Advertisers using Microsoft Ads can now target users based on job seniority, adding another layer of B2B audience precision powered by LinkedIn data.
What’s happening. Microsoft Advertising expanded its LinkedIn Profile targeting capabilities to include job seniority targeting across Search and Audience campaigns, according to Product Liaison Navah Hopkins.
The update allows advertisers to target or observe users based on 10 standardized seniority levels: CXO, VP, Director, Manager, Senior, Entry, Owner, Partner, Training, and Volunteer.
The feature is available at both the campaign and ad group level, giving advertisers more flexibility when segmenting audiences.
Why we care. B2B marketers have long struggled to distinguish between decision-makers and practitioners within search campaigns. The addition of job seniority targeting gives advertisers a way to better align messaging, bidding strategies, and reporting with specific audience segments.
For organizations with longer sales cycles or multiple stakeholders involved in purchasing decisions, understanding who is engaging with ads can be as important as the conversion itself.
Between the lines. Unlike many audience targeting options available across advertising platforms, Microsoft’s integration with LinkedIn data offers a professional identity layer that can help advertisers better understand who is behind a click.
The new seniority filters can be applied directly within campaign settings or used in observation mode to gather performance insights without restricting reach.
How marketers can use it:
Tailor messaging by seniority
Advertisers can create separate ad groups for executives, managers, and individual contributors, adapting tone and messaging based on audience expectations.
An executive-focused campaign might emphasize strategic outcomes and business growth, while messaging aimed at practitioners could focus on workflows, implementation, or efficiency gains.
Identify who is actually converting
Observation mode allows marketers to analyze conversion performance across seniority levels without narrowing targeting.
This can help answer questions such as:
Are conversions coming from decision-makers or influencers?
Is budget being spent on audiences that rarely close?
Which seniority levels generate the highest-quality leads?
Improve audience testing
The additional reporting layer provides another signal for optimization and expansion decisions.
Advertisers importing campaigns from other platforms may find performance patterns differ on Microsoft Ads, making seniority reporting a useful source of testing and audience discovery.
Availability. The feature is currently available in selected markets across the Americas, EMEA, and APAC regions.
Americas: Argentina, Brazil, Canada, Chile, Colombia, Ecuador, Mexico, Peru, and the United States.
EMEA: Egypt, Nigeria, Saudi Arabia, and South Africa.
APAC: Australia, India, Indonesia, Japan, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Vietnam.
The bottom line. Microsoft Ads continues to lean into its LinkedIn integration as a differentiator in the B2B advertising market. The addition of job seniority targeting gives advertisers another way to connect search intent with professional identity, helping them better understand not just what audiences are searching for, but who is doing the searching.
The idea that AI is killing advertising misses the bigger shift. As AI expands across search, assistants, productivity tools, and transactions, advertising is moving with it.
Ad density may be changing within AI experiences, but advertising opportunities are expanding across a growing number of surfaces.
At the same time, paid and organic are becoming harder to separate. The same AI systems increasingly power ad campaigns, search experiences, and brand visibility across Google’s ecosystem.
That changes how brands should think about visibility.
Paid and organic are no longer separate channels competing for the same click. They are increasingly different ways of influencing the same AI systems, which means the signals shaping organic visibility may also affect paid performance.
The old model: Paid and organic on one finite SERP
Google’s SERP was a finite surface: 10 organic blue links, a few ad slots, and a knowledge panel on the right. The user landed, scanned, and clicked.
Paid and organic teams operated on separate budgets, separate tools, and separate quarterly reports, and rarely talked to each other because manual Google Ads kept the paid specialist busy full time. Titles, descriptions, bids, and campaign structure were all chosen by hand and required constant attention, which is why the organic team had no part in any of it.
DSA changed that for me. It read my organic pages to decide which ads to run, who to show them to, when, at what bid, and what title to use. I controlled the descriptions. The engine decided everything else, and it did it better than I would’ve done manually because it was reading the same signals the organic side was already optimizing for.
When someone at Google in Singapore explained how PMax worked, I thought, “That’s exactly what I was doing.”
PMax took the DSA logic and extended it across every Google surface simultaneously: Search, YouTube, Gmail, Display, Maps, and Shopping, all in one campaign, with the engine making every placement decision from your assets and audience signals.
AI Max brought the same intelligence into Search campaigns, specifically, with Gemini underneath instead of rules. PMax and AI Max run on the same Gemini brain: one focused on Search, the other spread across every surface, applying the same funnel logic to different contexts with different signal layers on top.
And if Gemini’s understanding of your brand is thin, it fills those decisions with whatever it thinks will work, which isn’t necessarily your brand narrative, and you have no direct way to override it. You train it, or you lose control of your own ads.
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The new model: Gemini sits inside every surface, and it carries ads with it
Gemini now sits inside every layer of the Google ecosystem:
Discovery (Search, Maps, YouTube, Lens, News, Discover, and Shopping), productivity (Gmail, Docs, Drive, Photos, and Calendar).
Distribution (Android, Chrome, Google Play, Pixel, Wear OS, Google TV, and Nest).
Transaction (Google Pay, Wallet, Flights, Hotels, and Travel).
Assistive surfaces themselves (AI Mode, AI Overviews, Assistant, NotebookLM, and the Gemini app).
That’s how many connected consumers spend most of their workday, and most of those surfaces either carry ads now or have the infrastructure to start carrying them.
Microsoft Advertising sits inside Copilot across Bing, Edge, Windows Consumer, Office Consumer, Teams Free, and GitHub.
OpenAI Ads launched in February for logged-in users on Free and Go tiers in the U.S., placing ads below ChatGPT responses and clearly labeling them as sponsored. By May, OpenAI had opened a self-serve Ads Manager and was expanding internationally.
The ads layer travels with the engine, the engine is everywhere, and ads therefore have the potential to be everywhere. Most brands still treat paid as a separate channel run by a separate team on a separate dashboard, which is a search-era inheritance that was never ideal but now needs to be dropped.
Performance Max already runs the auction across YouTube, Display, Search, Discover, Gmail, and Maps as one campaign type. Search is one surface among many, and the “ads are dying in AI search” narrative is measuring the wrong thing. It sees ad slots compress inside the assistive interface while ignoring that the surface base has multiplied by an order of magnitude.
Ad density follows the delegation the user has made to the machine
The dominant narrative in 2026 is that ads are dying because AI is replacing search, and ads inside AI are a problem nobody has fully solved yet. That’s partially correct: Ad density per session drops as AI takes more control, and nobody – including Google – has yet figured out how to insert ads into the AI response itself without killing the experience that makes the AI valuable in the first place.
But this is the part the analysis gets wrong: This doesn’t add up to fewer ads overall.
Search ads are Google’s goose with the golden egg, and the goose may be slowing down — though nobody outside Google actually knows, because Google doesn’t break out search ad revenue from YouTube, Display, and the rest. That ambiguity is doing a lot of work.
What we do know is that total ad revenue has kept growing even as AI has taken over more of the search experience, which proves the flock is already working.
Kodak invented the digital camera and then buried it to protect film-processing revenue, and we know how that ended. Google appears to be doing what Kodak didn’t: building the replacement while the original is still profitable.
Every surface Gemini sits inside is a new bird in the flock, each laying a smaller egg that grows over time, and when Google finally cracks ads inside the AI response itself, that’s one more goose. The surface base has expanded faster than density has dropped, and the ad-density problem in Search and AI is temporary.
The more the user delegates decisions to the machine, the less room the machine has to surface a paid option. Search keeps the user in charge, so the engine surfaces ads the user might pick. Assistive narrows the options, so a sponsored slot still has a chance. Agentic executes the decision, so the ad has nobody to persuade. Ad density follows that delegation, mode by mode, with AI deciding which brands win at each mode.
Ad density follows the delegation the user makes to the machine.
Google is running two moves at once, and it seems most people have noticed only the first one. Gemini is taking over the recommendation, targeting, and auction logic on surfaces that have carried ads for years. And Google is adding ads to surfaces where they were previously absent, with AI Overviews now eligible for ads above, below, and within the answer, and AI Mode testing conversational ad formats.
The first move is AI taking over the existing ad business. The second is the ad business expanding into surfaces it never occupied. The net effect is more AI-driven ads across more of the stack than ever before.
The freemium system still works, but the ad is becoming part of the surface
The monetization model that works at consumer internet scale is simple: pay with money, or pay with attention.
YouTube is Google’s clearest example — and proof that it works: free with ads, paid without, and the vast majority of users have always chosen ads.
Gmail draws the same line: Where the user pays directly, Google doesn’t insert ads. Where the user pays with attention, Google monetizes it.
I learned about freemium the hard way. When our children’s media company, Boowa & Kwala, survived the dot-com crash, we added a paid tier that removed the ads. Out of a million unique visitors a month, a few hundred paid. Almost nobody chose to pay.
The freemium contract — free access in exchange for ads — is the deal they actively prefer, and the numbers prove it. And for ad-driven businesses, pure volume makes the money. In Big Tech, Google has the clear advantage.
ChatGPT is already running ads on free tiers.
Gemini is ad-free without login, but that’s a launch state, not a permanent model.
Perplexity is blocking users instead of monetizing them, which is a different bet on the same problem — and a bet with a limited runway.
Every AI surface is in the process of landing on the same answer because there is no other answer.
What changes is how the ads appear. The classic SERP ad was clearly labeled and set off in a colored panel. The Gemini recommendation that surfaces a product inside a Gmail context, the Copilot suggestion that names a vendor inside a Word document, and the agent that picks a supplier on the user’s behalf are something else entirely.
The ad becomes ambient. It dissolves into the surface, and what advertising looks like becomes harder to identify as advertising. Gemini reads context and intent with enough precision that an ad placed in a meeting summary can feel useful rather than disruptive, which is a risk profile Google’s rules-based systems could never have accepted.
At Boowa & Kwala, when we scaled free ad-supported views from 100 million to 1 billion, revenue multiplied by roughly two, and costs rose by around 20%. Surface (a.k.a. pageviews) multiplied tenfold, revenue doubled, costs grew by a fifth, and we went from profitable to significantly more profitable.
The aim was never to push revenue up at the same rate as surface expansion. It was to keep expanding the surface, knowing the incremental delivery cost was negligible.
Google’s ratios at planetary scale differ from ours, but the structural shape almost certainly doesn’t: surface expansion plus near-zero incremental cost equals profit growth, regardless of whether revenue per surface keeps pace.
Cohort, intent, and profit drive both paid and organic
PMax, AI Max, AI Overviews, AI Mode — Gemini is driving all of them. The AI optimizing your paid campaigns is the same AI evaluating your organic content, reading the same user, in the same moment, with the same intent.
The engine reads three signals:
Cohort.
Intent.
Profit.
In paid, you declare all three explicitly when you structure your campaigns. In organic, the engine infers all three from behavior: clicks, dwell time, and return-to-search serve as proxies for the profit signal that is missing there. Google denied using behavioral signals for years. Its own court case documentation told a different story.
Which means the organic discipline the whole series has been building — the funnel query pathway, the entity home, and the corroboration stack — has always been pointing at one thing: engineer the page so precisely for the right cohort that the behavioral signal does the same job as a correctly structured PMax campaign. The user lands, stays, converts, and doesn’t go back and research the same thing again. Google reads that behavior and infers your profit tier.
My bet, and I want to be clear it’s a bet rather than a documented fact, is that Gemini can’t serve a paid ad in real time without grounding against current search results because the ad has to match the organic context it’s appearing in.
If it doesn’t ground, the ad is inconsistent with what the user sees organically, which breaks the experience and loses the click. So the grounding process for paid is the same process as for organic: same knowledge graph, same search index, same LLM.
That means training Gemini on your brand through organic improves your paid performance through the same mechanism. One training investment, two outputs. I’ll be proven right on this eventually, and this article is the timestamp.
The same AI runs your organic and your paid. Train it once, win twice.
You can’t directly target Gemini in AI surfaces. You can only train it.
Across AI-driven placements, Gemini decides everything: where to show your ad, what to show, how to show it, who to show it to, when, and at what bid. The advertiser feeds it information and sets the parameters, but Gemini makes every decision that matters.
What you’re buying when you spend on Google Ads in 2026 is the right to feed a recommendation system that analyzes your brand on its own terms. The explicit signals you declare in paid — cohort, intent, and profit — are a real advantage over organic, where the engine has to infer all three from behavior.
But your ability to dominate through pure campaign structure is vastly reduced when Gemini doesn’t understand or trust your brand. The control has shifted: you guide it through signal clarity, not through the settings dashboard, and that guidance works best when your organic foundation is solid.
Use paid to find the combinations that work, build organic pages around them
In a correctly structured PMax or AI Max campaign, you declare cohort, intent, and profit margin explicitly: this audience, this goal, this margin, in the same campaign. You don’t mix a luxury hotel and a budget guesthouse in the same ad group because the cohort is different, the profit margin is different, and handing the engine a mixed signal makes it spend your budget resolving a contradiction you created.
Organic doesn’t let you declare profit directly. The engine infers it from who landed, who stayed, who converted, and who never came back to search for the same thing. That behavioral signal is the only proxy it has for the profit tier, and it’s a thin signal compared to the explicit declaration you make in paid.
The smartest move for any brand running both is to treat them as a single loop. Run paid to find which cohort-intent-profit combinations actually convert. Build the organic pages around those combinations, designed so precisely for the right cohort that the behavior on the page sends the engine the same signal the paid campaign explicitly declared.
The paid side becomes cheaper because organic pages provide the behavioral confirmation the engine needs. The organic side gets stronger because the paid data tells you exactly which pages to build and for whom, and then feeds the engine the same signal the paid campaign declared explicitly, for free.
Most travel sites serve the same page template to a budget traveler looking for a €30 guesthouse in Bangkok and a wealthy traveler looking for a €3,000 suite at the Peninsula. Same layout, same fields, same photo grid, same review format.
The engine has to infer which cohort the page serves mostly from behavior because the differentiation of the pages is limited. Build the page for the person rather than the query, and you hand the engine the cohort signal it’s currently having to guess. That’s not a UX decision. That’s your profit margin declaration to an engine that can’t see your margins any other way.
And you win on all three fronts simultaneously. A page built precisely for the right person converts better because it works better for the human.
Better conversion behavior sends cleaner implicit signals to the engine, which improves your organic ranking for that cohort. And cleaner organic signals reduce your paid CPC because the engine has less to guess about. Better pages, more organic, cheaper paid – the same work produces all three.
When Gemini isn’t convinced about you, you pay on both sides simultaneously
The three revenue taxes — the doubt tax, the ghost tax, and the invisibility tax — operate on the organic side. Because the engine powering your organic results is the same one powering your paid placements, you pay all three on both sides simultaneously.
The doubt tax: When the engine hedges on basic facts about you organically, it rewrites your paid creative to soften the same claims.
The ghost tax: When the engine prefers competitors in organic comparisons, your paid creative gets passed over even when your bid is competitive.
The invisibility tax: When the engine doesn’t surface you organically, it doesn’t show your ad either. You’re not in the running.
Paid surfaces carry two additional taxes that don’t exist on the organic side, and one discount you earn when you get it right.
The taxes and discounts in AI-driven paid search include:
The mistrust tax: What you pay when the engine’s confidence in your brand is low. A CPC premium because Quality Score penalizes low entity trust, and message distortion because the Gemini Filter rewrites your creative away from your intended positioning. You can’t turn the filter off. The practical answer isn’t constraining it. It’s improving the entity confidence that the engine reads when deciding how to filter.
The intent tax: This is self-inflicted. Build an ad group with mixed intent, and you hand the engine a contradiction. Gemini will spend your money figuring out a mess you made. Each ad group should align on cohort, intent, and profit margin — any mix across those three, and Gemini is billing you to resolve the confusion.
The confidence discount: This is the blade cutting the other way. Every properly defined ad group is secretly doing two jobs: it buys you an efficient placement today, and it teaches the engine which cohort you serve tomorrow. When the engine trusts you, it stops second-guessing your ads, your CPC drops, and your creative lands cleaner. That’s worth more than any bid adjustment you make.
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Google has a structural advantage that Microsoft and OpenAI can’t match
Google has all the cards: the model, the surfaces, and the ads platform, all owned and tuned together in absolute harmony. Microsoft has the surfaces but lacks the LLM to drive them at the same level.
OpenAI has the model and launched a real ads business in February 2026, but lacks the surfaces – no Gmail, no YouTube, no Maps, no Play – and without surfaces, an ads business can’t compound at scale. Only Google has all three working as one system.
Paid and organic are now inseparable. The goose is fading, but Google can afford to let it. They know it rises like a phoenix, and in the meantime, they’ve got the biggest gaggle.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/Ad-density-follows-the-delegation-the-user-makes-to-the-machine-CDyaZO.png?fit=2048%2C1707&ssl=117072048Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-16 14:00:002026-06-16 14:00:00How AI is merging paid and organic visibility
As AI tool usage has become more common, I’ve seen impressive examples of people building tools to automate complex processes that once required significant manual effort. I’ve also seen teams adopt AI simply because it’s available, often with little practical benefit.
My approach is to focus on AI applications that save time and solve real problems.
Recently, I needed to align the SEO architecture for more than a dozen websites across three separate businesses, eight regional domains, and multiple languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.
Historically, mapping thousands of URLs to create cohesive hreflang XML sitemaps would have required specialized software or days of spreadsheet work. Instead, I used Google Gemini to build a custom Python script that handled the heavy lifting.
Here’s how the project evolved from an initial prompt into a highly customized automation tool, and what it taught me about using AI for technical SEO.
Where AI delivers the most value
I use AI primarily for practical, time-saving tasks, including:
Generating regex patterns when I need a quick solution without researching syntax from scratch.
Creating complex spreadsheet formulas for reporting workflows that rely on manual data exports.
Accelerating research and planning for projects that require competitive analysis across multiple business lines.
Building custom automation tools for recurring SEO and data-processing tasks.
The hreflang project discussed here falls into that final category.
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Mapping hreflang at scale
The challenge was clear: map thousands of URLs across more than a dozen multilingual websites into accurate hreflang XML sitemaps.
Rather than tackling the project manually, I used Google Gemini to help build a custom Python solution.
Here’s how the process unfolded.
Phase 1: Asking for an approach, not just a script
A common pitfall when using generative AI for coding is asking it to sprint before it knows the route. If you simply type, “Write a Python script to create an hreflang sitemap,” you’ll get a generic, fragile piece of code that breaks the moment it encounters real-world data.
Instead, I started by asking for an approach. I explained the scenario: multiple regional domains, organic growth over several years resulting in mismatched URL slugs, translated subfolders, and appended revision years.
Gemini suggested a multi-step, data-driven approach:
Crawl the websites to collect live URLs and their metadata.
Use Python in Google Colab to process the raw data.
Run an exact match cluster first to group identical slugs.
Use an advanced semantic AI model (such as SentenceTransformers) to fuzzy match translated pages based on their titles and normalized URLs.
Phase 2: Crawling and data collection
Following the strategy, I used a crawler to spider all the regional websites. The goal was to generate a unified comma-separated values (CSV) file containing the live URLs, status codes, title tags, and H1s. Screaming Frog worked perfectly for this application.
A critical point: Your AI output is only as good as your crawl data (remember the old saying, “garbage in, garbage out”).
An AI script will fail to map an obvious “exact match” if the target URL is a 404 or a 301 redirect in your source data. You must filter your CSV to include only indexable content before feeding it to the script.
Google Colab provides a free, cloud-based Jupyter notebook environment where you can write, paste, and execute Python code without worrying about local installations or environment variables. You can access it through Google Drive. I found the free version had enough capacity to handle this project.
I uploaded the CSV to Colab, and Gemini provided the initial Python script. The script used a domain-mapping routine to assign language codes, clean the URLs, and generate an XML tree. The initial output was far from perfect.
Phase 4: The iteration (where the real work happens)
If you expect AI to deliver a flawless, edge-case-proof script on the first try, you’ll be disappointed. You’ve probably heard the comparison of AI tools to interns, meaning you need to check their work. That’s very true.
The real value of AI lies in the iteration. As we ran the script, we encountered several unmatched URLs, leaving pages orphaned rather than grouping them with their international counterparts.
Here’s how I iteratively trained the AI to handle the nuances of human-managed websites.
The directory flattening problem
The U.S. site had recently reorganized its blog into topical folders, while the Mexican and Italian sites hadn’t yet been reorganized.
I prompted Gemini with these specific mismatched examples. It responded by adding a URL flattener function to the script, which stripped the topical folders behind the scenes so the translated slugs could align cleanly.
The aggressive semantic trap
To prevent the AI from mixing up different topics, we implemented concept traps. Initially, they were too strict. A UK article about the manufacturing sector wouldn’t match an Italian article because the U.S. title was slightly more generic.
I instructed Gemini to loosen the traps for generic industries while keeping them strictly enforced for critical acronyms (such as “SEO” versus “SEM”). This gave the AI the breathing room it needed to match creative translations.
The translated slug epiphany
The biggest breakthrough came while auditing the Mexican blog orphans. For example, the Spanish URL /detras-de-escenas-historias... is a direct translation of the English /behind-the-scenes-stories... I pointed this out to Gemini.
Instead of forcing me to hard-code hundreds of manual matches, Gemini updated the script to create a “Combined Semantic Signature.” It dynamically translated core operational phrases in the slugs, effectively bridging the language gap for the semantic matching model and connecting dozens of orphaned pages almost instantly.
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Lessons from building an AI-assisted SEO tool
The project reinforced a simple lesson: AI works best when it’s treated as a collaborator rather than a shortcut.
Be the strategist, let AI be the coder: Don’t just demand a final product. Discuss the architecture, edge cases, and logic first. Treat AI like a junior developer that needs clear architectural direction.
Provide concrete examples: When a script fails, don’t just say, “It’s broken.” For this project, I provided either exact URLs that failed and the URLs they should have matched with, or groups of URLs with mismatches. AI needs concrete patterns to fix its logic.
Embrace the iterative loop: Expect to run the code, identify anomalies, and feed them back into the prompt. Each iteration makes the tool significantly smarter.
Leverage Google Colab: You don’t need to be a Python expert to use Python for SEO. Colab bridges the technical gap, allowing you to run complex data science libraries directly in your browser.
By the end of the project, we had a robust, highly customized Python script that could process a massive CSV and generate a cross-referenced hreflang XML sitemap in minutes.
AI isn’t going to replace technical SEOs anytime soon. However, SEOs who know how to collaborate with AI to build custom, scalable, and useful tools will have a significant advantage.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/How-AI-helped-build-hreflang-XML-sitemaps-at-scale-ONn3vk.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-16 13:00:002026-06-16 13:00:00How AI helped build hreflang XML sitemaps at scale
Much of the GEO conversation focuses on how AI systems discover, extract, cite, and recommend content. That work matters. But visibility also depends on what the content contains once it’s found.
Next-question intent is a way to test whether a page provides enough information to support the user’s next decision, not just the initial query.
The first search is often only the starting point. Real decisions happen in the follow-up questions, comparisons, constraints, and objections that come next.
Content that helps answer those questions gives AI systems more useful material to summarize, compare, cite, and recommend.
From results to narratives: Traditional search vs. AI search
Traditional search was built around a results page: a ranked set of links users could scan, compare, and interpret for themselves. AI search is increasingly built around a synthesized answer drawn from multiple sources.
That changes what content must do. A page can rank, index, and appear technically sound, yet still fail to provide the information needed to support an AI-generated answer. That’s where next-question intent matters.
Search intent asks, “What is this user trying to do?”
Next-question intent asks, “What will the user need to know next before they can trust, compare, choose, buy, book, or move on?”
That question is becoming increasingly important because AI systems don’t simply match queries to pages. They assemble answers, comparisons, qualifications, and recommendations.
In that environment, content must support the full answer path, not just the first query.
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The first query is often only the doorway
A user’s first search is often broad, incomplete, or simply exploratory. It signals a direction. Real value appears in what comes next: the follow-up, the objection, the comparison, the constraint, the “practical anxiety,” the “Yes, but what about my very specific situation?” moment.
As the simplest example, someone searches “best CRM software for small business.” The first query becomes a doorway. But the actual buying process begins with the follow-up questions.
Which platform is easiest for a two-person team?
Which integrates best with QuickBooks?
Which one works for a business without a formal sales department?
Which one is best for a local service company rather than a software startup?
Which one won’t make an owner, office manager, or intern quietly resent tech?
These queries aren’t add-on or side questions. They’re the actual decision path.
Otherwise competent content fails at this stage. It answers the query, but doesn’t help complete the conversation. A page can define the category, mention benefits, include a few keywords, and still omit information buyers need to make decisions.
In traditional search, the user might click a few results and assemble context manually. In AI search, the system will assemble it for them. If your content lacks that useful context, it gives the system less to work with and may appear less visible.
Next-question intent is not just a writing exercise
The risk with any new content framework is that it becomes a fresh label for familiar advice. Next-question intent should do more than remind you to “write better content.” It should help you test whether a page contains enough context to support the next step in a user’s decision.
In practical terms, next-question intent means asking whether the content is answer-ready.
Answer-ready content addresses the user’s initial need, anticipates the next layer of decision-making, and provides specific, verifiable, and contextual information to support a synthesized answer.
This distinction matters because AI search visibility isn’t exclusively about rankings. It’s also about citations, mentions, recommendations, and whether a brand is recognized as a trusted answer in a given context.
Those outcomes require something more than volume. They depend on whether the brand’s content provides the system with enough substance to understand what the brand does, who it serves, when it’s useful, why it’s trustworthy, and how it compares to alternatives.
Where good content goes thin
Most brands have decent content that’s accurate, readable, and optimized around a keyword. There may even be an FAQ section, like a useful but decorative basket of afterthoughts.
In AI search, decent may not be enough.
AI systems need extractable clarity, but they also need context. They must understand what something is, who it’s for, when it’s useful (and when it’s not), what evidence supports the claim, and what the user should consider next.
This level of context is where many pages go thin.
As an example, a service page says, “We offer customized marketing strategies.” But what does customized mean?
A real strategy?
A lightly personalized template?
A monthly call where everyone nods at a dashboard no one has time to interpret?
The product page says “safe for families.” Safe for whom?
Babies?
Pets?
People with health issues?
A software page says, “built for small businesses.” What business?
A solo bookkeeper?
A nonprofit?
A 40-person heating and cooling company?
A founder doing payroll late at night after working all day?
Broad claims offer humans little to trust and AI systems little to use. Specific, structured, evidence-backed content offers something better.
A next-question audit looks beyond keyword coverage and asks whether a page contains the information needed to support the next step in the user’s journey.
For every important page, you should ask:
What’s the primary question this page answers?
What would a serious buyer, reader, or researcher ask next?
What objection would stop them from acting?
What comparisons would help them understand the category?
What proof would make this answer trustworthy?
What detail would make this financially, technically, locally, or personally relevant?
Where are we applying broad language because we haven’t done the harder thinking?
The best inputs for the audit often come from inside the business, not from keyword tools alone. Customer reviews, comparison queries, demo questions, sales calls, support tickets, chat logs, internal site search, and objection patterns can all reveal the questions real people ask when making decisions.
That information is often closer to the buyer’s actual path than a neat spreadsheet of keywords.
Examples of next-question content across industries
For a local service business, next-question content might involve service areas, prices, appointment windows, insurance, reviews, emergency availability, or what happens after someone books.
B2B software may invest in next-question content that involves integrations, user roles, implementation times, costs for switching, security, support, or whether a lower-tier plan is useful.
For higher-trust categories like medical, financial, and legal, next-question content involves scope, credentials, risk, regulation, evidence, or when to speak with a qualified professional.
The point isn’t to stuff pages with every possible question. It’s to build content around how people actually decide.
AI search rewards content that completes the answer
Next-question intent helps you avoid one of the least useful responses to AI search: publishing more content because visibility feels uncertain. The better move is more specific, decision-ready content.
If your page says, “I/we help small businesses grow,” explain which small businesses, what kind of growth, what constraints, what proof, what trade-offs, and what alternatives.
For example:
“We help local service businesses without in-house marketing teams improve search visibility and generate more qualified appointment requests by clarifying their website content, answering the questions clients actually ask, and building pages that support both traditional and AI-generated search. This is best for businesses looking for durable visibility rather than a quick paid-ad spike.”
In that same line of thought, if a page says “We’re eco-friendly,” explain the materials, sources, use cases, certifications, limitations, disposal issues, and even circumstances where that claim doesn’t apply.
If a page says “This is AI-powered,” explain what that AI tool actually does, what it automates, what remains human-led, what data it uses, and where users will still need judgment.
This isn’t writing for bots. It’s writing for real people whose decisions are increasingly being mediated by AI-generated answers. The goal is to make your expertise, relevance, and trustworthiness easier to understand and use.
If AI can’t find you, customers won’t either.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
See your AI visibility
The new visibility test
Traditional SEO asked whether a page could rank. AI search asks whether a page can contribute to the answer.
Any page can be indexed, optimized, and technically sound, yet still fail if it lacks substance. It might answer the initial query, but ignore the information users need to make a decision.
The opportunity isn’t to chase every new acronym or rebrand every content plan as a new discipline. It’s to build answer-ready content.
That means clearer definitions, stronger examples, honest comparisons, better proof, more precise positioning, and direct answers to the questions customers ask every day.
In traditional search, visibility belonged to the page that best matched the query. In AI search, it increasingly belongs to the content that helps people move forward.
Google updated its AI Search optimization guide to clarify that llms.txt files neither help nor hurt Google search rankings. It also confirmed that Google Search does not use llms.txt files.
What Google wrote. I bolded the portion that is new, where Google wrote that Google Search does not use AI text files, markup or Markdown files.
“You don’t need to create new machine readable files, AI text files, markup, or Markdown to appear in Google Search (including its generative AI capabilities), as Google Search itself doesn’t use them. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn’t mean that the file is treated in a special way.”
Google also added a new note that reads:
“It’s completely fine if you decide to create and maintain LLMS.txt files (or other similar files) for other services or systems that use these files. Doing so won’t harm (nor help) your visibility or rankings in Google Search, as Google Search ignores them.”
Why we care. There’s been a lot of confusion about how Google Search handles llms.txt, markdown, and other AI-related files. In short, Google Search may discover, crawl, and index these files, but it does not use them in any special way. Having them on your site won’t help your rankings, and it won’t hurt them either.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/google-llm-doc-update-scaled-6cRDQT.webp?fit=2048%2C950&ssl=19502048Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-15 20:03:332026-06-15 20:03:33Google says llms.txt files won’t harm or help your search rankings
While managing a major B2B SaaS account, Hallam PPC Lead, Simran Harichand tightened a target CPA to improve efficiency but failed to monitor the impact. The change dramatically reduced spend, leaving the account €30,000 short of its monthly budget target.
When underspending becomes a business problem
Underspending isn’t just a media issue — it can affect a client’s future budgets. In this case, unused funds had to be returned to finance, making it harder for the marketing team to justify similar investment levels in future planning cycles.
The hardest part wasn’t the mistake
The most difficult moment came when Simran had to explain the situation to the client. Rather than making excuses, she took full responsibility for the error and acknowledged the impact it had on their goals.
Trust is built after the mistake
Although the client was understanding, trust had been damaged. Simran rebuilt confidence by introducing weekly budget pacing updates, showing transparency and proving the issue wouldn’t happen again.
Why the “brilliant basics” matter
The experience reinforced the importance of fundamentals such as budget pacing, account monitoring and conversion tracking. No matter how advanced advertising platforms become, strong basics remain the foundation of good performance.
What she’d do differently today
Looking back, Simran says she underestimated how much influence a target CPA change could have on delivery. Today, she treats any spend-related adjustment as a significant account change that requires close monitoring.
The danger of relying on AI without oversight
Simran supports testing AI-powered tools but warns against blindly adopting every new feature. She believes advertisers should balance experimentation with human oversight and strategic thinking.
Why conversion tracking remains the industry’s biggest blind spot
One of the most common issues she sees in account audits is poor tracking implementation. Inaccurate conversion data can lead to flawed optimisation decisions, making reliable measurement more important than ever.
The human side of client relationships
Strong client relationships can help teams navigate difficult moments when mistakes happen. Building trust through communication and honesty often matters just as much as delivering strong performance.
The bottom line
Mistakes are inevitable in PPC, but accountability and learning from them are what matter most. For Simran, the experience was a reminder that long-term success is built on mastering the fundamentals and maintaining trust.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/rzl5tut9h3c-RGkb8K.jpg?fit=1280%2C720&ssl=17201280Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-15 18:25:542026-06-15 18:25:54How a €30,000 underspend taught Simran Harichand the importance of the basics
You’ve probably seen some version of these three claims:
Quote-led headlines outperform plain declarative ones by nearly 29%.
Question headlines underperform both, sometimes by 24%.
Format drives the result: Rewrite a statement as a quote, or add that magic word, and you should expect a real lift.
We tested all three against 1,674,518 English editorial articles and 1,690,295 French articles from the 1492.vision Discover corpus (November 2025 to May 2026): about 3.4 million editorial articles with at least one capture across our fleet.
They share a deeper flaw than any of their numbers.
All three treat headline format as a cause — a lever you pull to gain visibility. But the data shows, layer after layer, that a format’s measured effect is almost entirely a proxy for something else: which publisher used it, for which audience, and on which Discover surface.
The headline is a symptom of those choices, not an independent driver.
The clearest demonstration is Simpson’s paradox. Once you see it, you find it throughout the dataset.
A note on what we measure
Our metric isn’t clicks from Discover; no third party has that data. It’s hits per article: how often an article appears across the 1492.vision fleet we observe, a proxy for visibility.
The corpus is limited to editorial articles. YouTube and X are excluded because their headlines follow different conventions. We’ll return to both at the end—they sharpen the point more than anything else.
A word on why the volume matters: the entire argument depends on being able to slice 3.4 million articles by publisher, Discover surface, topic, and language while still retaining enough data in each segment for meaningful comparisons. That’s the difference between a number and an insight — and between a real format effect and a statistical mirage.
The number is real, at the wrong altitude
Mean hits by headline format, EN and FR
Pool all publishers together, and a clean gradient emerges: quote-led headlines at the top, statements at the bottom.
Lang
Format
Articles
Mean hits
Median
vs statement
EN
Quote-led
38,044
13.0
4
+37%
EN
Quote inside
75,463
11.5
4
+21%
EN
Question
53,081
10.2
4
+7%
EN
Statement
1,674,518
9.5
3
baseline
FR
Quote-led
179,472
52.8
13
+48%
FR
Quote inside
223,052
49.9
12
+40%
FR
Question
103,117
41.3
11
+16%
FR
Statement
1,690,295
35.7
9
baseline
The commonly cited +29% is conservative for pure editorial articles: quote-led headlines show a +37% lift in English and +48% in French. Questions, far from underperforming, also outperform statements (+7% EN, +16% FR).
At this level of aggregation, claim 1 looks understated and claim 2 looks plainly wrong.
This is the level of aggregation where most headline advice is born. Hold onto that +37% figure — the rest of this piece is about what it’s actually measuring.
Hidden variable 1: which publisher
The aggregate can’t answer a crucial objection on its own: the publishers that use quotes aren’t the same publishers that don’t.
Celebrity media, regional dailies, and buzz-driven sites lean heavily on quotes and earn more Discover hits per article regardless of headline format. Pure-play publishers, wire services, and utility-focused sites favor declarative headlines and tend to sit lower.
The raw comparison, then, isn’t quote versus statement. It’s one publisher population versus another.
Simpson’s paradox in one chart
This is a textbook Simpson’s paradox: a strong trend in the aggregate that weakens, disappears, or reverses once you segment by group.
To get anywhere near the effect of headline format itself, the grouping variable has to be the publisher.
So make each publisher its own baseline: compare quote versus statement within the same site, holding audience and topic mix constant.
Across 324 English and 439 French publishers with enough of both formats — at least 50 quote and 200 statement articles each:
Within-publisher: does quote beat statement at the same site?
Lang
Publishers
Quote wins (median site)
Quote wins (mean site)
Median within-publisher Δ
EN
324
31.5%
55.9%
+3.1%
FR
439
47.6%
57.4%
+5.5%
In English, statements outperform quotes at 68% of publishers by the median; quote-led headlines hurt more often than they help. In French, the result is close to a coin flip.
That leaves the underlying format effect at roughly +3% to +5%—about five to nine times smaller than the aggregate figure.
(The mean is higher than the median because a minority of publishers see large gains from quotes. The median is the more reliable measure of the typical publisher.)
Stop here and the lesson sounds like “segment your data.” But the collapse points to something larger.
If three-quarters of a +37% effect was really a publisher effect, the obvious next question is: what else is the headline metric standing in for?
The rest of this article is a tour of those hidden variables. And by this point, the answer to claim 3 is already coming into view: the format itself isn’t the driver.
The same substitution, in reverse: questions
Questions: same Simpson, opposite direction
The conventional advice says questions underperform by roughly 24%. The aggregate view of our data says the opposite: questions outperform statements (+7% EN, +16% FR).
Both conclusions are wrong for the same reason. Question headlines are disproportionately used by high-engagement publishers, which inflates their aggregate performance.
Within publishers, the picture settles.
In English, question headlines show a modest real underperformance (-3.7%), winning at only 29.3% of sites. In French, the effect is essentially neutral (-0.5%), with questions outperforming at 46.2% of sites.
The conventional advice gets the direction roughly right in English and neutral in French, but its usual magnitude is about sixfold too large.
The question mark isn’t the cause. The kind of publisher using it is. Same hidden variable, opposite sign.
The effect won’t even hold still
Monthly within-publisher quote advantage
Even that modest within-publisher effect drifts from month to month.
In English, it peaks at +2.5% and turns negative in March 2026, while statements outperform questions at 55% to 60% of sites each month. In French, it ranges from +3% to +12% — strongest in December and February, weakest in March — with no clear trend.
A genuine causal lever shouldn’t wobble like this. A correlation tied to a shifting content mix should.
Hidden variable 2: Which audience
Top EN publishers, quote vs statement
The +3-5% average hides a sharp, consistent split. In English:
Gainers: International general news (BBC +85%, Forbes +46%, CBS News +43%, Boston Globe), Yahoo aggregators, mass-market magazines (Parade, Good Housekeeping), Gizmodo.
Losers: Specialist sport (RugbyPass, Planet F1, ThisIsAnfield), entertainment (IMDb, TVInsider, People), and factual-leaning dailies (Standard, Washington Post).
Top FR publishers, quote vs statement
French data follows the same pattern in a different market.
Gainers: Regional newspapers (La Dépêche, La Montagne, L’Écho Républicain) and general-interest magazines (Grazia).
Losers: Specialist sports outlets (Foot National, le10sport, MadeInFoot), technology publishers (Les Numériques), and service-oriented titles (Journal des Femmes, Femme Actuelle).
The pattern is editorial, not algorithmic. Quotes tend to work where the audience comes for commentary, reaction, and framing, and fail where the audience comes for facts.
A publisher built around “what someone said” benefits from a quoted headline. One built around “what just happened” usually doesn’t.
The convergence between English and French is the giveaway. This isn’t a language effect; it’s a reader-intent effect.
What looks like a headline-format effect is, in this case, an audience effect wearing the clothes of a headline.
Hidden variable 3: Which Discover surface
Discover isn’t a single feed. It’s a collection of pipelines, each selecting articles in different ways:
First, rule out the obvious alternative explanation. Are quote-led articles simply being routed to higher-value Discover surfaces, making the apparent bonus a placement effect rather than a headline effect?
The data says no.
Comparing where quote and statement articles actually appear, the distributions are nearly identical. In English, the largest differences are small: content.f (+2.2 percentage points), aura.f (-1.9), and moonstone.f (+0.6).
Pipeline mix by format
The bonus isn’t about placement: quotes and statements appear on the same surfaces in the same proportions. It’s about intensity — how each format performs once it’s on a surface. There, the overall +3% to 5% breaks into a wide range: from +22% to -14% in EN and from +25% to -12% in FR.
Quote bonus by pipeline, EN, full picture
Grouped into functional families, the pattern is readable:
Quote-led headlines win where multiple headlines compete for attention at once — curation carousels, news clusters, and other surfaces where the title carries a social signal: someone said this. They lose on similarity-based recommendations, where the surface sells continuity (“because you read X, you’ll read Y”) and a quote disrupts the topic-clear promise with an out-of-context citation.
The largest pipeline by volume, Aura, ranks on topic affinity and barely reacts to format at all, with gains of just +0.6% to +1.8%.
Why is the net effect so small?
A single quote-led FR article doesn’t get one number; it gets a blend:
+10 to +25% on its curation share (moonstone, mustntmiss, astria)
~0% on its aura share, the largest slice of volume
-3% on its relatedcontentruby share (≈ 10% of captures)
-2 to -6% on shopping/viewer-related surfaces
Integrate those and you land at +4% to +7% net. The curatorial gains are real but partly offset by recommendation losses, which is why the aggregate is nowhere near +29%. The same format is both an asset and a liability, depending entirely on the surface serving it.
And +4–7% overstates how much the format itself matters because each pipeline’s ranking is a compound of signals unrelated to the title: engagement, scroll depth, topic affinity, E-E-A-T, entities, reading history, location, timing, and prior interactions.
A quote in the headline is, at best, one weak signal competing with all of those. Long before an article reaches a feed, it’s largely swamped by everything else.
Questions by pipeline, same story sharper
Question vs statement bonus, by pipeline
These are within-publisher medians (each publisher against itself), so they aren’t a crude artifact of FR using more questions. The format follows the same pipeline logic as quotes, but in a more polarized form:
FR curation leans positive on questions; EN curation leans negative.astria.f, the same pipeline in both languages, runs +9% in FR and -1% in EN; FR mustntmiss.f is +14%, EN moonstone.f is -13%.
Similarity-based recommendation penalizes questions everywhere, harder than quotes: relatedcontentruby.f FR -11.5% (306 publishers), EN -6.1% (119); itemitemcollaborativefiltering.f FR -14.5%.
aura stays neutral in both (+3.5% FR, -0.6% EN).
Two caveats point in the same direction:
A fleet-capture metric can’t distinguish an algorithmic penalty from an audience-eviction effect: readers see a question mark, decide “not now,” and scroll past. The fact that relatedcontentruby — which serves already-engaged readers — penalizes questions this heavily points to a behavioral signal, not just ranking.
Within-publisher pairing controls for each publisher against itself, but the median is still computed across a different set of publishers in FR and EN, on partly different surfaces. So “FR rewards questions, EN doesn’t” describes the publishers and topics occupying each cell, not an inherent property of the language or the question mark. It’s another hidden variable mistaken for a format effect.
Hidden variable 4: Which editor, and which judgment
Even the honest +3% to 5% comes with a caveat that outweighs its size. When a publisher writes a headline as a quote, they choose the best available quote for that story. So the within-publisher figure compares the best quote an editor selected with the average of all that publisher’s statements, not the same article written two ways.
It’s the subject-line A/B testing problem: a good alternative beats a bad one, but the average alternative doesn’t. Convert every headline to quote-led and you’d be writing average quotes, so most of the gain would disappear. The +3–5% is an upper bound on a selective practice, not the return from a blanket rule.
That’s the final reason “do it everywhere” fails:
Not every article has a quote. A sports result, a press release, a market analysis, a product test: forcing one means fabricating it.
The editor-selection bias above: The measured bonus is the best quote chosen, not a property of the format.
Recommendation pipelines are long-tail levers.relatedcontentruby and friends are how an article redeploys after its initial peak, the main mechanism for extending Discover lifetime. Optimizing the headline for the curation peak while breaking the promise on these surfaces can net negative.
The largest pipeline barely reacts.aura is 11% to 15% of FR captures and 7% to 9% of EN, with a +0.6% to 1.8% quote effect. A universal quote rule optimizes secondary surfaces while ignoring that the biggest one runs on topic affinity.
The clincher: the same format, opposite meaning
YouTube and x.com, quote bonus
We excluded YouTube and X from the main corpus, but their results are the clearest proof of the thesis. The same quote-led format produces opposite effects depending entirely on what the title is trying to do.
Domain
Lang
Quote articles
Statement
Mean hits quote
Mean hits stmt
Δ
YouTube
EN
43,476
734,986
11.6
10.2
+14%
YouTube
FR
16,509
93,912
59.0
29.1
+103%
x.com
EN
34,156
268,175
5.2
4.9
+6%
x.com
FR
32,201
114,914
21.4
24.6
-13%
On YouTube, the title is effectively a text thumbnail that has seconds to create curiosity. A quote serves as a content promise — “here’s the line worth hearing” — which helps explain the +103% result in French. On X, the title is the post itself, and a detected quote usually indicates that someone is repeating or responding to another person’s words, diluting the original message. That correlates with a -13% result.
Same characters. Same regex. Opposite outcome. The format didn’t change; the job it was doing did.
(Methodological footnote: a naive audit that folded YouTube into the editorial corpus would inflate the overall quote bonus by 20–30 points, while one that folded in X would dilute it. Any serious headline study has to isolate editorial articles before measuring headline effects.)
The headline was never the variable
Put the layers together. Three-quarters of the +37% raw bonus was explained by publisher differences. What remained split again by audience, then by Discover surface, then by which quote the editor selected, and finally reversed entirely when the title served a different function on another platform. At every step, removing context shrank or flipped the apparent format effect.
There’s no clean residue at the bottom where the headline acts independently. The effect is inseparable from the context that creates it.
That’s not a measurement failure; it’s the finding. We just saw the mechanism. Headline format is one weak signal among many stronger ones, all moving through pipelines that often pull in opposite directions.
The consequence is the point. An article’s visibility is the running score of that entire contest, not the verdict of any headline rule. A number measured across publishers is downstream of everything that travels with the format: who published it, what topic it covers, what the audience expects, the newsroom’s style and habits, and the conventions of the language itself.
So when an aggregate reports “+29% for quotes,” it isn’t isolating the quotation marks. It’s measuring a correlation with that whole bundle of factors and quietly relabeling it as causation.
None of this means aggregate data is the enemy. Everything above comes from aggregate data, just analyzed at the right level.
The trap is narrower: treating a single cosmetic variable, averaged across publishers that don’t belong in the same category, as a causal lever.
The same index that exposes that mistake also reveals the signals that genuinely drive Discover: which topics a publisher wins on, which entities are accelerating, who dominates a given surface, and what’s trending before it peaks. Those signals aren’t cosmetic, and they aren’t drowned out by stronger forces. They’re the underlying demand that headline format only weakly approximates.
The lesson isn’t “ignore the data.” It’s “stop averaging the wrong variable across the wrong population.”
This is why no cross-publisher average, corrected or not, converts into a rule for your site:
Visibility isn’t traffic. Two sites can earn identical Discover visibility on the same article and see very different CTRs because their audiences click for different reasons.
No two audiences are the same. A quote that reads as insider commentary to a magazine reader may read as vague or irrelevant to someone scanning sports scores.
A cross-publisher average of one cosmetic feature is the average of audiences you don’t have. Segment by your audience, your topics, and your surfaces, and it becomes information again.
The only test that answers your question is the one you run on your own site, with your own audience. Know who you’re writing for, then measure them. Slice the data by your audience, your topics, and your surfaces — not by a single number averaged across everyone.
So what about the three claims?
Each is real as a correlation and useless as a cause:
“Quotes beat statements by ~29%”: True in aggregate — larger than +29%, in fact — but mostly explained by publisher differences. At the publisher level, the residue is +3% to 5%, and even that compares the best quote an editor selected against the average of all statements, not the format itself.
“Questions underperform”: Directionally true in EN, neutral in FR, but the magnitude is about 6x too large. The actual effect is roughly -4% in EN and ~0% in FR.
“The format itself is the driver”: The claim the dataset refutes. The same article from the same publisher, mechanically rewritten as a quote, would not gain the aggregate effect.
The honest version, if you want one sentence to keep:
A quote-led headline can earn roughly +3% to 7% additional Discover visibility for audiences that value commentary and framing (general news, magazines, regional press), especially on curation surfaces, and lose for factual audiences (sports, tech, utility) and on similarity-based recommendation surfaces. There is no universal gain from quotation marks; the popular ~+29% figure overstates the format effect by roughly an order of magnitude. The useful question isn’t “Should I use a quote?” but “Who am I writing for, and which Discover surface drives my traffic?” The only place to answer that is with your own site, not anyone else’s average.
Methodology
Data and period: 1,674,518 EN and 1,690,295 FR editorial articles with Discover visibility from 1492.vision proprietary data, collected between 2025-11-01 and 2026-05-19. Editorial articles only; excludes ads, videos, AI Overviews, and showcases. Domain exclusions: x.com, twitter.com, m.twitter.com, youtube.com, www.youtube.com, and m.youtube.com (reported separately above).
Headline format detection (regex): Quote-led: title starts with a multi-word quoted phrase (“…”, «…», ‘…’, or ‘X…’:). Quote inside: a quoted phrase appears but not at the start. Question: ends with ?. Statement: everything else. Titles under 20 or over 300 characters are excluded. Detection deliberately errs toward false negatives in the quote bucket, biasing against finding a quote effect, so the +3–5% is conservative.
Three layers of analysis: (1) Raw aggregate: all publishers pooled, producing +37% / +48%. (2) Within-publisher: quote vs. statement inside each publisher with ≥50 quote and ≥200 statement articles; we report the share of publishers favoring quotes and the median per-publisher Δ. This neutralizes publisher-mix bias. (3) Monthly evolution: the same pairing, recomputed monthly with relaxed thresholds (≥10 quote, ≥40 statement).
Pipeline layer: Captures come from 1492.vision proprietary data, with each row representing one capture on a specific pipeline. For each (pipeline, format, publisher), captures per article = pipeline captures ÷ distinct articles. Within-publisher pairing includes publishers with ≥20 quote (or question) and ≥60 statement articles on that pipeline. A pipeline is shown only if ≥5 publishers qualify. Pipeline families are an empirical grouping (editorial curation, related reading, trends, similarity-based recommendation, and main personalization) that reflects how each surface behaves.
Metric: A “hit” is one capture of an article on Discover by the 1492.vision device fleet. It is a visibility proxy, not a visit.
Known limitations: (1) No traffic data: the metric is Discover visibility, not clicks, so a format could affect CTR independently without appearing here. (2) Regex detection misses edge cases and is biased toward under-counting quotes. (3) Within-publisher effects compare the best quote an editor selected against the average statement, not the counterfactual of making every headline quote-led. (4) Some negative pipelines have small publisher samples (<10); the consistent direction matters more than any individual magnitude.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/EN_chart_01_global_raw-qL3TLu.webp?fit=1528%2C572&ssl=15721528Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-15 17:26:042026-06-15 17:26:04Headline formats and Google Discover: What 3.4 million articles reveal
With our latest 27.8 release, we introduced performance optimizations that should reduce loading times throughout the plugin’s functionalities, especially noticeable in large sites with lots of posts and users.
Note: This post contains technical content and implementation details.
Offering well-tuned software with minimal overhead in servers and fast loading times is always at the forefront of everything Yoast developers do. However, Yoast SEO is installed in millions of websites so the variance of setups that we must be well-tuned for is big. This means we should be continuously going back to search for windows in optimizing the performance of the plugin. We’ve been known to do that consistently in the past, like when we improved our database system.
The 27.8 release is the outcome of one of those targeted reviews. We deliberately picked features whose behavior at scale offered the most headroom and reworked them to be leaner and faster. From modifying queries to make pages faster for sites with many users and shaving heavy operations in the admin for sites with many posts, to reducing rounds trips to the database for multiple features and generally applying performance best practices, this is a release meant to improve the user and developer experience in the Yoast SEO plugin.
We would also like to offer a technical summary of the improvements in this release here, focusing on their nitty-gritty details because it’s always nice to raise awareness about performance best practice (not to mention that it’s always fun to talk about code).
Significantly reduce loading times of the root sitemap on sites with many users
For context, for Yoast SEO to calculate the Last Modified value of the author sitemap, when it outputs the root sitemap, it uses the usermeta of the all the users that are eligible to be included in the author sitemap.
Calculating the eligible users was traditionally done by checking user capabilities. This was done by adding the ‘capability’ => [ ‘edit_posts’ ] argument in the get_users() call that was used. As a result, a very heavy query with multiple joins and no use of the indexes of the database was triggered.
Specifically, the resulting query added a clause like this:
AND ((((mt1.meta_key = 'wp_capabilities'
AND mt1.meta_value LIKE '%"edit\_posts"%')
OR (mt1.meta_key = 'wp_capabilities'
AND mt1.meta_value LIKE '%"administrator"%')
OR (mt1.meta_key = 'wp_capabilities'
AND mt1.meta_value LIKE '%"editor"%')
OR (mt1.meta_key = 'wp_capabilities'
AND mt1.meta_value LIKE '%"author"%')
OR (mt1.meta_key = 'wp_capabilities'
AND mt1.meta_value LIKE '%"contributor"%')
OR (mt1.meta_key = 'wp_capabilities'
AND mt1.meta_value LIKE '%"wpseo\_manager"%')
OR (mt1.meta_key = 'wp_capabilities'
AND mt1.meta_value LIKE '%"wpseo\_editor"%'))))
Since LIKE ‘%…%’ cannot use any B-tree index, MySQL must read each matching wp_capabilities row in full and do seven substring scans of the serialized PHP meta_value per row.
By modifying that calculation from using the capability check to looking for users with published posts (via using the ‘ has_published_posts ‘ => true argument), we instantly turned the resulting query to be one that uses indexes and that performs way better in sites with many users.
In fact, on one of our tests, on a site with around 2 million users, the time it took to complete each query (so approximately the time that took the root sitemap to render), went from over 300 seconds down to just 25 milliseconds! This means that the change has the potential for drastic improvements in loading times of root sitemaps in similar sites.
Finally, considering that the ‘ has_published_posts ‘ => true argument was already used in a later stage of the sitemap generation, the change itself should have little to no negative impact on the actual functionality of the feature.
Reduce loading times of the author sitemap on sites with many users
For Yoast SEO to render author sitemaps, it needs to calculate the eligible users. On sites with many users, this can be a very heavy operation. Aside from the above optimization, we noticed that while Yoast SEO was calculating eligible users, it also added a meta query to check whether the user_level of each user was over 0.
It turned out that this was a remnant from old times, because the user_level framework had been deprecated by WP core since version 3.0. While this didn’t break things in our sitemap feature, it unnecessarily added an INNER JOIN in the resulting query without much purpose and in sites with very big user and usermeta tables that was degrading performance. So we went and removed the unnecessary JOIN:
INNER JOIN wp_usermeta AS mt1 ON wp_users.ID = mt1.user_id
...
AND ( mt1.meta_key = 'wp_user_level' AND mt1.meta_value != '0' )
Since the user_level framework was deprecated a long time ago, we made the deliberate call to drop support for it, especially since doing so would make our feature smoother. In fact, we are comfortable shipping this optimization and expect minimal disruption as a result, exactly because of how old that deprecation is.
Prevent unnecessary expensive database queries in admin pages
In order to timely notify admins that they need to perform the necessary actions for their site data to be indexed optimally in our internal storage, Yoast SEO used to run a database query daily while admins navigated throughout the backend. For big sites, that database query had the potential to run for several seconds, slowing the rendering of admin pages periodically.
This can run complex queries like the ones below, which were running periodically on admin pages, slowing rendering on larger sites.
SELECT Count(P.id)
FROM wp_posts AS P
WHERE P.post_type IN ( 'post', 'page' )
AND P.post_status NOT IN ( 'auto-draft' )
AND P.id NOT IN (SELECT I.object_id
FROM wp_yoast_indexable AS I
WHERE I.object_type = 'post'
AND I.version = 2)
We managed to re-arrange the logic of the code responsible for the notification that told admins about pending actions in such a way that those heavy queries now run only once, at the moment it’s first detected that such a notification should be created.
and rely on cache invalidation that existed before our changes, but weren’t properly utilized. As a result, a potentially very heavy database query went from being triggered daily (and, on very busy sites with lots of concurrent users, once per 15 minutes) to being triggered only once in most sites.
Optimize expensive database queries in admin pages
Related to the above query-preventing change, not only did we manage to avoid running that aforementioned heavy database query more than once per site, but we also managed to optimize the query itself. An added benefit from that is that we made the SEO optimization tool much faster in sites with lots of posts.
Specifically, we went from:
AND P.ID NOT IN (
SELECT I.object_id FROM wp_yoast_indexable AS I
WHERE I.object_type = 'post'
)
To:
AND NOT EXISTS (
SELECT 1 FROM wp_yoast_indexable AS I
WHERE I.object_id = P.ID
AND I.object_type = 'post'
)
Since NOT IN (subquery) builds the entire list of object_ids, while the second query short-circuits the moment one row matches, the query runs considerable faster in sites with multiple thousands of posts.
Reduce roundtrips to the database
As a rule of thumb, roundtrips to the database are considered to be expensive operations that should be reduced to a minimum whenever possible. Our reviews discovered instances where we were retrieving data for multiple posts in sequential SELECT queries where we could have done a single batched SELECT query to gather data for all posts at once.
For example, a piece of code that looked like this:
That meant that for a chunk of 1000 posts, instead of performing 1000 SELECT queries that yielded a maximum of one row, we now perform a single SELECT query that yields a maximum of 1000 rows. Naturally, we made sure that the posts that will be requested each time do not exceed a certain threshold, to avoid reaching MySQL usage limits.
As a result, sites with e.g. 1000 posts would save 960 roundtrips to the database for certain operations like part of their SEO optimization or part of the output of the schema aggregation feature.
Improve post editor performance by preventing unnecessary re-renders
The WordPress editor re-renders Yoast’s sidebar panels whenever the data they pull from the store appears to have changed. Unfortunately, “appears to have changed” is decided by reference equality (JavaScript’s ===) not by comparing values. A selector that returns { items: [‘foo’] } looks identical to a human, but if it’s a fresh object literal each time, React treats it as new and re-renders the panel. And if we multiply that by a busy editor that dispatches state updates on every keystroke, the result is panels that re-render constantly for no reason.
With the 27.8 release, we identified multiple instances where data that weren’t actually changed triggered unnecessary re-renders in the post editor and patched them, making our editor integration much more robust and performant.
AI-generated content – Loved by some, feared by others… Ever since tools like ChatGPT, Gemini, Claude, and many other AI writers became part of everyday marketing, SEOs have been…
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/06/AI-generated-content-nCnR8x.png?fit=960%2C517&ssl=1517960Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-12 07:32:302026-06-12 07:32:30AI-generated content & SEO 🤖 Everything you need to know in 2026
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.
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.
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.
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.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-10 21:06:442026-06-10 21:06:44Query Fan-Out: What It Is and How It Affects AI Visibility
