<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Ads API v25.1 adds new tools for lift measurement, benchmarking, brand sentiment, and loyalty reporting.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google released Google Ads API v25.1, adding new capabilities for conversion and brand lift measurement, competitive benchmarking, brand sentiment analysis and loyalty program reporting.</p>



<p class="wp-block-paragraph">The Aug. 19 release is a drop-in upgrade for v25, but developers will need to update their client libraries and code to access the new features.</p>



<p class="wp-block-paragraph"><strong>What’s new.</strong> Google is expanding what advertisers and third-party platforms can retrieve and analyze through the API, with several of the biggest additions focused on measurement and deeper performance insights.</p>



<p class="wp-block-paragraph">The release includes comprehensive API support for lift studies, new benchmark metrics, brand sentiment data, loyalty program segmentation and a metric showing conversion value before Google&#8217;s value adjustments are applied.</p>



<p class="wp-block-paragraph"><strong>Lift measurement.</strong> Google has added read-only API resources for Conversion Lift and Brand Lift studies, allowing developers to inspect study configurations, flight dates, campaigns and conversion goals.</p>



<p class="wp-block-paragraph">The update also introduces 24 new Conversion Lift metrics, winner score metrics for statistical analysis and Brand Lift dimensions covering age range, campaign, device, gender and video.</p>



<p class="wp-block-paragraph">There&#8217;s an important limitation: Brand Lift and Conversion Lift API features are currently available only to allowlisted Google Ads accounts, with advertisers instructed to contact their Google representative for access.</p>



<p class="wp-block-paragraph"><strong>Benchmarking gets more granular.</strong> Google&#8217;s BenchmarksService can now compare performance against advertisers within specific Product &amp; Service Categories, rather than relying on broader benchmark groups.</p>



<p class="wp-block-paragraph">Google is also adding metrics including aggregate cost and views alongside share-based measurements such as share of voice, potentially giving advertisers and reporting platforms more context around competitive performance.</p>



<p class="wp-block-paragraph"><strong>Brand sentiment comes to the API.</strong> The ContentCreatorInsightsService now supports brand sentiment data. Developers can retrieve sentiment distributions and summaries for creators and brands, expanding the type of creator and brand intelligence that can be incorporated into advertising tools and reporting workflows.</p>



<p class="wp-block-paragraph"><strong>Loyalty reporting expands.</strong> Advertisers can now segment reporting metrics according to the loyalty program tier of users who converted. Google is also introducing loyalty membership as a primary dimension for conversion value rules, making it possible to identify when a loyalty membership condition was satisfied as part of a conversion value rule.</p>



<p class="wp-block-paragraph"><strong>Original conversion values.</strong> A new <code>original_conversion_value</code> metric provides the value of a biddable conversion before conversion value rules or customer lifecycle goal adjustments are applied.</p>



<p class="wp-block-paragraph">That could be particularly useful for advertisers and measurement platforms trying to understand how much Google&#8217;s value adjustments are changing the numbers ultimately used for reporting and optimization.</p>



<p class="wp-block-paragraph"><strong>Reach planning.</strong> Google is adding parental status targeting to ReachPlanService, allowing it to be used within targeting for reach forecasting and plannable product discovery.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> The standout additions are around measurement and visibility. Lift study data, category-specific benchmarks and original conversion values give advertisers and third-party tools more ways to understand performance beyond standard campaign metrics. The ability to see conversion values before Google&#8217;s adjustments is particularly useful for teams that want to distinguish the underlying conversion data from values subsequently modified by rules or lifecycle goals.</p>



<p class="wp-block-paragraph"><strong>What developers need to do.</strong> Google says v25.1 is a drop-in upgrade for v25. Developers using Google&#8217;s client libraries will need to download the latest versions before they can access the new capabilities.</p>



<p class="wp-block-paragraph">Google has also scheduled a live walkthrough of the release for Aug. 20 at 10 a.m. ET, with the session available through its developer community and YouTube.</p>



<p class="wp-block-paragraph"><strong>Bottom line.</strong> <a href="https://ads-developers.googleblog.com/2026/08/announcing-v251-of-google-ads-api.html">Google Ads API v25.1</a> is largely a measurement and insights release, giving developers deeper access to lift studies, competitive benchmarks, brand sentiment, loyalty performance and the underlying conversion values behind Google&#8217;s optimization adjustments.</p>


Google released Google Ads API v25.1, adding new capabilities for conversion and brand lift measurement, competitive benchmarking, brand sentiment analysis and loyalty program reporting.
The Aug. 19 release is a drop-in upgrade for v25, but developers will need to update their client libraries and code to access the new features.
What’s new. Google is expanding what advertisers and third-party platforms can retrieve and analyze through the API, with several of the biggest additions focused on measurement and deeper performance insights.
The release includes comprehensive API support for lift studies, new benchmark metrics, brand sentiment data, loyalty program segmentation and a metric showing conversion value before Google’s value adjustments are applied.
Lift measurement. Google has added read-only API resources for Conversion Lift and Brand Lift studies, allowing developers to inspect study configurations, flight dates, campaigns and conversion goals.
The update also introduces 24 new Conversion Lift metrics, winner score metrics for statistical analysis and Brand Lift dimensions covering age range, campaign, device, gender and video.
There’s an important limitation: Brand Lift and Conversion Lift API features are currently available only to allowlisted Google Ads accounts, with advertisers instructed to contact their Google representative for access.
Benchmarking gets more granular. Google’s BenchmarksService can now compare performance against advertisers within specific Product & Service Categories, rather than relying on broader benchmark groups.
Google is also adding metrics including aggregate cost and views alongside share-based measurements such as share of voice, potentially giving advertisers and reporting platforms more context around competitive performance.
Brand sentiment comes to the API. The ContentCreatorInsightsService now supports brand sentiment data. Developers can retrieve sentiment distributions and summaries for creators and brands, expanding the type of creator and brand intelligence that can be incorporated into advertising tools and reporting workflows.
Loyalty reporting expands. Advertisers can now segment reporting metrics according to the loyalty program tier of users who converted. Google is also introducing loyalty membership as a primary dimension for conversion value rules, making it possible to identify when a loyalty membership condition was satisfied as part of a conversion value rule.
Original conversion values. A new original_conversion_value metric provides the value of a biddable conversion before conversion value rules or customer lifecycle goal adjustments are applied.
That could be particularly useful for advertisers and measurement platforms trying to understand how much Google’s value adjustments are changing the numbers ultimately used for reporting and optimization.
Reach planning. Google is adding parental status targeting to ReachPlanService, allowing it to be used within targeting for reach forecasting and plannable product discovery.
Why we care. The standout additions are around measurement and visibility. Lift study data, category-specific benchmarks and original conversion values give advertisers and third-party tools more ways to understand performance beyond standard campaign metrics. The ability to see conversion values before Google’s adjustments is particularly useful for teams that want to distinguish the underlying conversion data from values subsequently modified by rules or lifecycle goals.
What developers need to do. Google says v25.1 is a drop-in upgrade for v25. Developers using Google’s client libraries will need to download the latest versions before they can access the new capabilities.
Google has also scheduled a live walkthrough of the release for Aug. 20 at 10 a.m. ET, with the session available through its developer community and YouTube.
Bottom line. Google Ads API v25.1 is largely a measurement and insights release, giving developers deeper access to lift studies, competitive benchmarks, brand sentiment, loyalty performance and the underlying conversion values behind Google’s optimization adjustments.

Google Ads API v25.1 adds new tools for lift measurement, benchmarking, brand sentiment, and loyalty reporting.

Google Ads API v25.1 expands measurement and insights

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Microsoft is rolling out AI Max globally, bringing AI targeting, creative and landing page optimization to Search campaigns.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Microsoft Advertising has begun rolling out AI Max globally, bringing a new suite of AI-powered targeting, creative and landing page capabilities to Search campaigns.</p>



<p class="wp-block-paragraph">Microsoft Advertising Product Liaison Navah Hopkins said the rollout combines broader search matching, automated creative generation and dynamic landing page selection, while keeping AI Max <strong>opt-in</strong> for new and existing campaigns.</p>



<p class="wp-block-paragraph"><strong>What’s happening.</strong> AI Max is starting to roll out across all Microsoft Advertising accounts globally. The suite includes three core features: Search term matching, Text customization and Final URL expansion.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="538" src="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Adv-AI-Max.jpeg" alt="" class="wp-image-485531" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Adv-AI-Max.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Adv-AI-Max-768x516.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p class="wp-block-paragraph">Microsoft is positioning the features as complementary, encouraging advertisers to test all three together while using brand controls, URL rules and ad group-level settings to manage how the automation operates.</p>



<p class="wp-block-paragraph"><strong>Search term matching.</strong> The feature expands campaign reach beyond an advertiser’s existing keyword list by using keywords, ads, landing pages, user intent and contextual signals to identify additional relevant searches.</p>



<p class="wp-block-paragraph">Microsoft says this could be particularly useful for reaching more complex, conversational queries, including searches happening through AI-powered experiences on Bing and Copilot.</p>



<p class="wp-block-paragraph"><strong>Text customization.</strong> Microsoft AI can use an advertiser’s existing creative assets and website content to generate additional messaging variations.</p>



<p class="wp-block-paragraph">The system then selects what it considers the most appropriate creative combinations at auction time, with the aim of making ads more relevant to individual searches and AI-native experiences.</p>



<p class="wp-block-paragraph"><strong>Final URL expansion.</strong> Instead of directing every user to a predetermined landing page, AI Max can select a page from the advertiser’s website that it determines is more closely aligned with the user&#8217;s intent.</p>



<p class="wp-block-paragraph">Microsoft says this is designed to create greater consistency between the search query, ad creative and the page users ultimately visit.</p>



<p class="wp-block-paragraph"><strong>Advertiser controls.</strong> Microsoft is emphasizing that automation doesn&#8217;t mean advertisers have to surrender all control. AI Max launches with brand controls, including term exclusions for text asset generation, as well as reporting, URL rules and ad group-level settings.</p>



<p class="wp-block-paragraph">Advertisers can also use optimization experiments to test the entire AI Max suite or individual features before applying them more broadly.</p>



<p class="wp-block-paragraph"><strong>Existing automation moves into AI Max.</strong> Advertisers already using autogenerated text assets for Search campaigns or Predictive matching will see those capabilities moved under the AI Max umbrella.</p>



<p class="wp-block-paragraph">Those corresponding AI Max settings will be switched on in affected campaigns. However, Microsoft says it won&#8217;t automatically activate the other AI Max features, and AI Max will remain an opt-in setting for new and existing campaigns.</p>



<p class="wp-block-paragraph"><strong>What about Google Ads imports?</strong> AI Max settings will also carry across when advertisers import eligible Search campaigns into Microsoft Advertising.</p>



<p class="wp-block-paragraph">If an imported Search campaign already has AI Max features enabled, Microsoft will preserve those corresponding settings. There is one notable exception: AI Max Search campaigns that originated from upgraded Dynamic Search Ads will be converted back into DSA campaigns on Microsoft Advertising while the company develops additional functionality.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> Microsoft is bringing more of the automation increasingly shaping paid search into a single Search campaign framework, while notably keeping AI Max optional and providing controls from launch. For advertisers already managing AI Max campaigns in Google Ads, the ability to carry settings across through imports could also reduce duplicated setup work, although differences between the two platforms mean imported campaigns will still need monitoring.</p>



<p class="wp-block-paragraph"><strong>Bottom line.</strong> <a href="https://www.linkedin.com/posts/navahhopkins_ai-max-is-here-microsoft-advertising-has-share-7495901209418088449-W0t-/">AI Max marks Microsoft&#8217;s latest push</a> to make Search campaigns work across both traditional search and emerging AI experiences, with broader query matching, generated creative and dynamic landing pages now rolling out globally.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0102ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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Microsoft Advertising has begun rolling out AI Max globally, bringing a new suite of AI-powered targeting, creative and landing page capabilities to Search campaigns.
Microsoft Advertising Product Liaison Navah Hopkins said the rollout combines broader search matching, automated creative generation and dynamic landing page selection, while keeping AI Max opt-in for new and existing campaigns.
What’s happening. AI Max is starting to roll out across all Microsoft Advertising accounts globally. The suite includes three core features: Search term matching, Text customization and Final URL expansion.




Microsoft is positioning the features as complementary, encouraging advertisers to test all three together while using brand controls, URL rules and ad group-level settings to manage how the automation operates.
Search term matching. The feature expands campaign reach beyond an advertiser’s existing keyword list by using keywords, ads, landing pages, user intent and contextual signals to identify additional relevant searches.
Microsoft says this could be particularly useful for reaching more complex, conversational queries, including searches happening through AI-powered experiences on Bing and Copilot.
Text customization. Microsoft AI can use an advertiser’s existing creative assets and website content to generate additional messaging variations.
The system then selects what it considers the most appropriate creative combinations at auction time, with the aim of making ads more relevant to individual searches and AI-native experiences.
Final URL expansion. Instead of directing every user to a predetermined landing page, AI Max can select a page from the advertiser’s website that it determines is more closely aligned with the user’s intent.
Microsoft says this is designed to create greater consistency between the search query, ad creative and the page users ultimately visit.
Advertiser controls. Microsoft is emphasizing that automation doesn’t mean advertisers have to surrender all control. AI Max launches with brand controls, including term exclusions for text asset generation, as well as reporting, URL rules and ad group-level settings.
Advertisers can also use optimization experiments to test the entire AI Max suite or individual features before applying them more broadly.
Existing automation moves into AI Max. Advertisers already using autogenerated text assets for Search campaigns or Predictive matching will see those capabilities moved under the AI Max umbrella.
Those corresponding AI Max settings will be switched on in affected campaigns. However, Microsoft says it won’t automatically activate the other AI Max features, and AI Max will remain an opt-in setting for new and existing campaigns.
What about Google Ads imports? AI Max settings will also carry across when advertisers import eligible Search campaigns into Microsoft Advertising.
If an imported Search campaign already has AI Max features enabled, Microsoft will preserve those corresponding settings. There is one notable exception: AI Max Search campaigns that originated from upgraded Dynamic Search Ads will be converted back into DSA campaigns on Microsoft Advertising while the company develops additional functionality.
Why we care. Microsoft is bringing more of the automation increasingly shaping paid search into a single Search campaign framework, while notably keeping AI Max optional and providing controls from launch. For advertisers already managing AI Max campaigns in Google Ads, the ability to carry settings across through imports could also reduce duplicated setup work, although differences between the two platforms mean imported campaigns will still need monitoring.
Bottom line. AI Max marks Microsoft’s latest push to make Search campaigns work across both traditional search and emerging AI experiences, with broader query matching, generated creative and dynamic landing pages now rolling out globally.
a customer you lose.
      
See who’s stealing your traffic

Microsoft is rolling out AI Max globally, bringing AI targeting, creative and landing page optimization to Search campaigns.

Microsoft Advertising rolls out AI Max globally for Search campaigns

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png" class="attachment-large size-large wp-post-image" alt="Google local AI search citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A study of 14,472 AI citations found business websites led Gemini's local results, but repeated searches rarely returned the same sources.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png" class="attachment-large size-large wp-post-image" alt="Google local AI search citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Business websites accounted for nearly 60% of Gemini&#8217;s citations in local AI search, according to a new study. Steady Demand&#8217;s analysis of 14,472 citations from 1,487 local search queries across 50 U.S. metro areas also found that identical searches often produced different AI recommendations.</p>



<p class="wp-block-paragraph"><strong>Business websites beat directories. </strong>Nearly 60% of Gemini&#8217;s citations pointed directly to businesses&#8217; own websites. That was more than the combined total for directories, review platforms, and forums.</p>



<ul class="wp-block-list">
<li>Reddit ranked second, accounting for 13.7% of citations. It outperformed the entire local-service directory category, including Angi, Thumbtack, and HomeAdvisor. </li>
</ul>



<p class="wp-block-paragraph"><strong>AI answers weren&#8217;t repeatable</strong>. The report&#8217;s biggest finding was what Steady Demand co-founder Ben Fisher called &#8220;Grounding Drift.&#8221;</p>



<ul class="wp-block-list">
<li>Repeating the same Gemini query produced overlapping cited sources only about 40% of the time. </li>



<li>In a control test, Google&#8217;s local pack returned the same top listing about 90% of the time. </li>



<li>Gemini recommended the same top business only about 7% of the time.</li>



<li>Fisher said a single AI search should be treated as a snapshot, not a reliable measure of long-term visibility.</li>
</ul>



<p class="wp-block-paragraph"><strong>ChatGPT cited different sources</strong>. The same 1,487 local search queries were also run through ChatGPT: </p>



<ul class="wp-block-list">
<li>Both AI platforms cited many of the same domains only 8% of the time. They recommended the same top business just 4.2% of the time. </li>



<li>Gemini mainly cited business websites, while ChatGPT relied much more on Reddit and business directories.</li>
</ul>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Never assume success in one AI engine will carry over to another. Gemini and ChatGPT rarely cited the same sources or recommended the same businesses. Monitoring only one platform could give you a misleading picture of your AI visibility.</p>



<p class="wp-block-paragraph"><strong>About the data</strong>. <a href="https://www.steadydemand.com/ai-citation-ledger/" target="_blank" rel="noopener">Steady Demand analyzed</a> 14,472 citations from 1,487 Gemini local search queries across 50 of the largest U.S. metro areas and 10 service categories. The researchers also repeated the same queries in ChatGPT to compare citation patterns across AI platforms.</p>


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Business websites accounted for nearly 60% of Gemini’s citations in local AI search, according to a new study. Steady Demand’s analysis of 14,472 citations from 1,487 local search queries across 50 U.S. metro areas also found that identical searches often produced different AI recommendations.
Business websites beat directories. Nearly 60% of Gemini’s citations pointed directly to businesses’ own websites. That was more than the combined total for directories, review platforms, and forums.
Reddit ranked second, accounting for 13.7% of citations. It outperformed the entire local-service directory category, including Angi, Thumbtack, and HomeAdvisor. 
AI answers weren’t repeatable. The report’s biggest finding was what Steady Demand co-founder Ben Fisher called “Grounding Drift.”
Repeating the same Gemini query produced overlapping cited sources only about 40% of the time. 
In a control test, Google’s local pack returned the same top listing about 90% of the time. 
Gemini recommended the same top business only about 7% of the time.
Fisher said a single AI search should be treated as a snapshot, not a reliable measure of long-term visibility.
ChatGPT cited different sources. The same 1,487 local search queries were also run through ChatGPT: 
Both AI platforms cited many of the same domains only 8% of the time. They recommended the same top business just 4.2% of the time. 
Gemini mainly cited business websites, while ChatGPT relied much more on Reddit and business directories.
Why we care. Never assume success in one AI engine will carry over to another. Gemini and ChatGPT rarely cited the same sources or recommended the same businesses. Monitoring only one platform could give you a misleading picture of your AI visibility.
About the data. Steady Demand analyzed 14,472 citations from 1,487 Gemini local search queries across 50 of the largest U.S. metro areas and 10 service categories. The researchers also repeated the same queries in ChatGPT to compare citation patterns across AI platforms.
customers won’t either.
      
See your AI visibility

A study of 14,472 AI citations found business websites led Gemini's local results, but repeated searches rarely returned the same sources.

Business websites dominate Gemini local AI search citations: Study

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Enhanced Matching could help advertisers expand the reach of their first-party audiences using consented publisher signals.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google Ads appears to be giving advertisers another way to expand the reach of their first-party customer audiences by matching consented users across participating publishers.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening</strong>. A new Enhanced matching option is appearing within Customer Match settings in some Google Ads accounts.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="272" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-enhanced-match.jpeg" alt="" class="wp-image-485491" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-enhanced-match.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/08/Google-enhanced-match-768x261.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p class="wp-block-paragraph">When enabled, Google says it can use connected customer lists to expand audience reach by matching consented users with consented users from publishers, where available.</p>



<p class="wp-block-paragraph">The setting appears under Account settings alongside Google&#8217;s existing Customer Match controls.</p>



<p class="wp-block-paragraph"><strong>The details</strong>. Enhanced matching joins several options advertisers can already use with Customer Match data, including:</p>



<ul class="wp-block-list">
<li>Smart Bidding.</li>



<li>Optimized Targeting.</li>



<li>Conversion-based customer lists.</li>
</ul>



<p class="wp-block-paragraph">The new option appears to be opt-in, with the setting shown unchecked in the interface spotted so far.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Customer Match traditionally lets advertisers use their own first-party customer data to reconnect with known audiences across Google properties. Enhanced matching could extend the usefulness of those lists beyond Google&#8217;s existing matching capabilities by incorporating consented publisher signals.</p>



<p class="wp-block-paragraph">That potentially gives advertisers greater audience reach from the same first-party data without requiring them to build additional customer lists.</p>



<p class="wp-block-paragraph"><strong>Between the lines</strong>. The language around consent is important. Google specifically describes the feature as matching consented users with other consented users from publishers, suggesting the expansion is designed to work within Google&#8217;s evolving privacy and data-sharing framework.</p>



<p class="wp-block-paragraph">It also reflects a broader shift toward advertising systems that combine advertiser first-party data with privacy-conscious signals from platforms and publishers.</p>



<p class="wp-block-paragraph"><strong>Yes, but</strong>. Google doesn&#8217;t appear to have publicly detailed exactly how much incremental reach Enhanced matching can provide, which publishers participate, or how the matching process differs from existing Customer Match functionality.</p>



<p class="wp-block-paragraph">Availability also appears limited for now, suggesting the feature may still be rolling out or undergoing testing.</p>



<p class="wp-block-paragraph"><strong>The bottom line</strong>. Enhanced matching could make Customer Match audiences more scalable by connecting advertisers&#8217; consented first-party data with additional consented publisher signals — potentially extending reach while keeping Customer Match at the center of the targeting strategy.</p>



<p class="wp-block-paragraph"><strong>First spotted</strong>. The new setting was noticed by Joey Bidner who shared the screenshot on <a href="https://www.linkedin.com/feed/update/urn:li:activity:7495132274326216705/">LinkedIn</a>. </p>


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Google Ads appears to be giving advertisers another way to expand the reach of their first-party customer audiences by matching consented users across participating publishers.
What’s happening. A new Enhanced matching option is appearing within Customer Match settings in some Google Ads accounts.




When enabled, Google says it can use connected customer lists to expand audience reach by matching consented users with consented users from publishers, where available.
The setting appears under Account settings alongside Google’s existing Customer Match controls.
The details. Enhanced matching joins several options advertisers can already use with Customer Match data, including:
Smart Bidding.
Optimized Targeting.
Conversion-based customer lists.
The new option appears to be opt-in, with the setting shown unchecked in the interface spotted so far.
Why we care. Customer Match traditionally lets advertisers use their own first-party customer data to reconnect with known audiences across Google properties. Enhanced matching could extend the usefulness of those lists beyond Google’s existing matching capabilities by incorporating consented publisher signals.
That potentially gives advertisers greater audience reach from the same first-party data without requiring them to build additional customer lists.
Between the lines. The language around consent is important. Google specifically describes the feature as matching consented users with other consented users from publishers, suggesting the expansion is designed to work within Google’s evolving privacy and data-sharing framework.
It also reflects a broader shift toward advertising systems that combine advertiser first-party data with privacy-conscious signals from platforms and publishers.
Yes, but. Google doesn’t appear to have publicly detailed exactly how much incremental reach Enhanced matching can provide, which publishers participate, or how the matching process differs from existing Customer Match functionality.
Availability also appears limited for now, suggesting the feature may still be rolling out or undergoing testing.
The bottom line. Enhanced matching could make Customer Match audiences more scalable by connecting advertisers’ consented first-party data with additional consented publisher signals — potentially extending reach while keeping Customer Match at the center of the targeting strategy.
First spotted. The new setting was noticed by Joey Bidner who shared the screenshot on LinkedIn. 
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Enhanced Matching could help advertisers expand the reach of their first-party audiences using consented publisher signals.

Google Ads tests Enhanced Matching for Customer Match

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search.png" class="attachment-large size-large wp-post-image" alt="JavaScript links can make your pages invisible to AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A 41-day experiment reveals how JavaScript-only navigation limits AI crawler discovery and why fixing it later can be harder.

“Google renders JavaScript” has been the standard answer to client-side rendering concerns for years, and it got easier to believe in 2019, when Googlebot went evergreen, continuously running on the latest version of Chromium.
The increasing flow of search traffic through AI assistants renders that wisdom incomplete because the crawlers for AI assistants behave nothing like Googlebot.
To get numbers rather than assumptions about what that means for JavaScript links, I ran a controlled experiment. I set up one site with half its internal links hard-coded in HTML and the other half injected by JavaScript (JS). I added a logging layer that recorded every bot request for 41 days.
The results were more lopsided than I expected. 
Google’s crawler stack was the only one that executed JavaScript and followed the injected links. 
Almost none of that work came from Googlebot, the crawler that actually builds the search index. 
Googlebot reached only 2% of the JavaScript-linked pages. 
Meanwhile, GPTBot, ClaudeBot, Bingbot, PerplexityBot, Meta’s and Amazon’s crawlers, and every other bot I tracked found exactly zero.
How the experiment worked
The test site is a Brazilian business classification directory with roughly 2,400 pages, a project I own and operate. The experiment ran on the site’s hierarchy: 21 top-level sections branching into divisions, groups, and classes, 1,062 pages in total. The remaining pages are flat leaf pages that sat outside the test.
I split the 21 sections into two groups, alternating to avoid clustering bias:
HTML group (11 sections): Every child link was hard-coded in the raw HTML so that any bot can parse and follow them.
JS group (10 sections): Every child link, at every level of the hierarchy, was injected by JavaScript after page load. The raw HTML contained an empty div, and a bot that doesn’t execute JavaScript would see no links at all.
To keep the test clean, I removed every alternative discovery path:
Sitemaps: Disabled so that all sitemap URLs returned 404.
Breadcrumbs: Removed so deep pages couldn’t leak links back up the hierarchy.
Internal hierarchy panels: Removed for the same reason.
The only way to reach a division page in the JS group was to render JavaScript on its parent section page.
Site hierarchy split into 11 HTML-linked sections and 10 JavaScript-linked sections, with all discovery paths below JS sections requiring JavaScript execution.
The site’s 21 sections alternated between hard-coded HTML links and JavaScript-injected links. Any crawl below the signal line in the JS group proves JavaScript execution.



Middleware logged every bot request to a database, including details on path, user agent, referrer, IP, and timestamp. Googlebot and Bingbot IPs were verified via reverse DNS to filter out spoofed user agents. Each request was tagged as belonging to the HTML group or the JS group based on its URL path.
One measurement detail matters. Section pages for both groups were always linked from the index in plain HTML. A bot hitting a JS-group section page proves nothing. 
The experiment signal starts one level deeper: A hit on a division page in the JS group is only possible if the bot rendered the parent page’s JavaScript and followed the injected link.
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Phase 1: Crawlers arrive as the test begins
Crawlers arrived fast and crawled hard for the first 72 hours.
GPTBot swept the site within hours of deployment and crawled all 759 pages of the HTML group on day one, all the way down to class level. In the JS group, it hit all 10 section pages and stopped. It never reached a single division page.
ClaudeBot followed the same pattern a day later: deep, systematic HTML crawling and a hard stop at the JavaScript boundary.
Google’s two crawlers behaved differently from each other. GoogleOther reached its first JS-injected division page 54 minutes after launch. Googlebot took 9.9 hours. Both worked progressively deeper into the JS hierarchy over the following days, which is only possible if they rendered the parent pages and followed the injected links. Google documents rendering for Googlebot. For GoogleOther, this is an observation from my logs, not documented behavior.
The crawl burst lasted three days. On day four, discovery dropped about 90% for both groups, and the coverage numbers froze.
Daily page discovery by crawler showing GoogleOther’s three-day burst, GPTBot’s single-day sweep, and Bingbot starting on day 10.
New unique pages discovered per day. GoogleOther’s burst lasted three days. GPTBot and ClaudeBot swept the HTML group in a single day each. Bingbot’s crawl burst began around day 10. Googlebot isn’t plotted because its volume, 42 pages across both groups, doesn’t register at this scale.



Only Google executed the JavaScript
The full scorecard at the end of Phase 1, after 27 days, displays counts covering hierarchy pages only (division level and deeper), where a JS-group hit is unambiguous proof of JavaScript execution. 
One sizing note: The HTML sections hold 748 hierarchy pages and the JS sections 293, so for Google, the table also shows coverage as a share of each group:

CrawlerRenders JS?HTML hierarchy pagesJS hierarchy pages

GooglebotYes35 (5%)7 (2%)
GoogleOtherYes495 (66%)142 (48%)
GPTBot (OpenAI)No7480
ClaudeBot (Anthropic)No7480
BingbotNo2823
OAI-SearchBot (ChatGPT search)No100
Meta-ExternalAgent (Meta AI)No5370
Amazonbot (Amazon)No7350
ChatGPT-User (live browsing)No460
PerplexityBotNo00






Even Google barely touched the JavaScript pages
The two Google rows are doing very different jobs, and the difference matters more than the raw counts. Google’s crawler stack, GoogleOther, passed the rendering test. Googlebot, the crawler that feeds Search, mostly sat it out. 
This means the 66% and 48% on the GoogleOther row say nothing about whether those pages can appear in Google. The row that speaks to search visibility is Googlebot: 5% of the HTML group and 2% of the JS group.
Coverage varied by agent



The two groups differ in size, so the fair comparison is the share of each group’s own pages that got crawled.
Googlebot reached 5% of the 748 HTML-linked hierarchy pages and 2% of the 293 JS-linked ones. Six of the 10 JS sections got no Googlebot hierarchy crawl at all in 27 days. GoogleOther is where the volume was: 66% of the HTML group and 48% of the JS group, with its JS coverage falling by depth, from 67% of divisions to 42% of classes. 
Those are useful numbers for understanding how rendering behaves, and they aren’t search visibility. For Search, the relevant figure is 2%.
One caveat on that gap. GoogleOther out-crawled Googlebot roughly 14:1 on HTML pages here. I read that as a crawl-budget artifact of a brand-new domain with no external links, not as evidence that GoogleOther is the more thorough crawler. On an established site, I would expect Googlebot’s share to be much higher.
What the split does show is that a site owner watching raw crawl logs can badly overestimate their search coverage if they count every Google user agent as one crawler.
JavaScript was discovered later than HTML 



GoogleOther accounts for nearly all of Google’s crawling here, so its timings are the only ones with enough volume to measure. Its JS-linked pages were discovered 18% to 27% later than its HTML-linked pages at every depth. 
The lag compounds: Google must fetch the parent, queue it for rendering, execute the script, extract the URLs, and then schedule the children. HTML links skip the middle steps.
Distribution was uneven



GoogleOther’s JS coverage wasn’t spread thin evenly. One JS section got 52 pages crawled while two others sat at one page each for weeks. Rendering appears to be a queue with its own budget, and some branches never came up.
Coverage of each link group over time for both Google crawlers, with GoogleOther flattening at 66% for HTML-linked pages and 48% for JavaScript-linked pages while Googlebot stays near the floor at 5% and 2%.
Share of each group’s pages discovered, by day, split by crawler. The two GoogleOther lines locked in during the initial burst and stayed flat. Googlebot, the indexing crawler, never left the bottom of the chart.



One result ran against my expectations: Once GoogleOther discovered a JS-linked page, it recrawled it as fast as comparable HTML pages, and at two of the three depth levels 24% to 27% faster. The JavaScript delay is entirely front-loaded in discovery. 
Once a page gets past that stage, it’s treated normally, sometimes even better than normal. The pages that never get past it are the ones that hurt. They aren’t ranking slowly. They just aren’t there at all.
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All other crawlers ignored the JavaScript group
My middleware initially lumped Meta-ExternalAgent and Amazonbot into a generic “unknown bot” bucket, but splitting the logs by user agent revealed them as two of the heaviest crawlers on the site, with roughly 2,000 hits each. 



Meta-ExternalAgent is the crawler Meta uses to gather training data for its AI models. Amazonbot feeds Amazon’s search and AI products. Both crawled the HTML group deeply, hit the 10 JS-group section pages, and never went one level further.
HTML versus JavaScript page discovery per crawler, with only Googlebot and GoogleOther showing any JavaScript-linked pages.
Unique hierarchy pages discovered per crawler during Phase 1, with Googlebot and GoogleOther counted separately. Only Google’s two crawlers registered any JavaScript-linked coverage, and almost all of it belongs to GoogleOther, which doesn’t build the search index.



The zeros held for the entire phase. Bingbot was active on 20 of the 27 days of Phase 1, recrawled the 10 JS-group section pages on 18 of those days, and never followed one injected link.
GPTBot crawled the entire HTML group and 0% of the JS hierarchy.
PerplexityBot stayed on the homepage and robots.txt for all of Phase 1. It reached its first hierarchy page on day 40, well after the recovery portion of the test began.
Something odd happened with Bingbot
Bingbot did reach three JS-group hierarchy pages on days 26 and 27, after three weeks of daily recrawls that never went past the section level. However, the crawl pattern points to discovery through another path rather than JavaScript execution, since Bingbot’s coverage of those sections never expanded beyond those isolated URLs.
Microsoft has documented an evergreen, Edge-based Bingbot that can render JavaScript since 2019, but across 20 days of active crawling on this site, it never followed a single injected link, so whatever rendering capacity exists wasn’t applied here.
User requests from AI could not reach JavaScript links
ChatGPT-User deserves attention because it represents real user demand. This is the agent that fetches pages when someone asks ChatGPT a question.
During the experiment, real user queries pulled it to dozens of HTML-linked pages. Pages behind JavaScript links were unreachable for those same queries. So the pages a real person was actively trying to reach were exactly the ones it couldn’t get to.
Crawl logs don’t reveal indexing
Everything above is discovery, not indexing. A page can be crawled and still not make it into the index, and indexing determines whether the page shows up in Search at all. 
Server logs are the right tool for proving what a crawler could reach, which is the question this experiment was built to answer, but they’re the wrong tool for proving visibility. Search Console is the arbiter there.
Phase 2: Not all crawlers passed the recovery test
After 27 days, I converted all JavaScript links to hard-coded HTML and watched what happened. Nothing about the pages themselves changed here. The URLs, the content, and the server were identical to Phase 1. The one thing I touched was the links, which now sat in the HTML instead of being injected by JavaScript.
Some crawlers were quick to catch up
GPTBot responded within two days. It swept every formerly blocked section in a single sweep lasting under three hours and picked up 250 pages it had never crawled before, against zero in 27 days of Phase 1.
Bingbot followed with a systematic sweep, adding 212 pages across the same sections it had ignored for weeks, reaching class level in sections where it had never gone past the entrance.
Meta-ExternalAgent joined by day 41 with a sweep of its own.
Others were not
Amazonbot had finished its crawl and left the site on day 21, six days before the conversion, so its JS-group coverage ended at zero.
ClaudeBot stayed at zero in a different way: It kept recrawling the site after the fix, hundreds of hits through day 41, but only on pages it already knew. It never picked up a single newly linked page. Whatever internal map ClaudeBot drew on its first pass, it seemed to keep working from that same map for weeks, even as it carried on hitting the site.
Cumulative page discovery before and after the conversion to HTML links after 27 days, showing immediate recovery by GPTBot and Bingbot, a flat GoogleOther line, and a Googlebot line at the floor.
Cumulative hierarchy pages found in the 10 JS-group sections, with the Google crawlers shown separately. After the HTML conversion, GPTBot swept 250 pages in one pass, and Bingbot and Meta-ExternalAgent climbed steadily, while both Google lines stayed flat. Amazonbot left before the fix. ClaudeBot returned only to recrawl pages it already knew.



The slowest responder, ironically, was Google. Its aggressive first-visit budget was already spent. By day 41, Googlebot had visited exactly one page in the converted sections, and GoogleOther had visited none. 
Fixing link architecture after launch recovered AI crawler coverage quickly, but with Google, that first crawl of a brand-new site looks a lot harder to win back a second time.
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What I’d tell a client
Iceberg-style diagram showing every HTML-linked page found by at least one crawler at every depth, against GoogleOther coverage of JavaScript-linked pages falling from 67% to 42% as depth increases and Googlebot coverage at 5% or below.
The whole experiment in one picture. Above the waterline, every crawler saw everything. Below it, every HTML-linked page was found by at least one crawler, while the JavaScript-linked side was reached only by Google, and mostly by GoogleOther rather than by the crawler that builds the index.



This is one site, one niche, one 41-day window, and a fresh domain with no external links pointing at the buried pages. Established sites with strong signals may see Googlebot invest more in rendering. 
The non-Google results leave less room for interpretation: Eight crawlers failed to execute the JavaScript and follow the injected links across every crawl burst and recrawl cycle.
My takeaways:
Audit what your raw HTML actually contains. Disable JavaScript and click through your critical paths, or compare the raw source against the rendered DOM. If navigation to revenue pages only exists after hydration, every AI crawler is blind to it.
Treat “Google renders JS” as the exception, and a partial one. Rendering happened, but the crawler that feeds Search reached 2% of my JS-linked pages. Check which Google user agent your logs are actually showing you before you count that coverage as visibility.
Prioritize server-side rendering for links above content. A widget that renders in JavaScript just makes a page slightly worse. Navigation that renders in JavaScript is a different problem because it can drop whole sections of your site out of every index that feeds an AI assistant.
Fix it sooner rather than later. GPTBot re-swept within 48 hours of my Phase 2 conversion, and Bingbot within a week. Google took weeks and still hadn’t returned to the converted sections by day 41.
The rendering question used to be about whether Google could see your content. With AI assistants becoming a real referral channel and the web reshaping itself around agents, the question is now whether other crawlers can. If your links live in JavaScript, they can’t.

A 41-day experiment reveals how JavaScript-only navigation limits AI crawler discovery and why fixing it later can be harder.

JavaScript links can make your pages invisible to AI search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations.png" class="attachment-large size-large wp-post-image" alt="Reddit ChatGPT citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Reddit's share of ChatGPT Search citations fell from 3.83% to an average of 0.52% between Aug. 14 and Aug. 17. Promptwatch says.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations.png" class="attachment-large size-large wp-post-image" alt="Reddit ChatGPT citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/reddit-chatgpt-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Reddit&#8217;s share of ChatGPT Search citations fell 86.4% in four days, according to new Promptwatch data. The decline began Aug. 14 after weeks of relative stability, the AI visibility platform reported.</p>



<p class="wp-block-paragraph"><strong>What happened. </strong>Reddit accounted for an average of 3.83% of ChatGPT Search citations between July 18 and Aug. 7, Promptwatch said. That share fell below 1% on Aug. 14, then averaged 0.52% through Aug. 17. </p>



<ul class="wp-block-list">
<li>The data shows <strong>when</strong> the shift occurred, not <strong>why</strong>, Promptwatch said, adding that the findings are provisional while it continues monitoring.</li>
</ul>



<p class="wp-block-paragraph"><strong>The first drop came Aug. 8. </strong>The report identified an earlier decline that began Aug. 8, the same day Promptwatch said ChatGPT Search changed its query fan-out behavior. Reddit&#8217;s citation share fell from the high 3% range into the mid-2% range. It then dropped again six days later.</p>



<ul class="wp-block-list">
<li>Promptwatch observed a larger decline starting Aug. 14, but it said the data doesn&#8217;t explain the change, and it can&#8217;t rule out a data collection issue.</li>
</ul>



<p class="wp-block-paragraph"><strong>The change was unique to ChatGPT.</strong> Promptwatch found no comparable one-day decline in Google&#8217;s AI products. Instead, Reddit&#8217;s share of citations in Google AI Overviews declined gradually from about 2.5% in early July to roughly 2.1% in August. </p>



<ul class="wp-block-list">
<li>Google AI Mode showed a similar pattern. Its citation share began a modest decline at the end of July and continued falling over the following weeks.</li>
</ul>



<p class="wp-block-paragraph"><strong>What the data measures. </strong>The analysis tracks Reddit&#8217;s share of AI citations, not the systems behind them. It can&#8217;t explain why Reddit appeared less often in ChatGPT citations or whether the decline reflects a lasting change in source selection.</p>



<p class="wp-block-paragraph"><strong>Why we care. </strong>This is another reminder that AI search visibility can change quickly. Reddit has been the most cited source by AI platforms for a long time now. But AI platforms can change which sources they surface with little warning, so keep a close eye on your own AI referral and citation trends.</p>



<p class="wp-block-paragraph"><strong>About the data.</strong> <a href="https://promptwatch.com/data/reddit-citations-are-dropping-in-chatgpt">The analysis</a> is based on millions of AI responses collected from the live interfaces of major AI platforms. The charts measure Reddit&#8217;s share of citations among responses that included at least one citation.</p>


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Reddit’s share of ChatGPT Search citations fell 86.4% in four days, according to new Promptwatch data. The decline began Aug. 14 after weeks of relative stability, the AI visibility platform reported.
What happened. Reddit accounted for an average of 3.83% of ChatGPT Search citations between July 18 and Aug. 7, Promptwatch said. That share fell below 1% on Aug. 14, then averaged 0.52% through Aug. 17. 
The data shows when the shift occurred, not why, Promptwatch said, adding that the findings are provisional while it continues monitoring.
The first drop came Aug. 8. The report identified an earlier decline that began Aug. 8, the same day Promptwatch said ChatGPT Search changed its query fan-out behavior. Reddit’s citation share fell from the high 3% range into the mid-2% range. It then dropped again six days later.
Promptwatch observed a larger decline starting Aug. 14, but it said the data doesn’t explain the change, and it can’t rule out a data collection issue.
The change was unique to ChatGPT. Promptwatch found no comparable one-day decline in Google’s AI products. Instead, Reddit’s share of citations in Google AI Overviews declined gradually from about 2.5% in early July to roughly 2.1% in August. 
Google AI Mode showed a similar pattern. Its citation share began a modest decline at the end of July and continued falling over the following weeks.
What the data measures. The analysis tracks Reddit’s share of AI citations, not the systems behind them. It can’t explain why Reddit appeared less often in ChatGPT citations or whether the decline reflects a lasting change in source selection.
Why we care. This is another reminder that AI search visibility can change quickly. Reddit has been the most cited source by AI platforms for a long time now. But AI platforms can change which sources they surface with little warning, so keep a close eye on your own AI referral and citation trends.
About the data. The analysis is based on millions of AI responses collected from the live interfaces of major AI platforms. The charts measure Reddit’s share of citations among responses that included at least one citation.
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Reddit's share of ChatGPT Search citations fell from 3.83% to an average of 0.52% between Aug. 14 and Aug. 17. Promptwatch says.

Reddit’s ChatGPT Search citations fell 86% in four days: Report

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Advertisers in Europe will gain access to a new high-intent channel as OpenAI significantly expands its global advertising footprint.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/ChatGPT-Ads-in-31-European-countries-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">OpenAI is bringing ChatGPT Ads to its biggest group of new markets yet, giving European advertisers access to an emerging ad channel built around conversations rather than traditional search queries or social feeds.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening:</strong> ChatGPT Ads will expand to 31 European countries next week, including Germany, France, Spain, Italy, Sweden, Norway, Denmark, the Netherlands and Austria.</p>



<p class="wp-block-paragraph">Advertisers won&#8217;t initially get direct self-service access. Campaigns will be available through OpenAI&#8217;s Ads Solutions team, agency partners and technology partners, with self-service access through Ads Manager expected later this summer.</p>



<p class="wp-block-paragraph"><strong>The details:</strong> Ads will continue to appear only for people using ChatGPT&#8217;s Free and Go plans.</p>



<p class="wp-block-paragraph">Plus, Pro and Enterprise users will remain ad-free.</p>



<p class="wp-block-paragraph">OpenAI says ads are clearly labeled and kept separate from ChatGPT&#8217;s answers. Advertisers don&#8217;t receive users&#8217; conversations, and advertising doesn&#8217;t influence the answers ChatGPT generates.</p>



<p class="wp-block-paragraph"><strong>Why we care:</strong> The European expansion significantly increases the potential scale of ChatGPT as an advertising channel.</p>



<p class="wp-block-paragraph">But the more interesting proposition for marketers is where those ads can appear in the customer journey.</p>



<p class="wp-block-paragraph">People increasingly use ChatGPT to research products, compare alternatives and work through purchasing decisions by explaining their goals, preferences and constraints in natural language.</p>



<p class="wp-block-paragraph">That creates an opportunity for advertisers to reach users while they&#8217;re actively evaluating what to do or buy — potentially providing a richer intent signal than a standalone keyword or passive social impression.</p>



<p class="wp-block-paragraph"><strong>By the numbers:</strong> OpenAI began testing ChatGPT Ads in the U.S. in February and says it has since expanded to eight additional markets. Adding 31 European markets represents its largest geographic expansion so far.</p>



<p class="wp-block-paragraph">OpenAI also says tens of thousands of marketers have now advertised on ChatGPT.</p>



<p class="wp-block-paragraph"><strong>Meanwhile:</strong> The ad platform itself has been evolving quickly.</p>



<p class="wp-block-paragraph">OpenAI says it has:</p>



<ul class="wp-block-list">
<li>Expanded beyond CPM and CPC bidding to include conversion optimization.</li>



<li>Added geo-targeting and custom audiences.</li>



<li>Introduced the OpenAI Pixel and Conversions API to measure outcomes beyond clicks.</li>



<li>Added third-party measurement integrations.</li>
</ul>



<p class="wp-block-paragraph">Those additions move ChatGPT Ads closer to the targeting, optimization and measurement infrastructure advertisers expect from established performance advertising platforms.</p>



<p class="wp-block-paragraph"><strong>The big picture:</strong> OpenAI is moving rapidly from an experimental U.S. advertising product toward a global advertising platform. <a href="https://openai.com/index/chatgpt-ads-expands-across-europe/">Europe is a major step in that transition</a>. The bigger question is whether marketers can turn ChatGPT&#8217;s conversational intent into measurable performance — and whether OpenAI can scale advertising without undermining the trust that makes those conversations valuable in the first place.</p>


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OpenAI is bringing ChatGPT Ads to its biggest group of new markets yet, giving European advertisers access to an emerging ad channel built around conversations rather than traditional search queries or social feeds.
What’s happening: ChatGPT Ads will expand to 31 European countries next week, including Germany, France, Spain, Italy, Sweden, Norway, Denmark, the Netherlands and Austria.
Advertisers won’t initially get direct self-service access. Campaigns will be available through OpenAI’s Ads Solutions team, agency partners and technology partners, with self-service access through Ads Manager expected later this summer.
The details: Ads will continue to appear only for people using ChatGPT’s Free and Go plans.
Plus, Pro and Enterprise users will remain ad-free.
OpenAI says ads are clearly labeled and kept separate from ChatGPT’s answers. Advertisers don’t receive users’ conversations, and advertising doesn’t influence the answers ChatGPT generates.
Why we care: The European expansion significantly increases the potential scale of ChatGPT as an advertising channel.
But the more interesting proposition for marketers is where those ads can appear in the customer journey.
People increasingly use ChatGPT to research products, compare alternatives and work through purchasing decisions by explaining their goals, preferences and constraints in natural language.
That creates an opportunity for advertisers to reach users while they’re actively evaluating what to do or buy — potentially providing a richer intent signal than a standalone keyword or passive social impression.
By the numbers: OpenAI began testing ChatGPT Ads in the U.S. in February and says it has since expanded to eight additional markets. Adding 31 European markets represents its largest geographic expansion so far.
OpenAI also says tens of thousands of marketers have now advertised on ChatGPT.
Meanwhile: The ad platform itself has been evolving quickly.
OpenAI says it has:
Expanded beyond CPM and CPC bidding to include conversion optimization.
Added geo-targeting and custom audiences.
Introduced the OpenAI Pixel and Conversions API to measure outcomes beyond clicks.
Added third-party measurement integrations.
Those additions move ChatGPT Ads closer to the targeting, optimization and measurement infrastructure advertisers expect from established performance advertising platforms.
The big picture: OpenAI is moving rapidly from an experimental U.S. advertising product toward a global advertising platform. Europe is a major step in that transition. The bigger question is whether marketers can turn ChatGPT’s conversational intent into measurable performance — and whether OpenAI can scale advertising without undermining the trust that makes those conversations valuable in the first place.
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Advertisers in Europe will gain access to a new high-intent channel as OpenAI significantly expands its global advertising footprint.

ChatGPT Ads are expanding to 31 European countries

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/Technical-SEO-for-AI-search-4-fundamentals-that-matter.png" class="attachment-large size-large wp-post-image" alt="Technical SEO for AI search- 4 fundamentals that matter" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Technical-SEO-for-AI-search-4-fundamentals-that-matter.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/Technical-SEO-for-AI-search-4-fundamentals-that-matter-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Technical-SEO-for-AI-search-4-fundamentals-that-matter-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Technical-SEO-for-AI-search-4-fundamentals-that-matter-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI systems can miss content that search engines handle well. Learn what to check so your pages can be crawled, understood, and cited.

Despite what your LinkedIn feed may tell you, AI retrieval isn’t a new discipline. It’s a stress test of technical SEO fundamentals that are already best practices. 
Websites struggling with AI visibility are usually affected by the same technical issues that have long reduced search rankings. There are some additional checkboxes to review, but there is no great secret to making sure your website is technically sound for AI visibility.
Below are four technical elements to review to ensure AI systems can crawl, understand, and cite your website.
1. Crawlability and bot access 
The rise of AI assistants has expanded the crawler ecosystem beyond Googlebot and Bingbot. OpenAI, Anthropic, and Perplexity now operate multiple bots — some for model training, some for retrieval, and some for user-initiated queries — each with different behaviors and implications for website owners. 
Yet many sites still rely on robots.txt files last updated years ago, inadvertently blocking the bots through which they now want to gain visibility.
The robots.txt file is the gatekeeper when it comes to AI crawlability, according to Cloudflare Radar.




Dig deeper: Crawling for AI search: Balancing access, control, and visibility



The modern AI crawler landscape
AI systems don’t rely on a single bot. They use families of crawlers with distinct purposes. For example, Anthropic has ClaudeBot, Claude-SearchBot, and Claude-User. ClaudeBot is used for training, whereas Claude-SearchBot and Claude-User handle retrieval and browsing.
Some AI retrieval bots behave more like on-demand browsers than traditional search crawlers. Retrieval bots fetch pages in real time when a user asks a question. Training bots crawl broadly to build model knowledge. Therefore, each needs to be treated appropriately.




What access you allow each bot comes down to a business decision. For industries such as news and publishers, content is their lifeline, and they often choose to block AI. 
Their concern is that AI systems may use their content without sending visitors back to the original source. Organizations in regulated sectors, including legal services, may also block AI crawlers because of compliance concerns.
A third group blocks AI crawlers unintentionally. Because many sites run robots.txt files written years ago, it’s not uncommon to find rules that unintentionally block retrieval bots while attempting to block training crawlers, or vice versa.
The result is invisible: AI systems can’t fetch your pages during user queries, and you won’t see a traditional search-engine-style ranking drop — you’ll simply be absent from AI-driven answers.
Fix: Ensure your intent is reflected in your robots.txt configuration. If you want to be discoverable by AI retrieval agents, treat them as first-class crawlers in your robots.txt. If you want to block training crawlers, do so explicitly and verify you’re not catching retrieval agents in the same rule.
Allowing a crawler through robots.txt is only the first step. It must also be able to access the page’s critical content.
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2. JavaScript rendering: The silent killer
JavaScript rendering is the single most overlooked technical risk for AI visibility. None of the main AI crawlers render JavaScript. That means any content injected in the browser — schema, text, product details, or critical metadata — can be invisible to AI systems even when Google and other search engines index the page perfectly.
Why is this?



Modern single-page applications (SPAs) often render core content client-side. If the server response contains only a shell and the JavaScript populates content later, an AI crawler that doesn’t run JS will see only the shell. Additionally, some crawlers intentionally avoid executing JS to reduce cost, latency, and complexity.
Even when a crawler can execute JavaScript, rate limits, timeouts, and headless-browser differences can produce incomplete renders. Client-side rendering is therefore not a dependable delivery method for critical content.
Let’s take an example. Consider a React SPA that performs well in Google because its content and schema are injected client-side, but remains invisible to AI retrieval systems. AI crawlers see a near-blank page due to the technology being used.





Fix: Use server-side rendering (SSR), static site generation (SSG), or hybrid rendering for critical content. Ensure the HTML response contains the canonical text, headings, and structured data. Your safest bet is to stay with tried-and-true HTML for critical content.
Once the content is accessible, the next challenge is making its meaning explicit.
Dig deeper: 5 JavaScript SEO lessons from top ecommerce sites




	
	
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3. Structured data and clarity
As AI retrieval systems grow, they rely heavily on structured signals embedded within the HTML. Because many AI crawlers don’t execute JavaScript, schema markup delivered server-side is the reliable way to ensure an AI system can understand your page’s content. In other words, structured data adds explicit meaning to the information contained in the raw HTML.
Structured data gives AI systems explicit meaning
Structured data tells AI systems exactly what an entity is, how it relates to other entities, and what matters. When GPTBot, ClaudeBot, or PerplexityBot fetch a page, they may only see the raw HTML. 
If your schema is injected client-side — via React, Vue, or any SPA framework — the crawler never sees it, meaning no entity recognition, factual extraction, or inclusion in AI-generated answers.
Structured data can improve factual accuracy
AI systems rely on clear, machine-readable signals to avoid misinterpretation. Schema.org markup provides canonical names, prices, dates, authors, product attributes, and relationships. This reduces ambiguity and helps AI systems quote or summarize your content correctly.
Include structured data in the initial HTML response
SSR and SSG can ensure that schema is present in the initial HTML response, making it accessible to crawlers that don’t execute JavaScript. The rule is simple: Never rely on JavaScript to deliver the information you want AI systems to understand.
Structured data can strengthen AI search visibility
As AI assistants increasingly become the first touchpoint for information retrieval, sites that provide clear, server-rendered structured data will be better positioned for inclusion in AI-generated answers. Those that rely on client-side injection will fade from AI visibility even if they perform well in traditional search.
Fix: Audit your website to confirm that schema markup code is rendered server-side and visible in the initial HTML response. Ensure critical structured data is accessible without JavaScript execution. Validate your code by fetching the pages as an AI crawler user agent to confirm.
Page-level clarity is only part of the equation. AI systems must also connect those facts to a consistent brand entity.




4. Entity consistency matters
When a brand is consistently represented as a single entity, AI systems have a clearer foundation for identifying and accurately describing it. This isn’t limited to just Google’s Knowledge Graph. Anthropic, OpenAI, and Microsoft all maintain internal entity layers that rely on stable, unambiguous brand identities.
One part of the challenge mirrors the old NAP consistency problem from local SEO, but at brand scale. You should minimize unnecessary name variations and make unavoidable relationships explicit. 
Inconsistent brand names across your website, schema, directories, social profiles, and external listings can create entity fragmentation. When variations such as “Acme,” “Acme Co.,” and “Acme Inc.” are unavoidable, connect them through consistent structured data and authoritative external profiles.
A unified entity footprint strengthens citation frequency, improves answer accuracy, and ensures your brand is recognized as a single, authoritative source across modern AI ecosystems.





Fix: Audit brand references and standardize spelling, punctuation, capitalization, and legal-name usage wherever possible. In addition, reinforce that identity with sameAs links to canonical external nodes such as Wikidata, LinkedIn, and Crunchbase. These act as grounding anchors, helping AI systems collapse variations into one authoritative entity.
None of these fixes should be considered complete until the final output has been tested.
Verify, test, repeat
Technical fixes are only valuable if crawlers can access and interpret the final result. Inspect important pages to confirm that critical copy, headings, internal links, and structured data are present before JavaScript runs.
Test access across robots.txt, meta robots directives, status codes, redirects, and firewall rules. Server logs can help confirm whether AI crawlers are reaching your pages and receiving successful responses. Validate structured data with the Schema Markup Validator and Google’s Rich Results Test.
Finally, track AI visibility separately from traditional rankings. Use a consistent set of prompts to monitor brand mentions, citations, source pages, and factual accuracy across relevant AI platforms. Individual responses will vary, so focus on patterns over time rather than isolated spot checks.
Technical SEO is the foundation of AI visibility
AI visibility may feel like a new discipline, but the technical requirements are familiar. Search engines and AI retrieval systems both need accessible pages, clear information, consistent entities, and machine-readable signals. The difference is that many AI crawlers operate with fewer rendering capabilities and less tolerance for ambiguity.
The goal isn’t to optimize for every crawler individually or chase each new AI platform. It’s to build a technically resilient website that exposes its most important information clearly. Sites that do this make it easier for search engines and AI systems to crawl, understand, verify, and cite their content.
Key takeaways
Ensure your robots.txt, firewall, and access controls reflect your actual policy for training and retrieval crawlers.
Don’t depend on JavaScript to deliver critical content or structured data.
Use relevant schema markup and include it in the initial server response.
Maintain a consistent brand entity across your website, structured data, profiles, and authoritative external sources.
Verify implementation through raw HTML inspection, crawler testing, schema validation, and repeatable AI citation tracking.
AI search has raised the stakes, but it hasn’t rewritten the technical playbook. Websites that get the fundamentals right will be better positioned for traditional search performance and the growing number of AI-driven discovery experiences.

AI systems can miss content that search engines handle well. Learn what to check so your pages can be crawled, understood, and cited.

Technical SEO for AI search: 4 fundamentals that matter

<div><img width="1921" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen.png" class="attachment-large size-large wp-post-image" alt="Google Ads is finally building a stronger foundation for B2B lead gen" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen.png 1921w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen-1200x675.png 1200w" sizes="(max-width: 1921px) 100vw, 1921px" /></div>Three months after Google Marketing Live, Google Ads is laying the groundwork for better B2B lead gen. Here’s what to act on now.


<div><img width="1921" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen.png" class="attachment-large size-large wp-post-image" alt="Google Ads is finally building a stronger foundation for B2B lead gen" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen.png 1921w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-is-finally-building-a-stronger-foundation-for-B2B-lead-gen-1200x675.png 1200w" sizes="(max-width: 1921px) 100vw, 1921px" /></div>
<p class="wp-block-paragraph"><a href="https://searchengineland.com/google-marketing-live-2026-everything-you-need-to-know-478167">Google Marketing Live</a> was almost three months ago, and enough of the announcements have now shipped to separate the meaningful changes from the features that still need time. For B2B advertisers, that distinction matters.</p>



<p class="wp-block-paragraph">B2B has often felt like an afterthought as Google has focused on commerce features, leaving advertisers to figure out how to adapt those tactics to lead generation. That can shift performance toward users with a different intent than you originally targeted.</p>



<p class="wp-block-paragraph">This year, however, Ginny Marvin released “<a href="https://business.google.com/us/accelerate/resources/articles/42-launches-redefining-lead-generation/" target="_blank" rel="noopener">42 Launches Redefining Lead Generation</a>,” the closest thing Google has given B2B advertisers to a roadmap. Looking at what has shipped since then, it’s clear that Google is finally laying the foundation for a transformative period for B2B lead gen in Google Ads.</p>



<h2 id="h-what-has-shipped" class="wp-block-heading">What has shipped</h2>



<p class="wp-block-paragraph">Starting Aug. 17, Google changed how limited-budget campaigns with target-based bid strategies are optimized. Campaigns with a &#8220;limited by budget&#8221; status on tCPA/tROAS will deliver more consistently to a stated target vs. overperforming it.</p>



<p class="wp-block-paragraph">Though this news has shocked the entirety of the Google Ads world, B2B accounts should treat this as more urgent than ecommerce. Lead gen campaigns have a tendency to sit in &#8220;limited by budget&#8221; often, and tCPA and tROAS values are often stale, set at launch, and not often revisited as the campaign matures. </p>



<p class="wp-block-paragraph">Any campaign that&#8217;s been quietly beating an old target now runs the risk of being exposed to expanded targeting into less desirable networks, where incremental cost can add up quickly.</p>



<p class="wp-block-paragraph">On June 15, Smart Bidding Exploration moved out of beta and became globally available in all languages for Performance Max campaigns without a product feed. Google reports search campaigns using Smart Bidding Exploration see 27% more unique converting users on average.&nbsp;</p>



<p class="wp-block-paragraph">Though this is a mature banking account that has done the measurement and first-party data work first, it shows that this feature can be extremely powerful for the B2B/lead gen advertiser.</p>



<p class="wp-block-paragraph">Finally, Google launched more direct integrations into Data Manager for Mailchimp, ActiveCampaign, Klaviyo, and Google Drive, as well as partner API connections with Zapier, Stape, Adswerve, Bloomtech, and Treasure Data. Map view was also launched to make it easier to audit where account data is actually deployed.</p>



<p class="wp-block-paragraph">Offline conversion import is a direct prerequisite for journey-aware bidding, value-based bidding, and multichannel campaigns like Performance Max, Demand Gen, and AI Max to deliver anything beyond cheap volume. </p>



<p class="wp-block-paragraph">The main reason B2B accounts don’t import CRM data into the platform is often an engineering queue issue rather than a strategic one. These direct connectors clear the path for B2B accounts to significantly improve their performance in Google Ads.</p>


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<h2 id="h-still-worth-the-excitement" class="wp-block-heading">Still worth the excitement</h2>



<p class="wp-block-paragraph">Journey-aware bidding lets a Target CPA search campaign learn from every stage in the lead-to-sale path rather than the form fill or limited down-funnel quality alone. </p>



<p class="wp-block-paragraph">The bidding algorithm now reads the entire funnel when deciding which clicks to bid on. This resolves a longstanding structural qualm in B2B PPC optimization. </p>



<p class="wp-block-paragraph">First announced at Think Week 2025, it’s been in beta for a long time, though that’s to be expected given the complexities and the need to ensure the feature works as it should as a transformational feature.</p>



<p class="wp-block-paragraph">GML focused heavily on AI innovation this year. While we’re going to see an influx of ads appearing in AI Mode, sans reporting, which is a different story, one key AI innovation for B2B/lead gen is business agents for leads, which serves as a Gemini chat agent inside the search ad, grounded by your website, that answers questions and fires a pre-filled form after the user shows intent.</p>



<p class="wp-block-paragraph">As of now, this feature is only being tested in specific verticals, with a lot of work needed to be done before it’s widely available. </p>



<p class="wp-block-paragraph">In the meantime, advertisers should ensure their pricing, features, security, and integration pages are accurate and easily understood by LLMs to convey information to users who are comparison shopping.</p>



<h2 id="h-what-has-already-lost-its-luster" class="wp-block-heading">What has already lost its luster</h2>



<p class="wp-block-paragraph">AI Max has been out for about a year and got a lot of stage time at GML, but it’s one that should be heavily scrutinized for the B2B advertiser. A study from <a href="https://www.ppc.live/post/google-s-ai-max-for-search-what-the-data-actually-shows-in-2026#viewer-vetmm79583" target="_blank" rel="noopener">PPC Live</a> showed average CPC dropped 59%, click volume nearly tripled, and CPC dropped 59%, while cost per lead went from $493 to $850.</p>



<p class="wp-block-paragraph">That’s a predictable result when the model optimizes to a form fill without understanding the down-funnel impact. Query expansion increases the top of the funnel, and landing page expansion pushes prospects to pages that educate rather than convert, while generated copy flattens positioning that took time to build manually.</p>



<p class="wp-block-paragraph">On June 11, Google gave advertisers a temporary delay on the automatic migration from Dynamic Search Ads to AI Max campaigns from September 2026 to February 2027, and restored DSA campaign creation capabilities on the 15th. </p>



<p class="wp-block-paragraph">While it’s not a full reprieve from the campaign type, the pullback signals that this feature isn’t ready for prime time. While we expect improvements between now and February, this false start should make B2B advertisers wary.</p>



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<h2 id="h-ads-in-ai-mode" class="wp-block-heading">Ads in AI Mode</h2>



<p class="wp-block-paragraph">One major theme at GML this year was Ads in AI Mode. While AI Overviews have represented the most transformative time in Google Search, there’s much to figure out before B2B advertisers can truly benefit from this feature.&nbsp;</p>



<p class="wp-block-paragraph">The only way to serve in AI Overviews is through AI Max and Performance Max, while reporting offers little transparency into what AI is saying about your brand, when an ad is serving, or what triggered it. </p>



<p class="wp-block-paragraph">The expectation that you just need to trust Google’s algorithm to understand your customers and find them when it matters through signals hasn’t been entirely frictionless over the years. </p>



<p class="wp-block-paragraph">It will be interesting to see whether Google makes any changes to the reporting scope related to ads in AI Overviews by next year&#8217;s GML. Similar to Performance Max channel reporting, it could take years of raising issues before we get this transparency. </p>



<p class="wp-block-paragraph">In the meantime, B2B advertisers should ensure their conversion actions optimize beyond form fills to qualified down-funnel actions, that they’re using Customer Match as an audience signal to optimize PMax, and that they’re keeping an eye on lead quality as ads become more prevalent in AI Overviews.</p>



<h2 id="h-the-b2b-google-ads-priorities-for-the-next-two-quarters" class="wp-block-heading">The B2B Google Ads priorities for the next two quarters</h2>



<p class="wp-block-paragraph">Google has shipped a lot of the foundation, but many of the transformational features remain in beta.&nbsp;</p>



<p class="wp-block-paragraph">The teams that come out ahead in the next two quarters are the ones that invest in offline conversion data, address stale bid targets, and run controlled experiments while a control group still exists.</p>

Google Marketing Live was almost three months ago, and enough of the announcements have now shipped to separate the meaningful changes from the features that still need time. For B2B advertisers, that distinction matters.
B2B has often felt like an afterthought as Google has focused on commerce features, leaving advertisers to figure out how to adapt those tactics to lead generation. That can shift performance toward users with a different intent than you originally targeted.
This year, however, Ginny Marvin released “42 Launches Redefining Lead Generation,” the closest thing Google has given B2B advertisers to a roadmap. Looking at what has shipped since then, it’s clear that Google is finally laying the foundation for a transformative period for B2B lead gen in Google Ads.
What has shipped
Starting Aug. 17, Google changed how limited-budget campaigns with target-based bid strategies are optimized. Campaigns with a “limited by budget” status on tCPA/tROAS will deliver more consistently to a stated target vs. overperforming it.
Though this news has shocked the entirety of the Google Ads world, B2B accounts should treat this as more urgent than ecommerce. Lead gen campaigns have a tendency to sit in “limited by budget” often, and tCPA and tROAS values are often stale, set at launch, and not often revisited as the campaign matures. 
Any campaign that’s been quietly beating an old target now runs the risk of being exposed to expanded targeting into less desirable networks, where incremental cost can add up quickly.
On June 15, Smart Bidding Exploration moved out of beta and became globally available in all languages for Performance Max campaigns without a product feed. Google reports search campaigns using Smart Bidding Exploration see 27% more unique converting users on average. 
Though this is a mature banking account that has done the measurement and first-party data work first, it shows that this feature can be extremely powerful for the B2B/lead gen advertiser.
Finally, Google launched more direct integrations into Data Manager for Mailchimp, ActiveCampaign, Klaviyo, and Google Drive, as well as partner API connections with Zapier, Stape, Adswerve, Bloomtech, and Treasure Data. Map view was also launched to make it easier to audit where account data is actually deployed.
Offline conversion import is a direct prerequisite for journey-aware bidding, value-based bidding, and multichannel campaigns like Performance Max, Demand Gen, and AI Max to deliver anything beyond cheap volume. 
The main reason B2B accounts don’t import CRM data into the platform is often an engineering queue issue rather than a strategic one. These direct connectors clear the path for B2B accounts to significantly improve their performance in Google Ads.
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Still worth the excitement
Journey-aware bidding lets a Target CPA search campaign learn from every stage in the lead-to-sale path rather than the form fill or limited down-funnel quality alone. 
The bidding algorithm now reads the entire funnel when deciding which clicks to bid on. This resolves a longstanding structural qualm in B2B PPC optimization. 
First announced at Think Week 2025, it’s been in beta for a long time, though that’s to be expected given the complexities and the need to ensure the feature works as it should as a transformational feature.
GML focused heavily on AI innovation this year. While we’re going to see an influx of ads appearing in AI Mode, sans reporting, which is a different story, one key AI innovation for B2B/lead gen is business agents for leads, which serves as a Gemini chat agent inside the search ad, grounded by your website, that answers questions and fires a pre-filled form after the user shows intent.
As of now, this feature is only being tested in specific verticals, with a lot of work needed to be done before it’s widely available. 
In the meantime, advertisers should ensure their pricing, features, security, and integration pages are accurate and easily understood by LLMs to convey information to users who are comparison shopping.
What has already lost its luster
AI Max has been out for about a year and got a lot of stage time at GML, but it’s one that should be heavily scrutinized for the B2B advertiser. A study from PPC Live showed average CPC dropped 59%, click volume nearly tripled, and CPC dropped 59%, while cost per lead went from $493 to $850.
That’s a predictable result when the model optimizes to a form fill without understanding the down-funnel impact. Query expansion increases the top of the funnel, and landing page expansion pushes prospects to pages that educate rather than convert, while generated copy flattens positioning that took time to build manually.
On June 11, Google gave advertisers a temporary delay on the automatic migration from Dynamic Search Ads to AI Max campaigns from September 2026 to February 2027, and restored DSA campaign creation capabilities on the 15th. 
While it’s not a full reprieve from the campaign type, the pullback signals that this feature isn’t ready for prime time. While we expect improvements between now and February, this false start should make B2B advertisers wary.
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Ads in AI Mode
One major theme at GML this year was Ads in AI Mode. While AI Overviews have represented the most transformative time in Google Search, there’s much to figure out before B2B advertisers can truly benefit from this feature. 
The only way to serve in AI Overviews is through AI Max and Performance Max, while reporting offers little transparency into what AI is saying about your brand, when an ad is serving, or what triggered it. 
The expectation that you just need to trust Google’s algorithm to understand your customers and find them when it matters through signals hasn’t been entirely frictionless over the years. 
It will be interesting to see whether Google makes any changes to the reporting scope related to ads in AI Overviews by next year’s GML. Similar to Performance Max channel reporting, it could take years of raising issues before we get this transparency. 
In the meantime, B2B advertisers should ensure their conversion actions optimize beyond form fills to qualified down-funnel actions, that they’re using Customer Match as an audience signal to optimize PMax, and that they’re keeping an eye on lead quality as ads become more prevalent in AI Overviews.
The B2B Google Ads priorities for the next two quarters
Google has shipped a lot of the foundation, but many of the transformational features remain in beta. 
The teams that come out ahead in the next two quarters are the ones that invest in offline conversion data, address stale bid targets, and run controlled experiments while a control group still exists.

Three months after Google Marketing Live, Google Ads is laying the groundwork for better B2B lead gen. Here’s what to act on now.

Google Ads is finally building a stronger foundation for B2B lead gen

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/Slop-antibodies-The-link-between-AI-slop-watermarking-and-commodity-content-featured-image.png" class="attachment-large size-large wp-post-image" alt="Slop antibodies- The link between AI slop, watermarking, and commodity content featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Slop-antibodies-The-link-between-AI-slop-watermarking-and-commodity-content-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/Slop-antibodies-The-link-between-AI-slop-watermarking-and-commodity-content-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Slop-antibodies-The-link-between-AI-slop-watermarking-and-commodity-content-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Slop-antibodies-The-link-between-AI-slop-watermarking-and-commodity-content-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From LinkedIn’s anti-slop systems to AI watermarking, platforms are building new ways to identify low-value content. Here’s what survives.


Most comments on my LinkedIn posts look like this lately:




Slop. Of course.
Viruses have no metabolism of their own and need a host to reproduce. Slop comments regurgitate back to me what I wrote (the host) with zero value. My desire to be on LinkedIn is getting closer to dentist-visit level ever since the platform enabled and encouraged the use of AI.
But LinkedIn is seeing the problem. In 10 weeks, the platform:
Deployed systems that identify slop and limit its distribution beyond a poster’s immediate network.
Started testing a “Seems like AI slop” button for users to flag posts and comments.
Killed its own “Enhance post” button, replacing it with a proofreader that fixes grammar without rewriting your voice.
Antibodies do three things: spot a target, neutralize it, and remember it so the next encounter is faster. Anti-slop systems flag low-value content, throttle its reach, and feed every catch back into the model. LinkedIn is building Slop Antibodies, and every other content platform does as well:
Substack rolled out site-wide use of Pangram to detect AI use in writing.
YouTube demonetizes “repetitive, low-effort, emotionally manipulative video”. In January 2026, it terminated 11 channels and wiped 6 more, erasing ~4.7 billion lifetime views, 35 million subscribers, and ~$9.8 million in annual revenue.
Reddit went the anti-manipulation route rather than labeling. July 2026: AI-based detection of “manipulated and spammy content,” claiming 23 million spam views blocked and ~2 million inauthentic votes revoked daily.
Pinterest uses AI detection to label content plus feed control that lets users dial down “AI-modified” content in specific categories.
TikTok requires AI labels, embeds invisible metadata watermarks, shipped a “limit AI content” feed toggle in November 2025, and in July 2026 started testing detection aimed at accounts dedicated to AI spam.
Meta has “AI info” labels on Facebook, Instagram, and Threads since 2024, extended to ads in June 2026. Labels only, no user-side filter.
Spotify attacked supply: 75 million+ spammy tracks removed, an impersonation policy, spam filters, and DDEX-based AI disclosure in credits.




Cheap synthetic content floods platforms and the immune systems react: labels, reporting flows, filters, demonetization, spam detection, and supply-side removals. LinkedIn’s defense system has 94% accuracy. That’s 60x worse than Gmail’s spam filters, which would mean that one in 17 spam emails would be flagged as a false positive.
This changes how demand flows in profound ways: Production cost collapses to near zero, which puts pressure on distribution as it moves under machine control. So the scarce asset is selection. Content production efficiencies are worthless if that content doesn’t reach the target audience.
View embedded content



Watermark panic
On August 11, Anthropic announced machine-readable watermarks on Claude text and file output at the model level worldwide, so it persists across the API, Claude, Claude Code, and cloud access through AWS, Google Cloud, and Microsoft Foundry.
Anthropic didn’t do this out of the goodness of its heart. The EU AI Act’s Article 50 requires providers of generative systems to mark their output in a machine-readable format, with penalties up to €15M or 3% of global turnover. Every lab serving the EU owes that duty, whether or not it also signed the Commission’s voluntary code on top.
Anthropic’s watermarking is the upstream version of the same immune response from content platforms: Instead of asking LinkedIn, YouTube, or Reddit to infer whether a post was generated by AI after it enters the feed, Claude can make generated text easier to identify at the source.
Source: Detection performance of SynthID-Text from Scalable watermarking for identifying large language model outputs



I’m not worried about the impact of watermarking (on marketing):
Watermarks just prove that an AI model touched text, but not to what extent. It’s not an indicator of quality.
Detectors need a minimum amount of text to work. Published benchmarks land at roughly 100 tokens at best, and SynthID’s own evaluation truncates everything to 200. A LinkedIn comment is 20 to 50 tokens. The slop I opened this piece with sits below the floor.
Editing, paraphrasing, translating, combining the response with other text or chaining models can weaken or remove the mark.
If Anthropic makes watermark detection publicly available, it will lead to a cat-and-mouse game in which actors reverse-engineer ways to break the watermark, and Anthropic must figure out how to harden it.
A paraphrasing attack presented at ICML 2025 achieved near 100% success against seven recent watermarking methods at $0.88 per million tokens.
The escape hatch is open weights. Watermarking is applied by the sampling pipeline at inference, so if you run the model yourself, there is nothing to strip. If marked output ever gets penalized, slop moves to the models nobody marks.
Fun fact: Gemini has been applying SynthID watermarks since August 2023, but there was no outcry. The web’s reaction to Anthropic’s announcement says a lot about Google’s AI position.
Source: SynthID: Tools for watermarking and detecting LLM-generated Text







At the heart of this fear of watermarking is my key argument: We shouldn’t confuse “AI-generated” with “bad.”



One could argue Slop existed before AI: poor or machine-automated work, legal and finance speak, press releases, and every SEO article that opened with “in today’s digital landscape”. It’s is ultimately about quality. Where the fear is justified is that quality has become a blurry but critical filter for distribution.
Distribution bottleneck
The advice to avoid generic content isn’t new, just widely ignored. But AI raises the viral load faster than the filter can clear it.
Content production is no longer the constraint. Permission to distribute is.



What we learned over the last 24 months is that the production gains from AI do not offset the losses in distribution. You could argue that AI Overviews reduce clicks by 50% on average, but you can grow content output +2x to compensate for it. But that output growth comes at the expense of quality, which can ultimately hurt your overall distribution. An article or post that smells like AI can cost you fragile trust with readers to an irreparable degree.
The census above was platforms protecting their own feeds. The layer that decides what AI answers cite is doing the same thing, one level up. 
Google shipped a spam update in June aimed at scaled content abuse. Wikipedia went furthest: speedy deletion for suspected LLM-generated articles in August 2025, then an outright ban on using LLMs to write or rewrite article content in March 2026. 
Google demotes, Reddit detects, Wikipedia bans.
Shouldn’t AI Slop hurt the distribution of content platforms themselves? After all, LinkedIn, Reddit, and YouTube are the most-cited domains and bigger slop slingers.
Why are they not penalized by search engines and LLMs?
They are, but more targeted. Reddit’s machine-translated ?tl= pages collapsed from 6.14% of ChatGPT’s Reddit citations in April 2026 to 0.30% by early June, while Reddit rose in aggregate over the same period.
We don’t know what LLMs filter out for training data versus live retrieval.
Engagement may act as a rough filter. Semrush’s study of 89,000 cited LinkedIn URLs found the cited ones carry at least decent engagement.
Most cited posts have moderate engagement (15-25 reactions), while about 75% of cited authors post frequently (5+ posts in four weeks) and nearly half have over 2,000 followers.
So, it seems that at least some sort of slop filtering of content platforms is happening. But what’s the cost to distribution for companies? Several studies looked into this.
A Copenhagen Business School study published in Electronic Markets ran two experiments (n=325, n=371) on Instagram content labeled human-created, AI-enhanced, or AI-generated, and found labeling as AI-generated or AI-enhanced reduced both affective and behavioral engagement by about half, with the effect strongest on emotional content and weakest on rational or informational content.
Source: Springer



TikTok field data (1 million posts) shows AI disclosure leads to ~7% less engagement because people infer lower effort. Pretty tame in my mind. 
Pangram scanned 1 million+ posts between April and June 2026 and found 41% of LinkedIn long-form posts and 23% of comments were fully AI-generated, the highest of any platform.
Source: Pangram



Run the numbers on yourself. LinkedIn says it catches 94% of slop and caps flagged posts at your immediate network. In my analytics, 71% of impressions come from beyond that network. 
Multiply the two: an account posting nothing but AI should expect to lose roughly two-thirds of its LinkedIn reach. Post AI a third of the time and it lands closer to 20%. Add 7% on short-form video, and 40% to 95% in SEO if you scaled a content operation.
That LinkedIn figure is derived from two published numbers and my own baseline. LinkedIn published a catch rate and nothing at all about false positives.
Commodity content
At the beginning of the article, I mentioned that LinkedIn slop comments regurgitate my original post and provide zero value. That might remind you of the concept of information gain and commodity content — and it should. In 2021 (!), I wrote:
“Second, if the key feature is easy to replicate, you have a problem. You have a commodity; you’re one choice of many. Commodities compete heavily on price and cost. As a business, you have to weigh the cost it takes to create the product against the returns it brings. Are you in a stronger position when you compete with minimal advantage for a small share of ad views?”
Fast forward to today, Danny Sullivan shared an important slide at the Search Console Live Toronto conference about commodity vs. non-commodity content.
Source: Jean-Christophe Chouinard



In Sullivan’s words, non-commodity content is:
Unique: Brings a viewpoint, information, or has content that others lack or can’t easily replicate.
Specific: Talks about a specific instance, situation, or thing, not general rules, steps, or generic information.
Authentic: Demonstrates first-hand knowledge or experience.
It hits the whole slop discussion on the head. It’s the universal anti-slop recipe. Look at how similar LinkedIn’s VP Laura Lorenzetti’s frame of AI Slop is to Google’s:
“This includes technology systems built in partnership with our editorial team that have been trained to recognize signals of AI slop and learn over time by identifying content that adds perspective, context, or expertise and content that feels generic or repetitive, even if it appears polished on the surface.”
Or what Reddit CEO Steve Huffman recently said on the company’s Q2 earnings call:
“As AI makes information more abundant, the challenge is no longer finding content; it’s finding context, personal opinion, and first-hand accounts. Everything online feels flat, polished, generated, or sponsored, so consumers are overwhelmed and increasingly skeptical,” Reddit CEO Steve Huffman said.
Three organizations coming to the same conclusion: generic content has no value. Is that new? No! 
What is new is the binary nature and cost-effectiveness. Generic content could gain some residual traffic until a few years ago.
Now, Google won’t even index it, and LinkedIn downranks it. Panda ran on proxies: Links, clicks, dwell time, pogo-sticking, because judging whether a document contained a (good) idea was economically impossible when it launched in 2011. Now, an LLM can assess this quickly and at low cost.
A COLM 2026 paper from Maryland and Google DeepMind ran 61,608 stories through a classifier that was deliberately denied every style signal and told to work from structure alone. It kept over 97% of the accuracy of models that were allowed to see word choice and sentence rhythm. The finding: Slop has a shape pattern.




Now, scroll back up and look at the shape of my LinkedIn comments from the beginning. Spotting the uniform shape?
But there are even more similarities between the COLM study and slop: AI states the moral outright in 77% of stories versus 52% for humans, and 79% of AI stories contain zero subplots versus 57% for humans. Over-explaining and single-track tidiness is exactly the LinkedIn comment pathology.




So the antidote is owning something a model cannot produce on demand:
Proprietary information: Your own data, your own experiments, your own customer conversations. If a model can generate it, it is a commodity by definition.
Attributable identity: A named author with a track record, and on LinkedIn, a verified one. Lorenzetti’s post buried the tell: you can now filter the feed for LinkedIn’s 100 million+ verified members. Verification is an antibody.
An owned channel: Email, community, direct relationships. Somewhere, the filter is not standing between you and the reader.
The test for all three is the same: would it be expensive for someone else to fake?
Wish for the filters to work. Every throttled slop comment is oxygen back for someone who actually had something to say. Just make sure that when the feed finally clears, you’re the source the machine decided to keep.
This post first appeared on the author’s website and is republished here with permission.

From LinkedIn’s anti-slop systems to AI watermarking, platforms are building new ways to identify low-value content. Here’s what survives.

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