In today’s digital landscape, where artificial intelligence (AI) is transforming every aspect of our lives, search engines are no exception. A recent episode of Google’s Ads Decoded podcast shed light on key changes in search user behavior under the influence of AI. These changes directly impact content marketing strategies, especially mass video posting. According to data presented by Ginny Marvin, Google Ads Product Liaison, and Shivani Mohan, VP of Data and UX Research for Google Search, users are asking longer questions and starting their solution research earlier.
Transformation of Search Behavior under AI Influence
Users in AI search mode ask questions that are three times longer, actively use clarifying queries, and combine voice and visual search. This allows them to delve deeper into complex decisions, adding personal nuances to the search process. The processes of information discovery and option evaluation can now occur within a single dialogue.
Key Changes in Queries
Increased query length: Questions have become 3 times longer.
Multimodal search: More than 1 in 6 queries in AI mode in the US are multimodal.
Early research: Users begin their search at the problem consideration stage, not just for ready-made solutions.
“Users value the conciseness of AI summaries, but they also actively seek rich details, lived experiences, and authoritative opinions on the open web,” noted Shivani Mohan.
Content Strategies for Mass Posting in the New Search Era
For effective content distribution, especially with mass video posting, it is necessary to adapt strategies to changed user queries. The goal is to seamlessly integrate your content into their search journey, starting from the research stage.
Adapting Content to Search Trends
Users are looking for personal experiences and demonstrations that a product cannot provide in its description. Video reviews, podcasts, and forums are becoming key sources of information. Your mass posting service should take these trends into account, allowing you to quickly and effectively publish content that addresses such queries.
Identify needs: Analyze customer questions, sales team conversations, and search queries to identify recurring problems.
Test ad messages: Create ads that address identified needs, clearly explaining your offer.
Informative landing pages: Ensure that pages easily answer questions about price, compatibility, setup, and limitations.
Link to supporting content: Include links to relevant reviews, demonstrations, and customer success stories.
Conversion analysis: Track whether visits to comparative or educational pages lead to subsequent purchases. Evaluate results within the normal buying cycle.
Mass video upload: automation and reach
In an environment where users are looking for diverse and in-depth content, automatic publication of short videos becomes critically important. Use a single panel for TikTok, YouTube, Instagram and other platforms to ensure scheduled reach without manual labor.
Benefits of publishing automation
Time saving: While you sleep, videos are already being released on all platforms.
Scaling: Ability to publish 100+ videos per day to 10+ accounts.
Optimization: Using proxies and anti-detect for stable operation during mass upload.
Efficiency:Scheduled auto-posting allows you to plan content a month in advance, for example, a mass posting package for a month with a cost of 500 publications.
Frequently asked questions
How to choose a mass posting service, considering changes in search?
Choose a service that offers short video distribution to all key platforms (TikTok, YouTube Shorts, Instagram Reels) through a single panel. The ability to flexibly schedule and support mass uploads with proxies and anti-detect is important to ensure account stability and security. Make sure the service allows you to publish content that answers deep and multifaceted user queries.
How to adapt content for the early stages of the sales funnel?
Create videos that answer user questions at the problem research stage. These can be educational videos, comparisons, reviews, demonstrations of product use in various scenarios. Focus on solving problems and providing valuable information, not just direct promotion. Use video cross-posting for maximum audience reach on different platforms.
What is the role of the human factor in content when AI dominates?
Despite the rise of AI summaries, users still seek “live” experiences, reviews, and opinions from real people. Your content should include elements that demonstrate this experience: testimonials, user-generated content, expert opinions. This builds trust and helps users make decisions, which is especially important for automatic short video publishing, where quick information transfer is crucial.
Conclusion
Changes in search behavior caused by AI dictate new rules for content creation and distribution. Effective mass video posting today is not just automatic publishing, but a strategic approach to content distribution that responds to complex and multifaceted user queries. Integrate these principles into your strategy to ensure maximum reach and engagement. Start using tools for mass posting via API so your content works while you grow your business.
In the ever-evolving landscape of digital marketing, understanding how YouTube: how to trick the algorithm and get 100 unique views, becomes critically important for marketers. Are you wondering why YouTube views have skyrocketed, but leads and sales have remained flat? Do you want to know which YouTube metrics truly matter now and how to make your videos discoverable in Ask YouTube, ChatGPT, and other AI search tools? In this article, you will learn how new YouTube view metrics work, which analytics to track instead of the old ones, and how to optimize videos, chapters, and Shorts for AI search and discovery.
Now, the YouTube view metric is closer to an impression. If someone scrolls through a long video, and it automatically plays for a fraction of a second, YouTube counts it as a view. What YouTube previously called a view is now called an engaged view, meaning the person showed some interest in the video, rather than just scrolling past it.
YouTube Changes the Game: What Marketers Need to Know
According to Justin Brown, this change is YouTube’s attempt to catch up with other platforms. For a long time, creators who published the same video on multiple platforms noticed that YouTube reported fewer views than TikTok or Facebook, because platforms count views differently. On some platforms, scrolling past a video has been counted as a view for many years.
In the old system, YouTube never published a precise definition of a view. Rumors attributed a 30-second threshold, but Justin argues that this was never a good metric because it didn’t work for short videos. A view was simply someone who watched enough of a video for it to be considered meaningful, which could mean a few seconds on one video and most of another.
YouTube also hasn’t explained what an engaged view is, and Justin suspects the platform intentionally left the definition vague. Google owns the web browser, operating systems, Chromebooks, and Android phones, so it can use signals like whether a browser tab is open or whether a person watched a previous video and is more likely to watch the next one when it plays automatically.
Justin believes these behavioral signals now carry more weight than the percentage of video watched. His best guess is that engaged viewing depends on the video’s duration and starts with a couple of seconds—enough time for a person to decide if the video is right for them. From there, the score should depend on what happens next: a viewer who leaves immediately probably shouldn’t be counted, while one who stays should.
Autoplay and its Impact on Engagement
Autoplay also changes how people watch videos. Videos that previously didn’t autoplay now do so on TVs, mobile devices, and computers. Justin has caught himself watching videos in the mobile app that he never clicked on, because they started playing as he scrolled through the feed.
YouTube now plays these previews from the beginning of the video, and if he scrolls through the feed and returns, playback resumes from where he left off. Since the YouTube algorithm understands viewer behavior and preferences, Justin sees that this autoplay experience leads to more engaged views, not just views.
On the Primal Video channel, the number of views noticeably jumped on the day the changes took effect, and the baseline is now higher. A search-oriented video without a clickbait title used to get a few thousand views in the first 24 hours. Now that number is closer to 5,000-7,000 views. Engaged views, in contrast, are at roughly the same level or slightly higher.
Justin views the higher view counts as a new baseline rather than a performance gain, because the internal tracking his business relies on has barely changed. The rollout also appears uneven. Justin sees different view metrics on desktop than in the mobile app, especially for live streams, which he says is typical of how YouTube implements changes.
Key Metrics for Business: Not Just Views
For a business, the most important YouTube metric is the one tied to the video’s goal. Justin says that views are only a small part. Before evaluating a video, he recommends deciding whether its task is audience building, growing an email list, or driving sales. For companies building a brand, the goal is to generate leads, sales, and customers, and Justin works backward from that.
Views are nice, but by themselves, they say little about business results. Among the available YouTube analytics, engaged views (the equivalent of the old view count), watch time, and click-through rate provide a clearer picture of whether the right people are clicking and watching.
Justin believes every metric is worth looking at, but says each needs to be considered on a case-by-case basis. Comparing average view duration works when videos are similar, such as a recurring series. On a channel like Primal Video, where one week’s video is about editing software and the next is about shooting on an iPhone, a longer watch time on one video doesn’t mean viewers got more value from it. A viewer who watched a video for less time might have gotten exactly what they needed, solved their problem, and become a loyal, returning viewer.
Performance Analysis: Focus on Anomalies
Instead of spending hours in YouTube Analytics, Justin recommends doing future-focused analysis based on anomalies. He identifies the best- and worst-performing videos over a period, whether by engaged views, views, or watch time, and then compares their retention graphs to find where people are dropping off.
A video that performed poorly might have lost most of its audience early on, while the people who stayed watched the rest. This pattern indicates one of two problems: a weak hook or YouTube showing the video to the wrong audience, who clicked on it and immediately left. No single metric explains performance in isolation, so Justin looks at average view duration, watch time, and retention together to understand what’s happening with people who watch beyond the first 30 seconds.
How a video performs initially doesn’t reflect how it might perform over time, especially for YouTube search-focused channels. Primal Video doesn’t get big numbers on day one or week one, even with its channel size. Over time, videos get picked up by YouTube search and, increasingly, AI-driven traffic, and then they start to “snowball.” Some of the channel’s biggest videos flopped at launch.
Because of this, Justin usually leaves a poorly performing video alone and moves on to the next one. Re-editing has also become riskier. The Primal Video team used to shoot multiple “hooks,” publish the video, and use the YouTube editor to cut out the versions that weren’t working, with the option to restore them later. Now, edits to a published video can only remove footage. Nothing can be added back, and once an edit is saved, it can’t be undone.
Pro Tip: The YouTube editor can still be used to test a longer intro. Justin’s approach is to publish a video with an extended section and, if it doesn’t work, trim it with a clean cut. Justin says viewers watching free content rarely notice a jump cut or a skip in the music.
Co-Viewing: A New Metric or Just an Estimate?
YouTube also added a metric called “views (co-viewed)” which estimates when more than one person is watching a video together, such as a family watching on a living room TV. YouTube bases the estimate on factors like demographics, video genre, and time of day.
Justin doesn’t recommend changing content to encourage group viewing, such as asking viewers to invite a friend or spouse. From everything he’s seen, the metric is still an estimate on YouTube’s part, and shared family accounts on TVs are already skewing the data. Until YouTube finds a more robust way to track co-viewing, he doesn’t see it as a meaningful lever for content strategy.
The value of co-viewing data is in brand deals and sponsorships because it can show that a creator’s reach is larger than just the view count suggests, especially for family-friendly content. Justin expects the ratio of views to engaged views to become a metric creators compare, replacing the traditional comparison of impressions to click-through rate. Because the co-viewing metric is so new, it’s too early to establish benchmarks.
Content Uniqueness: Packaging Is Everything
But turning a view into an engaged view still comes down to packaging: the title, thumbnail, and especially the beginning of the video. When a video auto-plays, the thumbnail matters less because the viewer may never see it. What happens in the first few seconds matters more. This isn’t entirely new.
When YouTube first introduced auto-play in the feed, the Primal Video team noticed people were watching without sound, without clicking on the video — a behavior Justin calls “toilet TV.” Justin’s answer was to put text on the screen at the beginning of each video explaining what the video is about. This isn’t closed captions that viewers can turn on or off. This is a title embedded in the video, such as “Best Mac Editing Software 2026,” so anyone seeing the video auto-play knows exactly what it’s about, even if they never saw the thumbnail or title.
The text appears in about the first 3 seconds, as the action starts on the screen, stating the video’s promise: the result or transformation the viewer will experience. Justin says this made a significant impact for Primal Video when auto-play launched because it moved viewers into an engaged state. A talking head with no on-screen text makes it difficult for a silent viewer to understand what the video is about.
Pro Tip: The on-screen title text doesn’t require a specialized video editor. Justin describes it as simple on-screen text stating the video’s promise that any editor can add.
Ask YouTube and AI Search: A New Era of Content Discovery
YouTube is rolling out a feature called Ask YouTube, starting on TV and expanding to mobile and desktop. It allows users to have a conversation with YouTube to explore complex topics beyond traditional keyword search, and answers can include text as well as relevant videos, Shorts, and video clips. YouTube states that creators can improve their chances of appearing in Ask YouTube results by continuing to create unique, high-quality content and adding clear video chapters and descriptive titles.
Justin sees Ask YouTube as part of a broader shift. People expect to get to the outcome faster, whether they want to learn something or be entertained, and AI tools have raised those expectations. He anticipates that viewers will increasingly jump to specific sections of videos rather than watching the entire thing. The hope is that a short clip or summary will entice viewers to watch the full video, much as Google search snippets help some searchers on the spot while still driving traffic to videos.
AI search is already a significant traffic driver for Primal Video. The number one traffic source for the channel is YouTube search, and the second is Google search, from people who never started on YouTube. The third, and fastest-growing, is AI tools. ChatGPT and Claude are sending viewers to Primal Video’s YouTube videos, and the channel regularly gets comments from people saying ChatGPT sent them there.
SEO for AI: What’s Working Now
Keyword research isn’t gone. Primal Video still looks at search volume to gauge demand before creating a video, rather than publishing and hoping. But keyword order matters less than it used to. YouTube has long transcribed what’s said in videos, and now AI allows it to understand what’s actually happening in a video. The longer a video is live, the more YouTube learns from watch time which parts viewers find compelling, and Justin expects YouTube to direct viewers to those sections.
This means YouTube optimization now comes down to viewer experience. Justin focuses on what viewers want, what they would type into Google or YouTube to find a video, and what would grab their attention even if they weren’t looking for it.
As quick answers increasingly come from AI, Justin believes the part of video that AI can’t replace is the human part. A viewer can get a banner size or a setting from AI, but they want to hear how a specific person would approach it, what choices that person would make, and why. By sharing opinions, experiences, beliefs, and frameworks, you give viewers something no one else can offer. Justin says people are already “burning out” on AI content and that he leaves as soon as he hears an AI voice, so he expects human-created content to become more valuable, not less.
Optimizing Chapters: New “Fishing Lines” for Search
Primal Video treats chapters the way it used to treat titles. For each chapter, the team asks what someone would type in to find that section if it were a standalone video, researches keywords and search volume, and titles the chapter accordingly. Timestamps and chapter markers in the video description remain a place for optimization for both YouTube search and Google, and each is a direct link to that moment in the video.
Instead of writing chapter titles as “hooks” that tease what’s coming next, Justin recommends titles that communicate what each section actually covers. For a video about five things AI edits well, each chapter would name a specific thing. Revealing the answer in the timestamps helps YouTube surface that section, gives AI tools like ChatGPT a direct path to the answer, and prevents it from being hidden in a cryptic title that viewers may or may not click.
Justin views search-optimized chapters as additional “fishing lines” that help viewers find content. This approach works for channels built for search. Primal Video educates rather than entertains, so instead of competing with entertainment creators like MrBeast for a spot on the homepage, it aims to appear at the top when someone has a specific problem. He sees the same opportunity for most business owners, who can share their insights and talk about their products without competing for entertainment views.
AI tools make optimizing chapters much faster than doing it manually. Justin points to vidIQ, whose AI tools generate titles and chapter markers based on search volume. The Primal Video team connected vidIQ to their AI assistant via an MCP connection. After a video is complete, the team uploads a VTT or SRT subtitle file, which includes timings for everything said, and asks the AI to generate chapters based on key phrases around each section, while keeping the titles readable
A Comprehensive Approach to SEO: How Brands Adapt to AI Search
The modern digital landscape is constantly changing, and with the advent of artificial intelligence in search engines, brands have to find new ways to maintain their visibility. Companies like Fender and Marshall are actively investing in branded content not only to attract consumer attention but also to optimize their presence for AI models and chatbots.
These tools are becoming increasingly popular: 7 out of 10 American consumers already use them to search for products and information. For brands, this means that traditional SEO methods are no longer sufficient. It is necessary to create content that will not only be informative and appealing to humans but also easily interpretable and valuable to artificial intelligence algorithms.
Fender: From Transactional Site to Storytelling Platform
Fender, the world-renowned guitar manufacturer, has taken significant steps to rethink its online presence. According to interim CMO Kelly Magrath, their goal is to transform the website from a simple marketplace into “a front door that we can use as a storytelling platform.”
Video Content: The updated Fender website features short but impactful videos. They not only tell the brand’s history but also showcase legendary musicians, such as Jimi Hendrix, who played their guitars. Attention is also paid to the craftsmen who create these instruments, emphasizing their skill and dedication.
Social Media: A significant portion of this editorial content is actively distributed through Fender’s own social channels, including YouTube. Here, you can find entire video series dedicated to important milestones, such as the 75th anniversary of the Telecaster model, or detailed demonstrations of effects pedals.
Goal: The primary objective of these efforts is to improve Fender’s visibility in the early stages of product discovery and the consumer journey. By creating rich and diverse content, Fender aims to be top-of-mind when consumers are looking for information about musical instruments.
Marshall Group: Revitalizing Musical History to Promote the Brand
Marshall Group, a renowned manufacturer of amplifiers and headphones, also actively uses content marketing to strengthen its brand. They launched a unique YouTube series called “Dead Venues.”
In this series, new music bands perform on the stages of long-gone but legendary clubs, such as London Fog or China Chalet. This approach allows Marshall not only to support the young music scene but also to remind people of its rich heritage in the world of music.
“We’re challengers. We’re known as an iconic guitar amp brand, and as we move into new categories, we really need to rethink what we want to be in the eyes of consumers,” — Christine Kennedy Grant, VP of Brand Marketing at Marshall Group.
The “Dead Venues” campaign, launched in May, aims to create a strong connection between supporting the modern live music scene and the company’s products. Special attention is paid to standing out in the highly competitive headphone market, where Marshall seeks to take a leading position, relying on its history and cultural influence.
The Impact of Content on AI Search and Consumer Behavior
According to a survey conducted by Skyword, 7 out of 10 consumers in the US actively use ChatGPT or other AI summaries when searching for products and companies. This data highlights the critical importance of content that can be effectively used by AI models to form answers and recommendations.
Why is content becoming a key success factor in AI search?
More Data for AI: High-quality, well-structured branded content provides AI models with a vast amount of information for analysis. This increases the likelihood that the brand will be recommended or mentioned in AI-generated responses, as AI strives to provide the most complete and relevant data.
“ChatGPT is the new word-of-mouth”: Urvashi Manral, Associate Director of Discoverability at Attention Arc, states that for dominating AI search engines, content is the fastest and most effective way. The more quality content about a brand is available, the higher the chances of its mention and positive perception by AI.
Originality — Key to Differentiation: Emma Sibbles, Head of Content at IDHL, emphasizes that unique, original content reflecting a brand’s expertise and perspective is valued much higher by AI platforms. Unlike keyword-stuffed posts, such content demonstrates genuine value and authority.
Emily Stutzman, CEO of creative agency Happylucky, adds: “A brand with a real point of view, expertise, examples, experience, and helpful context provides a much richer source for an AI platform to pull from than a blog post designed to ‘sprinkle’ 20-30 keywords.”
Not Just AI: The Human Factor and Brand Building
It’s important to note that these content creation efforts are not solely aimed at “robot readers.” They also serve the crucial function of brand building for human customers. Content that tells stories, demonstrates values, and expertise creates a deep emotional connection with the audience.
According to Ben Hennes, Chief Creative Officer at Happylucky, “A brand that consistently publishes original ideas, helpful experiences, distinctive opinions, and firsthand experiences creates a knowledge base that can become a brand advantage when consumers are searching.” This means that quality content serves a dual purpose: optimizing for AI and strengthening loyalty among consumers.
Strategies and Prospects: What’s Next?
Marketers worldwide are actively responding to these changes in the search landscape. A Kantar survey showed that 75% of marketers plan to increase spending on visibility in AI assistants next year. This indicates widespread recognition of the importance of adapting to the new reality of search marketing.
Jim Yu, founder and CEO of SEO company BrightEdge, advises: “One of the best places to start is with content where you have the right to win.” This means creating content that aligns with the brand’s core expertise and values, where the company can demonstrate its uniqueness and authority.
Stephanie Estes, Media Director at The Shipyard agency, notes that her agency is actively experimenting with its own content, creator activity, and how “promoting” brand-created videos on platforms like YouTube can help their perception by LLMs (large language models). This highlights the importance of not only creating content but also strategically distributing it.
Frequently Asked Questions
What is full-cycle turnkey video production and how does it help with AI search?
Full-cycle turnkey video production is a comprehensive set of services for creating video content, covering all stages: from idea and script development to shooting, editing, post-production, and publication. In the context of AI search, high-quality, original videos, similar to those created by Fender or Marshall, provide AI models with a rich source of information about the brand, its history, products, and values. This significantly improves the brand’s citability and visibility in AI-generated responses, as AI prefers reliable, unique, and in-depth data, which is often contained in professionally created video content.
How to choose a contractor for creating video content optimized for AI search?
When choosing a contractor for full-cycle video production that will be optimized for AI search, pay attention to the following key criteria:
Storytelling experience: Ensure that the contractor is capable of creating an engaging and emotionally resonant narrative that will effectively convey your brand’s key messages to the target audience.
Understanding of SEO and AI: It is important that the contractor’s team has an understanding of how content will be perceived not only by people but also by search engines and AI models. This includes knowledge of best practices for video structuring, metadata usage, and keywords.
Production quality: High image and sound quality are critical for content perception. Professionally produced video inspires more trust and better holds attention.
Portfolio: Review the contractor’s work examples. Successful projects for other brands, especially in your niche, can be a good indicator of their competence.
Comprehensive approach: An ideal contractor offers not only video creation but also a strategy for its distribution, including optimization for YouTube, social media, and other platforms, which is an integral part of AI optimization.
Is it worth investing in repurposing old content or creating new content from scratch for AI optimization?
Both approaches have their advantages and can be effective depending on your goals and resources. Repurposing existing content (e.g., turning long articles into short videos, infographics, or audio podcasts) can be a cost-effective way to increase the amount of data available to AI. This allows you to extract additional value from already created materials.
However, creating new, original content from scratch, designed specifically for AI search, often yields better results. Such content is initially formed taking into account the requirements for uniqueness, depth, expertise, and structure, which are highly valued by AI models. It allows the brand to take a leading position for new or niche queries.
The best strategy is a combination of both approaches: repurposing valuable old content to expand its reach and creating new, strategically important content that will be purposefully optimized for modern AI algorithms and search engines.
Conclusion
The era of AI search requires brands to deeply rethink their approach to content. Investing in original, high-quality storytelling, as successfully demonstrated by Fender and Marshall, becomes not just a way to attract audience attention, but also a key factor for improving visibility in the ever-changing digital landscape. For your brand to be visible in AI responses and stand out from competitors, it is necessary to order a full cycle of video production that will not only tell your unique story but also be carefully optimized for modern search algorithms and AI models. Contact us today to find out how we can help your brand become a leader in the era of AI search and effectively utilize the potential of content marketing.
In the era of mass AI video generation and other types of content, many companies face a problem: their new pages, created specifically to improve visibility in AI search, are not being indexed and are not bringing the expected results. Why does this happen, and how can one build an effective workflow for AI video for advertising and other formats? The answer lies in understanding not just content gaps, but solution gaps.
The Problem of “Content Parity” in AI Search
One client found that their new pages, created for AI search, were not being indexed by Google. The team followed all recommendations: analyzed AI responses, studied competitors, identified gaps, and created more comprehensive content. However, in Google Search Console, these pages had the status “Crawled – currently not indexed.”
“Indexing takes time,” said the SEO consultant, but a month had already passed.
The problem lay in what is called “content parity.” The content was high-quality and well-organized, but it merely repeated existing information without adding anything new. This did not lead to information gain, which AI values so much.
Differences Between Information Gain and Competitive Gaps
Competitive gaps: This is what you lack but your competitors have. Filling these gaps is important, but not always sufficient for information gain.
Information gain: This is new, unique information that helps the user or AI make a decision.
Solution Gaps: The Key to Effective Content
Instead of simply filling competitive gaps, it is necessary to focus on solution gaps. This is information that the user or LLM needs to make a decision, but which neither you nor your competitors have.
Example: “Best Family Resort in Cancun”
The query “What is the best all-inclusive family resort on the beach in Cancun?” is not just a set of keywords. It is a request for a decision. The AI system must evaluate criteria: “all-inclusive,” “family,” “on the beach.”
“All-inclusive”: Not as Simple as It Seems
The definition of “all-inclusive” is often vague. Marketing is great at selling a promise but poor at defining boundaries. For example, one resort states in its FAQ that the package includes accommodation, meals, drinks, sports, entertainment, and children’s programs, but excursions and spa treatments may be at an additional cost.
Another resort dedicates an entire page to “all-inclusive” services, but a precise definition of what exactly is included is hard to find.
The problem is not the lack of content, but that it doesn’t fully resolve the criteria. The user or AI has to piece together the definition to understand where “all-inclusive” ends and “additional costs” begin.
“Family-friendly”: what lies behind the beautiful words
A similar situation applies to “family-friendly.” A resort may have an excellent section for children and families, but the description of the program for teenagers aged 14-17 might be too general. A parent wants to know specifics: is it supervised childcare or optional activities? What are the operating hours? What activities are offered? Do teenagers leave the premises? What level of supervision is provided? These questions remain unanswered.
Content analysis tools might show that the resort has “strong thematic coverage” for teen programs. But a parent doesn’t evaluate content architecture; they make a decision that will meet their family’s needs.
A new approach to content creation: from criteria to knowledge
To create content that is truly indexed and useful, we need to change our approach. Instead of simply generating content, we must:
Define the client’s solution: What is the client or LLM trying to solve?
Identify solution criteria: What criteria are important for making this decision?
Find existing evidence: Where is the information that supports these criteria?
Discover gaps in evidence: What information is missing?
Qualify the gaps: Is it a knowledge gap, information fragmentation, or intentional omission?
Acquire knowledge: If information is missing, how can it be obtained?
Validate: Check the accuracy and completeness of the information.
Create content: Using AI, transform knowledge into content.
Where to get new knowledge?
AI can analyze thousands of pages, but it cannot “produce” an organizational fact. If the policy for supervising a teen program is not documented, the model will not know it. If no one has measured the actual distance from the room to the beach, AI will not create that knowledge.
Answers can be found in:
Support tickets
Call center transcripts
Internal site search queries
CRM records
Product documentation
Surveys
Training materials
The minds of expert employees
Sometimes even Reddit, reviews, forums, and other communities where people discuss details that companies miss are useful. For example, a resort describes itself as “family-friendly,” and a frustrated parent explains what happened when they tried to enroll a three-year-old in the kids’ club.
AI and the Future of Content
AI has significantly reduced the cost of converting knowledge into content. Now, subscription-based neuro-production or an AI pipeline generating 500 videos per day have become a reality. However, this does not negate the need for knowledge acquisition. Text-to-video generation or mass generation APIs do not create facts; they merely process existing data.
The advantage shifts up the chain: to a better understanding of customer decisions, identifying criteria that others have missed, uncovering operational knowledge hidden within the organization, connecting it to your products and experience, and validating the answer before publication. This is not just about creating AI commercials or AI UGC for a crypto project; it’s about creating value.
Sometimes this means analyzing queries, sometimes listening to support calls, sometimes discovering that Reddit users are answering a question the company never addressed. And sometimes it means something that seems old-fashioned: talking to an expert and gaining new knowledge.
AI can help us identify gaps, organize evidence, and transform acquired knowledge into useful content. But for true informational gain, there must always be something new to acquire.
Content parity is the creation of content that almost completely duplicates information already available from competitors or other sources. It is harmful for SEO because search engines, especially AI search, look for unique, valuable, and new information (informational gain) that helps the user make a decision, rather than simply copying what has already been said. Such content is often not indexed or ranks poorly.
How can AI help in creating content that brings informational gain?
AI can significantly speed up many stages of content creation: from competitor analysis and identifying existing gaps to organizing evidence and transforming knowledge into content. However, AI cannot independently create new, unique knowledge. It is a powerful tool for processing and structuring information, but human input in the form of research, expert knowledge, and understanding user needs remains critically important for achieving informational gain.
In what way do competitive gaps and solution gaps differ?
Competitive gaps are areas of content where your competitors have information, but you don’t. Filling these gaps helps you catch up with competitors. Solution gaps are information that a user (or AI) needs to make an important decision, but which neither you nor your competitors have, or it is fragmented and incomplete. Focusing on solution gaps allows you to create truly valuable and unique content.
Master new approaches to content creation so that your AI avatar with subtitles or AI face-swap clips not only generate videos but also provide real value. Discover how neural networks write scripts and edit, while you focus on the main thing – knowledge. Find out how much one AI creative costs or the cost of 1 minute of AI video, and how to maximize ROI by creating content that truly solves customer problems.
In a world where AI systems generate new queries at the speed of light, old hacking tricks of relevance are getting a second life. Greg Jarboe, a veteran SEO, recounts how back in 2003 he manually implemented what is now called a “fan query,” using the “Russian nesting dolls” principle. This method allows your content to be found for queries that don’t even exist yet, and is key to query repurposing in modern AI search.
Matryoshka SEO: How it worked before AI
Long before the terms “fan query” or “GEO” became common, Jarboe optimized press releases by embedding short phrases within longer ones. For example, if the target phrase was “flights to Philadelphia,” he would look for a four-word version, such as “cheap flights to Philadelphia,” and build the release around it.
This allowed for coverage of both short and long queries, making the content visible to a wide audience. This approach ensured maximum reach, even when search engines were less developed.
15% unknown queries: a Google constant
“I thought at some point most queries would have been done; people just ask the same thing over and over. But when we re-run those metrics, it’s always about 15%,” — John Mueller, Search Central Live NYC, March 2025.
Google first mentioned that 15% of daily queries are new in 2019 when introducing BERT. This figure remains stable despite the development of large language models. Multiply 15% by billions of Google queries, and you get hundreds of millions of unique formulations every day.
This is a huge layer of traffic that most SEO specialists underestimate. Understanding this phenomenon is critically important for effective query repurposing.
News as a query generator
Most of these new queries are not random. They are born from breaking news, new products, changed policies, or phrases coined by journalists. Press releases published on the day of an event are ideal for intercepting this traffic.
If the wording in the release accurately matches what a user types into Google, the content can rank for a query that did not exist at the time of writing. This demonstrates the power of timely content in capturing “new” search demand.
AI and reverse matryoshka: from broad to narrow
M.J. Cachon’s research showed that ChatGPT generates sub-queries that are also not random. Initially, the model uses general, conversational language, then narrows the search with the site: operator and begins to check exact quotes.
The use of quotes increases 25-fold between the first and last search. This is the “matryoshka” principle, but in reverse: AI starts broadly and narrows down to the exact phrase for verification. This AI approach to search opens up new opportunities for query repurposing.
Query Length: The New Reality of AI Search
According to Google data from May 2026, the average length of an AI query in the US is three times that of a traditional search query. Kachon’s data shows that one branded ChatGPT query branches into sub-queries, averaging seven words each.
Length is not a side effect of AI search, but a new norm that the industry has been moving towards for years. This means that content must be optimized for longer and more detailed queries to match AI behavior.
Practical Steps: How to Deceive Algorithms and Scale Content
To effectively use this knowledge for query repurposing, three key habits must be developed:
Look for nested phrases, not just keywords. For every three-word keyword phrase, find two or three four- and five-word phrases that naturally contain it. Build your introductory paragraphs or H2 headings around the longer version.
Publish at the speed of news. 15% of new queries are related to recent events. If your organization has a channel for same-day publication (press release, blog, rapid response page), this is your best chance to rank before competitors even know the phrase exists.
Write the verbatim answer as a quotable sentence. Kachon’s data shows that AI systems are increasingly verifying claims by looking for an exact quote from your content. Rewrite the sentence that answers the question so that it can be taken verbatim and still make sense.
These methods do not replace fundamental SEO principles but complement them, focusing on the long tail of queries that were previously underestimated. They allow for effective adaptation to changing AI search algorithms.
Frequently Asked Questions
What is a “query fan-out” in the context of AI?
“Query fan-out” is the process by which an initial user phrase or query branches into many more specific sub-queries generated by an AI system. This allows the AI to delve deeper into a topic and find more relevant information, narrowing the search from general to specific.
How is the “Russian matryoshka” principle related to SEO?
The “Russian nesting doll” principle in SEO means embedding short, high-frequency keywords into longer, low-frequency queries. For example, the phrase “airline tickets” can be nested into “cheap airline tickets to Philadelphia.” This allows content to be found for queries of various lengths, increasing its visibility and audience reach.
Why are 15% of Google queries always new?
These 15% of queries are always new because they are directly related to real-world developments: news, new product launches, changes in legislation, cultural phenomena. People search for information about what has just happened or appeared, generating unique formulations that did not exist before. This confirms the importance of timely content publication.
How do AI systems use quotes to verify information?
M.J. Kachon’s research showed that AI systems, such as ChatGPT, after a broad search, begin to verify information using exact quotes. They look for specific sentences in the content to ensure that the source actually states what the AI interprets. This emphasizes the need to formulate answers as precisely and quotably as possible.
Conclusion
In the era of AI dominance, understanding how systems generate and process queries becomes critically important. By applying the “matryoshka doll” principles, optimizing content for long and nested phrases, and publishing it promptly, you can outpace competitors and capture traffic that hasn’t even been conceived in users’ minds yet.
Start applying these hacking techniques today so that your content doesn’t just exist, but scales and tricks the matrix of search algorithms. Don’t miss the opportunity to become a leader in your niche by using advanced strategies of query repurposing!
The era of traditional SEO, where CPM is profitable and hypothesis testing is cheap, is becoming a thing of the past. Today, buyers are increasingly turning to AI assistants, such as ChatGPT, Gemini, or Perplexity, to get direct answers to their questions. This changes the rules of the game for marketing teams and requires a re-evaluation of budgets. If your company is not mentioned in these AI answers, you are losing potential customers.
Why the old marketing structure doesn’t work?
Traditional marketing department structures were geared towards ranking in Google. SEO specialists were responsible for positions, content teams generated articles based on keywords, and paid advertising filled the gaps in organic traffic.
All of this assumed that the buyer would see the results page and click on a link. However, today many skip this page, directly asking AI questions. For example, the query “best contract management software for mid-market legal teams” now yields a list of three vendors. If you’re not there, you’re out of the game.
“I asked one question: which department is responsible for ChatGPT recommending you? No one had an answer, because the answer was — no one.”
Ranking does not guarantee citation
Companies with first-page Google rankings are often mentioned in only a few of the 20 answers from AI assistants.
One of our clients with 14 keywords on Google’s first page was mentioned in only 4 of 20 AI answers.
Work that ensures AI citation, such as creating consistent entity signals, third-party endorsements, and structured original content, is often not part of anyone’s job description.
Because budgets follow organizational structure, money continues to be spent on methods that AI models no longer reward. Planning a year in advance only exacerbates the problem, locking in old bets.
Three role changes to adapt to AI search
You probably don’t need new employees. You need three changes in areas of responsibility and a clear answer to the question of ownership.
Usually, it’s the same person. Their task expands from “where do we rank” to “where are we cited.” This means managing the brand entity everywhere AI models read information: your website, LinkedIn, G2, Crunchbase, Reddit, industry directories.
Entity fragmentation is the most common problem. Different brand names, domains, conflicting descriptions — AI perceives this as several weak companies instead of one strong one. A single owner can fix this in a quarter, and it costs almost nothing but attention.
2. The content team shifts from volume to evidence
Halve your publishing calendar. Redirect those hours to creating content that can be cited by AI: original data, customer results with specific numbers, expert comments from company employees, and pages structured to extract clear statements.
Eight general posts a month lose to one article with unique data. Evaluate the team not by volume, but by the number of citations received and the impact on the sales funnel.
3. Digital PR moves from the brand budget to the performance budget
AI models reward consistency of information from independent sources. Mentions in industry publications, review platforms, and communities now do the same work that backlinks did a decade ago.
This means PR stops being a “soft” expense item that can be cut in a bad quarter and becomes an acquisition channel with quarterly goals and KPIs. CPM cheaper than 50 rubles is possible through PR with clear metrics.
Budget Math: How to Reallocate Funds
Consider an example of a client who spent $60,000 per month on marketing. Price comparison before and after restructuring:
Before restructuring:
Paid Search: $30,000
Content Production: $12,000
SEO Retainer: $8,000
Brand & PR: $5,000
Tools: $5,000
After one quarter:
Paid Search: $24,000 (20% reduction, to protect branded queries and converting non-branded campaigns)
Content: $10,000 (fewer articles, but with more substantial evidence)
AI Search Program: $10,000 (entity cleanup, structured data, measurements)
Digital PR with targeted citation metrics: $10,000
Tools: $6,000 (added AI visibility tracker, e.g., Peec AI or Semrush AI toolkit)
Don’t completely abandon paid search. It’s the purest source of data on buyer intent, which is necessary for testing AI assistants. The rule is simple: move 15-20% of the budget in the first quarter, then let the evidence guide the rest. No one should stop a working paid program “on faith.”
90-Day Action Plan
Don’t reorganize the team on day one. First, measure, then experiment, then scale what works.
Weeks 1-4: Baseline
Test 20 of your key buyer queries in four major AI assistants.
Record every answer, every mention, every competitor.
Fix entity fragmentation. It costs nothing and has a cumulative effect.
Weeks 5-8: Pilot Launch
Create a small team: an AI search leader, one content specialist, and a portion of the PR budget focused on a single product line.
Everything else operates as usual, providing a control group and reassuring the rest of the team.
Weeks 9-12: Compare and Scale
Compare citation rates for test queries, referral traffic from AI assistants, and the number of inbound deals that mentioned discovering you through an AI tool.
Move budget where the evidence points. Reorganization follows results, not the other way around.
Three Mistakes That Burn Budget
Hiring an AI specialist before conducting baseline measurement. It’s impossible to write a job description until you know where your citation gaps are.
Completely cutting classic SEO. AI assistants still rely on search indexes. Well-ranked pages are more often read by models. This is a rebalancing, not a burial.
Launching AI search as a side project. If it doesn’t have a separate line in the budget, it doesn’t have an owner, and work without an owner doesn’t move. Allocate a budget, even a small one, and assign responsibility.
Six months after restructuring, the mentioned software company increased its mentions in AI responses from three to 12 out of 20. Demo requests received through AI assistants are now tracked in their CRM. The size of the marketing team has not changed. Only the direction of investment has changed, aligning with current buyer behavior. Your organizational structure is a bet on how buyers will find you. Most structures still bet on the search results page, which fewer buyers are viewing each month. It’s time to change that bet.
Frequently Asked Questions
What is the role of an SEO specialist in the era of AI search?
The role of an SEO specialist expands: they become an AI search leader, responsible not only for ranking but also for the brand’s citation by AI models across all relevant platforms and directories. This includes managing brand entity and eliminating information fragmentation.
What’s cheaper: a banner or a blogger?
In the context of AI search, what’s cheaper: a banner or a blogger is an outdated question. More important is which channel provides citation and evidence for AI models. Digital PR, aimed at getting mentions in authoritative sources, can be more effective than direct advertising or influencers, as AI values consistency from independent sources.
What CPM should be considered profitable for AI-oriented marketing?
What CPM should be considered profitable now depends on citations. If you get a CPM cheaper than 50 rubles through Digital PR, which leads to mentions in AI responses and, as a result, to leads, this is more profitable than a high CPM in traditional advertising without such an effect.
Is AI worth it?
Yes, AI is worth it if you want to remain competitive. AI search is not just a trend, but a new reality. Investing in adapting your marketing strategy to AI search allows for cheap hypothesis testing and ensures visibility where your customers are.
How to measure the effectiveness of investments in AI search?
Effectiveness is measured by several indicators: the citation level of the brand by AI assistants, referral traffic from these assistants, and the number of inbound deals that indicate AI as the source of discovery. These metrics help to understand how much money each ruble spent will bring.
Conclusion
The marketing landscape is changing. The focus is shifting from traditional SEO to AI search, where price vs. result is evaluated by citation and impact on the sales funnel. Reallocate your budget, change roles, and test hypotheses to stay in the game. Your organizational structure should reflect how customers search for and find information today. Learn more about our AI marketing strategy.
In a world where AI chatbots are becoming the new entry point for customers, brands are changing their visibility strategies. A new item has appeared in creator briefs: AI visibility. Marketers have realized that traditional SEO and paid advertising methods are no longer sufficient when LLMs (large language models) actively scan social media, blogs, and news portals in search of answers to user queries. The key becomes the “video DNA” and “matrix” of content that AI can interpret.
AI Visibility: The New Reality of Content
Previously, the brand itself dictated what would be said about it online. Today, AI search takes the initiative, relying on external, more authentic sources.
“The machine moves to the most accessible public information and the most authentic to get closer to an answer. It’s not what comes from the brands themselves,” notes Joe Gagliese, CEO of Viral Nation.
Accessibility: Social networks and creator blogs are open data sources for LLMs.
Trust: Positive reviews and feedback from creators build a brand’s reputation in the eyes of AI.
The Zoom example demonstrates this trend: the company collaborated with journalist-creator Naima Raza to enhance authority through storytelling. This is a step towards bypassing the advertising ban when a direct advertising message doesn’t work.
Measuring and Optimizing AI Visibility
While the direct link between creator campaigns and AI visibility is still under development, the correlation is already evident. Christina Coughlin, SVP and General Manager at Trevant, notes that requests to track creator content in AI search results come in almost every new pitch.
This pushes agencies towards repurposing content and adapting strategies.
Agency Strategies for AI Visibility
Audit existing content: Trevant analyzes which creators and content types generate the most citations in LLMs.
Continuous monitoring: Tracking citations during campaigns to optimize and plan future partnerships.
Integration with SEO: Crispin’s teams, for example, work with SEO specialists to reverse engineer GEO processes to amplify creator influence.
This approach allows brands, previously skeptical of influencer marketing, to see the value in unique video and content for AI search.
Technical Aspects of AEO and Content
Creators are actively mastering AEO (Answer Engine Optimization), creating specialized sites and conducting audits. It’s important that social media post captions are machine-readable and contain enough information for LLMs to scan.
“In addition to simply sharing information and all the usual things you expect from a caption, we’re now suddenly saying, ‘Okay, this is the new long tail of online search, and we need to make sure this caption is working as hard as possible,’” emphasizes Daniella Wiley, CEO of Sway Group.
What creators need to consider:
“Gluing” information: Creating cohesive, informative content that is easy to “repackage” for AI.
“Splitting” content: Turning one webinar into 50 short clips for different platforms.
Metadata: Maximally detailed and relevant descriptions, tags.
However, experts warn against overdoing it. Scott Sutton, CEO of Later, notes that 300,000 TikToks about a new soap might not make it into AEO if they are not in the right place for indexing.
This highlights the importance of a uniqueness package and a strategic approach to content scaling.
Frequently Asked Questions
What is AI visibility and why is it important?
AI visibility is the ability of a brand’s content to be discovered and cited by large language models (LLMs) and AI chatbots when responding to user queries. It is important because more and more people are starting their purchasing journey with AI search, and a brand not present there loses potential customers. It’s like a “clone” of your content working for you in a new digital reality.
How can creators help brands improve AI visibility?
Creators can create authentic, informative, and well-structured content that is easily scannable by LLMs. Their positive reviews and testimonials are perceived by AI as more reliable than direct brand advertising. They help to “trick” algorithms by providing quality, natural content.
What is AEO and how does it differ from SEO?
AEO (Answer Engine Optimization) is the optimization of content for answer engines, such as AI chatbots, which aim to provide a direct answer to a user’s question. Unlike traditional SEO, which focuses on keywords for search engines, AEO is geared towards providing clear, authoritative, and complete answers that AI can easily extract and use. It’s like “repurposing video for advertising” with AI logic in mind.
Yes, repurposing and unique-ifying existing content is an effective approach. This can include changing the background and tone in videos, adapting for TikTok, Shorts, Reels, rewriting captions for machine readability. The goal is to create 100 unique pieces from one, but it’s important to ensure that the new content meets AEO requirements and will be correctly indexed by AI.
Conclusion
The era of AI search is changing the game for brands and creators. Investing in AI visibility through authentic creator content is becoming not just desirable, but critically important. This is not just a “price comparison from scratch”, but a strategic step towards dominating the new digital reality.
Start adapting your content strategy today so that your brand is not just visible, but also cited in tomorrow’s AI world. Explore the possibilities of generating variants for A/B tests and adapting for different social networks to make the most effective use of every bit of your content.
The era of fragmented queries is fading. Data from a year of Google’s AI search mode shows: users are asking complete questions, not fragments of phrases. This is critically important for stream highlights and any other content: now the main information should be at the beginning so that AI algorithms can instantly extract it and present it in their reviews. If you bury the essence in long introductions, your content risks going unnoticed.
Evolution of Search: From Keywords to Dialogue
Since the launch of AI mode, user queries have fundamentally changed. According to Shivani Mohan, Google’s VP of Data Science, AI mode has reached 1 billion active users per month. But it’s not just about the increase in the number of queries.
Query length has tripled: The average query in AI mode in the US is three times longer than traditional queries.
Multimodal search: More than 1 in 6 queries now include images, voice input, or interactive dialogue.
Growth of images: Image-based queries are growing by 40% monthly.
Follow-up questions: Users ask clarifying questions, indicating the depth of their search.
The most common first words in AI mode queries are “what,” “how,” “I,” “is,” and “can.” This demonstrates a shift from searching by individual words to task- and question-oriented search, similar to communicating with a human.
Five User Behavior Modes in AI Search
Mohan identifies five main user behavior modes, each requiring a specific approach to content:
Explore: Open-ended queries for brainstorming. Growing 30% faster than overall AI mode traffic.
Decide: Queries with comparison words (“which of,” “which one”). Growing 40% faster.
Learn: Understanding new concepts and professional development.
Create: Queries for image creation have tripled since the beginning of the year.
Do: Planning (workouts, travel, budgets). Growing 80% faster in the last six months.
“This is not just a change in ranking factors; it’s a transformation of user behavior. If you’re still building content around keywords, that model is rapidly becoming obsolete,” says Greg Jarboe.
To make your content effective in the new reality of AI search, follow these five steps:
1. Start with the main point
Place the main definition, key metrics, or main conclusions directly in the first sentence of each main section. This allows AI algorithms to quickly extract the necessary information.
2. Use specifics
Formulate sentences with specific brand names, geographical markers, exact dates, and verified numerical values. Avoid vague generalizations so that your text is “mathematically readable” for search engines and reduces the risk of AI “hallucinations.”
3. Structure for scanning
Organize the main text into short paragraphs (2-3 sentences). After section headings, use brief summaries, ordered lists, or structured tables. This facilitates segment extraction for AI and ensures readability for humans. This is especially important for mass cutting of 100+ clips, where each fragment must be self-contained and informative.
4. Prepare answers to follow-up questions
If multi-turn dialogues are growing by 40% per month, your content should answer the second and third questions a reader will ask after the first. Structure long content so that each section can serve as an answer to a specific follow-up question. This increases the likelihood that this particular “chunk” will be provided by the AI mode. For cutting game streams, this means creating clips that answer specific viewer questions.
5. Optimize multimodal content
With the growth of image queries (40% monthly) and a threefold increase in image creation requests, alt-text, image context, and visual content quality are becoming key ranking factors. They are no longer secondary but are part of what the AI mode reads.
Conclusion: From “assembly line” to “smart content production”
Success in modern SEO is not about flooding the internet with templated texts, but about mastering structural clarity that provides immediate and verified benefits to both algorithms and readers. Just as 19th-century journalists adapted to the telegraph, we must rethink our approach to content creation for the AI era. Cutting streams without losing quality, creating highlights, and cropping 9:16 from horizontal — all this requires not just technical skills, but also a strategic approach to information delivery. Remember that the person who cuts streams must now think like an SEO specialist so that every Twitch clipping or podcast video cutting works as effectively as possible to attract an audience. Contact us to find out how we can help you with this.
Frequently Asked Questions
How long does it take to cut a 2-hour stream?
The cutting time depends on the complexity of the content and the desired number of highlights. On average, professional cutting without loss of quality of a 2-hour stream into 5-10 clips can take from 1 to 3 hours, including adding subtitles for cuts and optimization. If you need bulk cutting of 100+ clips, automated tools such as Opus Clip analog service are used, which significantly speed up the process but require subsequent manual refinement.
What moments are usually cut from streams for highlights?
For highlight editing, the most emotional moments, key game events (e.g., “kills”, “win rates”), funny jokes, important announcements, Q&A sessions, and fragments that evoke a strong audience reaction are usually cut. The goal is to create cuts for TikTok from Twitch or YouTube that quickly grab attention.
Are subtitles needed for stream cuts?
Yes, subtitles for cuts are extremely important. They improve content accessibility, allow videos to be watched without sound (e.g., in public places), and increase engagement. In addition, subtitles contribute to better indexing of content by search engines, which is important for stream monetization through cuts.
What is the price for 1 hour of stream for cutting 20 clips?
The price for cutting 20 clips from a stream for 1 hour can vary depending on the specialist’s qualifications, the complexity of the editing, and the need to add graphics and subtitles. Approximately, the cost can range from 1000 to 3000 rubles per hour of source material, if it is a high-quality work taking into account SEO optimization and 9:16 cropping from horizontal for different platforms.
In August, SEO specialist Anastasia Kuru noticed queries in the Search Console report that didn’t look like search queries: “Yes,” “Yes, continue,” “Yes, price.” John Mueller confirmed that Search Console includes data from AI Overviews and AI Mode in the overall report, and subsequent questions within AI Mode are recorded as new queries. This opens up the opportunity to analyze fragments of AI dialogues that appear in your statistics.
In this article, we’ll break down how to recognize such fragments in Search Console data, how they differ from regular queries, and how to use this information for optimizing content for AI search.
Seven types of AI fragments in Search Console data
I analyzed 16 months of data from my site and identified seven categories of such queries. Each has its own origin and characteristics.
Short answers
These are single words or phrases like “yes,” “of course,” “show me.” They occur when a user responds to AI during a dialogue, and the response is treated as a search query. Positions in such cases are taken from the answer block, not from the search results.
Comparisons in dialogue
Phrases like “what about Resend?” or “what if we try Gemini?” — the user asks AI to compare an alternative with the already received answer. This signals a specific interest in your content.
Questions addressed to a conversational partner
The grammar of such queries implies the presence of a listener: “Can Meta Ray-Bans be hacked?”, “How do I sell this?”. They differ from regular search phrases.
Synthetic prompts from SEO tools
Regular queries, for example, “evaluate [company] by [criterion]” or “. my location is usa.” They repeat daily for months, indicating automation.
Full instructions for agents
Machines sometimes log entire prompts, for example, “find on the web… return 3 most relevant results… don’t invent URLs.” Such strings end up in the report as queries.
Errors and table headers
People and pipelines search as-is. In my data, there was an entire row from a ranking tracker CSV file that received 146 impressions.
Long queries without markers
Phrases of 10+ words without obvious signs of dialogue. The classifier sends them for manual review, as they could be either AI fragments or regular long-tail queries.
How to distinguish AI fragments from regular queries
The key difference is the addressee. A regular long query is addressed to no one, while a conversational one is directed at a conversational partner. Four signals reveal the conversational nature:
presence of an address;
interrogative construction;
incomplete sentences;
conversational particles.
Length itself is a weak signal, so queries of 10+ words with interrogative intonation are classified as conversational, while those without markers are sent to quarantine.
Practical conclusions from the analysis
On my site, AI fragments began appearing in March 2026, steadily 20–30 impressions per month. For example, the query “yes” gave 110 impressions and 6 clicks at an average position of 4.5 — this is impossible in regular search results, but typical for an AI answer block.
Comparative queries perfectly correlate with my articles: “what about Claude?” led to the WebMCP guide, “what about Xcode?” — to the post about Xcode. This shows that users actively compare alternatives, and your content can be mentioned in AI answers.
Frequently asked questions
Can AI Overviews be separated from AI Mode in the data?
No, Google combines them in the report as web search. The fragment indicates the fact of a dialogue, but not the specific type of surface.
Why does Google hide queries and clicks in the Generative AI report?
The report shows impressions, pages, countries, devices, and dates, but not queries and clicks. The API does not support this data, and the BigQuery export does not contain an AI column. The only way to get data is the export button in the interface.
How to set up a classifier for your site?
The classifier genai_conversation_queries is available in the free Search Console MCP v2.4.0. Install MCP with one command, select your project, and run the analysis. For large sites, use the BigQuery version v4.1.0.
Conclusion
Analyzing AI fragments in Search Console provides unique insights into how users interact with AI and find your content. Use the classifier to uncover hidden opportunities and optimize content for AI dialogues.
Start with the free tool and get data that most SEO specialists miss.
Five days in Cannes, dozens of conversations — and one clear signal for creator marketing. Brands have stopped asking if it works. Now they are frantically building the infrastructure to scale before competitors pull ahead.
The most candid discussions happened outside official panels — at breakfasts and lunches where brand leaders and content creators dropped the scripts. Creator marketing is finally measurable down to the last dollar. Brands that embrace this rigor are pulling ahead.
Marketers Finally Have the Measurements They Were Missing
Previously, you couldn’t pinpoint which post led to a purchase. That has changed. We can now show a brand exactly which post drove which purchase, who saw it, who clicked, what they bought, and at what price — fully attributed down to the dollar.
Without this transparency, every decision becomes a negotiation between the CMO and CFO, speaking different languages. Closing this attribution gap is the biggest breakthrough in the industry right now. That’s why creator marketing is moving from a test budget to a core line item in annual planning.
“Brands get more from integrating and supporting smaller creators than they realize. Snapchat gives audiences a unique, unfiltered view of the creator’s life, and this deeper access is what drives real connection.” — David Dobrik
Brands That Underestimate Small Creators Lose Real Connection
We opened the week with a conversation between top creator David Dobrik and Quincy Kevaan, head of creator partnerships at Snap. The discussion centered on the value of authentic closeness between a creator and their audience. This is something brand strategists often miss.
A creator with a smaller but engaged audience on a platform built for intimate communication can outperform a much larger creator on a broadcast platform — depending on the goal. The trust Dobrik speaks of is harder to measure than reach, but it’s what turns attention into action.
The Best Brand-Creator Partnerships Start Before the Brief
The most memorable conversation was a lunch discussion with Dhar Mann and Shira Lazar. Dhar built a 200-person studio without taking a single brand partner for the first five years. This gave him the leverage to say “no” to unsuitable deals.
In that conversation, one theme kept surfacing: strong partnerships rarely start with a price list. They begin with a shared perspective, closer to creative collaboration than a media buy. Shira added that creator sustainability and partnership quality are not separate issues. A burned-out or financially unstable creator cannot consistently deliver for a brand, no matter how good the brief.
Brands Still Cling to Creative Control They Don’t Need
In a discussion with Keiko Mori (TikTok), Sabrina Callahan (Southwest Airlines), and comedian Connor Wood, a key insight emerged. Sabrina uses creator marketing to engage customers in real changes — including the airline’s controversial product shifts. This only works if the brand trusts creators enough to make content feel native to the platform, not committee-approved.
Connor put it simply: “When comedy feels forced, it’s dead.” Brands getting real returns from TikTok understand that the platform rewards content made for its feed, preserving the creator’s voice. Keiko noted that TikTok has long outgrown its reputation as a purely brand-awareness platform — discovery, consideration, and conversion all happen in a single scroll.
Creators Understand Your Brand Better Than Your Internal Team
Let’s be direct: a creator who communicates daily with your target audience for years understands how your brand is actually perceived better than any report or analysis. Marketers who accept this insight move faster and spend smarter than those who see it as a threat to control.
Advice for CMOs still hesitating: give creators strategic direction, then get out of the way and let them work.
Creator Content Becomes Raw Material for Brand Presence in AI Search
According to McKinsey, brand websites make up only 5–10% of what AI search references. The remaining 90–95% is third-party content: creator posts, community discussions, long-form videos, product reviews. Platforms leading in LLM citations include YouTube (16% of responses), Reddit (around 40%), Substack, and niche communities.
Brands fastest to build infrastructure for AI search understand that being good enough to rank in traditional search engines is no longer sufficient.
Cannes Confirmed the Model That Was Already Forming
Every conversation during the week returned to one idea: creator marketing has become part of the core marketing infrastructure. Brands that have embraced this are already building systems around it. As H2 planning progresses, the question has shifted from “whether to work with creators” to “how to weave creator content into every part of the funnel” — from awareness to conversion, into product pages, into paid traffic, into places where AI assistants pull answers.
Brands doing this well see growth and efficiency simultaneously. That combination convinces CFOs this is no longer a marketing experiment. Cannes confirmed the speed of this shift and provided the evidence.
Frequently Asked Questions
How do you measure the effectiveness of creator marketing?
Use end-to-end attribution: track specific posts to purchase via UTM tags, promo codes, and pixels. Modern platforms allow you to attribute every action down to the dollar.
Is it worth working with micro-creators?
Yes. A creator with a small but engaged audience on a platform built for close communication (e.g., Snapchat) can deliver higher conversion than a large blogger on a broadcast platform.
How does AI search impact creator marketing?
AI models increasingly reference creator content (YouTube, Reddit, Substack) rather than brand websites. Investing in such content boosts brand visibility in AI search.
If you’re building in this space, let’s talk. Plan, publish, and analyze campaigns with Later.
