<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png" class="attachment-large size-large wp-post-image" alt="ChatGPT ads" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A new ChatGPT Ads feature suggests that OpenAI is using AI to generate ads on its platform.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png" class="attachment-large size-large wp-post-image" alt="ChatGPT ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/chatgpt-ads-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>OpenAI rolled out a new feature to let ChatGPT Ads generate ads for you &#8211; I suspect using AI. The feature is available under the &#8220;Add new ad&#8221; option, and says &#8220;generate ads for you.&#8221; </p>



<p>The advertiser or marketer can then opt to let ChatGPT create the ad, and then review, edit and approve the ad for delivery on the ChatGPT Ad platform.</p>



<p><strong>What it looks like. </strong>Anthony Higman <a href="https://x.com/AnthonyHigman/status/2071636677920018490">posted</a> a screenshot of this feature on the social media platform named X. Here is that screenshot:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1401" height="1506" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-create-ads-ai-for-you-DoIC1C0f.png" alt="" class="wp-image-481652" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-create-ads-ai-for-you-DoIC1C0f.png 1401w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-create-ads-ai-for-you-DoIC1C0f-768x826.png 768w" sizes="(max-width: 1401px) 100vw, 1401px" /></figure>



<p>As you can see, it says, &#8220;We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you&#8217;re ready.&#8221; Then you can &#8220;Review and create.&#8221;</p>



<p>There is also a quick duplicate ad option that Higman spotted as well:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="668" height="625" src="https://searchengineland.com/wp-content/seloads/2026/07/duplicate-ad-chatgpt-QpzFKP71.png" alt="" class="wp-image-481653"/></figure>



<p><strong>Why we care.</strong> It makes sense that OpenAI would leverage AI to help advertisers create ads. The hope is it would lead to more ads being created and submitted on ChatGPT Ads sooner. This way the AI company can earn more money on ChatGPT.</p>



<p>As a marketer, you should be very careful when using AI to generate ads.  Make sure to review the ads that were generated for you carefully so they meet your branding and marketing criteria, including your ROI goals.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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OpenAI rolled out a new feature to let ChatGPT Ads generate ads for you – I suspect using AI. The feature is available under the “Add new ad” option, and says “generate ads for you.” 
The advertiser or marketer can then opt to let ChatGPT create the ad, and then review, edit and approve the ad for delivery on the ChatGPT Ad platform.
What it looks like. Anthony Higman posted a screenshot of this feature on the social media platform named X. Here is that screenshot:




As you can see, it says, “We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you’re ready.” Then you can “Review and create.”
There is also a quick duplicate ad option that Higman spotted as well:




Why we care. It makes sense that OpenAI would leverage AI to help advertisers create ads. The hope is it would lead to more ads being created and submitted on ChatGPT Ads sooner. This way the AI company can earn more money on ChatGPT.
As a marketer, you should be very careful when using AI to generate ads.  Make sure to review the ads that were generated for you carefully so they meet your branding and marketing criteria, including your ROI goals.
AI recommends.
      
See your AI visibility

A new ChatGPT Ads feature suggests that OpenAI is using AI to generate ads on its platform.

OpenAI can generate ChatGPT Ads for you

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search.png" class="attachment-large size-large wp-post-image" alt="AI traffic search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI referrals are surging, volatile, and shaped by internal search, landing page fit, and Claude’s quiet rise, new data shows.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search.png" class="attachment-large size-large wp-post-image" alt="AI traffic search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ai-traffic-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Twelve months ago, the industry was betting on which AI platform would win discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. Neither bet paid off.</p>



<p>Previsible (<em>disclosure: I’m its CPO and co-founder</em>) just published its third <a href="https://previsible.com/seo-strategy/ai-traffic-report-july-2026" target="_blank" rel="noopener">AI Traffic Study</a>, analyzing 6.77 million LLM-driven sessions. The data shows consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026. And 92.4% of that traffic comes from one platform.</p>



<h2 class="wp-block-heading" id="h-the-plateau-was-a-pause">The plateau was a pause</h2>



<p>In mid-2025, AI traffic appeared to be peaking in some sectors. It wasn’t.</p>



<p>Sessions rose from 65,249 in November 2024 to 396,278 by August 2025, then dropped sharply in November 2025, before hitting new highs of 428,203 in February 2026 and 644,478 in May.</p>



<p>That November dip needs context.</p>



<p>Sessions fell 50% in one month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms held steady. This was likely a model-related change; we’ve seen modest product tweaks massively swing referral traffic, like last fall when many sites lost half their ChatGPT traffic because the <a href="https://searchengineland.com/chatgpt-traffic-referrals-plummet-461027">model began favoring Wikipedia and Reddit</a>. Sessions recovered to 442,609 by December.</p>



<p>The lesson: one vendor’s product decisions can halve your AI traffic overnight. Plan for the volatility.</p>



<h2 class="wp-block-heading" id="h-consolidation-not-competition">Consolidation, not competition</h2>



<p>When we last published in December 2025, ChatGPT held roughly 84% share, followed by Perplexity at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field has collapsed toward the leader.</p>



<p>Across the full dataset, ChatGPT commands 92.4% of trackable LLM referral traffic, growing 12.8x over 19 months with no sign of slowing. It’s the only LLM sending meaningful referral volume at scale. Optimizing for “AI visibility” without prioritizing ChatGPT means optimizing for an abstraction.</p>



<p>Important framing: this measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined, but it operates on a different measurement paradigm and is excluded here.</p>



<h2 class="wp-block-heading" id="h-the-challengers-flipped">The challengers flipped</h2>



<p>The surprise isn’t at the top. It’s who’s moving underneath.</p>



<h3 class="wp-block-heading" id="h-claude">Claude</h3>



<p>Claude grew 64x, from 133 sessions in November 2024 to 8,528 in May 2026, and overtook Perplexity in March 2026 for the first time. It stayed ahead. </p>



<p>Claude was flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage the industry expected Copilot to win may be materializing for Claude instead. </p>



<p>If your audience includes technical buyers, developers, or professional services, Claude visibility is now material, and the window for early positioning is open.</p>



<h3 class="wp-block-heading" id="h-gemini">Gemini</h3>



<p>Gemini is the quiet number two: 3.2x growth with almost no volatility. Its Workspace and Android integration mean referral numbers likely undercount its real discovery footprint.</p>



<h3 class="wp-block-heading" id="h-perplexity-amp-copilot">Perplexity &amp; Copilot</h3>



<p>Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot collapsed 96% from its August 2025 peak, from 8,651 sessions to 339. </p>



<p>Neither is a growth bet for traffic acquisition anymore. Both are shifting toward keeping users inside their own experiences: browsers, agents, and modes where they don’t need to send you traffic at all.</p>



<h2 class="wp-block-heading" id="h-where-llms-send-users-and-why-it-should-change-your-roadmap">Where LLMs send users, and why it should change your roadmap</h2>



<p>The study’s most actionable finding isn’t market share. It’s landing pages.</p>



<p>ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search. </p>



<p>The model trusts your domain but can’t pick the right page, so it sends users to your search box and lets them navigate. This pattern persists across verticals and time periods, suggesting it’s structural to retrieval-augmented generation rather than a temporary quirk.</p>



<p>Think about what that means. The model did the hard work of choosing your domain. Your internal search UX now determines whether that high-intent visit converts or bounces. </p>



<p>For most sites, internal search is a neglected navigation feature, not an acquisition surface. That has to change.</p>



<p>The vertical view tells several different stories:</p>



<ul class="wp-block-list">
<li>SaaS traffic lands on search pages (34.6%). </li>



<li>Publisher traffic lands on news pages (54%), yet against 120+ million organic sessions, publisher penetration is 0.11%; publishers produce the content LLMs cite and capture almost none of the resulting traffic. </li>



<li>Ecommerce traffic lands on product pages, with purchase intent already formed. </li>



<li>Education traffic lands directly on course pages (52%), bypassing marketing content. </li>



<li>Health traffic lands on About pages (42.1%), with users evaluating the source before the content. </li>



<li>Legal traffic spreads across blog, about, contact, and location pages: the full evaluation arc.</li>
</ul>



<p>The platforms have personalities, too:</p>



<ul class="wp-block-list">
<li>ChatGPT and Gemini are search-pattern models: domain trust, page-level uncertainty. </li>



<li>Perplexity and Claude are content-selection models that pick specific pages and over-index on long-form. </li>
</ul>



<p>If your strategy depends on editorial content driving qualified traffic, Perplexity and Claude matter disproportionately to their share.</p>



<h2 class="wp-block-heading" id="h-what-to-do-now">What to do now</h2>



<ul class="wp-block-list">
<li>Optimize for ChatGPT first. Expand elsewhere when volume justifies it.</li>



<li>Monitor Claude. It overtook Perplexity in March. Early positioning compounds.</li>



<li>Treat product pages as AI entry points. Product pages capture 43% of e-commerce LLM traffic. Structured, comparable product data is a discoverability requirement now.</li>



<li>Make pricing machine-readable. &#8220;Contact us for pricing&#8221; gives AI systems nothing to summarize, compare, or recommend.</li>



<li>Prioritize internal search. It&#8217;s an acquisition tool, not a navigation feature.</li>



<li>Track AI traffic by page type, not site-wide. Your site average hides where AI traffic concentrates. Your pricing page might run 3x your site-wide penetration.</li>
</ul>



<p>The next question is the one nobody has answered: conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce? </p>



<p>We built this dataset to answer that. If the last 19 months are any indication, the answers will change faster than most teams are ready for.</p>



<h3 class="wp-block-heading" id="h-about-the-data">About the data</h3>



<p>166 GA4 properties, November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so the trajectories reflect behavioral change rather than sample expansion.</p>



<h3 class="wp-block-heading" id="h-the-report">The report</h3>



<p>You can find <a href="https://previsible.com/seo-strategy/ai-traffic-report-july-2026">the full report at </a><a href="http://previsible.io">previsible.io</a><em>.</em>






Twelve months ago, the industry was betting on which AI platform would win discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. Neither bet paid off.
Previsible (disclosure: I’m its CPO and co-founder) just published its third AI Traffic Study, analyzing 6.77 million LLM-driven sessions. The data shows consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026. And 92.4% of that traffic comes from one platform.
The plateau was a pause
In mid-2025, AI traffic appeared to be peaking in some sectors. It wasn’t.
Sessions rose from 65,249 in November 2024 to 396,278 by August 2025, then dropped sharply in November 2025, before hitting new highs of 428,203 in February 2026 and 644,478 in May.
That November dip needs context.
Sessions fell 50% in one month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms held steady. This was likely a model-related change; we’ve seen modest product tweaks massively swing referral traffic, like last fall when many sites lost half their ChatGPT traffic because the model began favoring Wikipedia and Reddit. Sessions recovered to 442,609 by December.
The lesson: one vendor’s product decisions can halve your AI traffic overnight. Plan for the volatility.
Consolidation, not competition
When we last published in December 2025, ChatGPT held roughly 84% share, followed by Perplexity at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field has collapsed toward the leader.
Across the full dataset, ChatGPT commands 92.4% of trackable LLM referral traffic, growing 12.8x over 19 months with no sign of slowing. It’s the only LLM sending meaningful referral volume at scale. Optimizing for “AI visibility” without prioritizing ChatGPT means optimizing for an abstraction.
Important framing: this measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined, but it operates on a different measurement paradigm and is excluded here.
The challengers flipped
The surprise isn’t at the top. It’s who’s moving underneath.
Claude
Claude grew 64x, from 133 sessions in November 2024 to 8,528 in May 2026, and overtook Perplexity in March 2026 for the first time. It stayed ahead. 
Claude was flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage the industry expected Copilot to win may be materializing for Claude instead. 
If your audience includes technical buyers, developers, or professional services, Claude visibility is now material, and the window for early positioning is open.
Gemini
Gemini is the quiet number two: 3.2x growth with almost no volatility. Its Workspace and Android integration mean referral numbers likely undercount its real discovery footprint.
Perplexity & Copilot
Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot collapsed 96% from its August 2025 peak, from 8,651 sessions to 339. 
Neither is a growth bet for traffic acquisition anymore. Both are shifting toward keeping users inside their own experiences: browsers, agents, and modes where they don’t need to send you traffic at all.
Where LLMs send users, and why it should change your roadmap
The study’s most actionable finding isn’t market share. It’s landing pages.
ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search. 
The model trusts your domain but can’t pick the right page, so it sends users to your search box and lets them navigate. This pattern persists across verticals and time periods, suggesting it’s structural to retrieval-augmented generation rather than a temporary quirk.
Think about what that means. The model did the hard work of choosing your domain. Your internal search UX now determines whether that high-intent visit converts or bounces. 
For most sites, internal search is a neglected navigation feature, not an acquisition surface. That has to change.
The vertical view tells several different stories:
SaaS traffic lands on search pages (34.6%). 
Publisher traffic lands on news pages (54%), yet against 120+ million organic sessions, publisher penetration is 0.11%; publishers produce the content LLMs cite and capture almost none of the resulting traffic. 
Ecommerce traffic lands on product pages, with purchase intent already formed. 
Education traffic lands directly on course pages (52%), bypassing marketing content. 
Health traffic lands on About pages (42.1%), with users evaluating the source before the content. 
Legal traffic spreads across blog, about, contact, and location pages: the full evaluation arc.
The platforms have personalities, too:
ChatGPT and Gemini are search-pattern models: domain trust, page-level uncertainty. 
Perplexity and Claude are content-selection models that pick specific pages and over-index on long-form. 
If your strategy depends on editorial content driving qualified traffic, Perplexity and Claude matter disproportionately to their share.
What to do now
Optimize for ChatGPT first. Expand elsewhere when volume justifies it.
Monitor Claude. It overtook Perplexity in March. Early positioning compounds.
Treat product pages as AI entry points. Product pages capture 43% of e-commerce LLM traffic. Structured, comparable product data is a discoverability requirement now.
Make pricing machine-readable. “Contact us for pricing” gives AI systems nothing to summarize, compare, or recommend.
Prioritize internal search. It’s an acquisition tool, not a navigation feature.
Track AI traffic by page type, not site-wide. Your site average hides where AI traffic concentrates. Your pricing page might run 3x your site-wide penetration.
The next question is the one nobody has answered: conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce? 
We built this dataset to answer that. If the last 19 months are any indication, the answers will change faster than most teams are ready for.
About the data
166 GA4 properties, November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so the trajectories reflect behavioral change rather than sample expansion.
The report
You can find the full report at previsible.io.

AI referrals are surging, volatile, and shaped by internal search, landing page fit, and Claude’s quiet rise, new data shows.

ChatGPT commands 92% of AI referral traffic. Here’s what 6.77 million sessions reveal.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai.jpg" class="attachment-large size-large wp-post-image" alt="Beyond navigation advanced architecture and AI" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Learn a proven five-phase framework for clearer navigation, stronger taxonomy, and content AI can access. Join us for the next SMX Now.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai.jpg" class="attachment-large size-large wp-post-image" alt="Beyond navigation advanced architecture and AI" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/07/beyond-navigation-advanced-architecture-ai-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Advanced architecture is no longer just technical structure. It determines whether your content can be found, understood, and surfaced by search engines and AI systems.</p>



<p>Our next <strong>SMX Now </strong>on July 15 features Shari Thurow, co-founder, information scientist, and search director at the Information Architecture Gateway. She’ll explain how advanced architecture works and where most AI, SEO, and site development workflows fall short.</p>



<p>The session introduces a five-phase framework Thurow has tested through decades of client work with organizations including Microsoft, Google Cloud, Abbott Laboratories, CVS Pharmacy, WebMD, Sony Music, the Library of Congress, Best Buy, and Merriam-Webster. You’ll learn how architecture decisions shape labeling systems, wayfinding networks, taxonomy, wireframes, and AI access to valuable content.</p>



<p>It also challenges long-standing misconceptions, including the three-click rule, the idea that taxonomy is only a hierarchy, and the belief that AI can generate effective wireframes without a deeper architectural model.</p>



<p>You’ll leave with a practical framework for building sites that communicate more clearly with users, search engines, and human-centered AI systems.</p>



<p class="has-text-align-center"><strong><a href="https://www.bigmarker.com/third-door-media/beyond-navigation-advanced-architecture-and-ai?utm_bmcr_source=Editorial">Save your spot</a></strong>


Advanced architecture is no longer just technical structure. It determines whether your content can be found, understood, and surfaced by search engines and AI systems.
Our next SMX Now on July 15 features Shari Thurow, co-founder, information scientist, and search director at the Information Architecture Gateway. She’ll explain how advanced architecture works and where most AI, SEO, and site development workflows fall short.
The session introduces a five-phase framework Thurow has tested through decades of client work with organizations including Microsoft, Google Cloud, Abbott Laboratories, CVS Pharmacy, WebMD, Sony Music, the Library of Congress, Best Buy, and Merriam-Webster. You’ll learn how architecture decisions shape labeling systems, wayfinding networks, taxonomy, wireframes, and AI access to valuable content.
It also challenges long-standing misconceptions, including the three-click rule, the idea that taxonomy is only a hierarchy, and the belief that AI can generate effective wireframes without a deeper architectural model.
You’ll leave with a practical framework for building sites that communicate more clearly with users, search engines, and human-centered AI systems.
Save your spot

Learn a proven five-phase framework for clearer navigation, stronger taxonomy, and content AI can access. Join us for the next SMX Now.

SMX Now: Build better site architecture for SEO, AI, and users

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search.png" class="attachment-large size-large wp-post-image" alt="6 SEO priorities to rethink for AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search is changing which SEO efforts have the biggest impact. Learn the three priorities to emphasize more and the three to emphasize less.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search.png" class="attachment-large size-large wp-post-image" alt="6 SEO priorities to rethink for AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/6-SEO-priorities-to-rethink-for-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Yes, <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> and <a href="https://searchengineland.com/answer-engine-optimization-ai-models-453509">AEO</a> have plenty of overlap, but they aren&#8217;t the same. Sticking with your tried-and-true SEO approach won&#8217;t get you as far in AI search visibility.</p>



<p>So what needs to change? </p>



<p>Rather than revisit <a href="https://searchengineland.com/optimize-content-generative-engines-actionable-tips-463036">content structure for AI search</a>, I&#8217;ll focus on three priorities that matter more in AI search and three that matter less.</p>



<h2 class="wp-block-heading" id="h-3-seo-priorities-to-emphasize-more">3 SEO priorities to emphasize more</h2>



<h3 class="wp-block-heading" id="h-establish-brand-authority-and-strong-entities">Establish brand authority and strong entities</h3>



<p>AI systems need to &#8220;know&#8221; your brand exists and what it stands for before they&#8217;ll cite you. Entity recognition is foundational to AI visibility in a way it never was for traditional search, although Google&#8217;s Knowledge Graph has long highlighted brands using this approach. LLM training data rewards brands with a consistent, cross-platform presence.</p>



<p>For our agency&#8217;s clients, we focus more on ensuring brand information is consistent across Wikipedia, LinkedIn, Crunchbase, industry directories, and anywhere else an LLM might pull entity data.</p>



<p>PR and SEO/AEO teams also need to work more closely together because earned media mentions are now entity-building signals.</p>



<p>E-E-A-T was already pointing in this direction, but building author entities matters even more now. Bylined experts with their own credible web presence add authority to the content they produce.</p>



<p>When we can invest in entity building, we tend to see faster AI citation gains when we produce strong content because the infrastructure is already in place.</p>


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<h3 class="wp-block-heading" id="h-build-topical-depth-with-content-clusters">Build topical depth with content clusters</h3>



<p>AI systems favor sources that demonstrate comprehensive authority on a topic, not just individual pages that rank for individual keywords. A thin content footprint is much more exposed in AI search than it was in traditional search.</p>



<p>Keyword-by-keyword content planning needs to give way to topic ownership planning. Stop asking, &#8220;What do we rank for?&#8221; and start asking, &#8220;What topics do we want AI systems to associate us with?&#8221;</p>



<p>Internal linking becomes more important because it signals topical relationships between pieces for LLM ingestion. Content audits should also become a regular exercise to identify gaps in topic coverage, not just underperforming pages.</p>



<p>When we can go deep on a niche, we tend to see content cited across multiple related queries. One strong content cluster can generate broad AI visibility.</p>



<p>Owning a topic cluster around the problem a client&#8217;s product solves positions them as the authoritative resource before the sales conversation even starts. We also get more feedback that buyers are seeing them in LLMs while researching before making a purchase.</p>



<h3 class="wp-block-heading" id="h-earn-unlinked-brand-mentions-and-community-presence">Earn unlinked brand mentions and community presence</h3>



<p>LLMs are trained on the broader web, beyond pages with backlinks. A mention of your brand on Reddit, Quora, a niche forum, or an industry community carries weight even without a link.</p>



<p>AI systems pattern-match what the web says about you across many sources, not only what ranks in Google. Owned content alone can&#8217;t manufacture that signal.</p>



<p>Trusted third-party communities like Reddit carry particular weight because LLMs have been heavily trained on them and often treat their content as authentic user sentiment.</p>



<p>Community participation and digital PR are now SEO-adjacent priorities. Getting your brand mentioned in the right places matters, whether a link is attached.</p>



<p>Monitoring unlinked brand mentions is becoming as important as tracking backlinks. Tools like Brandwatch and Mention, along with manual Reddit and Quora monitoring, can surface where your brand is and isn&#8217;t appearing organically.</p>



<p>Talk with your team less about who&#8217;s linking to you and more about where your brand is being discussed, and whether those conversations are positive and accurate.</p>



<p>Brands with an active presence in relevant communities tend to surface more naturally in AI answers to conversational, recommendation-style queries, such as &#8220;What does Reddit think about X?&#8221; or &#8220;What&#8217;s the best Y according to users?&#8221;</p>



<p>For challenger brands trying to break into a category, earned community mentions can build AI-visible authority faster than traditional link building, which takes time to accumulate.</p>



<p>B2C brands, in particular, benefit from genuine community presence. Consumer AI queries often skew toward social proof and peer recommendations over authoritative publications.</p>



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<h2 class="wp-block-heading" id="h-3-seo-priorities-to-emphasize-less">3 SEO priorities to emphasize less</h2>



<h3 class="wp-block-heading" id="h-chasing-high-volume-keywords-with-thin-content">Chasing high-volume keywords with thin content</h3>



<p>AI Overviews absorb the click for generic informational queries. Ranking No. 1 for a broad head term increasingly means you&#8217;ve put a lot of effort into attracting traffic that never arrives.</p>



<p>Volume alone is no longer a proxy for opportunity. A query with 50,000 monthly searches that triggers an AI Overview may deliver less traffic than a query with 2,000 searches that doesn&#8217;t.</p>



<p>Craft specific, authoritative content that answers a narrower question better than anything else available. Focus on queries where the searcher needs to take action, make a comparison, or access something only your site provides. Those are harder for AI to fully resolve.</p>



<p>The traffic potential of a keyword is no longer the primary metric. First ask whether someone will still need to click through after AI answers the query. If the answer is no, the opportunity isn&#8217;t what it used to be.</p>



<h3 class="wp-block-heading" id="h-pursuing-exact-match-and-manipulative-link-building">Pursuing exact-match and manipulative link building</h3>



<p>Low-quality link volume does nothing for AI citation likelihood. LLMs weight the authority and relevance of citing sources, not raw link counts. The publications that matter for AI citation are those with genuine editorial standards, which can&#8217;t be gamed the way link networks can.</p>



<p>Focus on earning coverage and links from the outlets AI systems actually draw from, such as trade publications, respected blogs, and academic-adjacent sources. Build content worth referencing instead of relying on outreach designed to extract a link.</p>



<p>A hundred low-quality links won&#8217;t get you cited in ChatGPT. Five links from publications your target audience actually reads might. The metric that matters is source authority, not link volume.</p>



<h3 class="wp-block-heading" id="h-optimizing-for-ctr-on-standard-blue-links">Optimizing for CTR on standard blue links</h3>



<p>A growing share of informational queries are resolved without a click. As a result, optimizing titles and meta descriptions for CTR on queries dominated by AI Overviews offers diminishing returns. Time and resources spent on micro-optimizing CTR for zero-click queries could be better spent earning the citation within the AI answer.</p>



<p>Aim to become the cited source within the AI answer rather than the blue link below it. For queries where clicks still happen, focus on transactional and navigational intent because those are more resistant to AI resolution.</p>



<p>CTR optimization assumes someone is choosing between results. For a growing share of queries, that choice is made before the blue links appear. The game has moved up the page.</p>


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<h2 class="wp-block-heading" id="h-the-payoff-isn-t-always-more-traffic">The payoff isn&#8217;t always more traffic</h2>



<p>There are certainly more shifts to consider, but these are the first ones I&#8217;d make. You may lose some volume in traditional SEO metrics like impressions and clicks, but that should have little downstream effect on the metrics that matter most, including conversions, pipeline, and revenue. That&#8217;s the tradeoff AI search increasingly rewards.</p>

Yes, SEO and AEO have plenty of overlap, but they aren’t the same. Sticking with your tried-and-true SEO approach won’t get you as far in AI search visibility.
So what needs to change? 
Rather than revisit content structure for AI search, I’ll focus on three priorities that matter more in AI search and three that matter less.
3 SEO priorities to emphasize more
Establish brand authority and strong entities
AI systems need to “know” your brand exists and what it stands for before they’ll cite you. Entity recognition is foundational to AI visibility in a way it never was for traditional search, although Google’s Knowledge Graph has long highlighted brands using this approach. LLM training data rewards brands with a consistent, cross-platform presence.
For our agency’s clients, we focus more on ensuring brand information is consistent across Wikipedia, LinkedIn, Crunchbase, industry directories, and anywhere else an LLM might pull entity data.
PR and SEO/AEO teams also need to work more closely together because earned media mentions are now entity-building signals.
E-E-A-T was already pointing in this direction, but building author entities matters even more now. Bylined experts with their own credible web presence add authority to the content they produce.
When we can invest in entity building, we tend to see faster AI citation gains when we produce strong content because the infrastructure is already in place.
AI recommends.
      
See your AI visibility
    
Build topical depth with content clusters
AI systems favor sources that demonstrate comprehensive authority on a topic, not just individual pages that rank for individual keywords. A thin content footprint is much more exposed in AI search than it was in traditional search.
Keyword-by-keyword content planning needs to give way to topic ownership planning. Stop asking, “What do we rank for?” and start asking, “What topics do we want AI systems to associate us with?”
Internal linking becomes more important because it signals topical relationships between pieces for LLM ingestion. Content audits should also become a regular exercise to identify gaps in topic coverage, not just underperforming pages.
When we can go deep on a niche, we tend to see content cited across multiple related queries. One strong content cluster can generate broad AI visibility.
Owning a topic cluster around the problem a client’s product solves positions them as the authoritative resource before the sales conversation even starts. We also get more feedback that buyers are seeing them in LLMs while researching before making a purchase.
Earn unlinked brand mentions and community presence
LLMs are trained on the broader web, beyond pages with backlinks. A mention of your brand on Reddit, Quora, a niche forum, or an industry community carries weight even without a link.
AI systems pattern-match what the web says about you across many sources, not only what ranks in Google. Owned content alone can’t manufacture that signal.
Trusted third-party communities like Reddit carry particular weight because LLMs have been heavily trained on them and often treat their content as authentic user sentiment.
Community participation and digital PR are now SEO-adjacent priorities. Getting your brand mentioned in the right places matters, whether a link is attached.
Monitoring unlinked brand mentions is becoming as important as tracking backlinks. Tools like Brandwatch and Mention, along with manual Reddit and Quora monitoring, can surface where your brand is and isn’t appearing organically.
Talk with your team less about who’s linking to you and more about where your brand is being discussed, and whether those conversations are positive and accurate.
Brands with an active presence in relevant communities tend to surface more naturally in AI answers to conversational, recommendation-style queries, such as “What does Reddit think about X?” or “What’s the best Y according to users?”
For challenger brands trying to break into a category, earned community mentions can build AI-visible authority faster than traditional link building, which takes time to accumulate.
B2C brands, in particular, benefit from genuine community presence. Consumer AI queries often skew toward social proof and peer recommendations over authoritative publications.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
3 SEO priorities to emphasize less
Chasing high-volume keywords with thin content
AI Overviews absorb the click for generic informational queries. Ranking No. 1 for a broad head term increasingly means you’ve put a lot of effort into attracting traffic that never arrives.
Volume alone is no longer a proxy for opportunity. A query with 50,000 monthly searches that triggers an AI Overview may deliver less traffic than a query with 2,000 searches that doesn’t.
Craft specific, authoritative content that answers a narrower question better than anything else available. Focus on queries where the searcher needs to take action, make a comparison, or access something only your site provides. Those are harder for AI to fully resolve.
The traffic potential of a keyword is no longer the primary metric. First ask whether someone will still need to click through after AI answers the query. If the answer is no, the opportunity isn’t what it used to be.
Pursuing exact-match and manipulative link building
Low-quality link volume does nothing for AI citation likelihood. LLMs weight the authority and relevance of citing sources, not raw link counts. The publications that matter for AI citation are those with genuine editorial standards, which can’t be gamed the way link networks can.
Focus on earning coverage and links from the outlets AI systems actually draw from, such as trade publications, respected blogs, and academic-adjacent sources. Build content worth referencing instead of relying on outreach designed to extract a link.
A hundred low-quality links won’t get you cited in ChatGPT. Five links from publications your target audience actually reads might. The metric that matters is source authority, not link volume.
Optimizing for CTR on standard blue links
A growing share of informational queries are resolved without a click. As a result, optimizing titles and meta descriptions for CTR on queries dominated by AI Overviews offers diminishing returns. Time and resources spent on micro-optimizing CTR for zero-click queries could be better spent earning the citation within the AI answer.
Aim to become the cited source within the AI answer rather than the blue link below it. For queries where clicks still happen, focus on transactional and navigational intent because those are more resistant to AI resolution.
CTR optimization assumes someone is choosing between results. For a growing share of queries, that choice is made before the blue links appear. The game has moved up the page.
customers won’t either.
      
See your AI visibility
    
The payoff isn’t always more traffic
There are certainly more shifts to consider, but these are the first ones I’d make. You may lose some volume in traditional SEO metrics like impressions and clicks, but that should have little downstream effect on the metrics that matter most, including conversions, pipeline, and revenue. That’s the tradeoff AI search increasingly rewards.

AI search is changing which SEO efforts have the biggest impact. Learn the three priorities to emphasize more and the three to emphasize less.

6 SEO priorities to rethink for AI search

<div><img width="1920" height="1081" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-measure-prompt-level-visibility-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="How to measure prompt-level visibility in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-measure-prompt-level-visibility-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-measure-prompt-level-visibility-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-measure-prompt-level-visibility-in-AI-search-1536x865.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-measure-prompt-level-visibility-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>You can't measure every AI recommendation, but you can build a reliable picture of your brand's presence. Here's how.

AI search doesn’t work like traditional search.
A prospect might ask ChatGPT for the best CRM for manufacturing companies, compare options in Google’s AI Mode, refine their requirements over several follow-up questions, and make a shortlist — all without ever clicking a website.
If your company appears in those conversations, you’ve influenced the buying process. The challenge is proving it.
Prompt-level visibility measurement has become one of the fastest-growing areas of AI search optimization. It’s also one of the most misunderstood. Many vendors promise complete visibility into AI conversations, but the reality is far messier.
Here’s what you can measure today, what you can’t, and how practitioners are building useful reporting despite the current limitations.
A 5-step framework for tracking AI visibility
1. Accept that AI doesn’t have ‘rankings’
The biggest mistake marketers make is trying to recreate traditional SEO reports — because there is no universal “position 1” inside ChatGPT.
The same prompt may produce different responses based on:
Conversation history
User location
Personalization
Follow-up questions
Model version
Available web retrieval
Time
Visibility is now probabilistic rather than deterministic. Instead of asking, “Do we rank?” the better question is, “How often are we included across the conversations that matter?”
That shift changes everything about measurement.
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2. Build a prompt library instead of a keyword list
Keywords are still useful. They’re just no longer enough.
Instead of tracking individual search terms, build a library of prompts that reflect how real people research purchases.
The easiest way is to organize prompts by search intent:

IntentExample prompt
DiscoveryWhat are the best workforce management platforms?
ComparisonRippling vs BambooHR vs Deel
EvaluationWhich HR platform works best for global hiring?
ValidationIs Company X actually worth the cost?
ObjectionsWhat are the disadvantages of using Company X?
AlternativesWhat should I use instead of Company X?
ImplementationHow difficult is Company X to implement?





Instead of monitoring 10 keywords, you might monitor 200 to 500 prompts covering the entire buying journey. That produces a much more realistic picture of AI visibility.
3. Use prompt clusters, not individual questions
One prompt rarely tells you anything useful.
For example, “best CRM software” might not mention your company. But “best CRM for manufacturing companies” might. And “CRM for manufacturers with field sales teams” might produce completely different recommendations.
Instead of focusing on individual prompts, group similar prompts into clusters.
For example:
Category cluster

Best project management software.
Best PM platform.
Project management tools.
Industry cluster

Best CRM for healthcare.
Best CRM for manufacturing.
Best CRM for finance.
Feature cluster

CRM with AI automation.
CRM with forecasting.
CRM for enterprise sales.
Patterns across clusters are far more reliable than individual prompt results.
4. Mix synthetic prompts with real user questions
This is where measurement gets tricky.
Most organizations don’t know what customers are actually typing into AI assistants. So they generate prompts synthetically.
That usually involves:
Expanding keyword research into conversational questions.
Generating prompt variations with AI.
Creating comparison, objection, and follow-up prompts.
Synthetic prompts are valuable because they’re repeatable. But they have limitations. Generated prompts often sound cleaner and more structured than real user behavior.
Actual conversations tend to look more like:
“We’re a 250-person SaaS company with a small HR team. We already use Workday but need something better for payroll. Budget isn’t a huge issue. What would you recommend?”
That’s much richer than “best payroll software.”
The best measurement programs use synthetic prompts for consistent benchmarking, then supplement them with real prompts collected from sources like:
Sales calls.
Customer interviews.
Support conversations.
Community discussions.
Internal search logs.
On-site search.
AI transcripts that customers voluntarily share.
No prompt library stays accurate forever. It should evolve as customer language changes.
5. Measure multi-turn conversations
Most AI buying journeys don’t happen in a single prompt. Someone might start by asking for the best cybersecurity vendors, then narrow the list to those strongest for healthcare, ask which ones integrate with CrowdStrike, and finally compare pricing.
Your company may not appear in the first response. But it might become highly recommended by the third.
If you’re only measuring the opening prompt, you’ll miss a large portion of meaningful visibility.
Modern prompt tracking should evaluate entire conversation paths, not just isolated questions. That often reveals different patterns than single-shot testing.
Metrics that actually matter
Many traditional SEO metrics don’t translate neatly to AI search. Rankings, clicks, and impressions still have value, but they no longer tell the whole story.
Instead, marketers are beginning to rely on different measurements that better reflect how brands appear — and how they’re positioned — inside AI-generated responses.
Inclusion rate
If you only track one AI visibility metric, make it this one.
Inclusion rate measures the percentage of tracked prompts where your brand appears in the AI’s response. For example, if you monitor 500 prompts and your company is mentioned in 185 of them, your inclusion rate is 37%.
On its own, that’s a useful benchmark. It becomes even more valuable when you segment it by factors like buying stage, product category, industry, geography, or AI model. Those slices often reveal opportunities that an overall average would hide.
Position within the response
Being mentioned isn’t the same as being recommended.
It’s worth tracking whether your brand is the first recommendation, one of the first few options, buried near the end of a list, or mentioned only as an alternative. If the response includes a comparison table, note where your company appears there as well.
AI answers don’t have traditional rankings, but prominence still matters. A top recommendation is naturally more likely to shape a buyer’s perception than a passing mention several paragraphs later.
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Brand framing
Visibility tells you whether you’re included. Brand framing tells you how you’re being described.
For example, there’s a meaningful difference between an AI describing your company as “widely considered an enterprise leader” and “best suited for smaller teams.” Both are positive, but they position the brand very differently.
Look for recurring themes around strengths, weaknesses, differentiators, pricing, ideal customer profile, and competitive comparisons. Over time, these patterns can highlight messaging gaps in your own content — or reveal how the broader web is shaping AI’s understanding of your brand.
Sentiment
Sentiment goes beyond simply labeling responses as positive or negative. It also captures the confidence with which AI presents your brand.
Compare these two statements:
“Company A is generally considered the strongest option…” 
“Company A may be worth considering.”
Neither is negative, but they carry different levels of conviction. Tracking confidence, uncertainty, caution, skepticism, and strong endorsement can provide a more nuanced view of how AI systems are presenting your company to prospective buyers.
Competitive share of voice
Your own visibility is only part of the picture. It’s equally important to understand how often competitors appear alongside — or instead of — you.
For example, if your inclusion rate stays at 40% month after month, that might seem disappointing. But if every major competitor also dropped by 20 percentage points after a model update, the story changes.
Conversely, if one competitor suddenly jumps from appearing in 35% of prompts to 70% while everyone else remains flat, that’s worth investigating.
Competitive share of voice helps you distinguish category-wide shifts from changes specific to your brand.
The current tool landscape
Over the past year, the market for AI visibility platforms has expanded quickly. While each product approaches the problem a little differently, most are trying to answer the same core questions: 
Does my brand appear? 
How often? 
In which AI models? 
Against which competitors? 
And how is it being described?
Many platforms also include features like prompt libraries, competitive benchmarking, citation tracking, answer monitoring, and trend reporting. These capabilities can dramatically reduce the manual effort required to test hundreds or even thousands of prompts on a recurring basis.
That said, it’s important to understand what these tools are – and aren’t – measuring.
None of them has access to every AI conversation happening in the wild. Most rely on controlled prompt libraries, repeatable testing environments, or sampled interactions to create a representative view of visibility.
That’s incredibly useful, but it isn’t the same thing as observing every real user interaction.
What you still can’t reliably track
This is the part many vendors tend to gloss over.
Despite how quickly AI measurement has evolved, there are still important things that simply aren’t observable today. No platform can comprehensively tell you:
Every prompt where your brand appeared.
Every conversation that influenced a purchase.
Every recommendation made inside ChatGPT.
Every citation shown to every individual user.
Exactly how personalization changed a response.
Every multi-turn conversation across every AI platform.
How often someone acted on an AI recommendation without ever clicking a link.
The underlying AI platforms simply don’t expose that level of data. If a vendor claims they can see every AI conversation involving your brand, it’s worth asking exactly how they’re collecting that information.
What a practical measurement framework looks like
Rather than chasing perfect attribution, the goal should be to build a repeatable measurement system that you can track consistently over time.
A practical dashboard might include four categories of metrics:
Visibility
Inclusion rate.
Competitive share of voice.
Prompt coverage.
Model coverage.
Response quality
Position within the response.
Brand framing.
Sentiment.
Message consistency.
Technical signals
Citation frequency.
Content retrieval success.
Entity consistency.
Freshness.
Business outcomes
AI referral traffic.
Assisted conversions.
Branded search lift.
Direct traffic trends.
Pipeline influenced by AI discovery.
No single metric tells the whole story. Together, however, they provide a much more complete picture of how your brand is showing up — and being perceived — across AI-assisted research.
customers won’t either.
      
See your AI visibility
    
The goal isn’t perfect measurement
Prompt-level visibility isn’t as mature today as keyword tracking became over the past two decades.
Some signals are still emerging, others remain inaccessible because AI platforms don’t expose the underlying data, and user behavior continues to evolve almost as quickly as the technology itself.
That doesn’t mean measurement is impossible. It simply means the objective has changed.
Instead of trying to reconstruct every AI conversation, focus on:
Building a representative prompt library.
Tracking visibility consistently over time.
Benchmarking against competitors.
Understanding how your brand is being framed.
Those trends are far more actionable than chasing a level of precision the ecosystem can’t yet support.
The organizations making the most progress in AI search aren’t waiting for perfect attribution. They’re establishing consistent baselines, watching for meaningful movement, and adapting as both the models and user behavior continue to evolve.

You can't measure every AI recommendation, but you can build a reliable picture of your brand's presence. Here's how.

How to measure prompt-level visibility in AI search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="Paid media is becoming an SEO investment in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Reviews, creator partnerships, and sponsored content can shape how AI systems understand and recommend your brand. Learn why.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="Paid media is becoming an SEO investment in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Paid-media-is-becoming-an-SEO-investment-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The lines between paid media, PR, and SEO are officially gone.&nbsp;</p>



<p>Investing in baked-in YouTube sponsorships, native UGC, and third-party review incentives is the modern equivalent of buying a high-DA backlink. When you fund these channels, you&#8217;re investing in information sources that shape how AI systems understand and recommend your brand.</p>



<p>A recent social media screenshot brought that shift into focus. A B2B brand was offering a $250 Amazon voucher to anyone who wrote a review on G2.</p>



<p>To a growth marketer, that&#8217;s a standard user acquisition tactic. As an SEO, I saw something different: a direct investment in the semantic infrastructure AI systems use to evaluate brands.</p>



<h2 class="wp-block-heading" id="h-the-evolution-of-the-authority-signal">The evolution of the authority signal</h2>



<p>To understand why a $250 G2 voucher or a paid YouTube sponsorship is an SEO strategy, we have to look at how LLMs define authority.</p>



<p>Authority used to be transactional and mathematical. You built or bought hyperlinks.</p>



<p>When I moved from link building into digital PR and influencer marketing, I realized Google was getting smarter. We needed unlinked brand mentions, high-tier media coverage, and contextual relevance. We were optimizing for Google&#8217;s Knowledge Graph.</p>



<p>Today, retrieval-augmented generation (RAG) systems and LLMs don&#8217;t just count links or parse knowledge graphs. They look for semantic consensus across the web.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1662" height="916" src="https://searchengineland.com/wp-content/seloads/2026/07/From-building-links-to-building-brand-consistency.png" alt="From building links to building brand consistency across the web" class="wp-image-481573" srcset="https://searchengineland.com/wp-content/seloads/2026/07/From-building-links-to-building-brand-consistency.png 1662w, https://searchengineland.com/wp-content/seloads/2026/07/From-building-links-to-building-brand-consistency-768x423.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/From-building-links-to-building-brand-consistency-1536x847.png 1536w" sizes="(max-width: 1662px) 100vw, 1662px" /></figure>



<p>When an AI engine like Perplexity or ChatGPT answers a user query, it crawls the data ecosystems it trusts most for that specific topic. For software, that&#8217;s G2 and Reddit. For consumer products, it&#8217;s TikTok transcripts, YouTube, and forums.</p>



<p>When you pay $250 for a G2 review, you&#8217;re buying a dense, text-based data point that an LLM can use to determine your brand&#8217;s sentiment, use cases, and vector positioning. You&#8217;re strengthening the signals AI systems use to recommend your brand.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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        Be the brand <span>AI recommends</span>.
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        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
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      <span id="semrush-one-cta">See your AI visibility</span>
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<h2 class="wp-block-heading" id="h-the-permanent-ad-why-sponsorships-and-ugc-are-the-new-organic-infrastructure">The permanent ad: Why sponsorships and UGC are the new organic infrastructure</h2>



<p>This reality shatters the traditional &#8220;church and state&#8221; separation of paid media and SEO.</p>



<p>Historically, paid ads were temporary. You turned off the budget, the traffic stopped, and SEO was left to clean up the long-term mess. If you run a dynamic programmatic ad on YouTube or a banner ad on a website, that model still holds true. LLM web scrapers ignore dynamic ad placements entirely.</p>



<p>Here&#8217;s what happens when you invest in baked-in influencer sponsorships, native user-generated content (UGC), or podcast reads:</p>



<ul class="wp-block-list">
<li><strong>The hardcoded transcript:</strong> When a YouTuber reads a native sponsor segment (&#8220;I use Brand X to manage my business taxes&#8230;&#8221;), that segment is baked into the video file. YouTube automatically transcribes it.</li>



<li><strong>LLM ingestion:</strong> When an LLM crawls the web or a multimodal AI watches the video, those spoken words are indexed. The AI associates your brand with the semantic concept of business taxes.</li>



<li><strong>The half-life of paid media:</strong> Your paid media spend suddenly delivers a lasting return. Long after the campaign ends and the initial views dry up, the transcript remains part of the information an LLM can access.</li>
</ul>



<p>As someone who spent years bridging the gap between digital PR and SEO, I used to judge a campaign&#8217;s ROI based on immediate referral traffic, brand search lift, and backlink quality. Today, we have to calculate the algorithmic half-life of our creative assets.</p>



<h2 class="wp-block-heading" id="h-activating-the-convincer-bringing-paid-and-pr-into-the-visibility-supply-chain">Activating the convincer: Bringing paid and PR into the visibility supply chain</h2>



<p>The <a href="https://searchengineland.com/seo-visibility-systems-469169">visibility supply chain</a> treats content like an industrial product that passes through strict organizational &#8220;gates&#8221; before it enters the digital ecosystem. Companies need to hire or elevate a strategic duo: the hacker (the technical architect) and the convincer (the cross-departmental visibility advocate).</p>



<p>This convergence of paid media and AI visibility is exactly where the convincer must step in.</p>



<p>If your paid media team is blindly buying YouTube sponsorships based entirely on demographic reach, or your product marketing team is buying G2 reviews just to hit a quarterly quota, they might be actively damaging your LLM visibility without knowing it.</p>



<p>Why? Because LLMs require information density and semantic alignment.</p>



<p>If a user writes a rushed, generic review (&#8220;Great tool, highly recommend!&#8221;) just to get their $250 voucher, it passes the human layer but fails the machine layer. To a RAG system, that sentence is low-density noise.</p>



<p>The Convincer&#8217;s job is to realign the review strategy and bring everyone internally on board with how every initiative can build LLM visibility. </p>



<p>For example, incentivize users to write detailed, context-rich problem/solution statements (&#8220;We used Brand X to solve our cross-border compliance issues in Europe&#8230;&#8221;). That gives AI the exact entity-relationship mapping it needs to recommend your brand for cross-border compliance.</p>



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			<p class="inline-form-text text-center mb-0">Get the newsletter search marketers rely on.</p>
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<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h2 class="wp-block-heading" id="h-the-new-marketing-playbook-optimizing-dataset-partnerships">The new marketing playbook: Optimizing dataset partnerships</h2>



<p>If you want your brand to be recommended by AI systems, you have to look at where the major AI players are getting their data.</p>



<p>We know OpenAI and Google have struck multimillion-dollar deals to train on Reddit&#8217;s real-time firehose. We know Grok trains on X. We know Apple and others are licensing major journalistic archives.</p>



<p>Your target audience research is no longer just about finding where your customers hang out. It&#8217;s about dataset matching.</p>



<p>If you&#8217;re planning an influencer campaign, a digital PR push, or a community-building initiative, ask yourself: Is this content entering a data pipeline that the primary LLMs trust and crawl in real time?</p>



<h2 class="wp-block-heading" id="h-stop-optimizing-pages-start-optimizing-budgets">Stop optimizing pages. Start optimizing budgets.</h2>



<p>Isolating SEO to a single technical department or a content blog no longer reflects how AI visibility is built.</p>



<p>The next time you sit in a budget allocation meeting and see a line item for influencer marketing, podcast sponsorships, or third-party review incentives, don&#8217;t let the team treat it as temporary media buying.</p>



<p>Reframe it. You&#8217;re building the digital infrastructure of your brand&#8217;s AI persona. You&#8217;re buying the AI equivalent of backlinks. If you aren&#8217;t intentionally structuring those paid assets to feed the visibility system, you&#8217;re leaving your brand&#8217;s future visibility up to chance.</p>

The lines between paid media, PR, and SEO are officially gone. 
Investing in baked-in YouTube sponsorships, native UGC, and third-party review incentives is the modern equivalent of buying a high-DA backlink. When you fund these channels, you’re investing in information sources that shape how AI systems understand and recommend your brand.
A recent social media screenshot brought that shift into focus. A B2B brand was offering a $250 Amazon voucher to anyone who wrote a review on G2.
To a growth marketer, that’s a standard user acquisition tactic. As an SEO, I saw something different: a direct investment in the semantic infrastructure AI systems use to evaluate brands.
The evolution of the authority signal
To understand why a $250 G2 voucher or a paid YouTube sponsorship is an SEO strategy, we have to look at how LLMs define authority.
Authority used to be transactional and mathematical. You built or bought hyperlinks.
When I moved from link building into digital PR and influencer marketing, I realized Google was getting smarter. We needed unlinked brand mentions, high-tier media coverage, and contextual relevance. We were optimizing for Google’s Knowledge Graph.
Today, retrieval-augmented generation (RAG) systems and LLMs don’t just count links or parse knowledge graphs. They look for semantic consensus across the web.




When an AI engine like Perplexity or ChatGPT answers a user query, it crawls the data ecosystems it trusts most for that specific topic. For software, that’s G2 and Reddit. For consumer products, it’s TikTok transcripts, YouTube, and forums.
When you pay $250 for a G2 review, you’re buying a dense, text-based data point that an LLM can use to determine your brand’s sentiment, use cases, and vector positioning. You’re strengthening the signals AI systems use to recommend your brand.
AI recommends.
      
See your AI visibility
    
The permanent ad: Why sponsorships and UGC are the new organic infrastructure
This reality shatters the traditional “church and state” separation of paid media and SEO.
Historically, paid ads were temporary. You turned off the budget, the traffic stopped, and SEO was left to clean up the long-term mess. If you run a dynamic programmatic ad on YouTube or a banner ad on a website, that model still holds true. LLM web scrapers ignore dynamic ad placements entirely.
Here’s what happens when you invest in baked-in influencer sponsorships, native user-generated content (UGC), or podcast reads:
The hardcoded transcript: When a YouTuber reads a native sponsor segment (“I use Brand X to manage my business taxes…”), that segment is baked into the video file. YouTube automatically transcribes it.
LLM ingestion: When an LLM crawls the web or a multimodal AI watches the video, those spoken words are indexed. The AI associates your brand with the semantic concept of business taxes.
The half-life of paid media: Your paid media spend suddenly delivers a lasting return. Long after the campaign ends and the initial views dry up, the transcript remains part of the information an LLM can access.
As someone who spent years bridging the gap between digital PR and SEO, I used to judge a campaign’s ROI based on immediate referral traffic, brand search lift, and backlink quality. Today, we have to calculate the algorithmic half-life of our creative assets.
Activating the convincer: Bringing paid and PR into the visibility supply chain
The visibility supply chain treats content like an industrial product that passes through strict organizational “gates” before it enters the digital ecosystem. Companies need to hire or elevate a strategic duo: the hacker (the technical architect) and the convincer (the cross-departmental visibility advocate).
This convergence of paid media and AI visibility is exactly where the convincer must step in.
If your paid media team is blindly buying YouTube sponsorships based entirely on demographic reach, or your product marketing team is buying G2 reviews just to hit a quarterly quota, they might be actively damaging your LLM visibility without knowing it.
Why? Because LLMs require information density and semantic alignment.
If a user writes a rushed, generic review (“Great tool, highly recommend!”) just to get their $250 voucher, it passes the human layer but fails the machine layer. To a RAG system, that sentence is low-density noise.
The Convincer’s job is to realign the review strategy and bring everyone internally on board with how every initiative can build LLM visibility. 
For example, incentivize users to write detailed, context-rich problem/solution statements (“We used Brand X to solve our cross-border compliance issues in Europe…”). That gives AI the exact entity-relationship mapping it needs to recommend your brand for cross-border compliance.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
The new marketing playbook: Optimizing dataset partnerships
If you want your brand to be recommended by AI systems, you have to look at where the major AI players are getting their data.
We know OpenAI and Google have struck multimillion-dollar deals to train on Reddit’s real-time firehose. We know Grok trains on X. We know Apple and others are licensing major journalistic archives.
Your target audience research is no longer just about finding where your customers hang out. It’s about dataset matching.
If you’re planning an influencer campaign, a digital PR push, or a community-building initiative, ask yourself: Is this content entering a data pipeline that the primary LLMs trust and crawl in real time?
Stop optimizing pages. Start optimizing budgets.
Isolating SEO to a single technical department or a content blog no longer reflects how AI visibility is built.
The next time you sit in a budget allocation meeting and see a line item for influencer marketing, podcast sponsorships, or third-party review incentives, don’t let the team treat it as temporary media buying.
Reframe it. You’re building the digital infrastructure of your brand’s AI persona. You’re buying the AI equivalent of backlinks. If you aren’t intentionally structuring those paid assets to feed the visibility system, you’re leaving your brand’s future visibility up to chance.

Reviews, creator partnerships, and sponsored content can shape how AI systems understand and recommend your brand. Learn why.

Paid media is becoming an SEO investment in AI search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Traffic Think Tank will continue operating as a private Slack community while expanding member benefits through Search Engine Land.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/ttt-sel-og-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><strong>[Boston, MA, July 6, 2026]</strong> — Search Engine Land today announced that Traffic Think Tank has officially joined the Search Engine Land family, expanding opportunities for search marketers to connect, collaborate, and learn through one of the industry&#8217;s most established professional communities.</p>



<p>Traffic Think Tank will continue operating as a private Slack community, providing members with a trusted space to exchange ideas, validate strategies, solve challenges, and stay current on search engine optimization, paid media, artificial intelligence, and related marketing topics.</p>



<p>As part of the relationship, Search Engine Land will support the community&#8217;s continued growth by increasing visibility across its editorial and marketing channels while preserving the collaborative environment members value.</p>



<p>“For years, Search Engine Land has represented the marketing community through its contributor network in a way few other sites have,” said Kyle Morley, Head of Sales and Marketing at Third Door Media, parent to Search Engine Land. “Launching a community like Traffic Think Tank feels like a natural extension of our identity, and I&#8217;m thrilled we now have more opportunity to connect with marketers in our space.”</p>



<p>David Broderick has been appointed Lead Community Manager and will oversee the day-to-day community experience. He will be supported by Liz Dougherty, who will play an active role in fostering member engagement and helping guide the community&#8217;s continued growth.</p>



<p>In addition to ongoing peer-to-peer discussions, members can expect:</p>



<ul class="wp-block-list">
<li>Expanded community programming and discussions</li>



<li>Increased visibility through Search Engine Land and Third Door Media channels</li>



<li>Exclusive discounts on Search Marketing Expo events and training</li>



<li>New opportunities to connect with search marketers across the industry</li>
</ul>



<p>Traffic Think Tank complements Search Engine Land&#8217;s mission of helping marketers stay informed and succeed in a rapidly evolving search landscape. Together, the publication and community provide marketers with trusted journalism, practical education, live events, and an active peer network for ongoing professional development.</p>



<p>Search marketers interested in joining the community can learn more at <a href="https://searchengineland.com/trafficthinktank">https://searchengineland.com/trafficthinktank</a>.&nbsp;</p>



<hr class="wp-block-separator has-alpha-channel-opacity"/>



<p><strong>About Search Engine Land</strong>



<p>Search Engine Land is the leading publication for news, insights, and education covering search engine optimization, paid media, artificial intelligence, and digital marketing. Through editorial coverage, events, training, and professional resources, Search Engine Land helps marketers stay ahead of industry change.</p>



<p><strong>About Traffic Think Tank</strong>



<p>Traffic Think Tank is a private community for search marketers that connects professionals through expert discussions, peer collaboration, and practical knowledge sharing. Members use the community to exchange ideas, solve challenges, validate strategies, and stay current on what&#8217;s working across search engine optimization, paid media, and artificial intelligence.</p>


[Boston, MA, July 6, 2026] — Search Engine Land today announced that Traffic Think Tank has officially joined the Search Engine Land family, expanding opportunities for search marketers to connect, collaborate, and learn through one of the industry’s most established professional communities.
Traffic Think Tank will continue operating as a private Slack community, providing members with a trusted space to exchange ideas, validate strategies, solve challenges, and stay current on search engine optimization, paid media, artificial intelligence, and related marketing topics.
As part of the relationship, Search Engine Land will support the community’s continued growth by increasing visibility across its editorial and marketing channels while preserving the collaborative environment members value.
“For years, Search Engine Land has represented the marketing community through its contributor network in a way few other sites have,” said Kyle Morley, Head of Sales and Marketing at Third Door Media, parent to Search Engine Land. “Launching a community like Traffic Think Tank feels like a natural extension of our identity, and I’m thrilled we now have more opportunity to connect with marketers in our space.”
David Broderick has been appointed Lead Community Manager and will oversee the day-to-day community experience. He will be supported by Liz Dougherty, who will play an active role in fostering member engagement and helping guide the community’s continued growth.
In addition to ongoing peer-to-peer discussions, members can expect:
Expanded community programming and discussions
Increased visibility through Search Engine Land and Third Door Media channels
Exclusive discounts on Search Marketing Expo events and training
New opportunities to connect with search marketers across the industry
Traffic Think Tank complements Search Engine Land’s mission of helping marketers stay informed and succeed in a rapidly evolving search landscape. Together, the publication and community provide marketers with trusted journalism, practical education, live events, and an active peer network for ongoing professional development.
Search marketers interested in joining the community can learn more at https://searchengineland.com/trafficthinktank. 
About Search Engine Land



Search Engine Land is the leading publication for news, insights, and education covering search engine optimization, paid media, artificial intelligence, and digital marketing. Through editorial coverage, events, training, and professional resources, Search Engine Land helps marketers stay ahead of industry change.
About Traffic Think Tank



Traffic Think Tank is a private community for search marketers that connects professionals through expert discussions, peer collaboration, and practical knowledge sharing. Members use the community to exchange ideas, solve challenges, validate strategies, and stay current on what’s working across search engine optimization, paid media, and artificial intelligence.

Traffic Think Tank will continue operating as a private Slack community while expanding member benefits through Search Engine Land.

Search Engine Land welcomes Traffic Think Tank to its community portfolio

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant.png" class="attachment-large size-large wp-post-image" alt="Why Meta AI could become search&#039;s sleeping giant" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Meta AI is bringing AI-powered search to Facebook, Instagram, and WhatsApp. Learn why its distribution advantage matters.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant.png" class="attachment-large size-large wp-post-image" alt="Why Meta AI could become search&#039;s sleeping giant" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-Meta-AI-could-become-searchs-sleeping-giant-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Not enough people are treating Meta AI as a serious AI search contender. </p>



<p>The <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> crowd talks about Google AI Mode, ChatGPT, Claude, Gemini, Perplexity, and RAG, and sure, those things matter. But Meta AI already has something most AI companies would give anything to replicate: scale.</p>



<p>By May 2025, Meta AI had reached one billion monthly active users across Meta&#8217;s apps, according to Mark Zuckerberg.</p>



<p>Zuckerberg has been clear about the direction: Meta AI as a leading personal AI, built around personalization, voice conversations, and entertainment, with monetization through paid recommendations or subscriptions already on the table.</p>



<p>Here&#8217;s why Meta AI is becoming a serious AI search contender.</p>



<h2 class="wp-block-heading" id="h-meta-s-advantage-is-distribution">Meta&#8217;s advantage is distribution</h2>



<p>The AI search debate is too preoccupied with model quality and channel ownership. Which tool is smarter? Which answer engine cites better? Is it just SEO?</p>



<p>Distribution matters more than the search industry is comfortable admitting.</p>



<p>Meta reported <a href="https://investor.atmeta.com/investor-news/press-release-details/2026/Meta-Reports-First-Quarter-2026-Results/default.aspx" type="link" id="https://investor.atmeta.com/investor-news/press-release-details/2026/Meta-Reports-First-Quarter-2026-Results/default.aspx" target="_blank" rel="noopener">3.56 billion family daily active people</a> across its apps in March. In that quarter, revenue came in at $56.31 billion, up 33% year over year.</p>



<p>WhatsApp passed <a href="https://techcrunch.com/2025/05/01/whatsapp-now-has-more-than-3-billion-users/" target="_blank" rel="noopener">3 billion monthly users</a> in 2025. Instagram hit 3 billion monthly active users in September 2025. Threads reached 500 million monthly active users in June.</p>



<p>Facebook isn&#8217;t cool. The metaverse stumbled. Threads still feels like a corporate answer to Elon Musk running or ruining the artist formerly known as Twitter.&nbsp;</p>



<p>That doesn&#8217;t change what matters. Meta can put AI inside the apps where people already spend their time and, in the process, bring the power of search to where discovery happens.</p>



<p>In doing so, it might push public AI usage and adoption faster than anyone else.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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      <div id="semrush-one-headline" class="headline-responsive">
        Be the brand <span>AI recommends</span>.
      </div>
      <p id="semrush-one-subhead">
        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>




<h2 class="wp-block-heading" id="h-the-first-search-is-the-search-that-matters">The first search is the search that matters</h2>



<p>Google&#8217;s historic power has always rested on something simple: When people wanted to know, compare, buy, find something local, or settle an argument, they started with Google. That starting point became the most valuable real estate on the internet.</p>



<p>AI search changes where that starting point is. Someone sees a product on Instagram. They don&#8217;t need to leave the app and search Google. They can ask Meta AI whether it&#8217;s any good, what the alternatives are, whether the brand can be trusted, or where they can buy it.</p>



<p>A WhatsApp group planning a weekend away doesn&#8217;t need to switch to Google to compare hotels, restaurants, venues, or train times. Meta AI will sit inside that conversation at the actual moment of intent.</p>



<p>Someone on Facebook scrolling through a local recommendation thread can ask Meta AI to summarize what people are saying in Groups, Reels, and public posts.</p>



<p>That&#8217;s not traditional SEO. That&#8217;s search behavior being absorbed into social platforms.</p>



<p>The strategic question is no longer just &#8220;Who ranks?&#8221; It&#8217;s &#8220;Where does the question begin?&#8221;</p>



<h2 class="wp-block-heading" id="h-meta-ai-is-more-than-just-another-chatbot">Meta AI is more than just another chatbot</h2>



<p>Search marketers tend to approach AI through a narrow lens. They find the chatbot, test a few brand queries, check what gets cited, and decide they&#8217;ve understood the platform.</p>



<p>That&#8217;s a mistake.</p>



<p>Meta AI is becoming a layer across feeds, chats, search, content creation, recommendations, smart glasses, and social discovery. According to Meta, it&#8217;s available across Facebook, Instagram, WhatsApp, and Messenger, including in feeds, chats, and search, giving users real-time information without leaving the app. Use cases include restaurant recommendations, travel planning, study help, and shopping inspiration.</p>



<p>The standalone Meta AI app, launched in 2025, was designed around a more personal AI. Meta says it can draw on information people have chosen to share across Meta products, profile data, and the content they engage with to deliver more relevant answers in supported markets.</p>



<p>You can see where this is heading. Meta AI becomes everyone&#8217;s free AI tool.</p>



<h2 class="wp-block-heading" id="h-how-meta-ai-could-become-consumer-ai">How Meta AI could become consumer AI</h2>



<p>ChatGPT and Claude feel like tools for work. Excellent ones, but tools you deliberately open because you&#8217;ve decided to do something.</p>



<p>Meta AI feels more like consumer AI. Messier. More visual. More embedded. Less like launching a productivity suite and more like stumbling into an answer while doing what you were already doing.</p>



<p>Opening ChatGPT still feels like a deliberate act if you&#8217;re not a tech person. Asking something inside WhatsApp or Instagram feels like nothing at all.</p>



<p>Meta doesn&#8217;t have to convince people to adopt AI. It can smuggle AI into existing behavior. And this is where it gets weird.</p>



<p>Meta AI is a plaything for people to try, and Meta gets to see how everyone uses it.</p>



<p>They see a 65-year-old grandma using it to animate photos and share them with her family WhatsApp group.</p>



<p>You&#8217;ll see a dog groomer using it to create mini videos of clients&#8217; dogs and share them on Instagram.</p>



<p>When AI goes mainstream and becomes easy to use, people will use it where they can reach other people.</p>



<p>In the process, Meta becomes better. More fun, more features, more usable.</p>



<h2 class="wp-block-heading" id="h-ai-becomes-social-visual-and-shoppable">AI becomes social, visual, and shoppable</h2>



<p>Then there&#8217;s Meta AI Studio.</p>



<p>Users can create AI characters built around their interests, work from templates or start from scratch, and put together assistants for advice, captions, entertainment, and creator interactions.</p>



<p>Then there&#8217;s Vibes. In September 2025, Meta introduced Vibes as a feed inside the Meta AI app and on Meta AI, where users can create, remix, and share short-form AI-generated videos, then distribute them through DMs, Instagram, and Facebook Stories and Reels.</p>



<p>Yes, parts of it feel genuinely strange. Generative AI video on social platforms is an odd mixture of creativity, slop, novelty, and nonsense. But early weirdness isn&#8217;t the same as strategic irrelevance.</p>



<p>AI was never going to land as one perfect super-app that everyone understood immediately. Meta is putting new formats into users&#8217; hands, watching what happens, and reshaping the product around that behavior.</p>



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<h2 class="wp-block-heading" id="h-the-ad-machine-makes-this-a-google-problem">The ad machine makes this a Google problem</h2>



<p>Forecasts suggest Meta will hit $243.46 billion in net worldwide ad revenue in 2026, putting it ahead of Google at $239.54 billion. The same forecast has Meta capturing 26.8% of worldwide digital ad spend, compared with Google&#8217;s 26.4%.</p>



<p>Those numbers should concentrate a few minds at Google.</p>



<p>If AI answers are monetized through paid recommendations, sponsored answers, shopping suggestions, or conversational ad units, the commercial value accumulates with whichever platform owns the query, not necessarily the best model.</p>



<p>Meta has the audience, the ad graph, creator relationships, commerce signals, and behavioral data built from years of social, messaging, and content engagement. It can promote Meta AI inside its own products to billions of existing users.</p>



<p>Google has search intent, which is still enormously powerful. But Meta has attention, habit, and context. Google is where people go when they&#8217;ve decided to search. Meta is where people already are.</p>



<h2 class="wp-block-heading" id="h-this-is-where-it-s-just-seo-gets-silly">This is where ‘it’s just SEO’ gets silly</h2>



<p>The AI optimization debate keeps collapsing into the same comforting line: it&#8217;s just SEO.</p>



<p>Sometimes, sure. Technical hygiene, crawlable content, authoritative pages, clear entities, strong brand signals, helpful content, and consistent information still matter.</p>



<p>But try answering a harder question: How exactly do you optimize for Meta AI?</p>



<p><a href="https://searchengineland.com/meta-ai-mode-facebook-search-480393">Facebook AI Mode</a> makes the difficulty obvious. In June, Meta introduced AI Mode as a Facebook search tab that uses Meta AI to surface answers rooted in public culture, opinions, and recommendations shared across Meta&#8217;s apps, rather than a list of links. It draws on what people are posting publicly in Groups and Reels to provide perspectives rather than results.</p>



<p>That&#8217;s a fundamentally different environment. If Meta AI pulls from public posts, Groups, Reels, creator content, user engagement, web information, social recommendations, product content, and eventually paid data, the standard SEO playbook isn&#8217;t enough.</p>



<p>Your website may still matter. Your public social content may matter, too. Your creator strategy may matter. Your product feed may matter. Your reviews may matter. You get the picture. It&#8217;s getting messy out there.</p>



<p>Nobody can say with any real confidence how it all works yet. Anyone who claims otherwise is selling you a dashboard and a dream.</p>



<p>The honest answer is irritating. We don&#8217;t know enough yet. That&#8217;s not an excuse to ignore it.</p>



<h2 class="wp-block-heading" id="h-google-is-being-attacked-from-every-angle">Google is being attacked from every angle</h2>



<p>Google is still Google. Let&#8217;s not be idiots about it. It remains central to search, commerce, publishing, advertising, and the open web.</p>



<p>But it&#8217;s being pushed from every direction at once: ChatGPT on answers, Perplexity on research, Amazon on product search, TikTok and Instagram on discovery, regulators on market power, publishers on AI content extraction, and Meta on attention, ads, and AI-assisted discovery.</p>



<p>In the UK, the Competition and Markets Authority imposed new conduct requirements on Google Search in June. Publishers will be able to opt out of having their content used to power AI features in Google Search, including AI Overviews. Google is also required to properly attribute publisher content with clear links in AI-generated results.</p>



<p>This is important because AI search isn&#8217;t just a product feature. It changes the exchange of value between users, publishers, platforms, and advertisers. While Google works through that problem, Meta is quietly building AI into social behavior.</p>



<h2 class="wp-block-heading" id="h-what-should-brands-and-seos-do-now">What should brands and SEOs do now?</h2>



<p>Don&#8217;t panic. Panic is rarely a strategy, even if it shows up in most marketing meetings. But do start testing.</p>



<p>Run brand, category, product, local, and comparison queries in Meta AI. Test Facebook, Instagram, WhatsApp, and the standalone app where you can, then compare what you get with Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude.</p>



<p>Track whether your brand appears, whether answers cite or link to you, and whether public Meta content appears to shape responses. Look at Facebook Groups, Reels, creator posts, Instagram content, product mentions, and recommendation language. If discovery moves into Meta&#8217;s AI layer, ask yourself what your brand actually needs to be visible there.</p>



<p>That might mean stronger public social content, clearer product information across Meta surfaces, creator partnerships, community management, entity consistency, or paid social becoming part of how you test AI visibility. Or it might mean none of those things yet. </p>



<p>Either way, you&#8217;ll have data, which is a more useful position than chanting &#8220;it&#8217;s just SEO&#8221; while the market reorganizes itself under your feet.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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<h2 class="wp-block-heading" id="h-the-sleeping-giant">The sleeping giant</h2>



<p>Meta AI might not beat Google at Google&#8217;s own version of search. It doesn&#8217;t need to.</p>



<p>It needs to:</p>



<ul class="wp-block-list">
<li>Absorb enough search behavior into the places where people already spend their time.&nbsp;</li>



<li>Become the casual AI layer for people who&#8217;ll never deliberately open ChatGPT.&nbsp;</li>



<li>Make product discovery, recommendations, local advice, content creation, and shopping assistance feel native inside social apps.</li>
</ul>



<p>That&#8217;s a serious threat. Meta AI is clunky right now, but so was most of the web in its early days.</p>



<p>The search industry should stop asking whether Meta AI looks like search. The better question is whether users care.</p>



<p>If people start asking Meta before they ask Google, the game changes. That&#8217;s how sleeping giants wake up.</p>

Not enough people are treating Meta AI as a serious AI search contender. 
The SEO crowd talks about Google AI Mode, ChatGPT, Claude, Gemini, Perplexity, and RAG, and sure, those things matter. But Meta AI already has something most AI companies would give anything to replicate: scale.
By May 2025, Meta AI had reached one billion monthly active users across Meta’s apps, according to Mark Zuckerberg.
Zuckerberg has been clear about the direction: Meta AI as a leading personal AI, built around personalization, voice conversations, and entertainment, with monetization through paid recommendations or subscriptions already on the table.
Here’s why Meta AI is becoming a serious AI search contender.
Meta’s advantage is distribution
The AI search debate is too preoccupied with model quality and channel ownership. Which tool is smarter? Which answer engine cites better? Is it just SEO?
Distribution matters more than the search industry is comfortable admitting.
Meta reported 3.56 billion family daily active people across its apps in March. In that quarter, revenue came in at $56.31 billion, up 33% year over year.
WhatsApp passed 3 billion monthly users in 2025. Instagram hit 3 billion monthly active users in September 2025. Threads reached 500 million monthly active users in June.
Facebook isn’t cool. The metaverse stumbled. Threads still feels like a corporate answer to Elon Musk running or ruining the artist formerly known as Twitter. 
That doesn’t change what matters. Meta can put AI inside the apps where people already spend their time and, in the process, bring the power of search to where discovery happens.
In doing so, it might push public AI usage and adoption faster than anyone else.
AI recommends.
      
See your AI visibility
    
The first search is the search that matters
Google’s historic power has always rested on something simple: When people wanted to know, compare, buy, find something local, or settle an argument, they started with Google. That starting point became the most valuable real estate on the internet.
AI search changes where that starting point is. Someone sees a product on Instagram. They don’t need to leave the app and search Google. They can ask Meta AI whether it’s any good, what the alternatives are, whether the brand can be trusted, or where they can buy it.
A WhatsApp group planning a weekend away doesn’t need to switch to Google to compare hotels, restaurants, venues, or train times. Meta AI will sit inside that conversation at the actual moment of intent.
Someone on Facebook scrolling through a local recommendation thread can ask Meta AI to summarize what people are saying in Groups, Reels, and public posts.
That’s not traditional SEO. That’s search behavior being absorbed into social platforms.
The strategic question is no longer just “Who ranks?” It’s “Where does the question begin?”
Meta AI is more than just another chatbot
Search marketers tend to approach AI through a narrow lens. They find the chatbot, test a few brand queries, check what gets cited, and decide they’ve understood the platform.
That’s a mistake.
Meta AI is becoming a layer across feeds, chats, search, content creation, recommendations, smart glasses, and social discovery. According to Meta, it’s available across Facebook, Instagram, WhatsApp, and Messenger, including in feeds, chats, and search, giving users real-time information without leaving the app. Use cases include restaurant recommendations, travel planning, study help, and shopping inspiration.
The standalone Meta AI app, launched in 2025, was designed around a more personal AI. Meta says it can draw on information people have chosen to share across Meta products, profile data, and the content they engage with to deliver more relevant answers in supported markets.
You can see where this is heading. Meta AI becomes everyone’s free AI tool.
How Meta AI could become consumer AI
ChatGPT and Claude feel like tools for work. Excellent ones, but tools you deliberately open because you’ve decided to do something.
Meta AI feels more like consumer AI. Messier. More visual. More embedded. Less like launching a productivity suite and more like stumbling into an answer while doing what you were already doing.
Opening ChatGPT still feels like a deliberate act if you’re not a tech person. Asking something inside WhatsApp or Instagram feels like nothing at all.
Meta doesn’t have to convince people to adopt AI. It can smuggle AI into existing behavior. And this is where it gets weird.
Meta AI is a plaything for people to try, and Meta gets to see how everyone uses it.
They see a 65-year-old grandma using it to animate photos and share them with her family WhatsApp group.
You’ll see a dog groomer using it to create mini videos of clients’ dogs and share them on Instagram.
When AI goes mainstream and becomes easy to use, people will use it where they can reach other people.
In the process, Meta becomes better. More fun, more features, more usable.
AI becomes social, visual, and shoppable
Then there’s Meta AI Studio.
Users can create AI characters built around their interests, work from templates or start from scratch, and put together assistants for advice, captions, entertainment, and creator interactions.
Then there’s Vibes. In September 2025, Meta introduced Vibes as a feed inside the Meta AI app and on Meta AI, where users can create, remix, and share short-form AI-generated videos, then distribute them through DMs, Instagram, and Facebook Stories and Reels.
Yes, parts of it feel genuinely strange. Generative AI video on social platforms is an odd mixture of creativity, slop, novelty, and nonsense. But early weirdness isn’t the same as strategic irrelevance.
AI was never going to land as one perfect super-app that everyone understood immediately. Meta is putting new formats into users’ hands, watching what happens, and reshaping the product around that behavior.
Get the newsletter search marketers rely on.
 
				
				
			
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The ad machine makes this a Google problem
Forecasts suggest Meta will hit $243.46 billion in net worldwide ad revenue in 2026, putting it ahead of Google at $239.54 billion. The same forecast has Meta capturing 26.8% of worldwide digital ad spend, compared with Google’s 26.4%.
Those numbers should concentrate a few minds at Google.
If AI answers are monetized through paid recommendations, sponsored answers, shopping suggestions, or conversational ad units, the commercial value accumulates with whichever platform owns the query, not necessarily the best model.
Meta has the audience, the ad graph, creator relationships, commerce signals, and behavioral data built from years of social, messaging, and content engagement. It can promote Meta AI inside its own products to billions of existing users.
Google has search intent, which is still enormously powerful. But Meta has attention, habit, and context. Google is where people go when they’ve decided to search. Meta is where people already are.
This is where ‘it’s just SEO’ gets silly
The AI optimization debate keeps collapsing into the same comforting line: it’s just SEO.
Sometimes, sure. Technical hygiene, crawlable content, authoritative pages, clear entities, strong brand signals, helpful content, and consistent information still matter.
But try answering a harder question: How exactly do you optimize for Meta AI?
Facebook AI Mode makes the difficulty obvious. In June, Meta introduced AI Mode as a Facebook search tab that uses Meta AI to surface answers rooted in public culture, opinions, and recommendations shared across Meta’s apps, rather than a list of links. It draws on what people are posting publicly in Groups and Reels to provide perspectives rather than results.
That’s a fundamentally different environment. If Meta AI pulls from public posts, Groups, Reels, creator content, user engagement, web information, social recommendations, product content, and eventually paid data, the standard SEO playbook isn’t enough.
Your website may still matter. Your public social content may matter, too. Your creator strategy may matter. Your product feed may matter. Your reviews may matter. You get the picture. It’s getting messy out there.
Nobody can say with any real confidence how it all works yet. Anyone who claims otherwise is selling you a dashboard and a dream.
The honest answer is irritating. We don’t know enough yet. That’s not an excuse to ignore it.
Google is being attacked from every angle
Google is still Google. Let’s not be idiots about it. It remains central to search, commerce, publishing, advertising, and the open web.
But it’s being pushed from every direction at once: ChatGPT on answers, Perplexity on research, Amazon on product search, TikTok and Instagram on discovery, regulators on market power, publishers on AI content extraction, and Meta on attention, ads, and AI-assisted discovery.
In the UK, the Competition and Markets Authority imposed new conduct requirements on Google Search in June. Publishers will be able to opt out of having their content used to power AI features in Google Search, including AI Overviews. Google is also required to properly attribute publisher content with clear links in AI-generated results.
This is important because AI search isn’t just a product feature. It changes the exchange of value between users, publishers, platforms, and advertisers. While Google works through that problem, Meta is quietly building AI into social behavior.
What should brands and SEOs do now?
Don’t panic. Panic is rarely a strategy, even if it shows up in most marketing meetings. But do start testing.
Run brand, category, product, local, and comparison queries in Meta AI. Test Facebook, Instagram, WhatsApp, and the standalone app where you can, then compare what you get with Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude.
Track whether your brand appears, whether answers cite or link to you, and whether public Meta content appears to shape responses. Look at Facebook Groups, Reels, creator posts, Instagram content, product mentions, and recommendation language. If discovery moves into Meta’s AI layer, ask yourself what your brand actually needs to be visible there.
That might mean stronger public social content, clearer product information across Meta surfaces, creator partnerships, community management, entity consistency, or paid social becoming part of how you test AI visibility. Or it might mean none of those things yet. 
Either way, you’ll have data, which is a more useful position than chanting “it’s just SEO” while the market reorganizes itself under your feet.
customers won’t either.
      
See your AI visibility
    
The sleeping giant
Meta AI might not beat Google at Google’s own version of search. It doesn’t need to.
It needs to:
Absorb enough search behavior into the places where people already spend their time. 
Become the casual AI layer for people who’ll never deliberately open ChatGPT. 
Make product discovery, recommendations, local advice, content creation, and shopping assistance feel native inside social apps.
That’s a serious threat. Meta AI is clunky right now, but so was most of the web in its early days.
The search industry should stop asking whether Meta AI looks like search. The better question is whether users care.
If people start asking Meta before they ask Google, the game changes. That’s how sleeping giants wake up.

Meta AI is bringing AI-powered search to Facebook, Instagram, and WhatsApp. Learn why its distribution advantage matters.

Why Meta AI could become search’s sleeping giant

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google has confirmed it is investigating the reports of bugs with reviews in Google Business Profiles after several days of complaints from businesses. 

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/11/broken-google-review-stars-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>After several days of complaints from businesses, Google has confirmed it is investigating reports of missing reviews from Google Local listings and has paused the ability of Google Business Profile listings to receive reviews.</p>



<p><strong>What Google said. </strong>A Google spokesperson told us:</p>



<ul class="wp-block-list">
<li>&#8220;When our systems detect suspicious reviews, we take a range of actions including removing reviews and temporarily pausing reviews on the profile to prevent further abuse. We are investigating the issue and will restore any reviews that were incorrectly removed.&#8221;</li>
</ul>



<p><strong>What is the issue. </strong>As I documented on the <a href="https://www.seroundtable.com/google-business-profile-reviews-vanish-41620.html">Search Engine Roundtable</a>, there are dozens of complaints in the&nbsp;<a href="https://support.google.com/business/community?hl=en">Google Business Profile Forums</a> with business owners and local SEOs saying reviews have mysteriously disappeared. In addition to reviews going missing, many of these businesses are not even able to get new reviews on their local listings. </p>



<p>It seems Google&#8217;s review spam detection algorithms are picking up on patterns and aggressively removing and blocking reviews on suspected Google Business Profiles. It is unclear if this is caused by spammers abusing some Business Profiles or Google&#8217;s algorithms were tweaked and being too trigger happy.</p>



<p><strong>More details. </strong>Amy Toman, a volunteer Google Product Expert in the Google Business Profiles, posted on&nbsp;<a href="https://www.linkedin.com/posts/amy-toman-%F0%9F%97%BA%EF%B8%8F-25945047_there-is-a-new-issue-im-seeing-on-google-activity-7478530628053815296-SHM7/">LinkedIn</a>:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>If you or a client are in this situation, you can post on the forum if you&#8217;d like, but know that Google is aware and working on it. No timeline for resolution stated yet.</p>



<p>There is a new issue I&#8217;m seeing on Google listings, where after reporting fake or spam reviews, listings are receiving a review block and all reviews are being hidden. And in at least one case, their rating is reduced to 0.</p>



<p>Google has been alerted and they now say they&#8217;re aware of the missing review issue and are working to resolve it.</p>
</blockquote>



<p><strong>Why we care. </strong>If you noticed that many of your reviews went missing and/or you are not getting any new reviews this week, this might be related this issue.</p>



<p>Google is investigating the issue and hopefully we will see many, if not all, of these reviews come back and new reviews resume.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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      <div id="semrush-one-headline" class="headline-responsive">
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      </p>
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      <span id="semrush-one-cta">See your AI visibility</span>
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After several days of complaints from businesses, Google has confirmed it is investigating reports of missing reviews from Google Local listings and has paused the ability of Google Business Profile listings to receive reviews.
What Google said. A Google spokesperson told us:
“When our systems detect suspicious reviews, we take a range of actions including removing reviews and temporarily pausing reviews on the profile to prevent further abuse. We are investigating the issue and will restore any reviews that were incorrectly removed.”
What is the issue. As I documented on the Search Engine Roundtable, there are dozens of complaints in the Google Business Profile Forums with business owners and local SEOs saying reviews have mysteriously disappeared. In addition to reviews going missing, many of these businesses are not even able to get new reviews on their local listings. 
It seems Google’s review spam detection algorithms are picking up on patterns and aggressively removing and blocking reviews on suspected Google Business Profiles. It is unclear if this is caused by spammers abusing some Business Profiles or Google’s algorithms were tweaked and being too trigger happy.
More details. Amy Toman, a volunteer Google Product Expert in the Google Business Profiles, posted on LinkedIn:
If you or a client are in this situation, you can post on the forum if you’d like, but know that Google is aware and working on it. No timeline for resolution stated yet.
There is a new issue I’m seeing on Google listings, where after reporting fake or spam reviews, listings are receiving a review block and all reviews are being hidden. And in at least one case, their rating is reduced to 0.
Google has been alerted and they now say they’re aware of the missing review issue and are working to resolve it.
Why we care. If you noticed that many of your reviews went missing and/or you are not getting any new reviews this week, this might be related this issue.
Google is investigating the issue and hopefully we will see many, if not all, of these reviews come back and new reviews resume.
customers won’t either.
      
See your AI visibility

Google has confirmed it is investigating the reports of bugs with reviews in Google Business Profiles after several days of complaints from businesses.

Google is investigating reports of reviews going missing and pausing reviews on local listings

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>After a long three-week delay, Google has finally updated the page indexing report with new data.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-pages-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The page indexing report within <a href="https://searchengineland.com/library/platforms/google/google-search-console">Google Search Console</a> has finally <a href="https://www.seroundtable.com/google-page-indexing-report-fixed-and-updated-41626.html">been updated</a> after a long <a href="https://searchengineland.com/page-indexing-report-in-google-search-console-delayed-481210">three-week delay</a>. Instead of it showing data from June 11, 2206, it now shows data from June 29, 2026 &#8211; note that it was just updated within the hour.</p>



<p><strong>The delay. </strong>As we documented previously, this is what the page indexing report was stuck at &#8211; June 11th:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1404" height="276" src="https://searchengineland.com/wp-content/seloads/2026/07/google-search-console-page-indexing-june11-u1oaVv40.png" alt="" class="wp-image-481612" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-search-console-page-indexing-june11-u1oaVv40.png 1404w, https://searchengineland.com/wp-content/seloads/2026/07/google-search-console-page-indexing-june11-u1oaVv40-768x151.png 768w" sizes="(max-width: 1404px) 100vw, 1404px" /></figure>



<p>Now, as of this morning, it is now showing data as recent as June 29th:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1552" height="374" src="https://searchengineland.com/wp-content/seloads/2026/07/google-page-indexing-june29-LVVcOdMA.png" alt="" class="wp-image-481613" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-page-indexing-june29-LVVcOdMA.png 1552w, https://searchengineland.com/wp-content/seloads/2026/07/google-page-indexing-june29-LVVcOdMA-768x185.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-page-indexing-june29-LVVcOdMA-1536x370.png 1536w" sizes="(max-width: 1552px) 100vw, 1552px" /></figure>



<p><strong>Page indexing report.&nbsp;</strong>This report shows you which pages Google can find and index on your site. It can also teach you about any indexing problems Google has encountered while crawling your website.</p>



<p>You can access the report in Search Console&nbsp;<a href="https://search.google.com/search-console/not-verified?original_url=/search-console/index?utm_medium%3Dreferral%26utm_campaign%3D7440203&amp;original_resource_id">over here</a>&nbsp;or by going to the Indexing section and then Pages.</p>



<p>The report shows a chart of indexed pages in green and not indexed pages in gray, you can even overlay impressions over the chart. It then lists out the reasons why pages on website are not being indexed, below that chart.</p>



<p>For more information on the page indexing report, see&nbsp;<a href="https://support.google.com/webmasters/answer/7440203?hl=en">this help document</a>.</p>



<p><strong>Why we care.</strong>&nbsp;If you are trying to debug why Google has not been indexing specific pages on your website over the past couple of weeks, well, you were out of luck. </p>



<p>Now, Friday, July 3rd, the data has finally been updated with data through June 29th.  So have at it and process the indexing report as you see best.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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The page indexing report within Google Search Console has finally been updated after a long three-week delay. Instead of it showing data from June 11, 2206, it now shows data from June 29, 2026 – note that it was just updated within the hour.
The delay. As we documented previously, this is what the page indexing report was stuck at – June 11th:




Now, as of this morning, it is now showing data as recent as June 29th:




Page indexing report. This report shows you which pages Google can find and index on your site. It can also teach you about any indexing problems Google has encountered while crawling your website.
You can access the report in Search Console over here or by going to the Indexing section and then Pages.
The report shows a chart of indexed pages in green and not indexed pages in gray, you can even overlay impressions over the chart. It then lists out the reasons why pages on website are not being indexed, below that chart.
For more information on the page indexing report, see this help document.
Why we care. If you are trying to debug why Google has not been indexing specific pages on your website over the past couple of weeks, well, you were out of luck. 
Now, Friday, July 3rd, the data has finally been updated with data through June 29th.  So have at it and process the indexing report as you see best.
AI recommends.
      
See your AI visibility

After a long three-week delay, Google has finally updated the page indexing report with new data.

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Read the Latest Trends and Headlines in Digital Marketing.

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