information, comparisons, or real-world data.
When I asked which car I should buy (Toyota vs. Honda) in Thinking mode, ChatGPT spent about 22 seconds reasoning through the question.
Then, it produced an answer drawn from 41 cited sources
That’s query fan-out in action: one prompt, varied sources, and multiple sub-queries running behind the scenes.
By default, you can’t see the sub-queries ChatGPT runs. But I’ll show you how to find them (don’t worry — it’s easier than it looks).
Note: This DevTools method only works in the web version of ChatGPT. You can’t access sub-query data on mobile or in the desktop app.
First, search a money prompt in ChatGPT.
Then, look at your browser’s address bar and copy the slug that appears after chatgpt.com/c/ — that’s the unique ID for your conversation
Next, right-click anywhere on the page and select “Inspect.”
A developer panel will open on the side of your screen:
- Click “Network” at the top of that panel
- Paste the slug you copied into the filter bar
- Refresh the page
Click on the fetch version of the slug (here, it’s the second option under the Name column).
Then, open the Response tab.
Once it loads, press Ctrl+F (or Cmd+F on Mac) and search for the word “queries.”
What appears is the exact set of internal searches ChatGPT ran before producing its answer.
For the Toyota vs Honda prompt, ChatGPT generated queries around:
- Vehicle specifications
- Fuel economy
- Reliability
- Safety ratings
- Long-term ownership costs
Once you have the sub-queries, cross-reference them against your content.
Are you targeting each one? Do your pages use the same language ChatGPT is searching for — “long-term ownership costs” rather than just “value”?
ChatGPT often pulls from third-party sources like Reddit threads, review sites, and comparison pages.
So topical authority matters here — not just what’s on your site, but whether your brand shows up across the sources ChatGPT is likely to retrieve.
Perplexity
Perplexity runs two types of fan-out simultaneously:
- Internal fan-out — scans your prior conversation history for relevant context
- External fan-out — searches the external web for relevant information
The final answer draws on both layers, which means your content needs to work for a range of user situations, not just one.
For the Toyota vs. Honda question, Perplexity’s first batch of sub-queries had nothing to do with the cars.
Instead, it checked whether I’d previously mentioned anything that could shape its recommendation.
Like budget constraints, driving habits, or past questions about either brand.
Only after that internal scan did it launch external searches about reliability, ownership cost, and safety ratings.
What this means for your content: Perplexity may pair your page with context you can’t predict: a user’s past questions, constraints, or preferences.
Your content needs to be specific and self-contained enough to remain accurate and useful no matter the surrounding context.
Claude
Claude takes a different approach.
Rather than immediately running sub-queries, it asks clarifying questions first. Then, it generates a response tailored to your answers.
When I asked the Toyota vs. Honda question, Claude presented a preference widget before producing an answer.
Once I responded, it generated a recommendation tailored to my priorities.
Because it clarifies intent before searching, Claude tends to generate fewer, more targeted fan-out sub-queries than other platforms.
The implication for your content: Answer specific, well-defined use cases directly rather than trying to cover every angle on a single page.
Google AI Overviews and AI Mode
AI Overviews appear as concise, AI-generated summaries with sources listed in a clickable sidebar.
They work by synthesizing Google’s existing web index into a tighter, more contained summary.
AI Mode, by contrast, is a dedicated conversational search tab designed for complex, multi‑part questions.
Like AI Overviews, it draws on Google’s index to generate answers, but it offers more interaction and depth.
Neither platform exposes the sub-queries it runs.
But SEOs have found a way to extract Google’s fan-outs using Screaming Frog configured with a Gemini API. Watch Dan Hinckley’s tutorial for a full walkthrough.
For both, the optimization focus is the same: Front-load your answers, use descriptive subheadings, and structure content so individual passages stand on their own.
AI Search Runs on Query Fan-Out — Your Content Strategy Should Too
High rankings alone won’t earn AI mentions.
The brands showing up are the ones covering the questions their audience is actually asking and making that content easy for AI to extract and cite.
You’ve got the query fan-out framework. Now it’s about execution.
Start with one money prompt, map the sub-queries, and audit where your content stands.
Then work through the gaps, one topic at a time.
Next, dive deeper into how to get your brand seen and trusted across AI platforms with our AI search strategy guide.
The post Query Fan-Out: What It Is and How It Affects AI Visibility appeared first on Backlinko.
The shift to TV‑based consumption, the Shorts monetization gap, and the professionalization pressure all point to the same pattern: YouTube has evolved into infrastructure for building sustainable media businesses, not just a platform for viral‑content gambling.
If you’re still treating YouTube as a marketing channel for short‑term campaign distribution, you’re missing the structural opportunity. The platform now functions as a digital headquarters where:
- Content compounds over time.
- Audiences discover you through interest alignment rather than follower relationships.
- Monetization comes from integrated revenue streams, not ad revenue alone.
That’s not a trend. That’s a fundamental recalibration of how content creates commercial value.
Call to Action: Turn This Strategy Into a System
If you’re a business owner or marketing leader who wants to turn YouTube from a guessing game into a predictable growth engine, you don’t have to figure this all out alone.
Book a strategy session with our team and we’ll help you:
- Audit your current content and channel positioning.
- Design an interest‑based content plan that works with YouTube’s modern algorithm.
- Build a barbell strategy that uses Shorts for discovery and long‑form for revenue.
- Identify the right platforms and revenue streams for your specific business model.
Click here to schedule your session now and start turning your content into a compounding, monetizable asset instead of disposable posts.
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Keywords found in this article:
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