<div><img width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield.jpeg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield.jpeg 1280w, https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield-768x432.jpeg 768w, https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield-1200x675.jpeg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></div>Chloe Varnfield  shares sneaky Google settings, Friday mistakes, and Google rep advice that tanked her campaigns — and the hard-won lessons that came after.

<div><img width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield.jpeg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield.jpeg 1280w, https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield-768x432.jpeg 768w, https://searchengineland.com/wp-content/seloads/2026/03/PL-Podcast-Chlow-Varnfield-1200x675.jpeg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></div>
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<iframe title="EP355 - Sneaky Google Ads Settings &amp; Tanked Campaigns ft Chloe Varnfield" width="500" height="281" src="https://www.youtube.com/embed/qwoElOsoJxA?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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<p>Chloe Varnfield, a digital marketing specialist at Atelier Studios with nearly eight years in PPC, joined me to share the mistakes that shaped her career — and the lessons every advertiser should take from them.</p>



<h2 class="wp-block-heading" id="h-when-google-sneaks-settings-past-you">When Google sneaks settings past you</h2>



<p>Chloe&#8217;s first story centers on Google&#8217;s account-level automated assets setting — a feature so well hidden that many advertisers don&#8217;t know it exists until a client sends a screenshot asking why their headline looks completely wrong. The setting, buried behind a three-dot menu, defaults to &#8220;on&#8221;, meaning Google can automatically generate and serve headlines advertisers never wrote or approved. The takeaway: always audit your account-level settings, and treat every Google update as a potential default you&#8217;ll need to turn off.</p>



<h2 class="wp-block-heading" id="h-why-you-should-never-make-changes-on-a-friday">Why you should never make changes on a Friday</h2>



<p>A client asked Chloe to narrow their campaign&#8217;s location targeting mid-call. She made the change quickly — and accidentally excluded the UK entirely while targeting only the desired regions. Campaigns stopped delivering. It took three days of head-scratching before she audited the full campaign and found the culprit. The lesson she now swears by: never make significant changes on a Friday, and when something stops working, go straight to a full audit rather than waiting for the algorithm to &#8220;fix itself.&#8221;</p>



<h2 class="wp-block-heading" id="h-the-time-she-listened-to-a-google-rep-and-tanked-performance-for-two-months">The time she listened to a Google rep — and tanked performance for two months</h2>



<p>Chloe&#8217;s most costly story involves a campaign that was performing at its best in years. A Google rep recommended switching bid strategy from Maximise Conversions to Maximise Conversion Value. She made the switch — and performance collapsed. For small to medium-sized businesses that already struggle to hit the conversion volume thresholds needed for smart bidding to work effectively, changing bid strategy is a high-stakes decision that shouldn&#8217;t be made on the spot. It took two months to recover, with the pressure of a major seasonal sale looming. She fixed it — but the lesson stuck: don&#8217;t let enthusiasm or a rep&#8217;s insistence override your judgment. Sit on big decisions. Trust your gut.</p>



<h2 class="wp-block-heading" id="h-the-account-mistakes-that-still-happen-in-2026">The account mistakes that still happen in 2026</h2>



<p>When auditing inherited accounts, Chloe consistently sees the same three problems: broken or absent conversion tracking (sometimes still pulling from Universal Analytics), broad match applied to brand campaigns — which makes it impossible to know whether results are genuinely driven by non-brand keywords — and accounts with zero negative keywords. These aren&#8217;t minor structural issues. They directly distort performance data and waste budget.</p>



<h2 class="wp-block-heading" id="h-on-honesty-client-relationships-and-not-spiralling">On honesty, client relationships, and not spiralling</h2>



<p>Across all three of her own stories, Chloe&#8217;s client relationships survived because she communicated transparently — explaining what had gone wrong, what she was doing to fix it, and what the next step would be if that didn&#8217;t work. Her advice to anyone mid-crisis: breathe, be kind to yourself, stay calm, and remember that no one has died. The ability to fix problems under pressure is what builds expertise — and fixing something difficult often becomes your proudest professional moment.</p>



<h2 class="wp-block-heading" id="h-the-ai-mistake-too-many-marketers-are-making">The AI mistake too many marketers are making</h2>



<p>On AI, Chloe is clear: using it to generate ad copy or proposals without reviewing or editing the output is lazy and obvious. AI should make you faster, not replace your judgment. Always put your own voice and review back into whatever it produces.</p>


Chloe Varnfield, a digital marketing specialist at Atelier Studios with nearly eight years in PPC, joined me to share the mistakes that shaped her career — and the lessons every advertiser should take from them.
When Google sneaks settings past you
Chloe’s first story centers on Google’s account-level automated assets setting — a feature so well hidden that many advertisers don’t know it exists until a client sends a screenshot asking why their headline looks completely wrong. The setting, buried behind a three-dot menu, defaults to “on”, meaning Google can automatically generate and serve headlines advertisers never wrote or approved. The takeaway: always audit your account-level settings, and treat every Google update as a potential default you’ll need to turn off.
Why you should never make changes on a Friday
A client asked Chloe to narrow their campaign’s location targeting mid-call. She made the change quickly — and accidentally excluded the UK entirely while targeting only the desired regions. Campaigns stopped delivering. It took three days of head-scratching before she audited the full campaign and found the culprit. The lesson she now swears by: never make significant changes on a Friday, and when something stops working, go straight to a full audit rather than waiting for the algorithm to “fix itself.”
The time she listened to a Google rep — and tanked performance for two months
Chloe’s most costly story involves a campaign that was performing at its best in years. A Google rep recommended switching bid strategy from Maximise Conversions to Maximise Conversion Value. She made the switch — and performance collapsed. For small to medium-sized businesses that already struggle to hit the conversion volume thresholds needed for smart bidding to work effectively, changing bid strategy is a high-stakes decision that shouldn’t be made on the spot. It took two months to recover, with the pressure of a major seasonal sale looming. She fixed it — but the lesson stuck: don’t let enthusiasm or a rep’s insistence override your judgment. Sit on big decisions. Trust your gut.
The account mistakes that still happen in 2026
When auditing inherited accounts, Chloe consistently sees the same three problems: broken or absent conversion tracking (sometimes still pulling from Universal Analytics), broad match applied to brand campaigns — which makes it impossible to know whether results are genuinely driven by non-brand keywords — and accounts with zero negative keywords. These aren’t minor structural issues. They directly distort performance data and waste budget.
On honesty, client relationships, and not spiralling
Across all three of her own stories, Chloe’s client relationships survived because she communicated transparently — explaining what had gone wrong, what she was doing to fix it, and what the next step would be if that didn’t work. Her advice to anyone mid-crisis: breathe, be kind to yourself, stay calm, and remember that no one has died. The ability to fix problems under pressure is what builds expertise — and fixing something difficult often becomes your proudest professional moment.
The AI mistake too many marketers are making
On AI, Chloe is clear: using it to generate ad copy or proposals without reviewing or editing the output is lazy and obvious. AI should make you faster, not replace your judgment. Always put your own voice and review back into whatever it produces.

Chloe Varnfield  shares sneaky Google settings, Friday mistakes, and Google rep advice that tanked her campaigns — and the hard-won lessons that came after.

Chloe Varnfield talks sneaky Google Ads settings and tanking performance

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Search marketing jobs" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-550x310.jpg 550w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

Looking to take the next step in your search marketing career? 
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
Newest SEO Jobs 
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(Provided to Search Engine Land by SEOjobs.com) 

Digital Marketing Specialist (Content, Technical SEO)
March 13, 2026 
Job Description Salary: $55k-$65k Digital Marketing Specialist Location: Oberlin, Ohio Full-Time About AdeptAg AdeptAg LLC is a North American leader in controlled environment agriculture, integrating innovative growing, automation, and irrigation solutions for customers both domestic and international. We support todays growers with forward-thinking, cost-efficient systems designed to meet the evolving challenges of modern agriculture. Our […]

Digital Marketing & Listing Specialist (SEO/SEM)
March 13, 2026 
Benefits: 401(k) matching Dental insurance Health insurance Vision insurance Digital Marketing & Listing SpecialistPosition Title: Digital Marketing & Listing Specialist Department: Marketing & Revenue Reports To: General Manager (Victoria Swinford) Location: Santa Rosa Beach, FL (On-site preferred; hybrid considered) Employment Type: Full-Time, Salaried Position Summary Southern Holiday Homes is seeking a highly creative, detail-oriented Digital […]

Global SEO Specialist (SEO/AI/GEO)
March 13, 2026 
Job Description Biointron is a global antibody services CRO seeking a client-facing, detail-oriented, and self-starting Marketing Associate to join our fast-growing team. Reporting directly to the Marketing Manager, this role is responsible for implementing marketing initiatives and collaborating with the global Biointron marketing team and regional business development teams to support company objectives. The ideal […]

Content Marketing Manager
March 13, 2026 
Job Description Hi, we’re TechnologyAdvice. At TechnologyAdvice, we pride ourselves on helping B2B tech buyers manage the complexity and risk of the buying process. We are a trusted source of information for tech buyers, delivering advice and facilitating connections between our buyers and the world’s leading sellers of business technology. Headquartered in Nashville, Tennessee, we […]

Director of Digital Marketing (SEO/SEM, Local SEO)
March 11, 2026 
About Haven Services Haven Services LLC is a $100MM residential and commercial plumbing, HVAC, and electrical services and contracting company. Haven Services is executing a growth strategy targeting $200MM in revenue by 2031. We are committed to delivering exceptional service to the homeowners and businesses we serve, and we’re looking for a results-driven digital marketing […]

Digital Marketing Associate (Technical SEO/Content)
March 11, 2026 
Who We Are iPullRank is a eleven-year-old digital marketing remote agency based in New York City, founded by industry trailblazer Michael King. We’re not here to follow trends—we set them. Our team blends technical expertise with creativity to deliver SEO, Content, and Generative AI services that drive results. We work with some of the biggest […]

Director, Digital Marketing (SEO/SEM)
March 11, 2026 
Job Description MetTel is a global communications solutions provider with the most complete suite of fully managed services that focus on secure connectivity, and network and mobility services. We simplify communications and networking for business and government agencies. Our customers include many of the Fortune 500, and Gartner recognizes us as an industry leader. We […]

Content Marketing Manager
March 11, 2026 
Job Description Salary: $105k – $115k/yr. Company: IPS Group is a design, engineering and manufacturing company focused on low power wireless telecommunications and parking technologies. IPS manufactures its products in the United States of America and has been delivering world-class solutions to the telecommunications and parking industries for over 25 years. The company is best […]

Senior Digital Marketing Manager (SEO/GEO/SEM)
March 10, 2026 
About Definitive Healthcare: At Definitive Healthcare (NASDAQ: DH), we’re passionate about turning data, analytics, and expertise into meaningful intelligence that helps our customers achieve success and shape the future of healthcare. We empower them to uncover the right markets, opportunities, and people—paving the way for smarter decisions and greater impact. Headquartered just outside of Boston, […]

Director of Organic Search
March 9, 2026 
The best organic search strategies don’t just move rankings—they move businesses. As Director of Organic Search, you’ll lead a team of specialists in building strategies that tie SEO and AEO performance directly to the outcomes clients care about: revenue, market share, and competitive advantage. This is a role for someone who thinks like a business […]

Newest PPC and paid media jobs
(Provided to Search Engine Land by PPCjobs.com) 
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Paid Social Manager
March 13, 2026 
Tombras, a 450+ person, full-service, national advertising agency with a digital mindset, is seeking a Paid Social Strategist. You’ll be joining one of the top independent agencies in North America.Connecting Data & Creativity for Business Results® is working for our clients and creating a flywheel affect fueling both client and agency growth. You’ll be a […]

PPC Specialist
March 13, 2026 
Do you love writing and designing compelling ads and landing pages that convert? Are you proven at building PPC strategies, launching campaigns, testing ideas, and continuously improving results … not just talking about performance, but driving it? Are you both strategic and hands‑on… someone who doesn’t just plan campaigns, but builds, optimizes, and owns them […]

PPC Specialist
March 13, 2026 
Join the Blacksmith Team! Blacksmith Agency is a boutique digital agency based out of Phoenix, AZ, specializing in top‑of‑the‑line, custom website design and development. By forging digital products and online experiences rooted in user expectations and data, Blacksmith helps partners grow, innovate, and exceed their business objectives. Top clients include Google, General Electric, Voss Water, […]

Paid Social Strategist
March 13, 2026 
About Delve Deeper Delve Deeper is a Performance Media Agency focused on helping clients grow their customer base by integrating the power of Tech & Data in Media. We act as one highly functioning team that is powered by our professional “Fire in the Belly”, with a passion for creating exceptional value by delighting our […]

Paid Social Manager
March 12, 2026 
McGarrah Jessee is looking for a Paid Social Manager — someone who doesn’t just launch campaigns, but architects how social drives full-funnel growth for a major retail business. This role sits at the intersection of social strategy, performance marketing, creative collaboration and retail commerce — translating business ambition into orchestrated social ecosystems that drive traffic, […]

Other roles you may be interested in
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Salary: $73,000 – $83,000
Develop and maintain strategic keyword and topic targeting plans for client campaigns
Monitor keyword rankings, search visibility, and performance trends to inform optimization strategies
Paid Ads/Growth Manager, Robert Half (Hybrid, Atlanta Metropolitan Area)
Salary: $65,000 – $85,000
Manage, optimize, and scale paid campaigns across Google Ads (Search, Display, YouTube) and Meta Ads (Facebook/Instagram).
Continuously refine targeting, bidding strategies, and creative to improve CPL, conversion rates, and overall ROAS.
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Salary: $60,000 – $75,000
Execute day-to-day SEO tactics across multiple client accounts, ensuring alignment with predefined campaign objectives.
Implement optimization strategies, including technical SEO audits and recommendations.
Marketing Manager – SEO & GEO, Care.com (Hybrid, Austin Texas)
Salary: $85,000 – $95,000
Organic Growth: Build and execute the SEO roadmap across technical, content, and off-page. Own the numbers: traffic, rankings, conversions. No handoffs, no excuses.
AI-Optimized Search (AIO): Define and drive CARE.com’s strategy for visibility in AI-generated results — Google AI Overviews, ChatGPT, Perplexity, and whatever comes next. Optimize entity coverage, content structure, and schema to ensure we’re the answer, not just a result.
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Manage Amazon advertising across Sponsored Products, Brands, and Display campaigns.
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Salary: $100,000 -$110,000
Develop and implement strategic advertising plans for Etail (Ecomm/Retail) accounts.
Analyzing advertising performance data with related ROAS & TACoS evaluations.
Programmatic Advertising Manager, We Are Stellar (Remote)
Salary: $75,000
Manage the day-to-day programmatic campaign approach, execution, trafficking optimization, and reporting across the relevant DSPs for your clients.
Build and present directly to client stakeholders programmatic campaign performance, analysis, and insights.
Marketing Manager, Backstage (Remote)
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Manage and optimize campaigns daily across Meta Ads, Google Ads, and other kay partners
Own forecasting, pacing, budget allocation, and optimization for high-scale monthly budgets..
Demand Generation Manager, Shoplift (Remote)
Salary: $100,000 – $110,000
Design and execute inbound-led outbound campaigns—reaching prospects who’ve shown intent (visited pricing page, downloaded resources, engaged with content) at precisely the right moment
Build and optimize Apollo sequences, LinkedIn outreach, and multi-touch campaigns that book qualified demos for AEs
Search Engine Optimization Manager, Confidential (Hybrid, Miami-Fort Lauderdale Area)
Salary: $75,000 – $105,000
Serve as a strategic SEO partner for client accounts, translating business goals into actionable search initiatives
Communicate SEO insights, priorities, and performance clearly to clients and internal stakeholders
Meta Ads Manager, Cardone Ventures (Scottsdale, AZ)
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Provide ongoing actionable insights into campaign performance to relevant stakeholders
Paid Search Marketing Manager, LawnStarter (Remote)
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Manage and optimize large-scale, complex SEM campaigns across Google Ads, Bing Ads, Meta Ads and other search platforms
Activate, optimize and make efficient Local Services Ads (LSA) at scale
Note: We update this post weekly. So make sure to bookmark this page and check back.

Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

The latest jobs in search marketing

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi.png" class="attachment-large size-large wp-post-image" alt="Reddit vs SerpApi" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>In its dismissal bid, SerpApi argues reading public Google results isn't DMCA circumvention and warns Reddit is stretching platform power.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi.png" class="attachment-large size-large wp-post-image" alt="Reddit vs SerpApi" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/reddit-vs-serpapi-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>SerpApi is asking a federal court to dismiss Reddit’s lawsuit over alleged scraping of Reddit content from Google Search, saying Reddit is trying to use copyright law to control user posts and public search results.</p>



<ul class="wp-block-list">
<li>The motion follows Reddit’s amended complaint filed in February. </li>



<li>SerpApi says the filing still fails to show copyright ownership, circumvention of technical protections, or concrete harm.</li>
</ul>



<p><strong>SerpApi’s argument.</strong> SerpApi CEO Julien Khaleghy, in a blog post today, argued the lawsuit fails for several reasons:</p>



<ul class="wp-block-list">
<li>Reddit doesn&#8217;t own most of the content at issue. Its user agreement states that users retain ownership.</li>



<li>Reddit holds only a non-exclusive license to user posts.</li>



<li>The snippets cited in the complaint (e.g., dates, addresses, short fragments) aren&#8217;t copyrightable.</li>



<li>SerpApi accessed Google Search pages, not Reddit itself.</li>
</ul>



<p><strong>DMCA.</strong> Khaleghy said Reddit claims SerpApi violated the Digital Millennium Copyright Act (DMCA) by circumventing technical protections. SerpApi disputes that claim, saying it retrieves the same search results visible to anyone who enters a query in Google. Khaleghy argued that:</p>



<ul class="wp-block-list">
<li>SerpApi doesn&#8217;t break encryption or bypass authentication.</li>



<li>Accessing public webpages isn&#8217;t “circumvention” under the DMCA.</li>



<li>Reddit is trying to enforce copyright protections it doesn&#8217;t own.</li>



<li>Reddit’s privacy policy states that public posts may appear in search results.</li>
</ul>



<p><strong>Catch up quick.</strong> Legal fights over search scraping and AI data have intensified in recent months:</p>



<ul class="wp-block-list">
<li><strong>Oct. 22:</strong> <a href="https://searchengineland.com/reddit-sues-perplexity-serpapi-scraping-google-463681">Reddit sued SerpApi</a>, Perplexity, Oxylabs, and AWMProxy, alleging they scraped Reddit content through Google Search and reused it at scale. Reddit said it planted a “trap” post visible only to Google’s crawler that later appeared in Perplexity responses.</li>



<li><strong>Oct. 29:</strong> <a href="https://searchengineland.com/serpapi-reddit-lawsuit-response-463962">SerpApi said it would fight the lawsuit</a>, calling the allegations inflammatory and defending access to public search data.</li>



<li><strong>Dec. 19:</strong> <a href="https://searchengineland.com/google-sues-serpapi-466541">Google sued SerpApi</a>, alleging the company bypassed bot protections and scraped licensed search features.</li>



<li><strong>Feb. 23:</strong> <a href="https://searchengineland.com/serpapi-motion-dismiss-google-scraping-lawsuit-469889">SerpApi asked a federal court to dismiss Google’s lawsuit</a>, arguing Google is misusing the DMCA to restrict access to public search results.</li>
</ul>



<p><strong>Why we care.</strong> The case tests whether companies can extract information from Google’s search results without violating copyright or the DMCA. The outcome could affect SEO tools and AI training data.</p>



<p><strong>What’s next.</strong> The court must decide whether Reddit’s amended complaint can proceed. If the judge dismisses the case with prejudice, Reddit’s claims against SerpApi in this lawsuit would end.</p>



<p><strong>SerpApi&#8217;s blog post.</strong> <a href="https://serpapi.com/blog/reddits-lawsuit-is-a-dangerous-attempt-to-expand-platform-power/">Reddit&#8217;s Lawsuit is a Dangerous Attempt to Expand Platform Power</a></p>


SerpApi is asking a federal court to dismiss Reddit’s lawsuit over alleged scraping of Reddit content from Google Search, saying Reddit is trying to use copyright law to control user posts and public search results.
The motion follows Reddit’s amended complaint filed in February. 
SerpApi says the filing still fails to show copyright ownership, circumvention of technical protections, or concrete harm.
SerpApi’s argument. SerpApi CEO Julien Khaleghy, in a blog post today, argued the lawsuit fails for several reasons:
Reddit doesn’t own most of the content at issue. Its user agreement states that users retain ownership.
Reddit holds only a non-exclusive license to user posts.
The snippets cited in the complaint (e.g., dates, addresses, short fragments) aren’t copyrightable.
SerpApi accessed Google Search pages, not Reddit itself.
DMCA. Khaleghy said Reddit claims SerpApi violated the Digital Millennium Copyright Act (DMCA) by circumventing technical protections. SerpApi disputes that claim, saying it retrieves the same search results visible to anyone who enters a query in Google. Khaleghy argued that:
SerpApi doesn’t break encryption or bypass authentication.
Accessing public webpages isn’t “circumvention” under the DMCA.
Reddit is trying to enforce copyright protections it doesn’t own.
Reddit’s privacy policy states that public posts may appear in search results.
Catch up quick. Legal fights over search scraping and AI data have intensified in recent months:
Oct. 22: Reddit sued SerpApi, Perplexity, Oxylabs, and AWMProxy, alleging they scraped Reddit content through Google Search and reused it at scale. Reddit said it planted a “trap” post visible only to Google’s crawler that later appeared in Perplexity responses.
Oct. 29: SerpApi said it would fight the lawsuit, calling the allegations inflammatory and defending access to public search data.
Dec. 19: Google sued SerpApi, alleging the company bypassed bot protections and scraped licensed search features.
Feb. 23: SerpApi asked a federal court to dismiss Google’s lawsuit, arguing Google is misusing the DMCA to restrict access to public search results.
Why we care. The case tests whether companies can extract information from Google’s search results without violating copyright or the DMCA. The outcome could affect SEO tools and AI training data.
What’s next. The court must decide whether Reddit’s amended complaint can proceed. If the judge dismisses the case with prejudice, Reddit’s claims against SerpApi in this lawsuit would end.
SerpApi’s blog post. Reddit’s Lawsuit is a Dangerous Attempt to Expand Platform Power

In its dismissal bid, SerpApi argues reading public Google results isn't DMCA circumvention and warns Reddit is stretching platform power.

SerpApi asks court to throw out Reddit scraping complaint

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/ChatGPT-Image-Google-Ads-AI-Max.png" class="attachment-large size-large wp-post-image" alt="Google Ads dashboard concept" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/ChatGPT-Image-Google-Ads-AI-Max.png 1920w, https://searchengineland.com/wp-content/seloads/2025/09/ChatGPT-Image-Google-Ads-AI-Max-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/09/ChatGPT-Image-Google-Ads-AI-Max-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>PPC experts explain how AI Max and Performance Max reshape search, when automation wins, and where human strategy still drives better leads.

The days of building campaigns around long lists of keywords are fading. Today, AI-powered Google campaigns and features like Performance Max (PMax) and AI Max are changing the rules.
These keywordless campaigns lean on automation, audience signals, and machine learning to find new opportunities, often faster and at greater scale than humans can.
At SMX Next, three PPC pros — Nikki Kuhlman, VP of search at Jumpfly; Brad Geddes, founder of Adalysis; and Christine Zirnheld, director of lead gen at Cypress North — explained where PMax and AI Max fit into your broader campaign strategy, where humans still make the difference, and how to strike the right balance between automation and control.
AI Max for Search: Best practices and what not to do
AI Max for Search is not a new campaign type. It’s a one-click opt-in setting within existing Search campaigns.
Without requiring you to switch to broad match, it expands your keywords — similar to broad match or Dynamic Search Ads — using your landing pages and other site assets. It then personalizes the ad copy and landing page the searcher sees.




The evolution from traditional setup
In the old setup, you might have used a keyword like “skincare for dry sensitive skin” that sent users to a moisturizer page with generic ad copy because you couldn’t capture every variation. With Google’s current matching, a specific ad group no longer guarantees that keyword will trigger that ad group.
AI Max for Search addresses this by generating ad copy based on the search query, making it more relevant and directing users to a landing page that better matches their needs.
Success with blog content
One area where AI Max for Search is seeing success beyond the norm is blog content. While DSA campaigns traditionally excluded blogs, AI Max for Search can now serve blogs as landing pages—and they’re converting. The key is that these blogs guide readers to specific products, not just general content.
The generated headlines are compelling and longer than what traditional RSAs allow, creating a more engaging user experience.




Best Practices for AI Max for Search
Do:
Use it on existing campaigns with history and data, not brand new campaigns
Test it as a 50/50 experiment instead of an outright change
Use it on brand campaigns with brand inclusion capabilities
Apply it to campaigns not hitting budget that could use more volume
Review landing pages and utilize URL exclusions (individual or rule-based)
Use landing page inclusions at the ad group level
Review search queries regularly and add negative search terms
Enable both text customization and final URL expansion for maximum value
Turn off AI Max at the ad group level when specific ad groups drive poor traffic
Don’t:
Use it on brand new campaigns without data
Change all campaigns at once without testing
Use it on brand campaigns without name recognition or brand inclusion ability
Apply it to budget-constrained campaigns
Turn off both URL expansion and text customization — if you’re not using both features, stick with broad match and smart bidding
Assume it works universally — test on individual campaigns




Your action plan
Week 1: Pick a search campaign to test (brand with brand inclusion, with budget capability, needing more volume). Review landing page URLs and add inclusions or exclusions.
Week 2: Review search queries and add negatives.
Week 3: Continue optimization and turn off AI Max at the ad group level as needed.
Experiment checklist:
Ensure enough volume for a 50/50 experiment
Give the experiment 6 weeks to 2 months
Set up a custom experiment if you need to enable brand inclusion or update settings
For one-click experiments, change confidence level to medium and turn off auto-apply




Match type performance: What the data shows
A comprehensive study analyzing over 16,000 campaigns revealed surprising insights about match type performance across different bidding strategies.




Match type basics
Exact match: Should match only when the search term has the same intent as your keyword. Misspellings and word order haven’t mattered for years — focus on user intent.
Phrase match: The search intent should match your keyword, but could have additional information around it, whether modifiers, phone numbers, or websites.
Broad match: Shows for anything related to the search intent. The key difference is that broad match uses additional signals that exact and phrase don’t, such as content on the landing page, other keywords in the ad group, and, most powerfully, previous search history for that user.




Performance by bidding strategy
Max Conversion strategies (Max Conversions, Max Conversion Value):
Most campaigns using max bid strategies have under 30 conversions per month, giving machines limited data to work with. The findings:
Exact match has the best click-through rates and conversion rates
Broad match had the worst conversion rates but the best return on ad spend
Broad match also had lower CPA than phrase match
Phrase match performed worst overall
Recommendation: Start with exact match, then skip phrase match entirely and layer in broad match if you have more budget to spend.




Target Bid Strategies (Target CPA, Target ROAS):
Most campaigns using these strategies have over 30 conversions per month, with many at 50 or 100+, giving machines substantially more data. The findings:
Exact match is again the best match type
Phrase match comes second
Broad match is third
Phrase match performs better with more data
Recommendation: Start with exact match, layer in phrase match with more budget, then add broad match if additional budget is available.
The phrase match puzzle
Why does phrase match perform poorly with limited data but better with more data? 
Broad match uses additional signals, particularly previous search queries, to determine bids. When conversion data is limited (under 30 conversions monthly), broad match’s ability to leverage previous search history makes it much stronger than phrase match.
However, with sufficient data (50–100+ conversions), Google can properly match phrase match keywords using machine-learning pattern matching.
Brand vs. non-brand considerations
When you combine brand and non-brand data, exact match becomes even more powerful, delivering significantly higher click-through rates, higher conversion rates, lower CPAs, and much higher return on ad spend. That’s why segmenting keywords by brand and non-brand is crucial when determining your match type strategy.
Ecommerce exception
For ecommerce companies, broad match (and sometimes phrase match) can produce higher average order values than exact match. When someone searches for a specific product, and you carry that exact item, conversion rates are high, but they’re usually buying a single product with a lower checkout value.
When shoppers haven’t decided on a product, they tend to match broader keywords and build larger carts — resulting in lower conversion rates but higher order values.




Performance Max for lead generation
There’s a common misconception that Performance Max only works for ecommerce and is too difficult for lead generation. That couldn’t be further from the truth.
The critical success factor
The biggest mistake you can make—one you should avoid entirely—is optimizing campaigns for form submissions alone. If you treat every form submission as your campaign goal, you’ll end up with spammy submissions and frustrated sales teams.




The solution: integrate your Google Ads account with your CRM and import bottom-of-funnel leads—sales-qualified leads (SQLs), marketing-qualified leads (MQLs), opportunities, or even customers if the sales cycle is short. 
When you tell Google Ads what you actually want and set it as your campaign goal, Performance Max can cast a wide net while still bringing in qualified prospects.
Available controls for regulated industries
Performance Max has significantly more controls now than at launch, making it viable for highly regulated industries:
Brand exclusions: Exclude all brand traffic from Performance Max campaigns
Campaign-level negative keywords: Exclude unwanted search terms directly
Search term reports: See what’s triggering your ads and exclude accordingly
Channel reporting: View spending and performance across different networks
Page feeds: Control where you send traffic on your site
Final URL expansion toggle: Turn it off completely if needed
Text enhancement controls: Optional feature that can be disabled entirely
Text guidelines: Specify words to avoid (e.g., “discount” or “directory”)




Device control: The secret weapon for B2B
One of the most underutilized levers for B2B and regulated industries is device control, introduced at the beginning of 2025. You can turn off any device from your Performance Max campaign.
A B2B SaaS example demonstrates the impact: Before device segmentation in January, the account had 224 SQLs from desktop at an acceptable CPA, but 33 from mobile at $319 CPA (above goal). After creating separate mobile campaigns with more aggressive target CPAs, they achieved 190 desktop SQLs and 37 mobile SQLs in a shorter month, with mobile CPA dropping to $204 and overall Performance Max CPA declining from $238 to $204.
Real Performance Max results for B2B SaaS
Despite lower conversion rates from Performance Max compared to search campaigns (due to broader reach), the results speak for themselves. In September 2025, one B2B SaaS account achieved:
Search Campaigns: 150 SQLs at $237 CPA
Performance Max: 204 SQLs at $220 CPA




Performance Max cast a wider net with cheaper CPCs, bringing in not just more leads but more sales-qualified leads at a lower cost.
How they did it:
Optimized for SQLs, not form submissions
Set lower target CPAs in Performance Max than search (to control spend while casting wider net)
Created separate campaigns for off-hours to control weekend spending
Turned off final URL expansion and text enhancements (client preference)
Implemented separate mobile and tablet campaigns with aggressive target CPAs
AI Max for Search in lead generation
AI Max for Search brings the power of Performance Max to the search network, where bottom-of-funnel intent is strongest. This is especially valuable for lead generation accounts that spend on other networks in Performance Max but don’t generate leads from them.
Early results: Higher ed financial services
A higher education financial client (loan products) showed promising early results:




Approved applications (primary KPI):
Standard Search: 86 approved applications at $660 CPA
AI Max: 70 approved applications at $579 CPA
AI Max brought in qualified leads cheaper despite the highly competitive keyword environment.
Down-funnel performance
Beyond the initial conversion action (soft credit check), AI Max showed superior performance throughout the funnel:
42% of AI Max form submissions resulted in soft pulls vs. 36% for standard search
9.9% of AI Max form submissions resulted in bookings vs. 5.58% for standard search
AI Max isn’t just bringing more qualified prospects at the top—lead quality remains higher throughout the entire funnel.
How they did it:
Optimized for approved applications, not form submissions
Set lower target CPAs in AI Max than standard search
Used high-performing bottom-of-funnel keywords with broad match types
Kept final URL expansion and text enhancements disabled (still worked well without them)
Win with AI without losing control
PPC success requires embracing AI-driven campaigns while maintaining strategic human oversight. Whether you use AI Max for Search, Performance Max for lead generation, or adjust match types based on bidding methods and data volume, the key is understanding how these tools work and applying best practices aligned with your business goals.
The data is clear: exact match remains powerful across scenarios, but phrase and broad match perform differently depending on bidding strategy and data volume. For lead generation, the game changer is optimizing for true bottom-of-funnel conversions rather than form submissions, combined with strategic device controls and proper campaign segmentation.
The future of PPC depends on knowing when — and how — to apply automation and control for maximum impact.

PPC experts explain how AI Max and Performance Max reshape search, when automation wins, and where human strategy still drives better leads.

Beyond keywords: Mastering AI-driven campaigns

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling.png" class="attachment-large size-large wp-post-image" alt="Google search monolith crumbling" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Advice around AI search is getting simplistic. The real edge comes from knowledge graphs, expert entities, and influence in trusted datasets.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling.png" class="attachment-large size-large wp-post-image" alt="Google search monolith crumbling" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-monolith-crumbling-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>A recent <a href="https://hbr.org/2026/03/llms-are-overtaking-search-heres-how-to-adjust-your-online-presence" target="_blank" rel="noopener">Harvard Business Review</a> piece echoes the shift we’re sseeing in the SEO industry: at a macro level, LLMs and Google’s AI-powered SERP features, such as AI Overviews, aren’t just creating a <a href="https://searchengineland.com/guide/zero-click-searches">zero-click</a> environment, but also changing user journeys and behavior.</p>



<p>They’re collapsing what used to be multi-touch customer journeys into a single synthesized answer.</p>



<p>For a more visual and emphatic metaphor, the monolith of “Search” is crumbling.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="600" height="628" src="https://searchengineland.com/wp-content/seloads/2026/03/google-search-crumbling.png" alt="" class="wp-image-471517"/></figure>
</div>


<p>When that happens, brands lose many of the touchpoints they once owned, and your marketing strategy must change accordingly. HBR captures this moment well, arguing that marketing now has a new audience and that algorithms increasingly shape first impressions.</p>



<p>That said, while the article points in the right direction on the broader trend, its tactical advice is generic and falls back on shallow tactics.</p>



<p>Much of the guidance returns to familiar marketing playbook ideas that sound strategic and innovative but lack real operational depth. That gap matters for the longevity and sustainability of visibility.</p>



<p>The narrative may be easy for you to understand and repeat at the executive level, but it glosses over the deeper structural changes you must actually make to adapt to the new search ecosystem.</p>



<h2 class="wp-block-heading" id="h-the-problem-with-flock-tactics">The problem with flock tactics</h2>



<p>The HBR article centers on schema, authorship signals, and branded concepts. These recommendations risk becoming what I call “flock tactics.”</p>



<p>These ideas spread quickly because they’re easy to explain, but they offer little lasting competitive advantage once everyone adopts them.</p>



<h3 class="wp-block-heading" id="h-schema-nbsp">Schema&nbsp;</h3>



<p>Schema has been one of the most debated topics in LLM and AI optimization. <a href="https://searchengineland.com/microsoft-bing-copilot-use-schema-for-its-llms-453455">Microsoft Bing confirmed</a> it uses schema for its LLMs, but the relationship between Google’s models and third-party LLMs isn’t as straightforward.</p>



<p>While it isn’t necessarily wrong to recommend schema as part of your overall search optimization activities (SEO and AI), positioning it as a table-stakes tactic ignores diminishing returns once competitors implement similar markup and it becomes standard.</p>



<p>Another gap is the role of external knowledge systems, such as Wikidata or authoritative publishers. Much of the information LLMs rely on comes from those sources rather than a single company’s website.</p>



<p>This is less linear to understand, explain, and demonstrate as a single line item on an activity tracker, but these are nuances you now have to deal with, whether you like it or not. </p>



<p>What’s also missing is any exploration — or even a nod — to how models ingest and prioritize structured data compared with the many unstructured signals they rely on.</p>



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<h3 class="wp-block-heading" id="h-e-e-a-t-shallow-authorship-signals">E-E-A-T — shallow authorship signals</h3>



<p>Attaching the names, credentials, and biographies of real experts follows familiar E-E-A-T logic and represents reasonable hygiene.</p>



<p>The problem is that the treatment remains superficial. It risks pushing you to focus on cosmetic signals such as bios, headshots, and credential lists without strengthening the underlying expertise pipeline.</p>



<p>There is a meaningful difference between placing an author bio on a page and cultivating a genuine expert entity whose work appears in conferences, third-party publications, standards committees, or academic collaborations.</p>



<p>Only the latter produces signals that models are more likely to recognize and trust.</p>



<h3 class="wp-block-heading" id="h-vanity-concepts">Vanity concepts</h3>



<p>The article also suggests creating branded frameworks or concepts — for example, something like “The Acme Index” — to help models associate ideas with your company. In theory this sounds appealing, but in practice it’s extremely difficult to execute.</p>



<p>Unless those ideas spread into the trusted datasets LLMs tend to prioritize, they rarely gain traction.</p>



<p>You need those concepts and frameworks adopted and discussed by entities other than yourself, including academic journals, technical standards, widely used software ecosystems, and other prominent entities in your category.</p>



<p>What often results instead is a proliferation of branded labels that remain largely invisible to the models they were meant to influence.</p>



<h2 class="wp-block-heading" id="h-the-structural-blind-spots">The structural blind spots</h2>



<p>Beyond these tactical issues, the analysis overlooks deeper structural challenges. It treats AI primarily as an external platform shift.</p>



<p>The implication is that you must simply adapt to it rather than actively shaping your own environment.</p>



<h3 class="wp-block-heading" id="h-internalizing-ai-infrastructure">Internalizing AI infrastructure</h3>



<p>HBR never seriously considers the possibility of building AI into your own infrastructure. You can deploy assistants, RAG systems, and domain-specific agents within your own products and customer experiences.</p>



<p>These systems operate in logged-in, transactional contexts where first-party data and controlled interfaces still matter enormously.</p>



<p>In those environments, traditional concerns such as site architecture, structured data, and product design remain deeply relevant, though they operate differently from public search optimization.</p>



<h3 class="wp-block-heading" id="h-it-s-not-just-seo">It’s not just SEO</h3>



<p>The discussion also frames SEO primarily as a page-ranking problem tied to discovery.</p>



<p>That perspective misses the broader shift toward <a href="https://searchengineland.com/guide/entity-first-content-optimization">entity-level knowledge management</a> (things, not strings).</p>



<p>Visibility within LLMs increasingly depends on how well you structure entities, taxonomies, and knowledge graphs, and on how those systems connect with external data sources.</p>



<p>Most LLMs don’t process data at the petabyte scale Google uses to understand entity relationships. There is a strong correlation that when something ranks well on Google, third-party LLMs often correlate and “trust” Google’s guidance on which brands to show, for what, and when.</p>



<p>HBR’s phrase “engineering recall” points directly to this deeper data engineering work, yet the implications aren’t expanded.</p>



<h3 class="wp-block-heading" id="h-llm-model-heterogeneity">LLM model heterogeneity</h3>



<p>Another major omission is the diversity of AI systems themselves.</p>



<p>Different AI assistants and models rely on different training datasets, refresh cycles, retrieval mechanisms, and safety layers.</p>



<p>That heterogeneity means you can’t assume a single optimization strategy will work across all AI surfaces.</p>



<p>It also doesn’t explore the risk of broad-stroke approaches. If you try to increase visibility within AI models without accounting for safety filters, attribution errors, or hallucinations, you may gain visibility in ways that are inaccurate or reputationally damaging.</p>



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<h2 class="wp-block-heading" id="h-surface-level-tactics-won-t-build-ai-visibility">Surface-level tactics won’t build AI visibility</h2>



<p>HBR’s article works well as a high-level explanation of how AI is changing marketing. It helps you understand that traditional SEO alone is no longer enough and that you must consider how AI systems see and describe your brand.</p>



<p>As a practical guide, however, the advice is thin. Most recommendations focus on surface-level tactics that many companies will quickly copy, reinforcing the echo chamber of flock tactics that are easy to sell and quantify, but risk narrowing your focus to short-term wins at the expense of longer-term strategy.</p>



<p>The real challenge is deeper. You need clear entity definitions, structured knowledge systems, reliable data in trusted sources AI models use, testing across how different models represent you, and AI-powered experiences within your own products.</p>



<p>“Winning” in the AI era will depend less on cosmetic SEO improvements and more on the harder structural work behind the scenes.</p>



<p></p>


A recent Harvard Business Review piece echoes the shift we’re sseeing in the SEO industry: at a macro level, LLMs and Google’s AI-powered SERP features, such as AI Overviews, aren’t just creating a zero-click environment, but also changing user journeys and behavior.
They’re collapsing what used to be multi-touch customer journeys into a single synthesized answer.
For a more visual and emphatic metaphor, the monolith of “Search” is crumbling.




When that happens, brands lose many of the touchpoints they once owned, and your marketing strategy must change accordingly. HBR captures this moment well, arguing that marketing now has a new audience and that algorithms increasingly shape first impressions.
That said, while the article points in the right direction on the broader trend, its tactical advice is generic and falls back on shallow tactics.
Much of the guidance returns to familiar marketing playbook ideas that sound strategic and innovative but lack real operational depth. That gap matters for the longevity and sustainability of visibility.
The narrative may be easy for you to understand and repeat at the executive level, but it glosses over the deeper structural changes you must actually make to adapt to the new search ecosystem.
The problem with flock tactics
The HBR article centers on schema, authorship signals, and branded concepts. These recommendations risk becoming what I call “flock tactics.”
These ideas spread quickly because they’re easy to explain, but they offer little lasting competitive advantage once everyone adopts them.
Schema 
Schema has been one of the most debated topics in LLM and AI optimization. Microsoft Bing confirmed it uses schema for its LLMs, but the relationship between Google’s models and third-party LLMs isn’t as straightforward.
While it isn’t necessarily wrong to recommend schema as part of your overall search optimization activities (SEO and AI), positioning it as a table-stakes tactic ignores diminishing returns once competitors implement similar markup and it becomes standard.
Another gap is the role of external knowledge systems, such as Wikidata or authoritative publishers. Much of the information LLMs rely on comes from those sources rather than a single company’s website.
This is less linear to understand, explain, and demonstrate as a single line item on an activity tracker, but these are nuances you now have to deal with, whether you like it or not. 
What’s also missing is any exploration — or even a nod — to how models ingest and prioritize structured data compared with the many unstructured signals they rely on.
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E-E-A-T — shallow authorship signals
Attaching the names, credentials, and biographies of real experts follows familiar E-E-A-T logic and represents reasonable hygiene.
The problem is that the treatment remains superficial. It risks pushing you to focus on cosmetic signals such as bios, headshots, and credential lists without strengthening the underlying expertise pipeline.
There is a meaningful difference between placing an author bio on a page and cultivating a genuine expert entity whose work appears in conferences, third-party publications, standards committees, or academic collaborations.
Only the latter produces signals that models are more likely to recognize and trust.
Vanity concepts
The article also suggests creating branded frameworks or concepts — for example, something like “The Acme Index” — to help models associate ideas with your company. In theory this sounds appealing, but in practice it’s extremely difficult to execute.
Unless those ideas spread into the trusted datasets LLMs tend to prioritize, they rarely gain traction.
You need those concepts and frameworks adopted and discussed by entities other than yourself, including academic journals, technical standards, widely used software ecosystems, and other prominent entities in your category.
What often results instead is a proliferation of branded labels that remain largely invisible to the models they were meant to influence.
The structural blind spots
Beyond these tactical issues, the analysis overlooks deeper structural challenges. It treats AI primarily as an external platform shift.
The implication is that you must simply adapt to it rather than actively shaping your own environment.
Internalizing AI infrastructure
HBR never seriously considers the possibility of building AI into your own infrastructure. You can deploy assistants, RAG systems, and domain-specific agents within your own products and customer experiences.
These systems operate in logged-in, transactional contexts where first-party data and controlled interfaces still matter enormously.
In those environments, traditional concerns such as site architecture, structured data, and product design remain deeply relevant, though they operate differently from public search optimization.
It’s not just SEO
The discussion also frames SEO primarily as a page-ranking problem tied to discovery.
That perspective misses the broader shift toward entity-level knowledge management (things, not strings).
Visibility within LLMs increasingly depends on how well you structure entities, taxonomies, and knowledge graphs, and on how those systems connect with external data sources.
Most LLMs don’t process data at the petabyte scale Google uses to understand entity relationships. There is a strong correlation that when something ranks well on Google, third-party LLMs often correlate and “trust” Google’s guidance on which brands to show, for what, and when.
HBR’s phrase “engineering recall” points directly to this deeper data engineering work, yet the implications aren’t expanded.
LLM model heterogeneity
Another major omission is the diversity of AI systems themselves.
Different AI assistants and models rely on different training datasets, refresh cycles, retrieval mechanisms, and safety layers.
That heterogeneity means you can’t assume a single optimization strategy will work across all AI surfaces.
It also doesn’t explore the risk of broad-stroke approaches. If you try to increase visibility within AI models without accounting for safety filters, attribution errors, or hallucinations, you may gain visibility in ways that are inaccurate or reputationally damaging.
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Surface-level tactics won’t build AI visibility
HBR’s article works well as a high-level explanation of how AI is changing marketing. It helps you understand that traditional SEO alone is no longer enough and that you must consider how AI systems see and describe your brand.
As a practical guide, however, the advice is thin. Most recommendations focus on surface-level tactics that many companies will quickly copy, reinforcing the echo chamber of flock tactics that are easy to sell and quantify, but risk narrowing your focus to short-term wins at the expense of longer-term strategy.
The real challenge is deeper. You need clear entity definitions, structured knowledge systems, reliable data in trusted sources AI models use, testing across how different models represent you, and AI-powered experiences within your own products.
“Winning” in the AI era will depend less on cosmetic SEO improvements and more on the harder structural work behind the scenes.

Advice around AI search is getting simplistic. The real edge comes from knowledge graphs, expert entities, and influence in trusted datasets.

Why surface-level SEO tactics won’t build lasting AI search visibility

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out.png" class="attachment-large size-large wp-post-image" alt="AI search fan out" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Most brands chase AI visibility, but discovery isn’t the win. Pages are retrieved, evaluated, and often rejected before an answer is written.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out.png" class="attachment-large size-large wp-post-image" alt="AI search fan out" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/ai-search-fan-out-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>ChatGPT retrieves far more webpages than it cites. A new AirOps analysis found that 85% of discovered sources never appear in the final answer.</p>



<p><strong>Why we care.</strong> If you want your content cited in AI-generated answers, discovery isn’t enough. Most retrieved pages never become visible to users.</p>



<p><strong>Key finding.</strong> In AI answers, retrieval doesn’t equal citation. Your page can rank and be retrieved yet still lose the citation to a source that better matches the prompt or supporting context.</p>



<ul class="wp-block-list">
<li>This shifts optimization toward earning selection inside the AI synthesis process—not just appearing in search results, per the report.</li>
</ul>



<p><strong>By the numbers</strong>:</p>



<ul class="wp-block-list">
<li>82,108 citations appeared in final responses.</li>



<li>Only <strong>15%</strong> of retrieved pages were cited.</li>



<li><strong>85%</strong> of pages surfaced during research never appeared in answers.</li>
</ul>



<p>Citation rates also varied by query type:</p>



<ul class="wp-block-list">
<li><strong>18.3%</strong> for product discovery queries</li>



<li><strong>16.9%</strong> for how-to queries</li>



<li><strong>11.3%</strong> for validation searches</li>
</ul>



<p><strong>Fan-out queries.</strong> ChatGPT often expands prompts with additional internal searches while generating an answer, creating what the report calls a “second citation surface.” Across the dataset:</p>



<ul class="wp-block-list">
<li>89.6% of prompts triggered two or more follow-up searches.</li>



<li>Fan-out searches expanded 15,000 prompts into 43,233 queries.</li>



<li>32.9% of cited pages appeared only in fan-out results—not the original prompt.</li>



<li>95% of fan-out queries had zero traditional search volume.</li>
</ul>



<p><strong>Google ranking correlation.</strong> High Google rankings strongly correlated with citations:</p>



<ul class="wp-block-list">
<li>55.8% of cited pages ranked in Google’s top 20.</li>



<li>Pages ranking in Position 1 were cited 3.5 times more often than pages outside the top 20.</li>
</ul>



<p><strong>About the data.</strong> AirOps analyzed 548,534 pages retrieved across 15,000 prompts to examine how ChatGPT expands queries and selects citations.</p>



<p><strong>The study.</strong> <a href="https://www.airops.com/report/influence-of-retrieval-fanout-and-google-serps-in-chatgpt">The Influence of Retrieval, Fan-out, and Google SERPs on ChatGPT Citations</a></p>


ChatGPT retrieves far more webpages than it cites. A new AirOps analysis found that 85% of discovered sources never appear in the final answer.
Why we care. If you want your content cited in AI-generated answers, discovery isn’t enough. Most retrieved pages never become visible to users.
Key finding. In AI answers, retrieval doesn’t equal citation. Your page can rank and be retrieved yet still lose the citation to a source that better matches the prompt or supporting context.
This shifts optimization toward earning selection inside the AI synthesis process—not just appearing in search results, per the report.
By the numbers:
82,108 citations appeared in final responses.
Only 15% of retrieved pages were cited.
85% of pages surfaced during research never appeared in answers.
Citation rates also varied by query type:
18.3% for product discovery queries
16.9% for how-to queries
11.3% for validation searches
Fan-out queries. ChatGPT often expands prompts with additional internal searches while generating an answer, creating what the report calls a “second citation surface.” Across the dataset:
89.6% of prompts triggered two or more follow-up searches.
Fan-out searches expanded 15,000 prompts into 43,233 queries.
32.9% of cited pages appeared only in fan-out results—not the original prompt.
95% of fan-out queries had zero traditional search volume.
Google ranking correlation. High Google rankings strongly correlated with citations:
55.8% of cited pages ranked in Google’s top 20.
Pages ranking in Position 1 were cited 3.5 times more often than pages outside the top 20.
About the data. AirOps analyzed 548,534 pages retrieved across 15,000 prompts to examine how ChatGPT expands queries and selects citations.
The study. The Influence of Retrieval, Fan-out, and Google SERPs on ChatGPT Citations

Most brands chase AI visibility, but discovery isn’t the win. Pages are retrieved, evaluated, and often rejected before an answer is written.

Only 15% of pages retrieved by ChatGPT appear in final answers: Report

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue.png" class="attachment-large size-large wp-post-image" alt="Vanity metrics vs revenue" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The new search playbook ties content to revenue: win AI recommendations, cut defensive ad spend, and hold agencies to P&#038;L results.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue.png" class="attachment-large size-large wp-post-image" alt="Vanity metrics vs revenue" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/vanity-metrics-vs-revenue-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The standard agency reporting call is broken. Budgets are under extreme scrutiny, yet you still invest in vendors that celebrate arbitrary traffic gains while your sales pipeline stays flat.</p>



<p>Optimizing for raw traffic volume is a legacy mindset that hides real commercial performance. The new mandate is to build an acquisition engine that influences buyers and protects your profit and loss (P&amp;L) long before the transaction.</p>



<p>To survive as a marketing leader today, you must ruthlessly challenge your internal teams and external agencies. Stop accepting reports on operational output and demand hard financial accountability: pipeline contribution, customer lifetime value (LTV) to customer acquisition cost (CAC) ratios, and reduced paid media dependency.</p>



<h2 class="wp-block-heading" id="h-the-new-path-to-purchase-why-traffic-is-bleeding-your-budget">The new path to purchase: Why traffic is bleeding your budget</h2>



<p>Chasing top-of-funnel informational traffic is a trap. If the users clicking your links aren’t actively buying, you’re paying for vanity metrics, not business outcomes.</p>



<p>This happens because many buyers now use large language models (LLMs) to conduct deep research before they reach a search engine’s transactional layer. If you aren’t the cited authority during that AI-driven research phase, you’re invisible by the time buyers finalize their purchase decisions.</p>



<h3 class="wp-block-heading" id="h-the-7-48-reality-the-power-of-the-educated-buyer">The 7.48% reality: The power of the educated buyer</h3>



<p>The contrast in traffic quality is staggering when you look at the data. Across our enterprise client base, traditional organic search converts at 2.75%, while AI search converts at 7.48%.</p>



<p>LLMs function as the ultimate trust proxy for today’s consumers. When tools like Gemini, ChatGPT, or Perplexity synthesize dozens of reviews, whitepapers, and Reddit threads to recommend your enterprise software, users trust the LLM’s consensus more than a branded blog post.</p>



<p>AI engines arm consumers with comprehensive data, comparisons, and consensus. By the time a user clicks your AI citation, they’ve already made their decision based on your authority and are prepared to transact.</p>



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<h2 class="wp-block-heading" id="h-from-found-to-cited-architecting-the-default-recommendation">From found to cited: Architecting the default recommendation</h2>



<p>Want to capture this 7.48% conversion rate? Your entire approach to digital asset creation must evolve. The strategy no longer centers on ranking among a list of links, but on being cited as the definitive option.</p>



<p>To win the AI consensus, you must translate your marketing strategy into structured capital management.</p>



<ul class="wp-block-list">
<li><strong>The old way: </strong>Publishing a 2,000-word blog post on top supply chain trends that generates 5,000 monthly visitors who bounce after reading and add zero value to your pipeline.</li>



<li><strong>The new way: </strong>Build a generative engine optimization (GEO) hub—a dedicated supply chain cost calculator page with proprietary data tables, expert author schema tagging your lead engineers, and strict answer-first formatting.</li>
</ul>



<p>LLMs require consensus and verifiable facts to generate confident answers. By structuring your digital assets with proprietary data and verifiable entities, you become the default recommendation.</p>



<p>This approach may yield only 500 highly qualified visitors, but it gives LLMs what they need to cite you in vendor comparison prompts and captures buyers at the exact moment of commercial evaluation.</p>



<h2 class="wp-block-heading" id="h-strategic-roi-using-citation-authority-to-reduce-ad-spend">Strategic ROI: Using citation authority to reduce ad spend</h2>



<p>It’s time to stop viewing SEO as a siloed traffic generator. You must treat organic citation authority as a strategic financial lever to reduce overall CAC.</p>



<p>Align your organic assets with your highest-CAC paid campaigns. When organic search owns the AI Overview, your paid team can confidently pull back defensive ad spend.</p>



<p>Here’s how to leverage paid and AI search:</p>



<ul class="wp-block-list">
<li><strong>IF</strong> your brand becomes the default AI recommendation for a high-cost commercial category, <strong>THEN</strong> your paid team must aggressively reduce defensive brand bidding to slash overall cost per acquisition (CPA).</li>



<li><strong>IF</strong> paid search identifies a highly profitable long-tail query, <strong>THEN</strong> SEO must prioritize building a structured asset to organically capture that exact demand in the future.</li>



<li><strong>IF</strong> an LLM cites your competitor as the superior enterprise solution, <strong>THEN</strong> your paid team must immediately deploy targeted, bottom-of-funnel conquesting ads to intercept that user before the transaction, while the organic team rapidly engineers a proprietary data asset to win back the consensus.</li>
</ul>



<h2 class="wp-block-heading" id="h-the-monthly-cannibalization-review-your-immediate-action-item">The monthly cannibalization review: Your immediate action item</h2>



<p>If your Head of Search and Head of Paid Media aren’t in the same room once a month mapping organic citations against paid brand bidding, you’re burning capital.</p>



<p>Align your teams and channels. Routinely audit where you’re paying for clicks on terms where you already own the AI citation and the top organic spot.</p>



<p>Treat this cannibalization review as a strict financial audit. Identify wasted defensive ad spend and immediately reallocate those dollars toward net-new market expansion.</p>



<h2 class="wp-block-heading" id="h-the-enterprise-scorecard-3-questions-to-ask-your-agency-tomorrow">The enterprise scorecard: 3 questions to ask your agency tomorrow</h2>



<p>To regain control of your P&amp;L, you must challenge your vendors to step up. Ask your agency these three questions tomorrow morning to see if they’re true business partners or order-takers.</p>



<h3 class="wp-block-heading" id="h-1-what-s-our-citation-share-of-voice-for-our-highest-margin-categories">1. What’s our citation share of voice for our highest-margin categories?</h3>



<p>Challenge your team to map their organic efforts directly to the AI research phase of your most profitable products.</p>



<p><strong>The answer you should hear</strong>: &#8220;We’ve mapped your 50 highest-margin queries. By securing the primary AI citation for these, we’ve generated $1.2 million in pipeline this quarter at a 3:1 LTV:CAC ratio.&#8221;</p>



<h3 class="wp-block-heading" id="h-2-how-is-our-citation-strategy-directly-reducing-our-paid-media-cac">2. How is our citation strategy directly reducing our paid media CAC?</h3>



<p>Require teams to prove how their organic authority captures demand that would otherwise require paid ad spend.</p>



<p><strong>The answer you should hear</strong>: &#8220;By capturing the definitive AI citation for [category], we paused paid bidding on those terms. This reduced our blended CAC by 18% and saved $45,000 in defensive ad spend — which we’ve immediately reallocated to net-new market expansion.&#8221;</p>



<h3 class="wp-block-heading" id="h-3-are-our-digital-assets-structured-for-llm-extraction">3. Are our digital assets structured for LLM extraction?</h3>



<p>Push your teams to explain their strategy for AI-driven search models. It’s no longer enough to publish standard web pages.</p>



<p><strong>The answer you should hear</strong>: &#8220;We’ve restructured your core commercial pages away from standard marketing copy, deploying answer-first&#8217; frameworks, proprietary data tables, and expert author entities to ensure LLMs confidently extract and recommend your brand. This structural shift has increased our inclusion in commercial AI Overviews by 40% this quarter, directly feeding our bottom-of-funnel pipeline.&#8221;</p>



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<h2 class="wp-block-heading" id="h-demand-commercial-outcomes-not-operational-output">Demand commercial outcomes, not operational output</h2>



<p>In a tough economy, SEO is a measurable business unit that must defend its budget with revenue data. <strong>Don’t accept operational output as proof of commercial success.</strong></p>



<p>Audit your reporting frameworks immediately. Stop accepting vanity metrics as evidence of success. Demand pipeline impact, LTV:CAC ratios, and a resilient acquisition engine.</p>



<p>Any agency or internal team unwilling to tie its work directly to your P&amp;L will become obsolete. Your job as an enterprise leader is to ensure your brand is cited as the authority long before the transaction begins.</p>


The standard agency reporting call is broken. Budgets are under extreme scrutiny, yet you still invest in vendors that celebrate arbitrary traffic gains while your sales pipeline stays flat.
Optimizing for raw traffic volume is a legacy mindset that hides real commercial performance. The new mandate is to build an acquisition engine that influences buyers and protects your profit and loss (P&L) long before the transaction.
To survive as a marketing leader today, you must ruthlessly challenge your internal teams and external agencies. Stop accepting reports on operational output and demand hard financial accountability: pipeline contribution, customer lifetime value (LTV) to customer acquisition cost (CAC) ratios, and reduced paid media dependency.
The new path to purchase: Why traffic is bleeding your budget
Chasing top-of-funnel informational traffic is a trap. If the users clicking your links aren’t actively buying, you’re paying for vanity metrics, not business outcomes.
This happens because many buyers now use large language models (LLMs) to conduct deep research before they reach a search engine’s transactional layer. If you aren’t the cited authority during that AI-driven research phase, you’re invisible by the time buyers finalize their purchase decisions.
The 7.48% reality: The power of the educated buyer
The contrast in traffic quality is staggering when you look at the data. Across our enterprise client base, traditional organic search converts at 2.75%, while AI search converts at 7.48%.
LLMs function as the ultimate trust proxy for today’s consumers. When tools like Gemini, ChatGPT, or Perplexity synthesize dozens of reviews, whitepapers, and Reddit threads to recommend your enterprise software, users trust the LLM’s consensus more than a branded blog post.
AI engines arm consumers with comprehensive data, comparisons, and consensus. By the time a user clicks your AI citation, they’ve already made their decision based on your authority and are prepared to transact.
shows up.
      
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Get started with
From found to cited: Architecting the default recommendation
Want to capture this 7.48% conversion rate? Your entire approach to digital asset creation must evolve. The strategy no longer centers on ranking among a list of links, but on being cited as the definitive option.
To win the AI consensus, you must translate your marketing strategy into structured capital management.
The old way: Publishing a 2,000-word blog post on top supply chain trends that generates 5,000 monthly visitors who bounce after reading and add zero value to your pipeline.
The new way: Build a generative engine optimization (GEO) hub—a dedicated supply chain cost calculator page with proprietary data tables, expert author schema tagging your lead engineers, and strict answer-first formatting.
LLMs require consensus and verifiable facts to generate confident answers. By structuring your digital assets with proprietary data and verifiable entities, you become the default recommendation.
This approach may yield only 500 highly qualified visitors, but it gives LLMs what they need to cite you in vendor comparison prompts and captures buyers at the exact moment of commercial evaluation.
Strategic ROI: Using citation authority to reduce ad spend
It’s time to stop viewing SEO as a siloed traffic generator. You must treat organic citation authority as a strategic financial lever to reduce overall CAC.
Align your organic assets with your highest-CAC paid campaigns. When organic search owns the AI Overview, your paid team can confidently pull back defensive ad spend.
Here’s how to leverage paid and AI search:
IF your brand becomes the default AI recommendation for a high-cost commercial category, THEN your paid team must aggressively reduce defensive brand bidding to slash overall cost per acquisition (CPA).
IF paid search identifies a highly profitable long-tail query, THEN SEO must prioritize building a structured asset to organically capture that exact demand in the future.
IF an LLM cites your competitor as the superior enterprise solution, THEN your paid team must immediately deploy targeted, bottom-of-funnel conquesting ads to intercept that user before the transaction, while the organic team rapidly engineers a proprietary data asset to win back the consensus.
The monthly cannibalization review: Your immediate action item
If your Head of Search and Head of Paid Media aren’t in the same room once a month mapping organic citations against paid brand bidding, you’re burning capital.
Align your teams and channels. Routinely audit where you’re paying for clicks on terms where you already own the AI citation and the top organic spot.
Treat this cannibalization review as a strict financial audit. Identify wasted defensive ad spend and immediately reallocate those dollars toward net-new market expansion.
The enterprise scorecard: 3 questions to ask your agency tomorrow
To regain control of your P&L, you must challenge your vendors to step up. Ask your agency these three questions tomorrow morning to see if they’re true business partners or order-takers.
1. What’s our citation share of voice for our highest-margin categories?
Challenge your team to map their organic efforts directly to the AI research phase of your most profitable products.
The answer you should hear: “We’ve mapped your 50 highest-margin queries. By securing the primary AI citation for these, we’ve generated $1.2 million in pipeline this quarter at a 3:1 LTV:CAC ratio.”
2. How is our citation strategy directly reducing our paid media CAC?
Require teams to prove how their organic authority captures demand that would otherwise require paid ad spend.
The answer you should hear: “By capturing the definitive AI citation for [category], we paused paid bidding on those terms. This reduced our blended CAC by 18% and saved $45,000 in defensive ad spend — which we’ve immediately reallocated to net-new market expansion.”
3. Are our digital assets structured for LLM extraction?
Push your teams to explain their strategy for AI-driven search models. It’s no longer enough to publish standard web pages.
The answer you should hear: “We’ve restructured your core commercial pages away from standard marketing copy, deploying answer-first’ frameworks, proprietary data tables, and expert author entities to ensure LLMs confidently extract and recommend your brand. This structural shift has increased our inclusion in commercial AI Overviews by 40% this quarter, directly feeding our bottom-of-funnel pipeline.”
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Demand commercial outcomes, not operational output
In a tough economy, SEO is a measurable business unit that must defend its budget with revenue data. Don’t accept operational output as proof of commercial success.
Audit your reporting frameworks immediately. Stop accepting vanity metrics as evidence of success. Demand pipeline impact, LTV:CAC ratios, and a resilient acquisition engine.
Any agency or internal team unwilling to tie its work directly to your P&L will become obsolete. Your job as an enterprise leader is to ensure your brand is cited as the authority long before the transaction begins.

The new search playbook ties content to revenue: win AI recommendations, cut defensive ad spend, and hold agencies to P&L results.

Stop paying for traffic: The enterprise CMO’s guide to ROI-driven SEO

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution.png" class="attachment-large size-large wp-post-image" alt="Google Search Ads value redistribution" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google Search Ad audits must now rebuild the visibility automation removed. Here's how. (A response to Google’s Ads Decoded podcast.)

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution.png" class="attachment-large size-large wp-post-image" alt="Google Search Ads value redistribution" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/google-search-ads-value-redistribution-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p id="h-brandon-ervin-director-of-product-management-for-google-search-ads-recently-discussed-campaign-consolidation-ai-max-and-what-advertiser-control-looks-like-in-2026-on-google-s-ads-decoded-podcast-the-conversation-was-serious-and-informed-and-reflected-a-product-team-that-understands-advertiser-concerns-and-is-actively-working-to-address-them">Brandon Ervin, Director of Product Management for Google Search Ads, recently discussed campaign consolidation, AI Max, and what advertiser control looks like in 2026 on Google&#8217;s Ads Decoded podcast. The conversation was serious and informed, and reflected a product team that understands advertiser concerns and is actively working to address them.</p>



<p>But the podcast is also incomplete. The gap between what Google said and what advertisers actually experience from their sales organization is large enough to warrant a direct response.</p>



<p>Ervin&#8217;s team is doing genuinely good work, but the platform&#8217;s structural incentives haven&#8217;t changed. Google&#8217;s evolving product is creating problems faster than it can solve them. Performance is now measured on economic standards, shaping how a search ads audit is performed.</p>



<h2 class="wp-block-heading" id="h-recent-improvements-to-google-search-ads">Recent improvements to Google Search Ads</h2>



<p>Recentish improvements are genuine:</p>



<ul class="wp-block-list">
<li>Brand exclusions in Performance Max and Demand Gen.</li>



<li>Site visitor and customer exclusions from PMax campaigns.</li>



<li>Network-level reporting within bundled campaigns.</li>



<li>Improved search term visibility.</li>



<li>Brand and geo controls inside AI Max at the ad group level.</li>



<li>Semantic modeling that doesn&#8217;t anchor on campaign or ad group IDs, reducing learning period risk during consolidation.</li>
</ul>



<p>These are meaningful. They are also solutions to issues introduced by bundling, opacity, and aggressive automation rollout.</p>



<p>These products have been mercilessly shopped to advertisers since 2021, and the controls that make it usable arrived years after the sales push began.</p>



<p>The ability to separate brand from non-brand traffic inside PMax/AI Max should not be framed as innovation. It restores a fundamental distinction that previously existed by default. The ability to see network performance inside a bundled campaign is not an expansion of control. It restores visibility that was removed.</p>



<p>An audit must ask whether new tools are genuinely expanding control or merely reintroducing baseline transparency.</p>



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<h2 class="wp-block-heading" id="h-table-stakes-what-everyone-agrees-on">Table stakes: What everyone agrees on</h2>



<p>Before the real audit begins, the fundamentals. These are uncontroversial and should already be in place:</p>



<ul class="wp-block-list">
<li>Run full ad extensions (sitelinks, callouts, structured snippets, image, call).</li>



<li>Use automated bidding with intentional target-setting and conversion action selection (I recognize there are still holdouts here but seems crazy to me).</li>



<li>Maintain negative keyword lists.</li>



<li>Write ads relevant to the queries they serve.</li>



<li>Audit automatically created assets for accuracy and brand safety.</li>



<li>Cut Search Partners and Display expansion from Search campaigns.</li>



<li>Separate brand and generic campaigns using brand controls.</li>



<li>Exclude site visitors and past customers from prospecting campaigns where appropriate.</li>



<li>Import offline conversion data (MQLs, SQLs, revenue, CLV, repeat rate,) to feed the algorithm downstream signals.</li>



<li>Weight conversion values by actual downstream conversion rates.</li>



<li>Account for mobile vs. desktop performance gaps.</li>
</ul>



<p>Those are table stakes. The real audit begins after that.</p>



<h2 class="wp-block-heading" id="h-what-a-2026-search-audit-must-focus-on">What a 2026 search audit must focus on</h2>



<p>With the prevalence of AI, advertisers need to focus on reconstructing economic visibility in systems designed around aggregation and automation.&nbsp;</p>



<h3 class="wp-block-heading" id="h-signal-architecture">Signal architecture</h3>



<p>In the podcast, Ervin says &#8220;control still exists, it just looks different.&#8221; Ad controls — where, when, and to whom ads appear — are still important and changing, some think, for the worse.</p>



<p>The old ad controls — exact match, manual bids, network selection, and device modifiers — gave advertisers direct influence over where ads appeared and what they paid.&nbsp;</p>



<p>However, the new controls are indirect. Control now lives in data quality, density, and selectivity. They influence the algorithm, but the algorithm makes the final call.</p>



<p>An audit should focus on three questions:</p>



<ul class="wp-block-list">
<li><strong>Quality</strong>: Are you importing revenue, pipeline stage, or qualified lead status, or only surface conversions?</li>



<li><strong>Density</strong>: Is there enough high-quality data for the model to learn from, or is it sparse and noisy?</li>



<li><strong>Selectivity</strong>: Are you intentionally limiting what Google can see, or are you passing everything indiscriminately?</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="936" height="473" src="https://searchengineland.com/wp-content/seloads/2026/03/Low-prediction-high-density.png" alt="Low prediction, high density" class="wp-image-471599" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Low-prediction-high-density.png 936w, https://searchengineland.com/wp-content/seloads/2026/03/Low-prediction-high-density-768x388.png 768w" sizes="(max-width: 936px) 100vw, 936px" /></figure>
</div>


<p>With these new tactics, you only pass net-new customers or high-value customers. The majority of the time, it is better to just pass the densest and most predictive conversion set.&nbsp;&nbsp;</p>



<h3 class="wp-block-heading" id="h-incrementality">Incrementality</h3>



<p>Google optimizes toward reported conversions, not incremental conversions. Brand search often captures existing demand. Retargeting often captures users already in motion. Pmax/AI Max frequently blends these signals.</p>



<p>Ervin was asked: Are AI-driven campaigns over-indexing on warm brand traffic to inflate blended ROAS (return on ad spend)? </p>



<p>He doesn&#8217;t dispute the problem, but points to partial solutions, including using brand controls, better theme your account, and looking at multi-campaign A/B testing.&nbsp;</p>



<p>If incrementality is not measured, automation amplifies non-incremental signals.</p>



<h3 class="wp-block-heading" id="h-marginal-returns">Marginal returns</h3>



<p>Google uses a blended cost-per-action (CPA). For example, the first $50K of spend might return a $30 CPA, while the next $50K might return $120.&nbsp;</p>



<p>With automation, money is spent until the blended metric falls within tolerance, meaning the last dollar is not spent efficiently. The vast majority of advertisers are bidding far beyond what they should be and have no idea it is happening.</p>



<p>An audit must:</p>



<ul class="wp-block-list">
<li>Plot spend against incremental conversions.</li>



<li>Estimate marginal CPA at each spend tier.</li>



<li>Identify diminishing return curves.</li>



<li>Compare marginal CPA to lifetime value.</li>
</ul>



<p>A lower target makes the algorithm more selective, competing in fewer high-value auctions. Google doesn&#8217;t suggest this because that would mean less spend and lower bids are less effective in general.&nbsp;&nbsp;</p>



<h3 class="wp-block-heading" id="h-query-resolution-and-ability-to-lower-targets">Query resolution and ability to lower targets</h3>



<p>On the podcast, Ervin acknowledges that some AI Max matches can &#8220;look a little wonky&#8221; and says his team is working on exposing the model&#8217;s reasoning.&nbsp;</p>



<p>Query mapping has gotten meaningfully worse over the past several years: queries landing in the wrong ad groups, matching to keywords with different intent, and broad match pulling in traffic unrelated to the keyword.</p>



<p>AI Max has accelerated this — there’s been an increase in the volume of irrelevant queries flowing through AI Max campaigns, with no connection to the advertiser&#8217;s business or keywords in the account.&nbsp;</p>



<p>Meanwhile, Google&#8217;s recommendations consistently push toward broad matching and large themed ad groups.&nbsp;&nbsp;</p>



<p>The issue is not whether broad match works, but whether high-value intent is being diluted in larger, broader ad groups. Fewer ad groups means that we cannot effectively or meaningfully lower targets without a massive structural negative schema, so performance differences have to be large enough to validate the new structure.&nbsp;</p>



<p>An audit should:</p>



<ul class="wp-block-list">
<li>Extract full search term reports.</li>



<li>Classify queries by intent tier.</li>



<li>Compare CPA and lifetime value by query type.</li>



<li>Quantify irrelevant or weakly related matches.</li>



<li>Measure performance drift across match types.</li>
</ul>



<h3 class="wp-block-heading" id="h-network-economics">Network economics</h3>



<p>Performance Max and Demand Gen bundle multiple networks into single campaigns, but offer limited visibility into which networks drive results. This makes it hard to cut the underperforming ones. The slow rollout of network-level controls systematically benefits Google&#8217;s less competitive inventory.</p>



<p>An audit must:</p>



<ul class="wp-block-list">
<li>Break out performance by network.</li>



<li>Compare CPA and lifetime value by placement.</li>



<li>Identify cross-subsidization.</li>



<li>Determine whether weaker networks are relying on surplus from strong search inventory.</li>
</ul>



<h3 class="wp-block-heading" id="h-value-redistribution">Value redistribution</h3>



<p>Combining these elements in your audit will help you succeed in this new world of ad search:&nbsp;</p>



<ul class="wp-block-list">
<li>Non-incremental traffic inflates conversion counts, making performance look better than it is.</li>



<li>Looser match types expand where ads appear, diluting intent precision and forcing fewer ad groups/spend and blanket-level targets/bids.</li>



<li>No clean marginal return visibility means it is much more difficult to find the point of negative return</li>



<li>Network bundling hides which channels actually perform.</li>
</ul>



<p>The cumulative effect is that the surplus value generated by your best inventory and high-intent, high-converting search queries gets redistributed across Google&#8217;s weaker inventory (i.e., Display, YouTube, Discover, Gmail, crazy tail queries).&nbsp;&nbsp;</p>



<p>This is how to get a dwindling supply of valuable search queries to inflate the cost-per-clicks (CPCs) of low-quality inventory.&nbsp;</p>



<p><strong>The Ads Decoded</strong> <strong>episode:</strong> <a href="https://blog.google/products/ads-commerce/is-your-campaign-structure-holding-you-back-in-the-era-of-ai/" target="_blank" rel="noopener">Is your campaign structure holding you back in the era of AI?</a></p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="Is your Search campaign structure holding back performance?" width="500" height="281" src="https://www.youtube.com/embed/vIf0EiCQtZI?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p>&nbsp;</p>


Brandon Ervin, Director of Product Management for Google Search Ads, recently discussed campaign consolidation, AI Max, and what advertiser control looks like in 2026 on Google’s Ads Decoded podcast. The conversation was serious and informed, and reflected a product team that understands advertiser concerns and is actively working to address them.
But the podcast is also incomplete. The gap between what Google said and what advertisers actually experience from their sales organization is large enough to warrant a direct response.
Ervin’s team is doing genuinely good work, but the platform’s structural incentives haven’t changed. Google’s evolving product is creating problems faster than it can solve them. Performance is now measured on economic standards, shaping how a search ads audit is performed.
Recent improvements to Google Search Ads
Recentish improvements are genuine:
Brand exclusions in Performance Max and Demand Gen.
Site visitor and customer exclusions from PMax campaigns.
Network-level reporting within bundled campaigns.
Improved search term visibility.
Brand and geo controls inside AI Max at the ad group level.
Semantic modeling that doesn’t anchor on campaign or ad group IDs, reducing learning period risk during consolidation.
These are meaningful. They are also solutions to issues introduced by bundling, opacity, and aggressive automation rollout.
These products have been mercilessly shopped to advertisers since 2021, and the controls that make it usable arrived years after the sales push began.
The ability to separate brand from non-brand traffic inside PMax/AI Max should not be framed as innovation. It restores a fundamental distinction that previously existed by default. The ability to see network performance inside a bundled campaign is not an expansion of control. It restores visibility that was removed.
An audit must ask whether new tools are genuinely expanding control or merely reintroducing baseline transparency.

  
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Table stakes: What everyone agrees on
Before the real audit begins, the fundamentals. These are uncontroversial and should already be in place:
Run full ad extensions (sitelinks, callouts, structured snippets, image, call).
Use automated bidding with intentional target-setting and conversion action selection (I recognize there are still holdouts here but seems crazy to me).
Maintain negative keyword lists.
Write ads relevant to the queries they serve.
Audit automatically created assets for accuracy and brand safety.
Cut Search Partners and Display expansion from Search campaigns.
Separate brand and generic campaigns using brand controls.
Exclude site visitors and past customers from prospecting campaigns where appropriate.
Import offline conversion data (MQLs, SQLs, revenue, CLV, repeat rate,) to feed the algorithm downstream signals.
Weight conversion values by actual downstream conversion rates.
Account for mobile vs. desktop performance gaps.
Those are table stakes. The real audit begins after that.
What a 2026 search audit must focus on
With the prevalence of AI, advertisers need to focus on reconstructing economic visibility in systems designed around aggregation and automation. 
Signal architecture
In the podcast, Ervin says “control still exists, it just looks different.” Ad controls — where, when, and to whom ads appear — are still important and changing, some think, for the worse.
The old ad controls — exact match, manual bids, network selection, and device modifiers — gave advertisers direct influence over where ads appeared and what they paid. 
However, the new controls are indirect. Control now lives in data quality, density, and selectivity. They influence the algorithm, but the algorithm makes the final call.
An audit should focus on three questions:
Quality: Are you importing revenue, pipeline stage, or qualified lead status, or only surface conversions?
Density: Is there enough high-quality data for the model to learn from, or is it sparse and noisy?
Selectivity: Are you intentionally limiting what Google can see, or are you passing everything indiscriminately?




With these new tactics, you only pass net-new customers or high-value customers. The majority of the time, it is better to just pass the densest and most predictive conversion set.  
Incrementality
Google optimizes toward reported conversions, not incremental conversions. Brand search often captures existing demand. Retargeting often captures users already in motion. Pmax/AI Max frequently blends these signals.
Ervin was asked: Are AI-driven campaigns over-indexing on warm brand traffic to inflate blended ROAS (return on ad spend)? 
He doesn’t dispute the problem, but points to partial solutions, including using brand controls, better theme your account, and looking at multi-campaign A/B testing. 
If incrementality is not measured, automation amplifies non-incremental signals.
Marginal returns
Google uses a blended cost-per-action (CPA). For example, the first $50K of spend might return a $30 CPA, while the next $50K might return $120. 
With automation, money is spent until the blended metric falls within tolerance, meaning the last dollar is not spent efficiently. The vast majority of advertisers are bidding far beyond what they should be and have no idea it is happening.
An audit must:
Plot spend against incremental conversions.
Estimate marginal CPA at each spend tier.
Identify diminishing return curves.
Compare marginal CPA to lifetime value.
A lower target makes the algorithm more selective, competing in fewer high-value auctions. Google doesn’t suggest this because that would mean less spend and lower bids are less effective in general.  
Query resolution and ability to lower targets
On the podcast, Ervin acknowledges that some AI Max matches can “look a little wonky” and says his team is working on exposing the model’s reasoning. 
Query mapping has gotten meaningfully worse over the past several years: queries landing in the wrong ad groups, matching to keywords with different intent, and broad match pulling in traffic unrelated to the keyword.
AI Max has accelerated this — there’s been an increase in the volume of irrelevant queries flowing through AI Max campaigns, with no connection to the advertiser’s business or keywords in the account. 
Meanwhile, Google’s recommendations consistently push toward broad matching and large themed ad groups.  
The issue is not whether broad match works, but whether high-value intent is being diluted in larger, broader ad groups. Fewer ad groups means that we cannot effectively or meaningfully lower targets without a massive structural negative schema, so performance differences have to be large enough to validate the new structure. 
An audit should:
Extract full search term reports.
Classify queries by intent tier.
Compare CPA and lifetime value by query type.
Quantify irrelevant or weakly related matches.
Measure performance drift across match types.
Network economics
Performance Max and Demand Gen bundle multiple networks into single campaigns, but offer limited visibility into which networks drive results. This makes it hard to cut the underperforming ones. The slow rollout of network-level controls systematically benefits Google’s less competitive inventory.
An audit must:
Break out performance by network.
Compare CPA and lifetime value by placement.
Identify cross-subsidization.
Determine whether weaker networks are relying on surplus from strong search inventory.
Value redistribution
Combining these elements in your audit will help you succeed in this new world of ad search: 
Non-incremental traffic inflates conversion counts, making performance look better than it is.
Looser match types expand where ads appear, diluting intent precision and forcing fewer ad groups/spend and blanket-level targets/bids.
No clean marginal return visibility means it is much more difficult to find the point of negative return
Network bundling hides which channels actually perform.
The cumulative effect is that the surplus value generated by your best inventory and high-intent, high-converting search queries gets redistributed across Google’s weaker inventory (i.e., Display, YouTube, Discover, Gmail, crazy tail queries).  
This is how to get a dwindling supply of valuable search queries to inflate the cost-per-clicks (CPCs) of low-quality inventory. 
The Ads Decoded episode: Is your campaign structure holding you back in the era of AI?

Google Search Ad audits must now rebuild the visibility automation removed. Here's how. (A response to Google’s Ads Decoded podcast.)

Google Search Ads in 2026 require a different kind of audit

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO.jpg" class="attachment-large size-large wp-post-image" alt="How to use Google Gemini for better SEO" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO.jpg 1920w, https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A senior Google executive says the company is "not ruling out" ads in its Gemini AI app — a notable reversal from denials made just months ago.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO.jpg" class="attachment-large size-large wp-post-image" alt="How to use Google Gemini for better SEO" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO.jpg 1920w, https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/10/How-to-use-Google-Gemini-for-better-SEO-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is leaving the door open to advertising in its Gemini AI app, with a senior executive telling WIRED the company is &#8220;not ruling them out&#8221; — a notable shift from the flat denials made just months ago.</p>



<p><strong>What&#8217;s changed:</strong> In January, <a href="https://searchengineland.com/google-rules-out-ads-in-gemini-for-now-467780">Google DeepMind CEO Demis Hassabis told reporters at Davos that Google had no plans to put ads in Gemini</a>. Now, SVP Nick Fox is saying otherwise — noting that learnings from ads in AI Mode will &#8220;likely carry over&#8221; to Gemini down the road.</p>



<p><strong>The current strategy</strong>. Rather than rushing into Gemini, Google is using AI Mode — its Gemini-powered Search product — as a testing ground for ad formats in AI experiences.</p>



<ul class="wp-block-list">
<li>Ads are kept separate from organic results and clearly labeled</li>



<li>Google says it only shows ads when they&#8217;re relevant — if nothing fits, nothing runs</li>



<li>The company is drawing on 20-plus years of Search ad experience to inform the approach</li>
</ul>



<p><strong>Why we care.</strong> Google&#8217;s entire business is built on advertising. How and if they bring ads into AI products will shape the future of the industry — and set the tone for every AI company trying to figure out how to monetize free users. The brands that figure out how to show up relevantly in conversational AI environments now — before the auction gets competitive — will have a significant first-mover advantage.</p>



<p><strong>The bigger picture</strong>. Google is in a stronger position than its rivals to take its time. The company crossed $400 billion in revenue in 2025, giving it the luxury of patience. OpenAI, by contrast, is under pressure to more than double its $30 billion in revenue this year — and has already started <a href="https://searchengineland.com/openai-starts-testing-chatgpt-ads-468593">testing ads in ChatGPT&#8217;s free tier</a>.</p>



<p><strong>Between the lines:</strong> Fox&#8217;s framing is careful but revealing. By positioning Gemini ads as a &#8220;prioritization question&#8221; rather than a values question, Google is signaling it&#8217;s a matter of when — not if.</p>



<p><strong>What to watch:</strong> Personal Intelligence — Gemini&#8217;s feature that pulls from a user&#8217;s Gmail, Photos, and Calendar — is the sleeper story here. Fox called personalization his &#8220;holy grail&#8221; for Search, and hinted it could eventually roll into the broader Search experience. If it does, advertisers would gain access to an entirely new layer of contextual targeting — though Fox was quick to add that user data will not be sold or shared.</p>



<p><strong>What&#8217;s next</strong>. Advertisers should start preparing now. As Google refines its AI ad formats in AI Mode, those learnings will eventually migrate to Gemini. Brands that understand how to show up relevantly in conversational, context-rich AI environments will have a significant head start when the floodgates open.</p>



<p><strong>Dig deeper. </strong><a href="https://www.wired.com/story/google-nick-fox-advertising-search-ai-gemini/">Google Is Not Ruling Out Ads in Gemini (registration needed)</a></p>


Google is leaving the door open to advertising in its Gemini AI app, with a senior executive telling WIRED the company is “not ruling them out” — a notable shift from the flat denials made just months ago.
What’s changed: In January, Google DeepMind CEO Demis Hassabis told reporters at Davos that Google had no plans to put ads in Gemini. Now, SVP Nick Fox is saying otherwise — noting that learnings from ads in AI Mode will “likely carry over” to Gemini down the road.
The current strategy. Rather than rushing into Gemini, Google is using AI Mode — its Gemini-powered Search product — as a testing ground for ad formats in AI experiences.
Ads are kept separate from organic results and clearly labeled
Google says it only shows ads when they’re relevant — if nothing fits, nothing runs
The company is drawing on 20-plus years of Search ad experience to inform the approach
Why we care. Google’s entire business is built on advertising. How and if they bring ads into AI products will shape the future of the industry — and set the tone for every AI company trying to figure out how to monetize free users. The brands that figure out how to show up relevantly in conversational AI environments now — before the auction gets competitive — will have a significant first-mover advantage.
The bigger picture. Google is in a stronger position than its rivals to take its time. The company crossed $400 billion in revenue in 2025, giving it the luxury of patience. OpenAI, by contrast, is under pressure to more than double its $30 billion in revenue this year — and has already started testing ads in ChatGPT’s free tier.
Between the lines: Fox’s framing is careful but revealing. By positioning Gemini ads as a “prioritization question” rather than a values question, Google is signaling it’s a matter of when — not if.
What to watch: Personal Intelligence — Gemini’s feature that pulls from a user’s Gmail, Photos, and Calendar — is the sleeper story here. Fox called personalization his “holy grail” for Search, and hinted it could eventually roll into the broader Search experience. If it does, advertisers would gain access to an entirely new layer of contextual targeting — though Fox was quick to add that user data will not be sold or shared.
What’s next. Advertisers should start preparing now. As Google refines its AI ad formats in AI Mode, those learnings will eventually migrate to Gemini. Brands that understand how to show up relevantly in conversational, context-rich AI environments will have a significant head start when the floodgates open.
Dig deeper. Google Is Not Ruling Out Ads in Gemini (registration needed)

A senior Google executive says the company is "not ruling out" ads in its Gemini AI app — a notable reversal from denials made just months ago.

Google leaves door open to ads in Gemini

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution.png" class="attachment-large size-large wp-post-image" alt="Google traffic redistribution" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Publishers are losing Google organic search traffic, but breaking news traffic jumped 103% and Discover clicks are surging.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution.png" class="attachment-large size-large wp-post-image" alt="Google traffic redistribution" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/google-traffic-redistribution-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><a href="https://searchengineland.com/google-ai-overviews-everything-you-need-to-know-449399">Google&#8217;s AI Overviews</a> may be reducing traditional search clicks, but publishers still have meaningful growth opportunities in breaking news and <a href="https://searchengineland.com/google-discover-qualifies-ranks-filters-content-research-470190">Google Discover</a>, according to new data from Define Media Group.</p>



<ul class="wp-block-list">
<li>Organic search clicks have fallen 42% since AI Overviews began expanding in Google Search, according to Define Media Group&#8217;s analysis of <a href="https://searchengineland.com/google-search-console-seo-guide-443942">Google Search Console</a> data across its portfolio of 64 sites.</li>
</ul>



<p><strong>Why we care</strong>. AI-generated answers are reshaping search traffic. Evergreen content is losing clicks, while real-time news coverage and Discover distribution are emerging as stronger traffic channels for publishers.</p>



<p><strong>By the numbers.</strong> Across Google Search, Discover, and Google News, breaking news traffic grew 103% from November 2024 through early 2026 in the company’s dataset. Losses were concentrated in informational and evergreen content:</p>



<ul class="wp-block-list">
<li>Organic search traffic averaged 1.7 billion clicks per quarter from Q1 2023 through Q1 2024.</li>



<li>After AI Overviews launched, traffic fell 16% immediately and never recovered.</li>



<li>As Google expanded AI Overviews in May 2025, declines accelerated.</li>



<li>By Q4 2025, search traffic was down 42% from the pre-AI Overviews baseline.</li>
</ul>



<p><strong>Discover’s role:</strong> Google Discover, which grew 30% across the portfolio, is now the main growth engine for breaking news distribution. Discover traffic rose steadily as web search traffic fell. For the first time in the dataset, Discover and web search now drive roughly equal traffic.</p>



<ul class="wp-block-list">
<li>The report also found a sharp increase in Discover traffic after the <a href="https://searchengineland.com/google-december-2025-core-update-rollout-is-now-complete-466362">December 2025 Google core update</a>, though some gains softened after <a href="https://searchengineland.com/google-february-2026-discover-core-update-is-now-complete-469450">Google’s February 2026 Discover core update</a>.</li>



<li>Meanwhile, Chartbeat data showed Discover was the primary driver of Google referrals to news sites as of last summer. See: <a href="https://searchengineland.com/google-traffic-news-publishers-steady-data-460944">Google traffic to news publishers is steady, but it isn’t traditional Search</a>.</li>
</ul>



<p><strong>Why is this happening?</strong> AI Overviews appear less often for news queries than for other topics. AI Overviews appeared for about 15% of news queries — nearly three times less often than in categories such as health and science — according to Ahrefs data cited in the report.</p>



<ul class="wp-block-list">
<li>News queries often trigger the <em>Top Stories</em> carousel, which links directly to publisher articles. Searches for major developing events, such as international conflicts, typically show <em>Top Stories</em> rather than AI summaries.</li>



<li>Define Media Group suggests Google may be avoiding AI-generated summaries for breaking news because events change rapidly, accuracy stakes are high, and generative systems can still hallucinate.</li>
</ul>



<p><strong>The report.</strong> <a href="https://www.definemg.com/breaking-news-thrives-in-the-age-of-ai/" target="_blank" rel="noopener">BREAKING! News Thrives in the Age of AI</a></p>


Google’s AI Overviews may be reducing traditional search clicks, but publishers still have meaningful growth opportunities in breaking news and Google Discover, according to new data from Define Media Group.
Organic search clicks have fallen 42% since AI Overviews began expanding in Google Search, according to Define Media Group’s analysis of Google Search Console data across its portfolio of 64 sites.
Why we care. AI-generated answers are reshaping search traffic. Evergreen content is losing clicks, while real-time news coverage and Discover distribution are emerging as stronger traffic channels for publishers.
By the numbers. Across Google Search, Discover, and Google News, breaking news traffic grew 103% from November 2024 through early 2026 in the company’s dataset. Losses were concentrated in informational and evergreen content:
Organic search traffic averaged 1.7 billion clicks per quarter from Q1 2023 through Q1 2024.
After AI Overviews launched, traffic fell 16% immediately and never recovered.
As Google expanded AI Overviews in May 2025, declines accelerated.
By Q4 2025, search traffic was down 42% from the pre-AI Overviews baseline.
Discover’s role: Google Discover, which grew 30% across the portfolio, is now the main growth engine for breaking news distribution. Discover traffic rose steadily as web search traffic fell. For the first time in the dataset, Discover and web search now drive roughly equal traffic.
The report also found a sharp increase in Discover traffic after the December 2025 Google core update, though some gains softened after Google’s February 2026 Discover core update.
Meanwhile, Chartbeat data showed Discover was the primary driver of Google referrals to news sites as of last summer. See: Google traffic to news publishers is steady, but it isn’t traditional Search.
Why is this happening? AI Overviews appear less often for news queries than for other topics. AI Overviews appeared for about 15% of news queries — nearly three times less often than in categories such as health and science — according to Ahrefs data cited in the report.
News queries often trigger the Top Stories carousel, which links directly to publisher articles. Searches for major developing events, such as international conflicts, typically show Top Stories rather than AI summaries.
Define Media Group suggests Google may be avoiding AI-generated summaries for breaking news because events change rapidly, accuracy stakes are high, and generative systems can still hallucinate.
The report. BREAKING! News Thrives in the Age of AI

Publishers are losing Google organic search traffic, but breaking news traffic jumped 103% and Discover clicks are surging.

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