<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google is expanding PMax video optimization with generative AI that automatically creates missing aspect ratios from existing ads.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/PMax-video-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google Ads is rolling out an asset optimization upgrade for existing Performance Max video ads.</p>



<p class="wp-block-paragraph">The update uses generative AI to extend videos into missing aspect ratios, allowing Google to create versions of an advertiser&#8217;s creative that can appear across more inventory.</p>



<p class="wp-block-paragraph"><strong>The details.</strong> The new capability builds on Google&#8217;s existing video enhancements, which can automatically convert horizontal videos into vertical and square formats.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="914" height="960" src="https://searchengineland.com/wp-content/seloads/2026/08/Asset-Optimization-Upgrade.jpeg" alt="" class="wp-image-485253" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Asset-Optimization-Upgrade.jpeg 914w, https://searchengineland.com/wp-content/seloads/2026/08/Asset-Optimization-Upgrade-768x807.jpeg 768w" sizes="(max-width: 914px) 100vw, 914px" /></figure>



<p class="wp-block-paragraph">The goal, according to Google, is to maximize video ad reach while improving the viewing experience.</p>



<p class="wp-block-paragraph">Advertisers who don&#8217;t want Google to apply the new generative AI optimization can contact their account management team or submit Google&#8217;s opt-out form by September 4th.</p>



<p class="wp-block-paragraph">Video settings can also be changed at any time from within a Google Ads account.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Performance Max advertisers are increasingly handing creative decisions to Google&#8217;s automation. Generating missing aspect ratios could help brands get more mileage from existing video assets without producing every format themselves, particularly for inventory that favors vertical or square creative.</p>



<p class="wp-block-paragraph"><strong>Yes, but</strong>. Automatically generated versions of brand creative won&#8217;t be appropriate for every advertiser. Extending or adapting a video with generative AI introduces another layer of automation that brands may want to review closely for visual consistency, product accuracy and brand guidelines.</p>



<p class="wp-block-paragraph">The opt-out deadline is therefore worth noting for advertisers that want tighter control over how their video assets are modified.</p>



<p class="wp-block-paragraph"><strong>The big picture</strong>. Google has steadily expanded Performance Max from automated media buying into automated creative production. This update pushes that further, with generative AI increasingly filling gaps in the assets advertisers provide rather than simply choosing how and where those assets run.</p>



<p class="wp-block-paragraph"><strong>First spotted. </strong>This update was shared by JXT Group founder Menachem Ani who shared the comms he received on <a href="https://x.com/MenachemAni/status/2089406204158099860">X</a>.</p>


Google Ads is rolling out an asset optimization upgrade for existing Performance Max video ads.
The update uses generative AI to extend videos into missing aspect ratios, allowing Google to create versions of an advertiser’s creative that can appear across more inventory.
The details. The new capability builds on Google’s existing video enhancements, which can automatically convert horizontal videos into vertical and square formats.




The goal, according to Google, is to maximize video ad reach while improving the viewing experience.
Advertisers who don’t want Google to apply the new generative AI optimization can contact their account management team or submit Google’s opt-out form by September 4th.
Video settings can also be changed at any time from within a Google Ads account.
Why we care. Performance Max advertisers are increasingly handing creative decisions to Google’s automation. Generating missing aspect ratios could help brands get more mileage from existing video assets without producing every format themselves, particularly for inventory that favors vertical or square creative.
Yes, but. Automatically generated versions of brand creative won’t be appropriate for every advertiser. Extending or adapting a video with generative AI introduces another layer of automation that brands may want to review closely for visual consistency, product accuracy and brand guidelines.
The opt-out deadline is therefore worth noting for advertisers that want tighter control over how their video assets are modified.
The big picture. Google has steadily expanded Performance Max from automated media buying into automated creative production. This update pushes that further, with generative AI increasingly filling gaps in the assets advertisers provide rather than simply choosing how and where those assets run.
First spotted. This update was shared by JXT Group founder Menachem Ani who shared the comms he received on X.

Google is expanding PMax video optimization with generative AI that automatically creates missing aspect ratios from existing ads.

Google Ads will use generative AI to resize Performance Max video ads

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>ChatGPT draws a higher share of Google clicks than any other top destination, highlighting overlap between search and AI discovery.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/give-me-a-16-9-featured-image-that-doesnt-include-a-face-the-title-of-the-article-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">iPullRank analyzed 13.1 billion search events from 9.1 million opted-in users, covering October 2024 through December 2025, to understand how Google users behave when they search and where their clicks ultimately go.</p>



<p class="wp-block-paragraph">One of the standout findings for paid search marketers was ChatGPT&#8217;s unusually high share of paid traffic.</p>



<p class="wp-block-paragraph"><strong>By the numbers.</strong> ChatGPT was the sixth-most-clicked destination from Google Search in the dataset, trailing only YouTube, Google&#8217;s own properties, Reddit, Facebook and Wikipedia. Millions of searches for ChatGPT occurred each week within the panel.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="795" height="477" src="https://searchengineland.com/wp-content/seloads/2026/08/Sites-ppl-click-from-google.png" alt="" class="wp-image-485250" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Sites-ppl-click-from-google.png 795w, https://searchengineland.com/wp-content/seloads/2026/08/Sites-ppl-click-from-google-768x461.png 768w" sizes="(max-width: 795px) 100vw, 795px" /></figure>



<p class="wp-block-paragraph">More significantly for advertisers, iPullRank found ChatGPT drew a greater proportion of paid clicks than any of the other leading Google destinations it analyzed.</p>



<p class="wp-block-paragraph">That suggests OpenAI isn&#8217;t relying solely on organic visibility or direct traffic to attract users. Paid search is also playing a role in moving people from Google to ChatGPT.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> This research suggests paid search is holding up even as Google sends fewer organic clicks, while ChatGPT itself is becoming a significant paid-search destination. It also shows that AI and Google Search aren’t necessarily separate customer journeys — users are moving between them, creating new opportunities for advertisers to think about how paid search fits into an increasingly AI-driven discovery landscape.</p>



<p class="wp-block-paragraph"><strong>The bigger picture.</strong> ChatGPT also appears relatively difficult for Google to replace with its own search features.</p>



<p class="wp-block-paragraph">Only 11.1% of searches that would otherwise lead toward OpenAI were intercepted by a zero-click Google experience, one of the lowest rates among the major destinations iPullRank studied.</p>



<p class="wp-block-paragraph">That is partly because many ChatGPT searches are navigational. Google can answer a factual question directly in Search, but it can&#8217;t fully satisfy someone whose intention is simply to open ChatGPT.</p>



<p class="wp-block-paragraph"><strong>Paid clicks are holding up.</strong> The research also contained another potentially important signal for Google Ads advertisers.</p>



<p class="wp-block-paragraph">Google&#8217;s overall zero-click rate rose about 2.6 percentage points during the 15-month study period, while the share of searches producing an organic click fell roughly 2.8 points.</p>



<p class="wp-block-paragraph">Paid clicks, however, showed no meaningful change during that period, according to iPullRank.</p>



<p class="wp-block-paragraph">That doesn&#8217;t mean paid search is immune to changing search behavior. But within this dataset, the growth of zero-click searches appears to have come primarily at the expense of organic clicks rather than ads.</p>



<p class="wp-block-paragraph"><strong>Brand bidding still matters.</strong> The study also found paid clicks were more common on branded searches.</p>



<p class="wp-block-paragraph">When branded searches resulted in a click, 4.4% were paid, compared with 3.3% for non-branded searches. iPullRank describes this as evidence of brands paying to defend their own names in search results.</p>



<p class="wp-block-paragraph">That context is particularly relevant to ChatGPT. People searching Google specifically for &#8220;ChatGPT&#8221; represent highly valuable navigational traffic, making prominent paid placement potentially useful even when the brand already has strong organic visibility.</p>



<p class="wp-block-paragraph">The research does not, however, reveal which queries OpenAI bids on or how much it spends, so it can&#8217;t establish how much of ChatGPT&#8217;s paid traffic comes specifically from defensive brand bidding.</p>



<p class="wp-block-paragraph"><strong>Search is still a discovery engine.</strong> Another notable finding is that only about 14% of Google clicks went to a website explicitly named in the user&#8217;s query. The remaining 86% were classified as discovery clicks, where Google sent users to destinations they hadn&#8217;t specifically requested.</p>



<p class="wp-block-paragraph">For advertisers, that&#8217;s important because it suggests Google&#8217;s value isn&#8217;t limited to capturing existing brand demand.</p>



<p class="wp-block-paragraph">A large majority of clicks remain potentially contestable territory where ads, rankings and other search experiences can introduce users to a destination.</p>



<p class="wp-block-paragraph"><strong>Bottom line.</strong> ChatGPT’s position as both a Google competitor and a significant recipient of paid Google traffic shows how intertwined traditional search and AI discovery are becoming. For paid search marketers, that suggests the emerging AI journey isn&#8217;t simply Google versus ChatGPT. Increasingly, it could be Google to ChatGPT — with an ad helping users get there.</p>



<p class="wp-block-paragraph"><strong>Dig deeper. </strong><a href="https://ipullrank.com/zero-click-behavior-analysis-q3-2026">What 13 Billion Google Searches Reveal About Zero-Click Behavior</a>


iPullRank analyzed 13.1 billion search events from 9.1 million opted-in users, covering October 2024 through December 2025, to understand how Google users behave when they search and where their clicks ultimately go.
One of the standout findings for paid search marketers was ChatGPT’s unusually high share of paid traffic.
By the numbers. ChatGPT was the sixth-most-clicked destination from Google Search in the dataset, trailing only YouTube, Google’s own properties, Reddit, Facebook and Wikipedia. Millions of searches for ChatGPT occurred each week within the panel.




More significantly for advertisers, iPullRank found ChatGPT drew a greater proportion of paid clicks than any of the other leading Google destinations it analyzed.
That suggests OpenAI isn’t relying solely on organic visibility or direct traffic to attract users. Paid search is also playing a role in moving people from Google to ChatGPT.
Why we care. This research suggests paid search is holding up even as Google sends fewer organic clicks, while ChatGPT itself is becoming a significant paid-search destination. It also shows that AI and Google Search aren’t necessarily separate customer journeys — users are moving between them, creating new opportunities for advertisers to think about how paid search fits into an increasingly AI-driven discovery landscape.
The bigger picture. ChatGPT also appears relatively difficult for Google to replace with its own search features.
Only 11.1% of searches that would otherwise lead toward OpenAI were intercepted by a zero-click Google experience, one of the lowest rates among the major destinations iPullRank studied.
That is partly because many ChatGPT searches are navigational. Google can answer a factual question directly in Search, but it can’t fully satisfy someone whose intention is simply to open ChatGPT.
Paid clicks are holding up. The research also contained another potentially important signal for Google Ads advertisers.
Google’s overall zero-click rate rose about 2.6 percentage points during the 15-month study period, while the share of searches producing an organic click fell roughly 2.8 points.
Paid clicks, however, showed no meaningful change during that period, according to iPullRank.
That doesn’t mean paid search is immune to changing search behavior. But within this dataset, the growth of zero-click searches appears to have come primarily at the expense of organic clicks rather than ads.
Brand bidding still matters. The study also found paid clicks were more common on branded searches.
When branded searches resulted in a click, 4.4% were paid, compared with 3.3% for non-branded searches. iPullRank describes this as evidence of brands paying to defend their own names in search results.
That context is particularly relevant to ChatGPT. People searching Google specifically for “ChatGPT” represent highly valuable navigational traffic, making prominent paid placement potentially useful even when the brand already has strong organic visibility.
The research does not, however, reveal which queries OpenAI bids on or how much it spends, so it can’t establish how much of ChatGPT’s paid traffic comes specifically from defensive brand bidding.
Search is still a discovery engine. Another notable finding is that only about 14% of Google clicks went to a website explicitly named in the user’s query. The remaining 86% were classified as discovery clicks, where Google sent users to destinations they hadn’t specifically requested.
For advertisers, that’s important because it suggests Google’s value isn’t limited to capturing existing brand demand.
A large majority of clicks remain potentially contestable territory where ads, rankings and other search experiences can introduce users to a destination.
Bottom line. ChatGPT’s position as both a Google competitor and a significant recipient of paid Google traffic shows how intertwined traditional search and AI discovery are becoming. For paid search marketers, that suggests the emerging AI journey isn’t simply Google versus ChatGPT. Increasingly, it could be Google to ChatGPT — with an ad helping users get there.
Dig deeper. What 13 Billion Google Searches Reveal About Zero-Click Behavior

ChatGPT draws a higher share of Google clicks than any other top destination, highlighting overlap between search and AI discovery.

ChatGPT draws more paid clicks from Google than any other top destination

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google has confirmed the issue starting on August 13, and the result is a decrease in impressions.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Google has confirmed the <a href="https://www.seroundtable.com/google-search-console-performance-reports-drop-41884.html">reports of a bug</a> in the Search Console performance reports, specifically with the Generative AI in Search performance report.  There is a data logging issue that has caused a decrease in impressions shown in the report.  This is just a reporting issue, not a true issue in Google Search.</p>



<p class="wp-block-paragraph"><strong>What was the issue. </strong>If you look at your Generative AI report for Search in Google Search Console&#8217;s performance report, you may see a deep dive in impressions reported starting on August 13th.</p>



<p class="wp-block-paragraph">Here is a screenshot of one example:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="956" src="https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-performance-reports-drop-Aqmaq2gf-scaled.png" alt="" class="wp-image-485217" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-performance-reports-drop-Aqmaq2gf-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-performance-reports-drop-Aqmaq2gf-768x358.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-search-console-performance-reports-drop-Aqmaq2gf-1536x717.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p class="wp-block-paragraph"><strong>What Google said. </strong>Google wrote on its <a href="https://support.google.com/webmasters/answer/6211453?hl=en#zippy=%2Cperformance-reports-search-results-discover-google-news">data anomalies</a> page:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p class="wp-block-paragraph">August 13 (Generative AI in Search)</p>



<p class="wp-block-paragraph">A logging error caused a decrease in impressions on the Generative AI performance report in Search for data starting on August 13, 2026. This issue affects data logging only and is ongoing.</p>
</blockquote>



<p class="wp-block-paragraph">John Mueller from Google added on Bluesky <a href="https://bsky.app/profile/johnmu.com/post/3mtchhfyizs25">saying</a>:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p class="wp-block-paragraph">We&#8217;re aware of this issue and working on resolving it. This is just a logging issue and not representative of visibility changes in Search. We&#8217;re adding an annotation in Search Console to let folks know about this in the near future.</p>
</blockquote>



<p class="wp-block-paragraph"><strong>Why we care. </strong>If you noticed the dip in impressions on this report, you are not alone. This is widespread; Google confirmed the issue and is working on fixing it going forward.</p>


Google has confirmed the reports of a bug in the Search Console performance reports, specifically with the Generative AI in Search performance report.  There is a data logging issue that has caused a decrease in impressions shown in the report.  This is just a reporting issue, not a true issue in Google Search.
What was the issue. If you look at your Generative AI report for Search in Google Search Console’s performance report, you may see a deep dive in impressions reported starting on August 13th.
Here is a screenshot of one example:




What Google said. Google wrote on its data anomalies page:
August 13 (Generative AI in Search)
A logging error caused a decrease in impressions on the Generative AI performance report in Search for data starting on August 13, 2026. This issue affects data logging only and is ongoing.
John Mueller from Google added on Bluesky saying:
We’re aware of this issue and working on resolving it. This is just a logging issue and not representative of visibility changes in Search. We’re adding an annotation in Search Console to let folks know about this in the near future.
Why we care. If you noticed the dip in impressions on this report, you are not alone. This is widespread; Google confirmed the issue and is working on fixing it going forward.

Google has confirmed the issue starting on August 13, and the result is a decrease in impressions.

Google Search Console Generative AI Performance Report in Search data bug

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin.png 1600w, https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>Google Ads Liaison Ginny Marvin discusses Smart Bidding, AI, PPC mistakes and why strong fundamentals still matter with the rise of AI. 

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin.png 1600w, https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/PLTP-with-Ginny-Marvin-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/5hXOwQ2slgo?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p class="wp-block-paragraph">Google Ads Liaison Ginny Marvin, joined me this week on PPC Live The Podcast for a very interesting episode of her sharing mistakes, lessons she&#8217;s learnt and lessons the PPC community should be learning. According to Marvin, advertisers navigating the accelerating pace of automation and AI should resist knee-jerk reactions to platform changes and focus instead on understanding how updates affect their actual business goals.</p>



<p class="wp-block-paragraph">Marvin discussed everything from costly campaign mistakes earlier in her career to Google&#8217;s August Smart Bidding changes, AI-powered tools and what advertisers should expect next.</p>



<h2 id="h-understand-an-update-before-reacting-to-it" class="wp-block-heading">Understand an update before reacting to it</h2>



<p class="wp-block-paragraph">Marvin said one mistake advertisers can make is immediately deciding that a Google Ads change is bad without first understanding exactly what is changing.</p>



<p class="wp-block-paragraph">Her advice is to go back to the fundamentals: determine which campaigns are affected, understand the existing implementation and assess what the change means for the strategy and targets already in place.</p>



<p class="wp-block-paragraph">That distinction is particularly important as Google introduces increasingly automated products and bidding systems.</p>



<h2 id="h-marvin-addresses-the-august-17-smart-bidding-change" class="wp-block-heading">Marvin addresses the August 17 Smart Bidding change</h2>



<p class="wp-block-paragraph">One of the biggest topics was Google&#8217;s August 17 <a href="https://searchengineland.com/google-expands-smart-bidding-exploration-adds-promotion-mode-480165">Smart Bidding update</a>.</p>



<p class="wp-block-paragraph">Marvin stressed that the change only affects campaigns that are both limited by budget and overachieving against their target. Campaigns that aren&#8217;t budget constrained already operate in this way, she said.</p>



<p class="wp-block-paragraph">For advertisers with fixed budgets, alternatives remain available. Marvin pointed to Maximize Conversions and Maximize Conversion Value without a target as options for advertisers who need to operate within a defined budget.</p>



<h2 id="h-targets-become-an-even-more-important-lever" class="wp-block-heading">Targets become an even more important lever</h2>



<p class="wp-block-paragraph">Advertisers running budget-constrained campaigns with targets should pay particularly close attention to current performance.</p>



<p class="wp-block-paragraph">Marvin recommended reviewing average target performance and accounting for the full conversion cycle. Advertisers that want to maintain their current average performance may need to adjust their targets accordingly.</p>



<p class="wp-block-paragraph">Those that keep their existing target could find themselves entering different auctions, potentially changing bids and CPCs.</p>



<p class="wp-block-paragraph">The bigger point is that advertisers should think about a campaign&#8217;s target as an <strong>efficiency lever</strong> tied directly to business objectives, rather than simply another Google Ads setting.</p>



<h2 id="h-business-goals-should-be-the-north-star" class="wp-block-heading">Business goals should be the North Star</h2>



<p class="wp-block-paragraph">That business-first approach was a recurring theme throughout the conversation.</p>



<p class="wp-block-paragraph">Marvin said bidding strategies should account for factors beyond what&#8217;s immediately visible inside Google Ads, including promotions, inventory availability and product margins.</p>



<p class="wp-block-paragraph">She later made a similar point about testing new products and features. If the objective is to increase conversion value, for example, CPCs may not be the right metric for judging whether a test succeeded.</p>



<p class="wp-block-paragraph">Instead, advertisers should identify the metrics that reflect the actual outcomes the business is trying to achieve.</p>



<h2 id="h-don-t-ignore-google-s-help-center" class="wp-block-heading">Don&#8217;t ignore Google&#8217;s Help Center</h2>



<p class="wp-block-paragraph">One surprisingly simple recommendation from Marvin: read the Help Center documentation.</p>



<p class="wp-block-paragraph">While acknowledging that documentation isn&#8217;t always updated as quickly as advertisers might want, Marvin said Google&#8217;s help pages can contain valuable details about how products and bidding systems actually work.</p>



<p class="wp-block-paragraph">She recalled discovering information in a Help Center article during her time at Search Engine Land despite believing she was already familiar with the subject.</p>



<p class="wp-block-paragraph">For advertisers unsure whether they fully understand how a feature works, revisiting the documentation can provide a useful refresher before making major account changes.</p>



<h2 id="h-don-t-expect-ai-tools-to-be-perfect" class="wp-block-heading">Don&#8217;t expect AI tools to be perfect</h2>



<p class="wp-block-paragraph">The conversation also turned to newer AI-powered Google Ads tools such as Ask Advisor.</p>



<p class="wp-block-paragraph">Marvin cautioned against expecting newer AI products to be 100% accurate, particularly as the underlying models continue to develop. Feedback from advertisers is important in helping those systems improve, she said.</p>



<p class="wp-block-paragraph">That means marketers still need to bring their own expertise and judgment to recommendations produced by AI rather than treating them as unquestionable instructions.</p>



<h2 id="h-tried-an-automated-feature-before-it-may-be-worth-testing-again" class="wp-block-heading">Tried an automated feature before? It may be worth testing again</h2>



<p class="wp-block-paragraph">The speed at which Google&#8217;s underlying systems are improving could also change how advertisers approach features they&#8217;ve previously tested unsuccessfully.</p>



<p class="wp-block-paragraph">Marvin argued that the old advice to try something again after it failed is increasingly relevant because many improvements now happen behind the scenes.</p>



<p class="wp-block-paragraph">A feature or campaign approach that underperformed in an earlier test may behave differently after months of changes to Google&#8217;s models and infrastructure.</p>



<p class="wp-block-paragraph">That doesn&#8217;t mean blindly retesting everything. Advertisers still need a clear hypothesis, appropriate success metrics and a defined testing strategy.</p>



<h2 id="h-build-a-testing-culture-around-google-ads" class="wp-block-heading">Build a testing culture around Google Ads</h2>



<p class="wp-block-paragraph">Marvin recommended establishing a dedicated testing budget where possible and developing a testing mindset across teams.</p>



<p class="wp-block-paragraph">That also requires setting expectations with clients and stakeholders. As Google Ads continues changing rapidly, advertisers that have room to experiment may be better positioned to understand new capabilities before those capabilities become more central to the platform.</p>



<h2 id="h-more-ai-and-automation-are-coming" class="wp-block-heading">More AI and automation are coming</h2>



<p class="wp-block-paragraph">Asked what advertisers should expect over the next six to 12 months, Marvin pointed to features announced at <a href="https://searchengineland.com/google-marketing-live-2026-everything-you-need-to-know-478167">Google Marketing Live</a> that are still rolling out.</p>



<p class="wp-block-paragraph">More broadly, she referenced comments from Google&#8217;s head of Ads and Commerce, Vidya Srinivasan, as a signal that Google remains committed to its current direction.</p>



<p class="wp-block-paragraph">For advertisers, Marvin&#8217;s message was to start planning rather than waiting for the direction of travel to reverse. Teams that aren&#8217;t already testing newer capabilities should consider how they can begin doing so and prepare clients for what&#8217;s ahead.</p>



<h2 id="h-mistakes-happen-how-you-respond-matters" class="wp-block-heading">Mistakes happen — how you respond matters</h2>



<p class="wp-block-paragraph">Marvin also shared a mistake from earlier in her PPC career involving an international client whose campaign budgets were accidentally set too high by a junior team member.</p>



<p class="wp-block-paragraph">Her team responded by acknowledging the error without becoming defensive, explaining what happened without over-explaining it and quickly shifting the conversation toward what they could do next. They also increased communication and introduced stronger internal processes.</p>



<p class="wp-block-paragraph">The relationship survived, and the client remained with the company for years.</p>



<h2 id="h-don-t-throw-junior-team-members-under-the-bus" class="wp-block-heading">Don&#8217;t throw junior team members under the bus</h2>



<p class="wp-block-paragraph">For managers dealing with a serious account mistake, Marvin was particularly clear about one thing: don&#8217;t blame the junior employee in front of the client.</p>



<p class="wp-block-paragraph">Leadership should take responsibility for the team&#8217;s work, even when an individual made the error. Marvin argued that blaming an employee doesn&#8217;t reflect well on the manager from the client&#8217;s perspective.</p>



<p class="wp-block-paragraph">Internally, mistakes should still be reviewed, but the goal of that conversation should be learning and growth rather than blame. Marvin recommended first resolving the immediate client problem, then giving people enough space before reviewing what happened and improving the processes that allowed it to occur.</p>



<h2 id="h-the-takeaway-for-ppc-marketers" class="wp-block-heading">The takeaway for PPC marketers</h2>



<p class="wp-block-paragraph">Despite the increasing role of AI, automation and Smart Bidding, Marvin&#8217;s message throughout the conversation was surprisingly consistent: the fundamentals haven&#8217;t gone away.</p>



<p class="wp-block-paragraph">Understand what you&#8217;re trying to achieve, connect campaign decisions to business outcomes, choose metrics that reflect those outcomes, test deliberately and understand how the systems you&#8217;re using actually work.</p>



<p class="wp-block-paragraph">Google Ads may be changing faster than ever, but advertisers still need to decide whether those changes make sense for the businesses they&#8217;re responsible for.</p>


Watch this video on YouTube




Google Ads Liaison Ginny Marvin, joined me this week on PPC Live The Podcast for a very interesting episode of her sharing mistakes, lessons she’s learnt and lessons the PPC community should be learning. According to Marvin, advertisers navigating the accelerating pace of automation and AI should resist knee-jerk reactions to platform changes and focus instead on understanding how updates affect their actual business goals.
Marvin discussed everything from costly campaign mistakes earlier in her career to Google’s August Smart Bidding changes, AI-powered tools and what advertisers should expect next.
Understand an update before reacting to it
Marvin said one mistake advertisers can make is immediately deciding that a Google Ads change is bad without first understanding exactly what is changing.
Her advice is to go back to the fundamentals: determine which campaigns are affected, understand the existing implementation and assess what the change means for the strategy and targets already in place.
That distinction is particularly important as Google introduces increasingly automated products and bidding systems.
Marvin addresses the August 17 Smart Bidding change
One of the biggest topics was Google’s August 17 Smart Bidding update.
Marvin stressed that the change only affects campaigns that are both limited by budget and overachieving against their target. Campaigns that aren’t budget constrained already operate in this way, she said.
For advertisers with fixed budgets, alternatives remain available. Marvin pointed to Maximize Conversions and Maximize Conversion Value without a target as options for advertisers who need to operate within a defined budget.
Targets become an even more important lever
Advertisers running budget-constrained campaigns with targets should pay particularly close attention to current performance.
Marvin recommended reviewing average target performance and accounting for the full conversion cycle. Advertisers that want to maintain their current average performance may need to adjust their targets accordingly.
Those that keep their existing target could find themselves entering different auctions, potentially changing bids and CPCs.
The bigger point is that advertisers should think about a campaign’s target as an efficiency lever tied directly to business objectives, rather than simply another Google Ads setting.
Business goals should be the North Star
That business-first approach was a recurring theme throughout the conversation.
Marvin said bidding strategies should account for factors beyond what’s immediately visible inside Google Ads, including promotions, inventory availability and product margins.
She later made a similar point about testing new products and features. If the objective is to increase conversion value, for example, CPCs may not be the right metric for judging whether a test succeeded.
Instead, advertisers should identify the metrics that reflect the actual outcomes the business is trying to achieve.
Don’t ignore Google’s Help Center
One surprisingly simple recommendation from Marvin: read the Help Center documentation.
While acknowledging that documentation isn’t always updated as quickly as advertisers might want, Marvin said Google’s help pages can contain valuable details about how products and bidding systems actually work.
She recalled discovering information in a Help Center article during her time at Search Engine Land despite believing she was already familiar with the subject.
For advertisers unsure whether they fully understand how a feature works, revisiting the documentation can provide a useful refresher before making major account changes.
Don’t expect AI tools to be perfect
The conversation also turned to newer AI-powered Google Ads tools such as Ask Advisor.
Marvin cautioned against expecting newer AI products to be 100% accurate, particularly as the underlying models continue to develop. Feedback from advertisers is important in helping those systems improve, she said.
That means marketers still need to bring their own expertise and judgment to recommendations produced by AI rather than treating them as unquestionable instructions.
Tried an automated feature before? It may be worth testing again
The speed at which Google’s underlying systems are improving could also change how advertisers approach features they’ve previously tested unsuccessfully.
Marvin argued that the old advice to try something again after it failed is increasingly relevant because many improvements now happen behind the scenes.
A feature or campaign approach that underperformed in an earlier test may behave differently after months of changes to Google’s models and infrastructure.
That doesn’t mean blindly retesting everything. Advertisers still need a clear hypothesis, appropriate success metrics and a defined testing strategy.
Build a testing culture around Google Ads
Marvin recommended establishing a dedicated testing budget where possible and developing a testing mindset across teams.
That also requires setting expectations with clients and stakeholders. As Google Ads continues changing rapidly, advertisers that have room to experiment may be better positioned to understand new capabilities before those capabilities become more central to the platform.
More AI and automation are coming
Asked what advertisers should expect over the next six to 12 months, Marvin pointed to features announced at Google Marketing Live that are still rolling out.
More broadly, she referenced comments from Google’s head of Ads and Commerce, Vidya Srinivasan, as a signal that Google remains committed to its current direction.
For advertisers, Marvin’s message was to start planning rather than waiting for the direction of travel to reverse. Teams that aren’t already testing newer capabilities should consider how they can begin doing so and prepare clients for what’s ahead.
Mistakes happen — how you respond matters
Marvin also shared a mistake from earlier in her PPC career involving an international client whose campaign budgets were accidentally set too high by a junior team member.
Her team responded by acknowledging the error without becoming defensive, explaining what happened without over-explaining it and quickly shifting the conversation toward what they could do next. They also increased communication and introduced stronger internal processes.
The relationship survived, and the client remained with the company for years.
Don’t throw junior team members under the bus
For managers dealing with a serious account mistake, Marvin was particularly clear about one thing: don’t blame the junior employee in front of the client.
Leadership should take responsibility for the team’s work, even when an individual made the error. Marvin argued that blaming an employee doesn’t reflect well on the manager from the client’s perspective.
Internally, mistakes should still be reviewed, but the goal of that conversation should be learning and growth rather than blame. Marvin recommended first resolving the immediate client problem, then giving people enough space before reviewing what happened and improving the processes that allowed it to occur.
The takeaway for PPC marketers
Despite the increasing role of AI, automation and Smart Bidding, Marvin’s message throughout the conversation was surprisingly consistent: the fundamentals haven’t gone away.
Understand what you’re trying to achieve, connect campaign decisions to business outcomes, choose metrics that reflect those outcomes, test deliberately and understand how the systems you’re using actually work.
Google Ads may be changing faster than ever, but advertisers still need to decide whether those changes make sense for the businesses they’re responsible for.

Google Ads Liaison Ginny Marvin discusses Smart Bidding, AI, PPC mistakes and why strong fundamentals still matter with the rise of AI.

Ginny Marvin on Smart Bidding, AI and why PPC fundamentals still matter

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Robby Stein from Google said, "This was a small experiment we're no longer running."

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/robot-painting-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Google has paused an experiment within Google Search to create AI-generated images within AI Overviews for recipe results.  Robby Stein, VP of Product at Google, replied to this test minutes ago saying, &#8220;This was a small experiment we&#8217;re no longer running.&#8221;</p>



<p class="wp-block-paragraph"><strong>What was the experiment. </strong>As I covered on Friday, Google was <a href="https://www.seroundtable.com/ai-generated-images-google-ai-overviews-41872.html">testing showing AI-generated images in AI Overviews</a> for recipe results. The AI-generated image summarized how to cook the dish, from prep to finish. This was spotted by the folks over at Inspired Taste who <a href="https://x.com/inspiredtaste/status/2087688326577090679">wrote</a> on X, &#8220;This AIO is directly trying to replace creators who buy groceries for testing, photography, and filming recipe videos. We also have conversations with our visitors who have questions. We are doing all of the work and Google is directly trying to over simplify everything and replace us with AIOs like this. There is also risk that a searcher might think that one of the websites cited in the AIO did the illustrations. It is very confusing and extremely oversimplified.&#8221;</p>



<p class="wp-block-paragraph"><strong>What it looked like. </strong>Here are some screenshots of this in action:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1074" height="2048" src="https://searchengineland.com/wp-content/seloads/2026/08/google-ai-overview-open-ai-image-1Dp7KlSG-scaled.jpg" alt="" class="wp-image-485150" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-ai-overview-open-ai-image-1Dp7KlSG-scaled.jpg 1074w, https://searchengineland.com/wp-content/seloads/2026/08/google-ai-overview-open-ai-image-1Dp7KlSG-768x1465.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-ai-overview-open-ai-image-1Dp7KlSG-805x1536.jpg 805w" sizes="(max-width: 1074px) 100vw, 1074px" /></figure>



<p class="wp-block-paragraph">Here are more:</p>



<figure class="wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex">
<figure class="wp-block-image size-large"><a href="https://searchengineland.com/wp-content/seloads/2026/08/HPoL-ZdaUAA-dNe-scaled.jpg"><img decoding="async" width="1094" height="2048" data-id="485153" src="https://searchengineland.com/wp-content/seloads/2026/08/HPoL-ZdaUAA-dNe-scaled.jpg" alt="" class="wp-image-485153" srcset="https://searchengineland.com/wp-content/seloads/2026/08/HPoL-ZdaUAA-dNe-scaled.jpg 1094w, https://searchengineland.com/wp-content/seloads/2026/08/HPoL-ZdaUAA-dNe-768x1437.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/HPoL-ZdaUAA-dNe-821x1536.jpg 821w" sizes="(max-width: 1094px) 100vw, 1094px" /></a></figure>



<figure class="wp-block-image size-large"><a href="https://searchengineland.com/wp-content/seloads/2026/08/HPoPwfAakAAXicF-scaled.jpg"><img decoding="async" width="1061" height="2048" data-id="485154" src="https://searchengineland.com/wp-content/seloads/2026/08/HPoPwfAakAAXicF-scaled.jpg" alt="" class="wp-image-485154" srcset="https://searchengineland.com/wp-content/seloads/2026/08/HPoPwfAakAAXicF-scaled.jpg 1061w, https://searchengineland.com/wp-content/seloads/2026/08/HPoPwfAakAAXicF-768x1482.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/HPoPwfAakAAXicF-796x1536.jpg 796w" sizes="(max-width: 1061px) 100vw, 1061px" /></a></figure>



<figure class="wp-block-image size-large"><a href="https://searchengineland.com/wp-content/seloads/2026/08/HPoNzlIbAAAUjgn-scaled.jpg"><img decoding="async" width="1119" height="2048" data-id="485151" src="https://searchengineland.com/wp-content/seloads/2026/08/HPoNzlIbAAAUjgn-scaled.jpg" alt="" class="wp-image-485151" srcset="https://searchengineland.com/wp-content/seloads/2026/08/HPoNzlIbAAAUjgn-scaled.jpg 1119w, https://searchengineland.com/wp-content/seloads/2026/08/HPoNzlIbAAAUjgn-768x1405.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/HPoNzlIbAAAUjgn-840x1536.jpg 840w" sizes="(max-width: 1119px) 100vw, 1119px" /></a></figure>
</figure>



<p class="wp-block-paragraph"><strong>Google dropped the experiment. </strong>Robby Stein from Google <a href="https://x.com/rmstein/status/2089391912960884909">said</a> on X, &#8220;This was a small experiment we&#8217;re no longer running.&#8221; </p>



<p class="wp-block-paragraph">He added that &#8220;this is not the Nano Banana feature we announced in July, which only triggers when users explicitly ask to generate an image.&#8221; That is <a href="https://searchengineland.com/google-ai-overviews-now-lets-you-create-image-482163">this story</a>, where Google said they would allow users to create AI-generated images in AI Overviews.</p>



<p class="wp-block-paragraph"><strong>Why we care. </strong>Not only would this feature result in a drop in traffic to the publishers of these recipes, it might also lead to the recipe being followed incorrectly and lead to a bad taste in more than one way.</p>



<p class="wp-block-paragraph">Adam from Inspired Taste replied to the experiment being dropped <a href="https://x.com/inspiredtaste/status/2089393029744664926">saying</a>, &#8220;We are extremely happy to hear that this experiment is no longer running.&#8221;</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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       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
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      <span id="semrush-one-cta">See your AI visibility</span>
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Google has paused an experiment within Google Search to create AI-generated images within AI Overviews for recipe results.  Robby Stein, VP of Product at Google, replied to this test minutes ago saying, “This was a small experiment we’re no longer running.”
What was the experiment. As I covered on Friday, Google was testing showing AI-generated images in AI Overviews for recipe results. The AI-generated image summarized how to cook the dish, from prep to finish. This was spotted by the folks over at Inspired Taste who wrote on X, “This AIO is directly trying to replace creators who buy groceries for testing, photography, and filming recipe videos. We also have conversations with our visitors who have questions. We are doing all of the work and Google is directly trying to over simplify everything and replace us with AIOs like this. There is also risk that a searcher might think that one of the websites cited in the AIO did the illustrations. It is very confusing and extremely oversimplified.”
What it looked like. Here are some screenshots of this in action:




Here are more:













Google dropped the experiment. Robby Stein from Google said on X, “This was a small experiment we’re no longer running.” 
He added that “this is not the Nano Banana feature we announced in July, which only triggers when users explicitly ask to generate an image.” That is this story, where Google said they would allow users to create AI-generated images in AI Overviews.
Why we care. Not only would this feature result in a drop in traffic to the publishers of these recipes, it might also lead to the recipe being followed incorrectly and lead to a bad taste in more than one way.
Adam from Inspired Taste replied to the experiment being dropped saying, “We are extremely happy to hear that this experiment is no longer running.”
customers won’t either.
      
See your AI visibility

Robby Stein from Google said, "This was a small experiment we're no longer running."

Google Search pauses AI-generated images within AI Overviews

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Join us on August 19th for the latest SMX Now where Reva Minkoff will break down Google's August 17 Smart Bidding changes

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/smxnow-featured-image-V1-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Google&#8217;s <a href="https://searchengineland.com/google-expands-smart-bidding-exploration-adds-promotion-mode-480165">upcoming changes to Smart Bidding</a> have sparked one of the biggest debates the PPC industry has seen in years. With the rollout scheduled to begin on August 17, many advertisers are questioning whether long-standing Target CPA and Target ROAS strategies will continue to work the way they always have.</p>



<p class="wp-block-paragraph">Our latest SMX Now installment won&#8217;t simply recap Google&#8217;s announcement. Instead, it promises to put the update into historical context, explaining why experienced PPC professionals believe this isn&#8217;t the first time Google has fundamentally changed how automated bidding behaves—and what advertisers can learn from previous transitions.</p>



<p class="wp-block-paragraph">The announcement has generated significant discussion throughout the PPC community, with many questioning whether advertisers will need to rethink years of bidding best practices before the rollout begins.</p>



<p class="wp-block-paragraph">Whether you agree with Google&#8217;s approach or share the concerns voiced across the PPC community, this is one of the most significant Google Ads changes of 2026.</p>



<p class="wp-block-paragraph">If your accounts rely on Target CPA or Target ROAS bidding, this webinar offers an opportunity to understand what&#8217;s changing, why it matters, and how to prepare in its early roll out phase.</p>



<p class="wp-block-paragraph">Join us Aug. 19, 1 p.m. ET</p>



<p class="has-text-align-center wp-block-paragraph"><a href="https://www.bigmarker.com/third-door-media/the-target-bidding-apocalypse-and-how-we-ve-been-here-before">Save your spot</a>


Google’s upcoming changes to Smart Bidding have sparked one of the biggest debates the PPC industry has seen in years. With the rollout scheduled to begin on August 17, many advertisers are questioning whether long-standing Target CPA and Target ROAS strategies will continue to work the way they always have.
Our latest SMX Now installment won’t simply recap Google’s announcement. Instead, it promises to put the update into historical context, explaining why experienced PPC professionals believe this isn’t the first time Google has fundamentally changed how automated bidding behaves—and what advertisers can learn from previous transitions.
The announcement has generated significant discussion throughout the PPC community, with many questioning whether advertisers will need to rethink years of bidding best practices before the rollout begins.
Whether you agree with Google’s approach or share the concerns voiced across the PPC community, this is one of the most significant Google Ads changes of 2026.
If your accounts rely on Target CPA or Target ROAS bidding, this webinar offers an opportunity to understand what’s changing, why it matters, and how to prepare in its early roll out phase.
Join us Aug. 19, 1 p.m. ET
Save your spot

Join us on August 19th for the latest SMX Now where Reva Minkoff will break down Google's August 17 Smart Bidding changes

SMX Now: The target bidding apocalypse and how we’ve been here before

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google is making view-through conversion optimization video-only for Demand Gen while expanding support to Display.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/emand-Gen-view-through-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google is changing how view-through conversion (VTC) optimization works in Demand Gen campaigns, expanding support to more inventory while shifting optimization to focus exclusively on video assets.</p>



<p class="wp-block-paragraph">The update also changes the default campaign settings and billing model for Display video ads.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening</strong>. Over the coming months, Google will roll out three major changes to VTC optimization for Demand Gen campaigns.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/08/google-vtc-billing-bidding-changes.jpg" alt="" class="wp-image-485128" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-vtc-billing-bidding-changes.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/08/google-vtc-billing-bidding-changes-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-vtc-billing-bidding-changes-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p class="wp-block-paragraph">First, VTC bidding will become video-only. While image asset view-through conversions will still be reported as secondary conversions in existing campaigns, they will no longer be eligible for bidding or appear in the primary Conversions column.</p>



<p class="wp-block-paragraph">Second, VTC optimization will be enabled by default for all new Demand Gen campaigns. Advertisers creating new campaigns will need to manually opt out if they don&#8217;t want to use the feature. Existing campaigns will retain their current settings and won&#8217;t automatically opt in.</p>



<p class="wp-block-paragraph">Third, Google is expanding VTC optimization beyond YouTube and Discover Feed to include the Google Display Network.</p>



<p class="wp-block-paragraph"><strong>Billing changes</strong>. As part of the Display expansion, Google is changing how video ads are billed.</p>



<p class="wp-block-paragraph">Video assets served on Display will move from cost-per-click (CPC) to cost-per-thousand impressions (CPM) billing. Google says this change applies to all Display video assets in Demand Gen, regardless of whether advertisers choose to use VTC optimization.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Advertisers relying on image-heavy campaigns will lose VTC bidding for those assets, while those using video should review how the shift to CPM billing could affect campaign costs and performance. The fact that VTC optimization will be enabled by default for new campaigns also means advertisers should verify campaign settings during setup rather than assuming the feature is disabled.</p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. Google is repositioning view-through conversion optimization as a video-first feature in Demand Gen, while expanding its reach across Display and making it the default optimization setting for new campaigns.</p>



<p class="wp-block-paragraph"><strong>First spotted. </strong>The update was shared by Paid Search expert Arpan Banerjee who shared the comms he received on <a href="https://www.linkedin.com/posts/arpan301_googleads-ppc-sem-activity-7494127574252462080-HmAj?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAJTV04Bw5M-1qqJtnImkpsEOFw9bHvfSSs">LinkedIn</a>. </p>


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Google is changing how view-through conversion (VTC) optimization works in Demand Gen campaigns, expanding support to more inventory while shifting optimization to focus exclusively on video assets.
The update also changes the default campaign settings and billing model for Display video ads.
What’s happening. Over the coming months, Google will roll out three major changes to VTC optimization for Demand Gen campaigns.




First, VTC bidding will become video-only. While image asset view-through conversions will still be reported as secondary conversions in existing campaigns, they will no longer be eligible for bidding or appear in the primary Conversions column.
Second, VTC optimization will be enabled by default for all new Demand Gen campaigns. Advertisers creating new campaigns will need to manually opt out if they don’t want to use the feature. Existing campaigns will retain their current settings and won’t automatically opt in.
Third, Google is expanding VTC optimization beyond YouTube and Discover Feed to include the Google Display Network.
Billing changes. As part of the Display expansion, Google is changing how video ads are billed.
Video assets served on Display will move from cost-per-click (CPC) to cost-per-thousand impressions (CPM) billing. Google says this change applies to all Display video assets in Demand Gen, regardless of whether advertisers choose to use VTC optimization.
Why we care. Advertisers relying on image-heavy campaigns will lose VTC bidding for those assets, while those using video should review how the shift to CPM billing could affect campaign costs and performance. The fact that VTC optimization will be enabled by default for new campaigns also means advertisers should verify campaign settings during setup rather than assuming the feature is disabled.
Bottom line. Google is repositioning view-through conversion optimization as a video-first feature in Demand Gen, while expanding its reach across Display and making it the default optimization setting for new campaigns.
First spotted. The update was shared by Paid Search expert Arpan Banerjee who shared the comms he received on LinkedIn. 
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Analyze your competitors

Google is making view-through conversion optimization video-only for Demand Gen while expanding support to Display.

Google updates Demand Gen view-through conversion optimization

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search.png" class="attachment-large size-large wp-post-image" alt="The AI visibility index- Which brands are vanishing from AI search?" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-AI-visibility-index-Which-brands-are-vanishing-from-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Some brands dominate Google yet barely surface in AI answers. Here’s where traditional authority and AI recall diverge.

Some of the brands with the strongest SEO footprints barely appear in AI answers. Others with much smaller search footprints show up again and again.
Fractl analyzed how consistently AI models recommend brands, which brands disappear despite strong SEO footprints, and which signals separate the brands that overperform from those that under-index. (Disclosure: I’m the co-founder of Fractl.)



The short answer: organic authority still matters, but it’s not the whole map.
More than 9 in 10 brands in the dataset behaved the way most SEOs would expect: stronger traditional search authority generally tracked with stronger AI visibility. But the outliers are where the findings get interesting.
The gap between traditional search authority and AI recall is where the visibility challenge gets more complicated.
AI answers have default brands
Some brands with high domain ratings, large keyword portfolios, and millions of monthly organic visits barely surfaced in their own categories. Others with smaller traditional search footprints appeared far more often than their SEO metrics would predict.
Every industry we reviewed had a short list of brands that surfaced again and again, regardless of how the prompt was phrased.
Those default answers weren’t always the biggest companies in the category. They weren’t always the brands with the most traffic. And they weren’t always the companies a traditional SEO leaderboard would put first.




We noticed a few patterns emerge immediately:
Travel was the most concentrated category: Booking.com (285 mentions), Airbnb (227), and Expedia (215) accounted for roughly 20% of the sector’s total mention volume. Three brands accounted for one out of every five recommendations.
HealthTech had a runaway leader: Teladoc (275) led Amwell (220), the runner-up, by roughly 25%. The models have settled.
Wellness had the deepest bench: Peloton, Headspace, Calm, Whoop, and Oura all cleared 168 mentions. No single brand dominates, which means the category-leader slot is still up for grabs.
Insurance broke the way industry watchers should have expected: Lemonade (213) ranked above State Farm (172). Root Insurance (165) ranked above Progressive (114). The models cited the digital-native carriers first and treated the legacy ones as the alternatives, rather than the default.
Lifestyle was the loudest signal of the methodology working: Patagonia, Allbirds, Eileen Fisher, and Everlane (sustainability-coded direct-to-consumer brands) outranked Sephora, Samsung, and Whirlpool. Whether that’s a function of how product roundup articles get written or a deeper bias in training data, the result is the same: The models prefer the brand story that gets told in third-party content.
The pattern held across every sector. A handful of categories do have winners the models clearly recognize, but plenty of the time, those picks don’t line up with what you’d expect. Stripe wins in FinTech, Notion in SaaS, Coursera in Education, and so on. Most of the time, though, the default names aren’t the ones you’d predict.
That’s the first strategic shift for SEO leaders.
The competitive set inside an AI answer is much smaller than the competitive set on a search results page. If your brand can’t crack the top five to 10 names the model recalls for your category, ranking well in Google may not be enough to get you into the AI-generated consideration set.
Dig deeper: Hidden gem publishers outperform major media on audience affinity: Study



  
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Traditional authority doesn’t guarantee AI recall
More than 9 in 10 brands in our dataset were broadly aligned: strong traditional search authority usually tracked with stronger AI visibility. That’s the expected outcome. It’s also why marketers shouldn’t throw out every SEO principle they’ve spent the last decade building around.
But the edges of the dataset show where the strategy changes. About 5% (471 brands) were underexposed in LLMs. These were companies with high domain ratings, very high organic traffic, and a deep keyword portfolio that still drew almost no references from the models. 
On SEO infrastructure, they look like market leaders. On LLM references, they look like companies nobody has ever written about.




About 4% (377 brands) were AI overperformers. The models referenced them far more often than their modest traditional signals would suggest, much more aggressively in their verticals than Ahrefs data says they should be.
Roughly 9% lined up with how often a brand appeared in third-party content that it did not create. Brands that showed up disproportionately in roundups, expert lists, and comparison reviews tended to be far more visible to the models because that kind of coverage gets ingested into the training data. Brands with plenty of search authority but little third-party coverage tended to land in the underrepresented group, regardless of vertical.
That’s the strategic shift: What a brand says about itself matters less for AI recall than what the rest of the web has repeatedly said about it. Owned content still matters. Technical SEO still matters. But for AI visibility, the corroboration layer is becoming harder to ignore.




Some brands are miscategorized
The brands underrepresented in AI are, in many cases, the blue chips of their categories by most traditional measures. These brands have domain ratings above 80, millions of monthly organic visits, and hundreds of thousands of keywords ranked. But the models don’t even mention them in their own sectors.
The issue is categorization.




A few of the patterns we spotted:
Microsoft and Spotify topped the list in FinTech: They’re the giants of traditional visibility, but the models didn’t categorize them as fintechs when prompted with fintech queries. That’s not a bug. Microsoft is heavily cited when you ask about productivity software or cloud platforms. It’s a categorization gap. The models have decided what counts as a fintech, and Microsoft wasn’t on the list.
Legacy insurance carriers got displaced: Aetna, Cigna, Humana, Liberty Mutual, and Trustpilot all sat at DR 80+ with millions of monthly visits. The models barely cited them. Lemonade and Root sat higher in the same prompts despite a fraction of the traditional footprint.
Legacy retail brands got displaced: Sephora, Samsung, Whirlpool, and GE Appliances. The models preferred Patagonia, Allbirds, and Eileen Fisher when the prompt asked for lifestyle recommendations. This is the most visible signal that the training data heavily weights direct-to-consumer brand narratives over legacy retail.
Healthcare giants were under-indexed. Medtronic, GoodRx, Humana, and 23andMe. The models cited telehealth-native brands like Teladoc, Amwell, and Doxy.me instead.
Low recall doesn’t always mean the model doesn’t know your brand. It may mean the model has filed your brand under a different category than the one your buyers are searching.
More content alone won’t fix that.
You need stronger category signals in the places models are likely to encounter and reinforce those associations: analyst content, industry publications, comparison pages, partner pages, customer stories, review sites, and category-specific media coverage. Before you chase more AI mentions, make sure the models understand which category you’re supposed to win.
Dig deeper: What 1 million keywords reveal about AI’s impact on search




	
	
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The AI overperformers show the playbook
The other side may be even more helpful to marketers, since it shows how brands punching above their weight actually pull it off. Smaller Ahrefs profiles. Larger model recall. And all 377 of these brands show up repeatedly in the third-party material the models were trained on.




This list tells the story:
Monday.com was the biggest overperformer in the dataset, with an AI Visibility Score of 0.71. Monday.com already has a lot of traditional traffic, close to 1 million organic visitors, and more than 50,000 ranked keywords. Its overperformer status tracks directly with how much more often the models referenced it than its SaaS peers.
Root Insurance owned the insurance category outright, drawing more AI references than any of the legacy carriers despite being a fraction of their size.
Six of the top 15 overperformers were in education: Stanford, MIT, Khan Academy, LinkedIn Learning, IBM Data Science, and Google Career Certificates. The models cited these for their credibility, not because they run high-Domain-Rating commercial platforms.
Smaller players followed the same pattern. Doxy.me, Nike Training Club, and Google Flights all got pulled into product roundups, showed up heavily on expert lists, and drew an outsized number of LLM references as a result.
The common thread isn’t just “good SEO.” It’s category-level presence in other people’s content. That pushes AI visibility work closer to digital PR, content distribution, and category authority building than many teams want to admit.
If you want to know why a competitor with less than half your organic traffic shows up in the model’s answer while you don’t, the question to ask isn’t, “What’s their SEO strategy?” It’s, “What’s their press coverage, and how did they earn it?”
You’re not just optimizing your own site. You’re shaping the set of sources AI systems use to understand who belongs in the category.
Most brands exist in only one model’s world
Only 900 brands in our dataset, or 11% of the total, were referenced by all three models. Another 12% appeared in two. The overwhelming majority, 77%, were referenced by only one model.




That’s a major measurement problem. A brand can win in ChatGPT and lose in Gemini. It can show up in Claude and disappear from ChatGPT. It can own one model’s answer set while barely registering in another.
Each model also had a different fingerprint.
Claude leaned more heavily toward SaaS and insurance brands, with Notion, Linear, and Lemonade appearing more often there than in the other two models.
Gemini leaned toward travel and healthcare, with Booking.com, Teladoc, and Livongo appearing at a much higher rate.
ChatGPT was the most consensus-driven of the three, with the most overlap between its top brands and the other models.
The heatmap shows how unevenly distributed even consensus brands are:




The gaps weren’t always subtle. Notion was cited 17 times more often by Claude than by Gemini. Whoop appeared almost four times more often in Claude than Gemini. State Farm drew almost half its mentions from ChatGPT and only about a quarter from Gemini.
This is why aggregate AI visibility scores can mislead senior teams. If a dashboard says your “AI visibility” improved, your first question should be: Where?
A single blended score can hide the actual problem. You may not have an AI visibility problem broadly. You may have a Gemini problem. Or a Claude problem. Or a category prompt problem. Or a source problem.
Measure each model separately. Track prompts by category and intent. Look at which sources appear repeatedly. Then decide where the gap is worth closing.
Some sectors have already been re-sorted
The disconnect between traditional authority and AI recall wasn’t evenly distributed across industries. Some sectors still look a lot like Google, while others have been almost completely re-ranked by the models.




The pattern breaks down differently by sector:
Insurance was the least represented, at 8%. Legacy carriers like Aetna, Cigna, Humana, and Liberty Mutual were replaced by digitally native carriers. If you’re an established carrier, you have the largest gap between your Google authority and your AI recall.
Education had the highest overperformer rate, at 7%, with Stanford, MIT, Khan Academy, IBM Data Science, Google Career Certificates, and LinkedIn Learning dominating. Professional education and institutions do better here than the commercial platforms because they generate credibility content.
HealthTech was neutral (underrepresented by 5%, overperforming by 6%), with a shift underway from pharma and medical device companies to consumer-facing digital health.
Travel had the smallest disconnect (4% under, 4% over) because LLM recall and traditional awareness lined up closely. In travel, Booking.com, Expedia, and Airbnb are the best-known names to both Google and the models. Travel’s top performers are stable.
SaaS and FinTech sat between 3% to 4% over and 6% under. That’s because in these categories, the story is one incumbent replacing another (Stripe vs. PayPal, Notion vs. Microsoft) rather than new entrants displacing the old guard entirely.
The pattern across sectors mirrors the broader finding. Where the legacy authority of a category was built primarily on SEO and traffic, the models have re-sorted. Where the category was built around brand stories that circulated in third-party media, the alignment is tighter.
What this means for brand strategy
These are my five main lessons from the study, roughly ordered by how quickly you can act on them.
Measure traditional search authority and AI recall separately 
Domain rating, organic traffic, and keyword rankings still matter. They just don’t tell the whole story. 
A brand can rank well on Google and still fail to show up in AI answers. Another brand can have a smaller traditional SEO footprint and still become a default recommendation. Track both. 
Measure where you rank in Google and where you appear in AI responses. Then look for mismatches. The mismatches are where the strategy lives.
Build third-party validation, not just owned content
The brands that outperform in AI responses tend to appear repeatedly in other people’s content. That includes:
Roundups.
Best-of lists.
Comparison articles.
Expert reviews.
Analyst pages.
Podcasts. 
YouTube transcripts.
Community discussions.
Category-specific publisher coverage. 
Owned content can help clarify your positioning, but it won’t replace external corroboration. If AI systems are building answers from the broader web’s understanding of your brand, digital PR becomes one of the most practical levers for AI visibility.
Track each model separately 
Only 11% of brands were referenced by all three models. That should kill the idea that “AI visibility” is one clean metric. Measure ChatGPT, Gemini, and Claude separately. Break prompts out by product category, buyer intent, and comparison set. 
Track whether your brand appears, how it’s described, which competitors appear with it, and which sources seem to support the answer. A model-specific miss gives you a much clearer action plan than a blended visibility score.
Fix categorization before you chase volume
If your brand has strong overall awareness but weak recall in your target category, the issue may be categorization. The model may know who you are. It just may not think you belong in the answer set for the queries that matter. That requires a different strategy. 
You need more category-specific proof: media coverage, comparison content, partner references, customer stories, awards, reviews, and third-party pages that repeatedly connect your brand to the category you want to own.
Move before the default-answer slot hardens 
Some categories are still wide open. Wellness, lifestyle, and parts of HealthTech still have room for new default answers. 
Others are already consolidating. Travel, major SaaS categories, and parts of FinTech are much harder to break into because the models already return a tight set of familiar names. 
The earlier you build category association, the easier it is to influence recall. Once a model repeatedly sees the same brands attached to the same category, dislodging them gets harder.
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What the web says about your brand matters
Your next brand visibility audit shouldn’t stop at your site. It should map the sources that teach models who belongs in your category: roundups, comparisons, expert lists, publisher coverage, reviews, partner pages, and community discussions.
That’s where the overperformers in this study separated themselves, not by being the biggest brands in organic search, but by being repeatedly reinforced in the places AI systems use to understand, compare, and recommend companies.
Methodology



Fractl analyzed AI visibility across eight industries using GPT-4o, Gemini 2.5 Flash, and Claude Sonnet 4.6. Each model received the same 96 industry-specific prompts, with each prompt run 15 times per model. The analysis produced 4,320 responses and more than 8,500 unique brand references.
Fractl then mapped the brands identified in those responses to Ahrefs data, including domain rating, organic traffic, referring domains, and keyword volume. This made it possible to compare each brand’s traditional search authority with how frequently it appeared in AI-generated responses.
The analysis grouped brands based on how their AI visibility compared with their traditional search performance. Brands that appeared less often than their SEO metrics would suggest were classified as underrepresented, while brands that appeared more often were classified as overperformers.

Some brands dominate Google yet barely surface in AI answers. Here’s where traditional authority and AI recall diverge.

The AI visibility index: Which brands are vanishing from AI search?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png" class="attachment-large size-large wp-post-image" alt="The $113K paid search test- How much traffic did organic really replace" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Paid search looked valuable in the channel report. A 13-week test revealed how much of that value the business actually needed to buy.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png" class="attachment-large size-large wp-post-image" alt="The $113K paid search test- How much traffic did organic really replace" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-113K-paid-search-test-How-much-traffic-did-organic-really-replace-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">I turned off $113,000 a month in <a href="https://searchengineland.com/library/ppc">paid search</a>. </p>



<p class="wp-block-paragraph">Thirteen weeks later, 65% of the paid-attributed revenue had been recaptured through organic and direct, and the analysis showed that nearly nine out of 10 dollars in the branded campaign I studied had been defending clicks organic was already positioned to win.</p>



<p class="wp-block-paragraph">Every performance marketer has the same recurring nightmare.</p>



<p class="wp-block-paragraph">You turn off paid search. Traffic goes rogue. Revenue goes full &#8220;Thelma &amp; Louise&#8221; and drives off a cliff. Then, during Monday’s leadership meeting, someone asks (very calmly, which is somehow worse) what you thought would happen.</p>



<p class="wp-block-paragraph">So paid stays on. Forever.&nbsp;</p>



<p class="wp-block-paragraph">It becomes the unwanted houseguest eating everything in your fridge with no indication of finding an apartment. I wanted to know if it deserved to be there.</p>



<h2 id="h-experiment-were-they-spending-113k-buying-customers-or-buying-our-own-clicks" class="wp-block-heading">Experiment: Were they spending $113K buying customers or buying our own clicks?</h2>



<p class="wp-block-paragraph">I was asked to answer this question:&nbsp;</p>



<p class="wp-block-paragraph">How much of our paid search budget was bringing in customers they wouldn’t have reached otherwise, and how much was paying Google for clicks our organic listings were already winning?</p>



<p class="wp-block-paragraph">This was what I wanted to test by turning it off.</p>



<p class="wp-block-paragraph">A company paused branded paid search across four of its largest markets: the United States, United Kingdom, Australia, and Canada.</p>



<p class="wp-block-paragraph">By the end of the month, most non-brand paid search had been paused, too. Branded search never came back.</p>



<p class="wp-block-paragraph">Before the pause, the company was spending $106,000 to $119,000 per month across branded search, non-brand search, Shopping, and <a href="https://searchengineland.com/google-performance-max-431821">Performance Max</a>, so I’ll say $113,000 per month on average.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/paid-search-clicks-double-organic-clicks-fall-study-469519">Paid search click share doubles as organic clicks fall: Study</a></em></strong>


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<h2 id="h-the-questions-that-make-paid-search-hard-to-turn-off" class="wp-block-heading">The questions that make paid search hard to turn off</h2>



<p class="wp-block-paragraph">There’s always a “<strong>but what if</strong>.”</p>



<ul class="wp-block-list">
<li>What if the ad at the top of the page makes someone trust the brand a little more? </li>



<li>What if seeing paid and organic together gives us more real estate on the SERP? </li>



<li>What if competitors start bidding on our name? </li>



<li>What if turning paid off doesn’t move the click downstairs to organic but makes the click disappear entirely?</li>
</ul>



<p class="wp-block-paragraph">This is the paid search halo.</p>



<p class="wp-block-paragraph">And unlike Bigfoot, there’s some evidence it exists.</p>



<p class="wp-block-paragraph">Google studied more than 400 advertisers that paused search campaigns and found that, on average,<a href="https://research.google/pubs/incremental-clicks-impact-of-search-advertising/"> 89% of ad clicks were incremental</a>. Organic search didn’t magically pick up most of the traffic when the ads went away.<a href="https://research.google/pubs/impact-of-ranking-of-organic-search-results-on-the-incrementality-of-search-ads/"> Google’s follow-up research</a> also found that incrementality changes depending on whether an organic result appears and where it ranks.</p>



<p class="wp-block-paragraph">Which sounds like a fairly compelling reason to leave the credit card where it is.</p>



<p class="wp-block-paragraph">Except there’s another famous experiment.</p>



<p class="wp-block-paragraph"><a href="https://cepr.org/voxeu/columns/experiment-estimate-effectiveness-branded-search-ads">eBay turned off branded paid search</a> and found that almost all of the missing paid clicks and attributed sales were immediately<a href="https://www.theguardian.com/technology/2013/mar/13/google-keyword-advertising-wastes-money-ebay"> captured by organic search</a>. For a household-name brand with strong organic visibility, the ads were intercepting customers who already knew where they were going.</p>



<p class="wp-block-paragraph">So which one is right? Annoyingly: both.</p>



<p class="wp-block-paragraph">Paid search can create incremental traffic. It can also charge you admission for customers who were already standing in line.</p>



<p class="wp-block-paragraph">The only way to know which one you’re buying is to measure it. So I did.</p>



<p class="wp-block-paragraph">I wanted to know what our halo cost and whether there was anything underneath it.</p>



<h2 id="h-putting-paid-and-organic-in-one-spreadsheet-nbsp" class="wp-block-heading">Putting paid and organic in one spreadsheet&nbsp;</h2>



<p class="wp-block-paragraph">I brought every Google Ads search term and its associated spend together with every matching Search Console query and its organic clicks.</p>



<p class="wp-block-paragraph">Then I matched them keyword by keyword across the same period to understand what the company was paying for.</p>



<p class="wp-block-paragraph">I sorted each query into one of three categories:</p>



<ul class="wp-block-list">
<li><strong>Cannibalized:</strong> The company paid for a click organic was already positioned to win.</li>



<li><strong>Dependent:</strong> Organic performance benefited from paid support and declined when the ads disappeared.</li>



<li>Incremental: Paid search reached a customer that organic search couldn’t have captured.</li>
</ul>



<p class="wp-block-paragraph">Then I ran one of the company’s branded campaigns through the model.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/prevent-ppc-cannibalizing-seo-efforts-451920">How to prevent PPC from cannibalizing your SEO efforts</a></em></strong>



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<h2 id="h-the-impact-89-defense-11-offense" class="wp-block-heading">The impact: 89% defense, 11% offense</h2>



<p class="wp-block-paragraph">On queries where<a href="https://searchengineland.com/get-ppc-seo-working-together-386083"> paid and organic overlapped</a>, organic was already winning 71% of clicks, even with the ads running.</p>



<p class="wp-block-paragraph">Of the $36,129 spent in the branded campaign I analyzed, only $3,945, or 10.9%, appeared to buy clicks organic couldn’t have captured.</p>



<p class="wp-block-paragraph">The other $32,184?</p>



<p class="wp-block-paragraph">Brand defense.</p>



<p class="wp-block-paragraph">Nearly nine out of every 10 dollars were going toward traffic the company was already positioned to receive.</p>



<p class="wp-block-paragraph">Turns out, a lot of our “paid acquisition” was organic traffic with a cover charge.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Metric</strong></td><td><strong>Result</strong></td></tr><tr><td>Branded spend</td><td>$36,129</td></tr><tr><td>Organic share of overlapping clicks</td><td>71%</td></tr><tr><td>Incremental spend</td><td>$3,945</td></tr><tr><td>Incremental share</td><td>10.9%</td></tr><tr><td>Brand-defense spend</td><td>$32,184</td></tr><tr><td>Brand-defense share</td><td>89.1%</td></tr><tr><td>Annualized incremental spend</td><td>~$15,000</td></tr><tr><td>Annualized cannibalized spend</td><td>~$128,000</td></tr></tbody></table></figure>



<div aria-hidden="true" class="wp-block-spacer"></div>



<h2 id="h-then-the-company-turned-off-paid-and-organic-moved-in" class="wp-block-heading">Then, the company turned off paid — and organic moved in</h2>



<p class="wp-block-paragraph">Six weeks after the pause, organic search began doing what I hoped it would do: reaching over and collecting some of the clicks paid search used to purchase.</p>



<p class="wp-block-paragraph">And once the full revenue data became available, the story became much easier to quantify.</p>



<p class="wp-block-paragraph">Organic revenue increased $18,000 to $28,000 per month over the pre-pause baseline.</p>



<p class="wp-block-paragraph">Direct revenue initially increased by about $14,000 per month. By week 13 and beyond, the direct revenue lift had climbed to roughly $54,000 per month.</p>



<p class="wp-block-paragraph">Combined organic and direct revenue recaptured 30% of the paid-attributed revenue within six weeks. By week 13, recapture had reached 65%.</p>



<p class="wp-block-paragraph">All while spending nothing to acquire those visits through paid search.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Time after pause</strong></td><td><strong>Organic revenue change/month</strong></td><td><strong>Direct revenue change/month</strong></td><td><strong>Recapture</strong></td></tr><tr><td>Weeks 1-6</td><td>+$17,800</td><td>+$14,500</td><td>30%</td></tr><tr><td>Weeks 7-12</td><td>+$28,100</td><td>+$14,100</td><td>39%</td></tr><tr><td>Weeks 13+</td><td>+$15,800</td><td>+$54,000</td><td>65%</td></tr></tbody></table></figure>



<div aria-hidden="true" class="wp-block-spacer"></div>



<p class="wp-block-paragraph">Comparing the paid-on period with the later paid-off period, organic clicks increased across several of the company’s most commercially valuable product terms.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Query type</strong></td><td><strong>Paid on</strong></td><td><strong>Paid off</strong></td><td><strong>Change</strong></td></tr><tr><td>Core product term</td><td>5,144</td><td>5,858</td><td><strong>+14%</strong></td></tr><tr><td>Premium product term</td><td>638</td><td>990</td><td><strong>+55%</strong></td></tr><tr><td>Product variant</td><td>228</td><td>402</td><td><strong>+76%</strong></td></tr><tr><td>Category plural</td><td>315</td><td>444</td><td><strong>+41%</strong></td></tr><tr><td>Product variant</td><td>206</td><td>289</td><td><strong>+40%</strong></td></tr><tr><td>Brand + category</td><td>265</td><td>352</td><td><strong>+33%</strong></td></tr><tr><td>Entry-level product</td><td>433</td><td>490</td><td><strong>+13%</strong></td></tr></tbody></table></figure>



<div aria-hidden="true" class="wp-block-spacer"></div>



<p class="wp-block-paragraph">Across every commercial query shown here, organic clicks went up after paid went away.</p>



<h2 id="h-clicks-came-back-faster-than-the-money" class="wp-block-heading">Clicks came back faster than the money</h2>



<p class="wp-block-paragraph">Paid and organic are two doors leading into the same room. Shut one, and customers don’t necessarily go home. Plenty of them use the other door.</p>



<p class="wp-block-paragraph">The catch is that they don’t all buy at the same rate. Paid traffic converted at about 2.9%. Organic converted closer to 2.3%.</p>



<p class="wp-block-paragraph">So the clicks came back faster than the revenue did.&nbsp;</p>



<p class="wp-block-paragraph">Did organic replace paid?</p>



<p class="wp-block-paragraph">Not perfectly. But far more of it than the original attribution ever suggested.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-ppc-trust-across-search-case-study-484383">How SEO and PPC build trust across search: Case study</a></em></strong>


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<h2 id="h-what-i-learned-revenue-isn-t-profit" class="wp-block-heading">What I learned: Revenue isn’t profit</h2>



<p class="wp-block-paragraph">This is usually where case studies start polishing the numbers until everyone involved looks brilliant. I won’t.</p>



<p class="wp-block-paragraph">Top-line revenue fell roughly $30,000 to $40,000 per month from the pre-pause baseline.</p>



<p class="wp-block-paragraph">That happened. But after accounting for media spend that was no longer going to Google, the working estimate put net P&amp;L roughly $5,000 to $20,000 per month higher.</p>



<p class="wp-block-paragraph">That may sound contradictory. It isn’t.</p>



<p class="wp-block-paragraph">Did the company sell slightly less? Yes.</p>



<p class="wp-block-paragraph">Did the business potentially make more money? Also yes.</p>



<p class="wp-block-paragraph">And that may be the most important thing this experiment uncovered.</p>



<p class="wp-block-paragraph">Channel dashboards are good at telling you what revenue an ad touched.</p>



<p class="wp-block-paragraph">They’re less good at telling you whether you needed to buy the touch in the first place.</p>



<p class="wp-block-paragraph">The company had been spending six figures a month on paid search. In the branded campaign I analyzed, 89% of the spend was defense rather than incremental acquisition.</p>



<p class="wp-block-paragraph">Six weeks after paid went dark, organic and direct had recaptured 30% of the lost paid-attributed revenue.</p>



<p class="wp-block-paragraph">By week 13, they’d recaptured 65%. That doesn’t mean branded paid search is bad.</p>



<p class="wp-block-paragraph">Competitors exist. SERPs change. Organic can’t capture every click. And in this experiment, paid traffic converted better.</p>



<p class="wp-block-paragraph">It means you should know what you’re paying for. “We’ve always run branded search” isn’t a strategy.</p>



<p class="wp-block-paragraph">Measure the halo. Price the defense. Give organic enough time to show you what it can carry.</p>



<p class="wp-block-paragraph">And above all, don’t confuse less attributed revenue with a worse business.</p>



<p class="wp-block-paragraph">This company turned off the money. The customers kept coming.</p>



<p class="wp-block-paragraph">They just stopped paying Google to introduce them to people who were already looking.</p>

I turned off $113,000 a month in paid search. 
Thirteen weeks later, 65% of the paid-attributed revenue had been recaptured through organic and direct, and the analysis showed that nearly nine out of 10 dollars in the branded campaign I studied had been defending clicks organic was already positioned to win.
Every performance marketer has the same recurring nightmare.
You turn off paid search. Traffic goes rogue. Revenue goes full “Thelma & Louise” and drives off a cliff. Then, during Monday’s leadership meeting, someone asks (very calmly, which is somehow worse) what you thought would happen.
So paid stays on. Forever. 
It becomes the unwanted houseguest eating everything in your fridge with no indication of finding an apartment. I wanted to know if it deserved to be there.
Experiment: Were they spending $113K buying customers or buying our own clicks?
I was asked to answer this question: 
How much of our paid search budget was bringing in customers they wouldn’t have reached otherwise, and how much was paying Google for clicks our organic listings were already winning?
This was what I wanted to test by turning it off.
A company paused branded paid search across four of its largest markets: the United States, United Kingdom, Australia, and Canada.
By the end of the month, most non-brand paid search had been paused, too. Branded search never came back.
Before the pause, the company was spending $106,000 to $119,000 per month across branded search, non-brand search, Shopping, and Performance Max, so I’ll say $113,000 per month on average.
Dig deeper: Paid search click share doubles as organic clicks fall: Study



  
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The questions that make paid search hard to turn off
There’s always a “but what if.”
What if the ad at the top of the page makes someone trust the brand a little more? 
What if seeing paid and organic together gives us more real estate on the SERP? 
What if competitors start bidding on our name? 
What if turning paid off doesn’t move the click downstairs to organic but makes the click disappear entirely?
This is the paid search halo.
And unlike Bigfoot, there’s some evidence it exists.
Google studied more than 400 advertisers that paused search campaigns and found that, on average, 89% of ad clicks were incremental. Organic search didn’t magically pick up most of the traffic when the ads went away. Google’s follow-up research also found that incrementality changes depending on whether an organic result appears and where it ranks.
Which sounds like a fairly compelling reason to leave the credit card where it is.
Except there’s another famous experiment.
eBay turned off branded paid search and found that almost all of the missing paid clicks and attributed sales were immediately captured by organic search. For a household-name brand with strong organic visibility, the ads were intercepting customers who already knew where they were going.
So which one is right? Annoyingly: both.
Paid search can create incremental traffic. It can also charge you admission for customers who were already standing in line.
The only way to know which one you’re buying is to measure it. So I did.
I wanted to know what our halo cost and whether there was anything underneath it.
Putting paid and organic in one spreadsheet 
I brought every Google Ads search term and its associated spend together with every matching Search Console query and its organic clicks.
Then I matched them keyword by keyword across the same period to understand what the company was paying for.
I sorted each query into one of three categories:
Cannibalized: The company paid for a click organic was already positioned to win.
Dependent: Organic performance benefited from paid support and declined when the ads disappeared.
Incremental: Paid search reached a customer that organic search couldn’t have captured.
Then I ran one of the company’s branded campaigns through the model.
Dig deeper: How to prevent PPC from cannibalizing your SEO efforts




	
	
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The impact: 89% defense, 11% offense
On queries where paid and organic overlapped, organic was already winning 71% of clicks, even with the ads running.
Of the $36,129 spent in the branded campaign I analyzed, only $3,945, or 10.9%, appeared to buy clicks organic couldn’t have captured.
The other $32,184?
Brand defense.
Nearly nine out of every 10 dollars were going toward traffic the company was already positioned to receive.
Turns out, a lot of our “paid acquisition” was organic traffic with a cover charge.

MetricResult
Branded spend$36,129
Organic share of overlapping clicks71%
Incremental spend$3,945
Incremental share10.9%
Brand-defense spend$32,184
Brand-defense share89.1%
Annualized incremental spend~$15,000
Annualized cannibalized spend~$128,000






Then, the company turned off paid — and organic moved in
Six weeks after the pause, organic search began doing what I hoped it would do: reaching over and collecting some of the clicks paid search used to purchase.
And once the full revenue data became available, the story became much easier to quantify.
Organic revenue increased $18,000 to $28,000 per month over the pre-pause baseline.
Direct revenue initially increased by about $14,000 per month. By week 13 and beyond, the direct revenue lift had climbed to roughly $54,000 per month.
Combined organic and direct revenue recaptured 30% of the paid-attributed revenue within six weeks. By week 13, recapture had reached 65%.
All while spending nothing to acquire those visits through paid search.

Time after pauseOrganic revenue change/monthDirect revenue change/monthRecapture
Weeks 1-6+$17,800+$14,50030%
Weeks 7-12+$28,100+$14,10039%
Weeks 13++$15,800+$54,00065%






Comparing the paid-on period with the later paid-off period, organic clicks increased across several of the company’s most commercially valuable product terms.

Query typePaid onPaid offChange
Core product term5,1445,858+14%
Premium product term638990+55%
Product variant228402+76%
Category plural315444+41%
Product variant206289+40%
Brand + category265352+33%
Entry-level product433490+13%






Across every commercial query shown here, organic clicks went up after paid went away.
Clicks came back faster than the money
Paid and organic are two doors leading into the same room. Shut one, and customers don’t necessarily go home. Plenty of them use the other door.
The catch is that they don’t all buy at the same rate. Paid traffic converted at about 2.9%. Organic converted closer to 2.3%.
So the clicks came back faster than the revenue did. 
Did organic replace paid?
Not perfectly. But far more of it than the original attribution ever suggested.
Dig deeper: How SEO and PPC build trust across search: Case study



  
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What I learned: Revenue isn’t profit
This is usually where case studies start polishing the numbers until everyone involved looks brilliant. I won’t.
Top-line revenue fell roughly $30,000 to $40,000 per month from the pre-pause baseline.
That happened. But after accounting for media spend that was no longer going to Google, the working estimate put net P&L roughly $5,000 to $20,000 per month higher.
That may sound contradictory. It isn’t.
Did the company sell slightly less? Yes.
Did the business potentially make more money? Also yes.
And that may be the most important thing this experiment uncovered.
Channel dashboards are good at telling you what revenue an ad touched.
They’re less good at telling you whether you needed to buy the touch in the first place.
The company had been spending six figures a month on paid search. In the branded campaign I analyzed, 89% of the spend was defense rather than incremental acquisition.
Six weeks after paid went dark, organic and direct had recaptured 30% of the lost paid-attributed revenue.
By week 13, they’d recaptured 65%. That doesn’t mean branded paid search is bad.
Competitors exist. SERPs change. Organic can’t capture every click. And in this experiment, paid traffic converted better.
It means you should know what you’re paying for. “We’ve always run branded search” isn’t a strategy.
Measure the halo. Price the defense. Give organic enough time to show you what it can carry.
And above all, don’t confuse less attributed revenue with a worse business.
This company turned off the money. The customers kept coming.
They just stopped paying Google to introduce them to people who were already looking.

Paid search looked valuable in the channel report. A 13-week test revealed how much of that value the business actually needed to buy.

The $113,000 paid search test: How much traffic did organic replace?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads.png" class="attachment-large size-large wp-post-image" alt="Inside ChatGPT&#039;s retrieval stack: The index, the cache, and the pages it actually reads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Inside-ChatGPTs-retrieval-stack-The-index-the-cache-and-the-pages-it-actually-reads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>New research exposes the mechanics behind ChatGPT citations, including what gets retrieved, what gets read, and what ultimately gets cited.


When ChatGPT cites a web page, where did that page come from?
In July, our team at RESONEO captured and dissected 1,200 ChatGPT answers, 88,000 search results, and 26,900 distinct pages to find out. (Disclosure: I’m the co-founder of RESONEO.)



We found three layers in how ChatGPT grounds its answers: a discovery index that finds pages, a reading cache that keeps full copies of pages it has fetched, and a small set of pages it opens live.
Each layer has its own rules, its own staleness, and its own blind spots. Understanding those layers makes much of ChatGPT’s citation behavior easier to explain — including why some pages are retrieved, some are read, and some ultimately get cited.
How we uncovered ChatGPT’s retrieval system
Our Chrome extension records the raw data stream ChatGPT sends to the browser, including fields the interface never displays. 
Until July 21, that stream contained a field called result_source that named the internal pipeline behind every single search result. 
Four values kept coming back: labrador, bright, oxylabs, and serp. None of them is documented by OpenAI, which only speaks publicly of “third-party search providers.”
Then the field vanished from the stream. It happened overnight, on every account we monitor. We now reidentify each pipeline by its formatting signatures (i.e., snippet length, title shape), with a classifier that gets it right about 98% of the time. V5 of our extension ships with that classifier built in. 
We also ran a fleet of accounts across tiers and countries, replayed the same prompts through OpenAI’s API, and published canary pages on our own domain with full server logs, so we could see exactly what OpenAI’s robots fetch rather than guess.
Dig deeper: Inside ChatGPT Search: how web.run and fan-out queries shape AI visibility



  
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The cast of pipelines
What we had established before this round, and what still holds:
labrador is OpenAI’s own retrieval hub. Besides web search, it returns news, academia (arXiv), Reddit, and YouTube results, so it acts as an orchestrator of in-house indexes, open repositories like Wikipedia, and news partnerships.
bright and oxylabs are scraped Google results, bought live.
P1/P2/P3 serve shopping from OpenAI’s own merchant feeds, with Google kept around as an oracle for prices and reviews.
B1/B3, Yelp, and TripAdvisor serve local results, with a twist: The links displayed to users point to Google Maps.
Shopping and local never touch web search. If you sell products or run a restaurant, your battle is fought in merchant feeds and business listings, on top of the classic citations. 
This article focuses on the web search side because that’s where the surprises were.
August update: The Think button changes which web ChatGPT sees
Six weeks after the July study, we replayed the same prompts across every ChatGPT configuration: free and paid, Instant, Think, and Thinking, at different effort levels. Until this rollout, Thinking was a paid-only product mode. Free users now effectively have two retrieval regimes: Instant and Think. The first surprise is that source volume and source origin are now two separate questions.
On a free account, clicking Think more than doubles the retrieval pool, from 15.1 to 35.3 URLs per conversation and from 9.8 to 16.3 distinct domains. That puts free Think at almost exactly the same source volume as paid Thinking at medium effort.
But the two modes don’t search the same web. Free Think gets 74.7% of its results from labrador, OpenAI’s own index, just 3.1% from classic scraped Google web results and 22.2% from oxylabs, which feeds its news channel. Paid Thinking is almost the mirror image: 75.3% scraped Google and 24.7% labrador.
That distinction matters for AI search visibility. Two people can ask the same question, retrieve roughly the same number of sources, and still get answers built from very different corpora. Ranking well in Google remains important for paid Thinking, but it doesn’t guarantee that a page will enter the free Think retrieval set, where OpenAI’s own index is the main gatekeeper.
Paid Thinking also became much narrower between July and August. On the same medium-effort prompts, fan-outs fell from 3.56 to 1.90 per conversation, URLs from 47.2 to 33.9, and distinct domains from 21.9 to 15.5. At advanced effort, the contraction is stronger still: 67 URLs became 40.5, and 29.5 domains became 14.7. Our captures date the break between July 26 and July 31, before OpenAI’s August 6 model refresh.
Free Instant didn’t show a detectable change in retrieval volume, but the product behavior around it did change sharply: reasoning appeared in 96.7% of our August answers, versus 0.6% in July, and the pipeline mix was redistributed.
The queries themselves also became more navigational. The site: operator appears in 3.5% of free Instant queries, 10.8% of free Think queries, 41.9% of medium-effort paid Thinking queries and 58.1% at high effort. 
At high effort, site: usage rose from 40.8% in late July to 58.1% in mid-August, even as the total query count fell. ChatGPT is searching less broadly and more often going straight to domains, brands and official sources it already expects to be useful.
One final caveat for anyone benchmarking through the API: The API doesn’t reproduce the product. We replayed identical prompts across four ChatGPT product regimes and four API models, then measured the overlap between the brands each answer mentioned using Jaccard similarity. 
API variants overlap with one another at 0.32–0.39, ChatGPT regimes at 0.25–0.37, but product-to-API similarity falls to just 0.23–0.27. The API is therefore useful for probing what the underlying model family knows about a brand, but not for predicting a ChatGPT answer. 
Even two ChatGPT regimes share only about a third of the brands they mention because the retrieval corpus, tools, system instructions, and compute differ. The closest pairs do line up with OpenAI’s announced mapping: free Think is closest to Luna and paid ChatGPT is closest to Sol, but those model names aren’t exposed in the ChatGPT UI.
OpenAI’s web index works differently from Bing
OpenAI has its own web index, and Bing doesn’t supply it. We checked three ways. Only 1.5% of labrador URLs appear in Bing’s top 20 for the same fan-outs. 
No labrador snippet matches a Bing snippet. Bing hard-caps titles at 75 characters, while 24% of labrador titles sail past that limit. The longest title we captured runs 289 characters.
That index feeds essentially all search citations in instant mode, the fast mode that free users get. The exception is questions whose answer requires near-live data (“who won last night’s game”), where it splits roughly 50/50 with scraped Google results.
What does a result from that index actually contain? Three things: 
The URL. 
The full page title, never truncated, even when it’s absurdly long.
A snippet of about 200 characters.
The snippet construction deserves a close look because it’s the text from your page that reaches the model in instant mode. It’s anchored on your H1, rendered in caps, plus whatever visible text sits immediately around it. That can include things you wouldn’t necessarily choose: 
The category label above the title.
The alt text of the image below it.
The byline.
The publication date.
A table of contents. 
We measured one case where the snippet was 100% table of contents and 0% content. Whatever occupies those 200 characters is what gets picked up, and nothing else. The meta description is ignored entirely.
One more thing about that snippet: It doesn’t depend on the query. The same URL returns the same frozen snippet whether the user asked about pricing, history, or side effects. It’s cut at indexing time and served as is until the next crawl.
By modern search standards, this is pretty crude. Query-independent snippets are the kind of thing web search left behind about two decades ago, and here they are in 2026, powering one of the world’s most-used AI assistants. 
We assume this is a placeholder and that it’ll improve fast. For now, it’s what your content gets judged on.
For comparison, bright results behave like Google results: titles are cut around 60 characters with an ellipsis, snippets are around 160 characters, and the meta description is used about one time in three. Your meta description does nothing for labrador and still works for the Google-fed pipelines.
Dig deeper: Bing, not Google, shapes which brands ChatGPT recommends



The routing is economic, not technical
Why does OpenAI maintain both? Cost is the answer.
In instant mode, ChatGPT must answer in a few seconds, and the user typically pays nothing. So it queries only what OpenAI already owns: the labrador hub. No page gets opened (zero pages opened in 93% of instant answers we captured). The response is grounded in titles and 200-character snippets, full stop.
In thinking mode, the user pays a subscription and accepts the wait. Now the expensive tools are invoked: scraped Google rankings through bright (75% of thinking search results) and real page opens performed by the ChatGPT-User robot. A thinking conversation pulls around 100 results across some 28 domains, compared with around 11 results in instant.
Everything that costs OpenAI money (paying scraping providers) or time (opening URLs, calling live APIs) is reserved for the mode where users pay and wait. Here’s why this matters commercially: More than 90% of ChatGPT users are on the free tier. 
Free means instant, and instant means labrador. The pipeline nobody had heard of six months ago is the one deciding visibility in the vast majority of ChatGPT conversations happening right now.
The funnel numbers make the stakes concrete. Across our corpus, 61,332 URLs were surfaced into the sources sidebar. 5,032 became the lead source behind a citation. 759 pages were actually opened, all in thinking mode. An opened page ends up cited 74% of the time. A page merely retrieved but never opened, 7%.
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A reading cache shared by everyone
This is the part Jérôme Salomon of Oncrawl dug into with us, and it changed how we think about “ChatGPT visited my page.”
Alongside the index, ChatGPT maintains a cache of every page it has ever fetched. Not snippets: full pages, converted from HTML to Markdown, and stored that way. The cache is keyed by URL and shared across all users and tiers. If a free user in Berlin asks about a page that a paying user in Ohio triggered a fetch on last week, Berlin gets Ohio’s copy.
The refresh logic is what engineers call stale-while-revalidate. A copy is considered fresh for about 30 minutes. Within that window, everyone gets the stored version, and your server sees nothing. 
Past 30 minutes, the user still gets the stale copy immediately, and a background fetch refreshes it for the next person. This means the recrawl schedule of your pages is set by one signal only: how often ChatGPT users ask about them. Popular pages stay fresh. Unpopular pages age indefinitely. 
Salomon documented copies served more than 90 days after the fetch, with no eviction cap in sight. Your Cache-Control: no-store header? Ignored. Your noindex? Ignored too.
The Markdown conversion has its own personality. 
Scripts, iframes, and JSON-LD are stripped (yes, your structured data never reaches the model through this path). 
Image alt text survives. 
Text hidden by CSS is still extracted, so the robot reads content human visitors never see. 
We’ll leave the implications of that as an exercise for the reader and note that cloaking detection does not yet appear to be OpenAI’s strong suit.
Two hard limits frame every fetch. The robot does not execute JavaScript, so client-side content is invisible to it. And pages are capped at exactly 4 MB. Past that, the page is not truncated. It is rejected outright with an HTTP 400, and the model reads nothing at all. A page that is too heavy is not partially read. It does not exist.
One surprise hides in all this: an API parameter returns the crawl date of the stored copy of a URL. Fetch your own pages through it, and you learn when a ChatGPT user last caused your page to be read. That is a real, nearly free exposure metric, and nobody is watching it.
The analytics blind spot
You’ve probably seen utm_source=chatgpt.com in your analytics reports. That parameter is appended to the clickable links displayed to users. It tracks outbound clicks. 
But not every citation carries that tracking parameter. We observed that the pages ChatGPT opens on its own in thinking mode carry no utm_source in the citations displayed to users.
So the pages the model actually read in full, the ones with a 74% citation rate, leave zero UTM trace. If you gauge your ChatGPT exposure by filtering on that parameter, you’re counting the clicks and missing the reads. Also, watch for the ChatGPT-User user agent in your server logs.
The mystery: Citations that come from nowhere
One finding resists explanation, and we want the community’s help on it.
A share of the results reaching the model carry no snippet at all, just a title and a URL. We see it for arXiv or Reddit results, which come from databases OpenAI has direct access to, so that part is no surprise. But there are many others. 
In instant mode, snippet-less URLs (excluding Reddit, YouTube, and arXiv) get cited more than URLs with snippets (14.9% versus 8.2%). The model prefers citing pages it has read the least about.
Stranger still, some cited links match no search result at all. Our hypothesis is that the model writes them from its parametric memory, the knowledge baked into its weights during training. These are almost always bare domain roots of well-known sites, dutifully suffixed with utm_source=chatgpt.com as if they came from a search. 
A meaningful share of what ChatGPT ends up citing may simply be what it remembers, not what it retrieved. We can observe the phenomenon, but we can’t yet size it precisely, and we would genuinely welcome challengers on this one.
A related oddity: arXiv was pulled more than 2,600 times in our corpus and cited 10 times. Reddit, same pattern. ChatGPT reads preprints and forums to think, then shows the reader ordinary web pages. Being retrieved and being cited are two different markets.
Dig deeper: ChatGPT topic ownership is rare, and SEO alone doesn’t explain it



So, should you write 289-character titles?
The full title reaches the model untruncated, so it’s part of your grounding budget. The 200 characters after your H1 are the only body text the model sees in instant mode.
As such, moving down the category labels, dates, and widgets that sit between your H1 and your first useful sentence directly improves what ChatGPT knows about your page. Both of those are real, measured, and actionable today.
Act on them with a light touch. 
Write the title as a self-contained sentence rather than a truncation-optimized label. 
Make the first 200 characters after the H1 carry your core message. 
Keep an informative alt text near the top. 
Keep your meta description anyway because the Google-fed pipelines still use it. 
Stay under 4 MB and readable without JavaScript.
But don’t rebuild your site around a quirk. Everything in this article describes a system that evolves very fast: the result_source field disappeared overnight, shopping providers were anonymized in the same week, and Google Shopping tokens went from readable to encrypted in four days. Any specific mechanism we documented can be gone by the time you deploy.
Two structural facts, though, are safe to build on. 
LLMs have knowledge with a cutoff date, past which they know nothing, so they’re condemned to lean on search engines to bridge the gap between their training data and today. Retrieval will remain a permanent dependency of every AI assistant. 
Every engine they lean on will be pushed toward Google-grade quality because rudimentary indexes produce rudimentary answers, and users notice. The 200-character snippet probably won’t last long. The need to be the best answer will. That’s why the classic fundamentals, crawlable pages, clear titles, and content that actually answers the question keep paying off no matter how the plumbing changes.
The durable battle: Owning the answers to real questions
Optimizing snippet mechanics is a tactic. The strategy is upstream: knowing what people actually ask AI assistants and being the source that answers it.
Nobody asks ChatGPT for “a stroller with a 49 cm frame width.” They ask for “a stroller that fits through metro ticket gates.” 
Nobody searches for the foam firmness of headphone ear pads. They ask which headphones stay comfortable when you wear glasses. They ask which sofa survives a cat that scratches. 
That gap between spec-sheet language and human language is exactly where AI visibility is decided because assistants answer conversational questions with whatever content maps to them best.
You already own half the corpus that reveals those questions: 
Customer reviews.
Support conversations.
The emails you receive every day.
Surveys nobody reopened. 
The other half lives outside your walls, in what people say about your products on forums, on social platforms, in videos, in the press, and on blogs. 
Read both sides and the key benefits and pain points of your products emerge in your customers’ own words, which are also the words AI assistants hear. 
Mine that language, answer it on pages the robots can read, and you’re optimizing for every retrieval system at once, including the ones OpenAI hasn’t built yet.
What remains open
We don’t know which robot populates the labrador index. Our canary pages caught OAI-SearchBot nearly silent while the index kept serving results. 
Reddit is the easy case, since OpenAI has enjoyed real-time access to Reddit’s data API under its 2024 partnership. 
YouTube is another story, with no public agreement and no visible fingerprint explaining how those results get in. And the over-citation of snippet-less URLs still rests on a hypothesis.
OpenAI’s stack keeps getting harder to observe. Fields vanish from the stream, providers get anonymized, routing shifts week to week, and the whole thing grows more complex over time. 
We published the full study, What ChatGPT pulls, what it shows, what it cites, including the methodology and data behind every number in this article, so others can challenge these findings or push further.

New research exposes the mechanics behind ChatGPT citations, including what gets retrieved, what gets read, and what ultimately gets cited.

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