Google used its Q3 earnings call to argue that AI features are expanding search usage rather than cannibalizing it.

CEO Sundar Pichai described an “expansionary moment for Search,” adding that Google’s AI experiences “highlight the web” and send “billions of clicks to sites every day.”

Pichai said overall queries and commercial queries both grew year over year, and that the growth rate increased in Q3 versus Q2, largely driven by AI Overviews and AI Mode.

What Did Google Report In Its Q3 Earnings?

AI Mode & AI Overviews

Pichai reported “strong and consistent” week-over-week growth for AI Mode in the U.S., with queries doubling in the quarter.

He said Google rolled AI Mode out globally across 40 languages, reached over 75 million daily active users, and shipped more than 100 improvements in Q3.

He also said AI Mode is already driving “incremental total query growth for Search.”

Pichai reiterated that AI Overviews “drive meaningful query growth,” noting the effect was “even stronger” in Q3 and more pronounced among younger users.

Revenue: By The Numbers

Alphabet posted $102.3 billion in revenue, its first $100B quarter. “Google Search & other” revenue reached $56.6 billion, up from $49.4 billion a year earlier.

YouTube ads revenue reached $10.26 billion in Q3. Pichai said YouTube “has remained number one in streaming watch time in the U.S. for more than two years, according to Nielsen.”

Pichai added that in the U.S. “Shorts now earn more revenue per watch hour than traditional in-stream.”

The quarter also included a $3.5 billion European Commission fine that Alphabet notes when discussing margins. Excluding that charge, operating margin was 33.9%.

Why It Matters

Google is telling Wall Street that AI surfaces expand search rather than replace it. If that holds, the company has reason to put AI Mode and AI Overviews in front of more queries.

The near-term implication for marketers is a distribution shift inside Google, not a pullback from search.

What’s missing is as important as what was said. Google didn’t share outbound click share from AI experiences or new reporting to track them. Expect adoption to grow while measurement lags. Teams will be relying on their own analytics to judge impact.

The revenue backdrop supports continued investment. “Search & other” rose year over year and Google highlighted growth in commercial queries. Paid budgets are likely to remain with Google as AI-led sessions take up a larger share of usage.

Looking Ahead

Google plans to keep pushing AI-led search surfaces. Pichai said the company is “looking forward to the release of Gemini 3 later this year,” which would give AI Mode and AI Overviews a stronger model foundation if the timing holds.

Google described Chrome as “a browser powered by AI” with deeper integrations to Gemini and AI Mode and “more agentic capabilities coming soon.”

The company also raised 2025 capex guidance to $91–$93 billion to meet AI demand, which supports continued investment in search infrastructure and features.

Featured Image: Photo Agency/Shutterstock

https://www.searchenginejournal.com/google-q3-report-ai-mode-ai-overviews-lift-total-search-usage/559597/

This is all based on the Google leak and tallies up with my experience of content that does well in Discover over time. I have pulled out what I think are the most prominent Discover proxies and grouped them into what seems like the appropriate workflow.

Like a disgraced BBC employee, thoughts are my own.

TL;DR

1. Your site needs to be seen as a “trusted source” with low SPAM, evaluated by proxies like publisher trust score, in order to be eligible.
2. Discover is driven by a six-part pipeline, using good vs. bad clicks (long dwell time vs. pogo-sticking) and repeat visits to continuously score and re-score content quality.
3. Fresh content gets an initial boost. Success hinges on a strong CTR and positive early-stage engagement (good clicks/shares from all channels count, not just Discover).
4. Content that aligns with a user’s interests is prioritised. To optimize, focus on your areas of topical authority, use a compelling headline(s), be entity-driven, and use large (1200px+) images.

Image Credit: Harry Clarkson-Bennett

I count 15 different proxies that Google uses to guide satiate the doomscrollers’ desperate need for quality content in the Discover feed. It’s not that different to how traditional Google search works.

But traditional search (a high-quality pull channel) is worlds apart from Discover. Audiences killing time on trains. At their in-laws. The toilet. Given they’re part of the same ecosystem, they’re bundled together into one monolithic entity.

And here’s how it works.

Image Credit: Harry Clarkson-Bennett

Google’s Discover Guidelines

This section is boring, and Google’s guidelines around eligibility are exceptionally vague:

• Content is automatically eligible to appear in Discover if it is indexed by Google and meets Discover’s content policies.
• Any kind of dangerous, spammy, deceptive, or violent/vulgar content gets filtered out.

“…Discover makes use of many of the same signals and systems used by Search to determine what is… helpful, reliable, people-first content.”

Then they give some solid, albeit beige advice around quality titles – clicky, not baity as John Shehata would say. Ensuring your featured image is at least 1200px wide and creating timely, value-added content.

But we can do better.

Discover’s Six-Part Content Pipeline

From cradle to grave, let’s review exactly how your content does or, in most cases, doesn’t appear in Discover. As always, remembering I have made these clusters up, albeit based on real Google proxies from the Google leak.

1. Eligibility check and baseline filtering.
2. Initial exposure and testing.
3. User quality assessment.
4. Engagement and feedback loop.
5. Personalization layer.
6. Decay and renewal cycles.

Eligibility And Baseline Filtering

For starters, your site has to be eligible for Google Discover. This means you are seen as a “trusted source” on the topic, and you have a low enough SPAM score that the threshold isn’t triggered.

There are three primary proxy scores to account for eligibility and baseline filtering:

• is_discover_feed_eligible: a Boolean feature that filters non-eligible pages.
• publisher_trustScore: a score that evaluates publisher reliability and reputation.
• topicAuthority_discover: a score that helps Discover identify trusted sources at the topic level.

The site’s reputation and topical authority are ranked for the topic at hand. These three metrics help evaluate whether your site is eligible to appear in Discover.

Initial Exposure And Testing

This is very much the freshness stage, where fresh content is given a temporary boost (because contemporary content is more likely to satiate a dopamine addicted mind).

• freshnessBoost_discover: provides a temporary boost for fresh content to keep the feed alive.
• discover_clicks: where early-stage article clicks are used as a predictor of popularity.
• headlineClickModel_discover: is a predictive CTR model based on the headline and image.

I would hypothesize that using a Bayesian style predictive model, Google applies learnings at a site and subfolder level to predict likely CTR. The more quality content you have published over time (presumably at a site, subfolder and author level), the more likely you are to feature.

Because there is less ambiguity. A key feature of SEO now.

User Quality Assessment

An article is ultimately judged by the quality of user engagement. Google uses the good and bad click style model from Navboost to establish what is and isn’t working for users. Low CTR and/or pogo-sticking style behavior downgrades an article’s chance of featuring.

Valuable content is decided by the good vs bad click ratio. Repeat visits are used to measure lasting satisfaction and re-rank top-performing content.

• discover_blacklist_score: Penalty for spam, misinformation, or clickbait.
• goodClicks_discover: Positive user interactions (long dwell time).
• badClicks_discover: Negative interactions (bounces, short dwell).
• nav_boosted_discover_clicks: Repeat or return engagement metric.

The quality of the article is then measured by its user engagement. As Discover is a personalized platform, this can be done accurately and at scale. Cohorts of users can be grouped together. People with the same general interests are served the content if, by the algorithm’s standard, they should be interested.

But if the overly clicky or misleading title delivers poor engagement (dwell time and on-page interactions), then the article may be downgraded. Over time, this kind of practice can compound and nerf your site completely.

Headlines like this are a one way ticket to devaluing your brand in the eyes of people and search engines (Image Credit: Harry Clarkson-Bennett)

Important to note that this click data doesn’t have to come from Discover. Once an article is out in the ether – it’s been published, shared on social, etc. – Chrome click data is stored and is applied to the algorithm.

So, the more quality click data and shares you can generate early in an article’s lifecycle (accounting for the importance of freshness), the better your chance of success on Discover. Treat it like a viral platform. Make noise. Do marketing.

Engagement And Feedback Loop

Once the article enters the proverbial fray, a scoring and rescoring loop begins. Continuous CTR, impressions, and explicit user feedback (like, hate, and “don’t show me this again, please” style buttons) feed models like Navboost to refine what gets shown.

• discover_impressions: The number of times an article appears in a Discover feed.
• discover_ctr: Clicks divided by impressions. Impressions and click data feed CTR modelling
• discover_feedback_negative: Specific user feedback, i.e., not interested suppresses content for individuals, groups, and on the platform as a whole.

These behavioral signals define an article’s success. It lives or dies on relatively simple metrics. And the more you use it, the better it gets. Because it knows what you and your cohort are more likely to click and enjoy.

This is as true in Discover as it is in the main algorithm. Google admitted as such in the DoJ rulings. (Image Credit: Harry Clarkson-Bennett)

I imagine headline and image data are stored so that the algorithm can apply some rigorous standards to statistical modelling. Once it knows what types of headlines, images and articles perform best for specific cohorts, personalisation becomes effective faster.

Personalization Layer

Google knows a lot about us. It’s what its business is built on. It collects a lot of non-anonymized data (credit card details, passwords, contact details, etc.) alongside every conceivable interaction you have with webpages.

Discover takes personalization to the next level. I think it may offer an insight into how part of the SERP could look like in the future. A personalized cluster of articles, videos, and social posts designed to hook you in embedded somewhere alongside search results and AI Mode.

All of this is designed to keep you on Google’s owned properties for longer. Because they make more money that way.

Hint: They want to keep you around because they make more money (Image Credit: Harry Clarkson-Bennett)

• contentEmbeddings_discover: Content embeddings determine how well the content aligns with the user’s interests. This powers Discover’s interest-matching engine.
• personalization_vector_match: This module dynamically personalises the user’s feed in real-time. It identifies similarity between content and user interest vectors.

Content that matches well with your personal and cohort’s interest will be boosted into your feed.

You can see the site’s you engage with frequently using the site engagement page in Chrome (from your toolbar: chrome://site-engagement/) and every stored interaction with histograms. This histogram data indirectly shows key interaction points you have with web pages, by measuring the browser’s response and performance around those interactions.

It doesn’t explicitly say user A clicked X, but logs the technical impact, i.e., how long did the browser spending processing said click or scroll.

Decay And Renewal Cycles

Discover boosts freshness because people are thirsty for it. By boosting fresh content, older or saturated stories naturally decay as the news cycle moves on and article engagement declines.

For successful stories, this is through market saturation.

• freshnessDecay_timer: This module measures recency decay after initial exposure, gradually reducing visibility to make way for fresher content.
• content_staleness_penalty: Outdated content or topics are given a lower priority once engagement starts to decline to keep the feed current.

Discover is Google’s answer to a social network. None of us spend time in Google. It’s not fun. I use the word fun loosely. It isn’t designed to hook us in and ruin our attention spans with constant spiking of dopamine.

But Google Discover is clearly on the way to that. They want to make it a destination. Hence, all the recent changes where you can “catch up” with creators and publishers you care about across multiple platforms.

Videos, social posts, articles … the whole nine yards. I wish they’d stop summarizing literally everything with AI, however.

My 11-Step Workflow To Get The Most Out Of Google Discover

Follow basic principles and you will put yourself in good stead. Understand where your site is topically strong and focus your time on content that will drive value. Multiple ways you can do this.

If you don’t feature much in Discover, you can use your Search Console click and impressions data to identify areas where you generate the highest value. Where you are topically authoritative. I would do this at a subfolder and entity level (e.g., politics and Rachel Reeves or the Labor Party).

Also worth breaking this down in total and by article. Or you can use something like Ahrefs’ Traffic Share report to determine your share of voice via third-party data.

Essentially share of voice data (Image Credit: Harry Clarkson-Bennett)

Then really focus your time on a) areas where you’re already authoritative and b) areas that drive value for your audience.

Assuming you’re not focusing on NSFW content and you’re vaguely eligible, here’s what I would do:

1. Make sure you’re meeting basic image requirements. 1200 pixels wide as a minimum.
2. Identify your areas of topical authority. Where do you already rank effectively at a subfolder level? Is there a specific author who performs best? Try to build on your valuable content hubs with content that should drive extra value in this area.
3. Invest in content that will drive real value (links and engagement) in these areas. Do not chase clicks via Discover. It’s a one-way ticket to clickbait city.
4. Make sure you’re plugged into the news cycle. Being first has a huge impact on your news visibility in search. If you’re not first on the scene, make sure you’re adding something additional to the conversation. Be bold. Add value. Understand how news SEO really works.
5. Be entity-driven. In your headlines, first paragraph, subheadings, structured data, and image alt text. Your page should remove ambiguity. You need to make it incredibly clear who this page is about. A lack of clarity is partly why Google rewrites headlines.
6. Use the Open Graph title. The OG title is a headline that doesn’t show on your page. Primarily designed for social media use, it is one of the most commonly picked up headlines in Discover. It can be jazzy. Curiosity led. Rich. Interesting. But still entity-focused.
7. Make sure you share content likely to do well on Discover across relevant push channels early in its lifecycle. It needs to outperform its predicted early-stage performance.*
8. Create a good page experience. Your page (and site) should be fast, secure, ad-lite, and memorable for the right reasons.
9. Try to drive quality onward journeys. If you can treat users from Discover differently to your main site, think about how you would link effectively for them. Maybe you use a pop-up “we think you’ll like this next” section based on a user’s scroll depth of dwell time.
10. Get the traffic to convert. While Discover is a personalized feed, the standard scroller is not very engaged. So, focus on easier conversions like registrations (if you’re a subscriber first company) or advertising revenue et al.
11. Keep a record of your best performers. Evergreen content can be refreshed and repubbed year after year. It can still drive value.

*What I mean here is if your content is predicted to drive three shares and two links, if you share it on social and in newsletters and it drives seven shares and nine links, it is more likely to go viral.

As such, the algorithm identifies it as ‘Discover-worthy.’

More Resources:

This was originally published on Leadership in SEO.

Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/how-google-discover-really-works/559304/

Capture Links, Mentions, and Citations That Make a Difference

Backlinks alone no longer move the authority needle. Brand mentions are just as critical for visibility, recognition, and long-term SEO success. Are your campaigns capturing both?

Join Michael Johnson, CEO of Resolve, for a webinar where he shares a replicable campaign framework that aligns media outreach, SEO impact, and brand visibility, helping your campaigns become long-term assets.

What You’ll Learn

• The Resolve Campaign Framework: Step-by-step approach to ideating, creating, and pitching SEO-focused digital PR campaigns.
  
• The Dual Outcome Strategy: How to design campaigns that earn both high-quality backlinks and brand mentions from top-tier media.
  
• Real Campaign Case Studies: Examples of campaigns that created a compounding effect of links, mentions, and brand recognition.
  
• Techniques for Measuring Success: How to evaluate the SEO and branding impact of your campaigns.

Why You Can’t Miss This Webinar

Successful SEO campaigns today capture authority on multiple fronts. This session provides actionable strategies for engineering campaigns that work hand in hand with SEO, GEO, and AEO to grow your brand.

📌 Register now to learn how to design campaigns that earn visibility, links, and citations.

🛑 Can’t attend live? Register anyway, and we’ll send you the recording so you don’t miss out.

https://www.searchenginejournal.com/turn-campaign-into-lasting-seo-authority/558685/

This post was sponsored by IQRush. The opinions expressed in this article are the sponsor’s own.

Your traditional SEO is winning. Your AI visibility is failing. Here’s how to fix it.

Your brand dominates page one of Google. Domain authority crushes competitors. Organic traffic trends upward quarter after quarter. Yet when customers ask ChatGPT, Perplexity, or others about your industry, your brand is nowhere to be found.

This is the AI visibility gap, which causes missed opportunities in awareness and sales.

“SEO ranking on page one doesn’t guarantee visibility in AI search.  The rules of ranking have shifted from optimization to verification.”

Raj Sapru, Netrush, Chief Strategy Officer

Recent analysis of AI-powered search patterns reveals a troubling reality: commercial brands with excellent traditional SEO performance often achieve minimal visibility in AI-generated responses. Meanwhile, educational institutions, industry publications, and comparison platforms consistently capture citations for product-related queries.

The problem isn’t your content quality. It’s that AI engines prioritize entirely different ranking factors than traditional search: semantic query matching over keyword density, verifiable authority markers over marketing claims, and machine-readable structure over persuasive copy.

This audit exposes 15 questions that separate AI-invisible brands from citation leaders.

We’re sharing the first 7 critical questions below, covering visibility assessment, authority verification, and measurement fundamentals. These questions will reveal your most urgent gaps and provide immediate action steps.

Question 1: Are We Visible in AI-Powered Search Results?

Why This Matters: Commercial brands with strong traditional SEO often achieve minimal AI citation visibility in their categories. A recent IQRush field audit found fewer than one in ten AI-generated answers included in the brand, showing how limited visibility remains, even for strong SEO performers. Educational institutions, industry publications, and comparison sites dominate AI responses for product queries—even when commercial sites have superior content depth. In regulated industries, this gap widens further as compliance constraints limit commercial messaging while educational content flows freely into AI training data.

How to Audit:

• Test core product or service queries through multiple AI platforms (ChatGPT, Perplexity, Claude)
• Document which sources AI engines cite: educational sites, industry publications, comparison platforms, or adjacent content providers
• Calculate your visibility rate: queries where your brand appears vs. total queries tested

Action: If educational/institutional sources dominate, implement their citation-driving elements:

• Add research references and authoritative citations to product content
• Create FAQ-formatted content with an explicit question-answer structure
• Deploy structured data markup (Product, FAQ, Organization schemas)
• Make commercial content as machine-readable as educational sources

IQRush tracks citation frequency across AI platforms. Competitive analysis shows which schema implementations, content formats, and authority signals your competitors use to capture citations you’re losing.

Question 2: Are Our Expertise Claims Actually Verifiable?

Why This Matters: Machine-readable validation drives AI citation decisions: research references, technical standards, certifications, and regulatory documentation. Marketing claims like “industry-leading” or “trusted by thousands” carry zero weight. In one IQRush client analysis, more than four out of five brand mentions were supported by citations—evidence that structured, verifiable content is far more likely to earn visibility. Companies frequently score high on human appeal—compelling copy, strong brand messaging—but lack the structured authority signals AI engines require. This mismatch explains why brands with excellent traditional marketing achieve limited citation visibility.

How to Audit:

• Review your priority pages and identify every factual claim made (performance stats, quality standards, methodology descriptions)
• For each claim, check whether it links to or cites an authoritative source (research, standards body, certification authority)
• Calculate verification ratio: claims with authoritative backing vs. total factual claims made

Action: For each unverified claim, either add authoritative backing or remove the statement:

• Add specific citations to key claims (research databases, technical standards, industry reports)
• Link technical specifications to recognized standards bodies
• Include certification or compliance verification details where applicable
• Remove marketing claims that can’t be substantiated with machine-verifiable sources

IQRush’s authority analysis identifies which claims need verification and recommends appropriate authoritative sources for your industry, eliminating research time while ensuring proper citation implementation.

Question 3: Does Our Content Match How People Query AI Engines?

Why This Matters: Semantic alignment matters more than keyword density. Pages optimized for traditional keyword targeting often fail in AI responses because they don’t match conversational query patterns. A page targeting “best project management software” may rank well in Google but miss AI citations if it doesn’t address how users actually ask: “What project management tool should I use for a remote team of 10?” In recent IQRush client audits, AI visibility clustered differently across verticals—consumer brands surfaced more frequently for transactional queries, while financial clients appeared mainly for informational intent. Intent mapping—informational, consideration, or transactional—determines whether AI engines surface your content or skip it.

How to Audit:

• Test sample queries customers would use in AI engines for your product category
• Evaluate whether your content is structured for the intent type (informational vs. transactional)
• Assess if content uses conversational language patterns vs. traditional keyword optimization

Action: Align content with natural question patterns and semantic intent:

• Restructure content to directly address how customers phrase questions
• Create content for each intent stage: informational (education), consideration (comparison), transactional (specifications)
• Use conversational language patterns that match AI engine interactions
• Ensure semantic relevance beyond just keyword matching

IQRush maps your content against natural query patterns customers use in AI platforms, showing where keyword-optimized pages miss conversational intent.

Question 4: Is Our Product Information Structured for AI Recommendations?

Why This Matters: Product recommendations require structured data. AI engines extract and compare specifications, pricing, availability, and features from schema markup—not from marketing copy. Products with a comprehensive Product schema capture more AI citations in comparison queries than products buried in unstructured text. Bottom-funnel transactional queries (“best X for Y,” product comparisons) depend almost entirely on machine-readable product data.

How to Audit:

• Check whether product pages include Product schema markup with complete specifications
• Review if technical details (dimensions, materials, certifications, compatibility) are machine-readable
• Test transactional queries (product comparisons, “best X for Y”) to see if your products appear
• Assess whether pricing, availability, and purchase information is structured

Action: Implement comprehensive product data structure:

• Deploy Product schema with complete technical specifications
• Structure comparison information (tables, lists) that AI can easily parse
• Include precise measurements, certifications, and compatibility details
• Add FAQ schema addressing common product selection questions
• Ensure pricing and availability data is machine-readable

IQRush’s ecommerce audit scans product pages for missing schema fields—price, availability, specifications, reviews—and prioritizes implementations based on query volume in your category.

Question 5: Is Our “Fresh” Content Actually Fresh to AI Engines?

Why This Matters: Recency signals matter, but timestamp manipulation doesn’t work. Pages with recent publication dates, but outdated information underperforms older pages with substantive updates: new research citations, current industry data, or refreshed technical specifications. Genuine content updates outweigh simple republishing with changed dates.

How to Audit:

• Review when your priority pages were last substantively updated (not just timestamp changes)
• Check whether content references recent research, current industry data, or updated standards
• Assess if “evergreen” content has been refreshed with current examples and information
• Compare your content recency to competitors appearing in AI responses

Action: Establish genuine content freshness practices:

• Update high-priority pages with current research, data, and examples
• Add recent case studies, industry developments, or regulatory changes
• Refresh citations to include latest research or technical standards
• Implement clear “last updated” dates that reflect substantive changes
• Create update schedules for key content categories

IQRush compares your content recency against competitors capturing citations in your category, flagging pages that need substantive updates (new research, current data) versus pages where timestamp optimization alone would help.

Question 6: How Do We Measure What’s Actually Working?

Why This Matters: Traditional SEO metrics—rankings, traffic, CTR—miss the consideration impact of AI citations. Brand mentions in AI responses influence purchase decisions without generating click-through attribution, functioning more like brand awareness channels than direct response. CMOs operating without AI visibility measurement can’t quantify ROI, allocate budgets effectively, or report business impact to executives.

How to Audit:

• Review your executive dashboards: Are AI visibility metrics present alongside SEO metrics?
• Examine your analytics capabilities: Can you track how citation frequency changes month-over-month?
• Assess competitive intelligence: Do you know your citation share relative to competitors?
• Evaluate coverage: Which query categories are you blind to?

Action: Establish AI citation measurement:

• Track citation frequency for core queries across AI platforms
• Monitor competitive citation share and positioning changes
• Measure sentiment and accuracy of brand mentions
• Add AI visibility metrics to executive dashboards
• Correlate AI visibility with consideration and conversion metrics

IQRush tracks citation frequency, competitive share, and month-over-month trends across across AI platforms. No manual testing or custom analytics development is required.

Question 7: Where Are Our Biggest Visibility Gaps?

Why This Matters: Brands typically achieve citation visibility for a small percentage of relevant queries, with dramatic variation by funnel stage and product category. IQRush analysis showed the same imbalance: consumer brands often surfaced in purchase-intent queries, while service firms appeared mostly in educational prompts. Most discovery moments generate zero brand visibility. Closing these gaps expands reach at stages where competitors currently dominate.

How to Audit:

• List queries customers would ask about your products/services across different funnel stages
• Group them by funnel stage (informational, consideration, transactional)
• Test each query in AI platforms and document: Does your brand appear?
• Calculate what percentage of queries produce brand mentions in each funnel stage
• Identify patterns in the queries where you’re absent

Action: Target the funnel stages with lowest visibility first:

• If weak at informational stage: Build educational content that answers “what is” and “how does” queries
• If weak at consideration stage: Create comparison content structured as tables or side-by-side frameworks
• If weak at transactional stage: Add comprehensive product specs with schema markup
• Focus resources on stages where small improvements yield largest reach gains

IQRush’s funnel analysis quantifies gap size by stage and estimates impact, showing which content investments will close the most visibility gaps fastest.

The Compounding Advantage of Early Action

The first seven questions and actions highlight the differences between traditional SEO performance and AI search visibility. Together, they explain why brands with strong organic rankings often have zero citations in AI answers.

The remaining 8 questions in the comprehensive audit help you take your marketing further. They focus on technical aspects: the structure of your content, the backbone of your technical infrastructure, and the semantic strategies that signal true authority to AI. 

“Visibility in AI search compounds, making it harder for your competition to break through. The brands that make themselves machine-readable today will own the conversation tomorrow.”
Raj Sapru, Netrush, Chief Strategy Officer

IQRush data shows the same thing across industries: early brands that adopt a new AI answer engine optimization strategy quickly start to lock in positions of trust that competitors can’t easily replace. Once your brand becomes the reliable answer source, AI engines will start to default to you for related queries, and the advantage snowballs.

The window to be an early adopter and take AI visibility for your brand will not stay open forever.  As more brands invest in AI visibility, the visibility race is heating up.

Download the Complete AI Search Visibility Audit with detailed assessment frameworks, implementation checklists, and the 8 strategic questions covering content architecture, technical infrastructure, and linguistic optimization. Each question includes specific audit steps and immediate action items to close your visibility gaps and establish authoritative positioning before your market becomes saturated with AI-optimized competitors.

Image Credits

Featured Image: Image by IQRush. Used with permission.

In-Post Images: Image by IQRush. Used with permission.

https://www.searchenginejournal.com/ai-visibility-audit-questions-iqrush-spa/558015/

Google’s John Mueller answered a question about canonicals, expressing his opinion that “hope” shouldn’t be a part of your SEO strategy with regard to canonicals. The implication is that hoping Google will figure it out on its own misses the point of what SEO is about.

Canonicals And Case Sensitivity

Rel=canonical is an HTML tag that enables a publisher or SEO to tell Google what their preferred URL is. For example, it’s useful for suggesting the best URL when there are multiple URLs with the same or similar content. Google isn’t obligated to obey the rel=canonical declaration, it’s treated as a strong hint.

Someone on Reddit was in the situation where a website has category names that they begin with a capitalized letter but the canonical tag contains a lowercase version. There is currently a redirect from the lowercase version to the uppercase.

They’re currently not seeing any negative impact from this state of the website and were asking if it’s okay to leave it as-is because it hasn’t affected search visibility.

The person asking the question wrote:

“…I’m running into something annoying on our blog and could use a sanity check before I push dev too hard to fix it. It’s been an issue for a month, after a redesign was launched.

All of our URLs resolve in this format: /site/Topic/topic-title/

…but the canonical tag uses a lowercase topic, like: /site/topic/topic-title/

So the canonical doesn’t exactly match the actual URL’s case. Lowercase topic 301 redirects to the correct, uppercase version.

I know that mismatched canonicals can send mixed signals to Google.

Dev is asking, “Are you seeing any real impact from this?” and technically, the answer is no — but I still think it’s worth fixing to follow best practices.”

If It Works Don’t Fix It?

This is an interesting case because in many things related to SEO if something’s working there’s little point trying to fix a small detail for fear of triggering a negative response. Relying on Google to figure things out is another fallback.

Google’s John Mueller has a different opinion. He responded:

“URL path, filename, and query parameters are case-sensitive, the hostname / domain name aren’t. Case-sensitivity matters for canonicalization, so it’s a good idea to be consistent there. If it serves the same content, it’ll probably be seen as a duplicate and folded together, but “hope” should not be a part of an SEO strategy.

Case-sensitivity in URLs also matters for robots.txt.”

Takeaway

I know that in highly competitive niches the SEO is on a generally flawless level. If there’s something to improve it gets improved. And there’s a good reason for that. Someone at one of the search engines once told me that anything you can do to make it easier for the crawlers is a win. They advised me to make sites easy to crawl and content easy to understand. That advice is still useful, it follows with Mueller’s advice to not “hope” that Google figures things out, implying that it’s best to make sure they do work out.

Featured Image by Shutterstock/MyronovDesign

https://www.searchenginejournal.com/googles-advice-on-canonicals-theyre-case-sensitive/559440/

Search is literally evolving faster than ever. Is your SEO strategy keeping up?

Are you struggling to stay visible in AI-powered search results? 

Does your visibility seem to vanish with each algorithm update or SERP overhaul? 

Worried that AI Overviews are cutting into your traffic, but unsure how to adapt?

Discover 5 proven tactics to protect your SERP visibility.

The rise of AI-driven search engines like ChatGPT, Google’s SGE, and Bing Copilot is changing how users discover and trust brands online.

What Worked Last Year May Not Work Tomorrow

Join Craig Smith, Chief Strategy Officer at OuterBox, and learn exactly how to adapt your strategy for a world run by answer engines and generative SERPs.

What You’ll Learn:

• How to structure your content and technical SEO practices for AI-generated answers.
• Which trust signals matter most when AI chooses which brand to cite.
• Where search is headed, plus how to stay ahead.

Watch on-demand and get the SEO playbook your competitors wish they had.

View the slides below or check out the full webinar for all the details.

https://www.searchenginejournal.com/5-seo-tactics-on-ai-search/557107/

SEO plays a role in Automattic’s counterclaim against WP Engine. The legal document mentions search engine optimization six times and SEO once as part of counterclaims asserting that WP Engine excessively used words like “WordPress” to rank in search engines as part of an “infringement” campaign that uses WordPress trademarks in commerce. A close look at those claims shows that some of the evidence may be biased and that claims about SEO rely on outdated information.

Automattic’s Claims About SEO

Automattic’s counterclaim asserts that WP Engine used SEO to rank for WordPress-related keywords and that this is causing confusion.

The counterclaim explains:

“WP Engine also has sown confusion in recent years by dramatically increasing the number of times Counterclaimants’ Marks appear on its websites. Starting in or around 2021, WP Engine began to sharply increase its use of the WordPress Marks, and starting in or around 2022, began to sharply increase its use of the WooCommerce Marks.”

Automattic next argues that the repetition of keywords on a web page is WP Engine’s SEO strategy. Here’s where their claims become controversial to those who know how search engines rank websites.

The counterclaim asserts:

“The increased number of appearances of the WordPress Marks on WP Engine’s website is particularly likely to cause confusion in the internet context.

On information and belief, internet search engines factor in the number of times a term appears in a website’s text in assessing the “relevance” of a website to the terms a user enters into the search engine when looking for websites.

WP Engine’s decision to increase the number of times the WordPress Marks appear on WP Engine’s website appears to be a conscious “search engine optimization” strategy to ensure that when internet users look for companies that offer services related to WordPress, they will be exposed to confusingly written and formatted links that take them to WP Engine’s sites rather than WordPress.org or WordPress.com.”

They call WP Engine’s strategy aggressive:

“WP Engine’s strategy included aggressive utilization of search engine optimization to use the WordPress and WooCommerce Marks extremely frequently and confuse consumers searching for authorized providers of WordPress and WooCommerce software;”

Is The Number Of Keywords Used A Ranking Factor?

I have twenty-five years of experience in search engine optimization and have a concomitantly deep understanding of how search engines rank content. The fact is that Automattic’s claim that search engines “factor in the number of times” a keyword is used in a website’s content is outdated and incorrect.

Modern search engines don’t factor in the number of times a keyword appears on a web page as a ranking factor. Google’s algorithms use models like BERT to gain a semantic understanding of the meaning and intent of the keyword phrases used in search queries and content, resulting in the ability to rank content that doesn’t even contain the user’s keywords.

Those aren’t just my opinions; Google’s web page about how search works explicitly says that content is ranked according to the user’s intent, regardless of keywords, which directly contradicts Automattic’s claim about WPE’s SEO:

“To return relevant results, we first need to establish what you’re looking for – the intent behind your query. To do this, we build language models to try to decipher how the relatively few words you enter into the search box match up to the most useful content available.

This involves steps as seemingly simple as recognizing and correcting spelling mistakes, and extends to our sophisticated synonym system that allows us to find relevant documents even if they don’t contain the exact words you used.”

If Google’s documentation is not convincing enough, take a look at the search results for the phrase “Managed WordPress Hosting.” WordPress.com ranks #2, despite the phrase being completely absent from its web page.

Screenshot Of WordPress.com In Search Results

What Is The Proof?

Automattic provides a graph comparing WP Engine’s average monthly mentions of the word “WordPress” with mentions published by 18 other web hosts. The comparison of 19 total web hosts dramatically illustrates that WP Engine mentions WordPress more often than any of the other hosting providers, by a large margin.

Screenshot Of Graph

Here’s a close-up of the graph (with the values inserted) showing that WP Engine’s monthly mentions of “WordPress” far exceed the number of times words containing WordPress are used on the web pages of the other hosts.

Screenshot Of Graph Closeup

People say that numbers don’t lie, and the graph presents compelling evidence that WP Engine is deploying an aggressive use of keywords with the word WordPress in them. Leaving aside the debunked idea that keyword-term spamming actually works, a closer look at the graph comparison shows that the evidence is not so strong because it is biased.

Automattic’s Comparison Is Arguably Biased

Automattic’s counterclaim compares eighteen web hosts against WP Engine. Of those eighteen hosts, only five (including WPE) are managed WordPress hosting platforms. The remaining fourteen are generalist hosting platforms that offer cloud hosting, VPS (virtual private servers), dedicated hosting, and domain name registrations.

The significance of this fact is that the comparison can be considered biased against WP Engine because the average mention of WordPress will naturally be lower across the entire website of a company that offers multiple services (like VPS, dedicated hosting, and domain name registrations) versus a site like WP Engine that offers only one service, managed WordPress hosting.

Two of the hosts listed in the comparison, Namecheap and GoDaddy, are primarily known as domain name registrars. Namecheap is the second biggest domain name registrar in the world. There’s no need to belabor the point that these two companies in Automattic’s comparison may be biased choices to compare against WP Engine.

Of the five hosts that offer WordPress hosting, two are plugin platforms: Elementor and WPMU Dev. Both are platforms built around their respective plugins, which means that the average number of mentions of WordPress is going to be lower because the average may be diluted by documentation and blog posts about the plugins. Those two companies are also arguably biased choices for this kind of comparison.

Of the eighteen hosts that Automattic chose to compare with WP Engine, only two of them are comparable in service to WP Engine: Kinsta and Rocket.net.

Comparison Of Managed WordPress Hosts

Automattic compares the monthly mentions of phrases with “WordPress” in them, and it’s clear that the choice of hosts in the comparison biases the results against WP Engine. A fairer comparison is to compare the top-ranked web page for the phrase “managed WordPress hosting.”

The following is a comparison of the top-ranked web page for each of the three managed WordPress hosts in Automattic’s comparison list, a straightforward one-to-one comparison. I used the phrase “managed WordPress hosting” plus the domain name appended to a search query in order to surface the top-ranked page from each website and then compared how many times the word “WordPress” is used on those pages.

Here are the results:

Rocket.net

The home page of Rocket.net ranks #1 for the phrase “rocket.net managed wordpress hosting.” The home page of Rocket.net contains the word “WordPress” 21 times.

Screenshot of Google’s Search Results

Kinsta

The top ranked Kinsta page is kinsta.com/wordpress-hosting/ and that page mentions the word “WordPress” 55 times.

WP Engine

The top ranked WP Engine web page is wpengine.com/managed-wordpress-hosting/ and that page mentions the word “WordPress” 27 times.

A fair one-to-one comparison of managed WordPress host providers, selected from Automattic’s own list, shows that WP Engine is not using the word “WordPress” more often than its competitors. Its use falls directly in the middle of a fair one-to-one comparison.

Number Of Times Page Mentions WordPress

• Rocket.net: 21 times
• WP Engine: 27 times
• Kinsta: 55 times

What About Other Managed WordPress Hosts?

For the sake of comparison, I compared an additional five managed WordPress hosts that Automattic omitted from its comparison to see how often the word “WordPress” was mentioned on the top-ranked web pages of WP Engine’s direct competitors.

Here are the results:

• WPX Hosting: 9
• Flywheel: 16
• InstaWP: 22
• Pressable: 23
• Pagely: 28

It’s apparent that WP Engine’s 27 mentions put it near the upper level in that comparison, but nowhere near the level at which Kinsta mentions “WordPress.” So far, we only see part of the story. As you’ll see, other web hosts use the word “WordPress” far more than Kinsta does, and it won’t look like such an outlier when compared to generalist web hosts.

A Comparison With Generalist Web Hosts

Next, we’ll compare the generalist web hosts listed in Automattic’s comparison.

I did the same kind of search for the generalist web hosts to surface their top-ranked pages for the query “managed WordPress hosting” plus the name of the website, which is a one-to-one comparison to WP Engine.

Other Web Hosts Compared To WP Engine:

1. InMotion Hosting: 101 times
2. Greengeeks: 97 times
3. Jethost: 71 times
4. Verpex: 52 times
5. GoDaddy: 49 times
6. Cloudways: 47 times
7. Namecheap: 41 times
8. Liquidweb: 40 times
9. Pair: 40 times
10. Hostwinds: 37 times
11. KnownHost: 33 times
12. Mochahost: 33 times
13. Panthen: 31 times
14. Siteground: 30 times
15. WP Engine: 27 times

Crazy, right? WP Engine uses the word “WordPress” less often than any of the other generalist web hosts. This one-to-one comparison contradicts Automattic’s graph.

And just for the record, WordPress.com’s top-ranked page wordpress.com/hosting/ uses the word “WordPress” 62 times, over twice as often as WP Engine’s web page.

Will Automattic’s SEO Claims Be Debunked?

Automattic’s claims about WP Engine’s use of SEO may be based on shaky foundations. The claims about how keywords work for SEO contradict Google’s own documentation, and the fact that WordPress.com’s own website ranks for the phrase “Managed WordPress Hosting” despite not using that exact phrase appears to debunk their assertion that search engines factor the number of times a user’s keywords are used on a web page.

The graph that Automattic presents in their counterclaim does not represent a comparison of direct competitors, which may contribute to a biased impression that WP Engine is aggressively using the “WordPress” keywords more often than competitors. However, a one-to-one comparison of the actual web pages that compete against each other for the phrase “Managed WordPress Hosting” shows that many of the web hosts in Automattic’s own list use the word “WordPress” far more often than WP Engine, which directly contradicts Automattic’s narrative.

I ran WP Engine’s Managed WordPress Hosting URL in a Keyword Density Tool, and it shows that WP Engine’s web page uses the word “WordPress” a mere 1.92% of the time, which, from an SEO point of view, could be considered a modest amount and far from excessive. It will be interesting to see how the judge decides the merits of Automattic’s SEO-related claims.

Featured Image by Shutterstock/file404

https://www.searchenginejournal.com/automattic-seo-counterclaim/559328/

The web – and the way in which humans interact with it – has definitely changed since the early days of SEO and the emergence of search engines in the early to mid-90s. In that time, we’ve witnessed the internet turn from something that nobody understood to something most of the world cannot operate without. This interview between Bill Gates and David Letterman puts this 30-year phenomenon into perspective:

[embedded content]

The attitude 30 years ago was that the internet was not understood at all and nor was its potential influence. Today, the concept of AI entering into our daily lives is taken much more seriously to the point that it is something that many look upon with fear – perhaps now because we [think] we have an accurate outlook on how this may progress.

This transformation isn’t so much about the skills we’ve developed over time, but rather about the evolution of the technology and channels that surround them. Those technologies and channels are evolving at a fast pace and causing some to panic over whether their inherent technological skills will still apply within today’s Search ecosystem.

The Technological Rat Race

Right now, it may feel like there’s something new to learn or a new product to experiment with every day, and it can be difficult to decide where to focus your attention and priorities. This is, unfortunately, a phase that I believe will continue for a good couple of years as the dust settles over this wild west of change.

Because these changes are impacting nearly everything an SEO would be responsible for as part of organic visibility, it may feel overwhelming to digest all these things – all while we seemingly take on the challenge of communicating these changes to our clients or stakeholders/board members.

But change does not equal the end of days. This “change” relates to the technology around what we’ve been doing for over a generation, and not the foundation of the discipline itself.

Old Hat Is New Hat

The major search engines have been actively telling you, including Google and Bing, that core SEO principles should still be at the forefront of what we do moving forward. Danny Sullivan, former Search Liaison at Google, also made this clear during his recent keynote at WordCamp USA:

[embedded content]

The consistent messages are clear:

• Produce well-optimized sites that perform well.
• Populate solid structured data and entity knowledge graphs.
• Re-enforce brand sentiment and perspective.
• Offer unique, valuable content for people.

The problem some may have is that the content we produce is moreso for agents than for people, and if this is true, what impact does this make?

The Web Is Splitting Into Two

The open web has been disrupted most of all, with some business models being uprooted by taking solved knowledge and serving it within their platform, appropriating the human visitor, which they rely on for income.

This has created a split from a complete open web into two – the “human” web and the “agentic” web – where these two audiences are both major considerations and will differ from site to site. SEOs will have to consider both sides of the web and how to serve both – which is where an SEO’s skill set becomes more valuable than it was before.

One example can be seen in the way that agents now take charge of ecommerce transactions, where OpenAI announced “Buy it in ChatGPT,” where the buying experience is even more seamless with instant checkouts. It also open-sourced the technology behind it, Agentic Commerce Protocol (ACP), and is already being adopted by content management system (CMS), including Shopify. This split between agentic and human engagement will still require optimization in order to ensure maximum discoverability.

When it comes to content, ensure everything is concise and avoid fluff, what I refer to as “tokenization spam.” Content isn’t just crawled; it’s processed, chunked, and tokenized. Agents will take preference to well-structured and formatted text.

“Short-Term Wins” Sounds Like Black Hat

Of course, during any technological shift, there will be some bad actors who may tell you about a brand-new tactic that is guaranteed to work to help you “rank in AI.” Remember that the dust has not yet settled when it comes to the maturity of these assistance engines, and you should compare this to the pre-Panda/Penguin era of SEO, where black hat SEO techniques were easier to achieve.

These algorithm updates closed those loopholes, and the same will happen again as these platforms improve – with increased speed as agents understand what is truly honest with increased precision.

Success Metrics Will Change, Not The Execution To Influence Them

In reality, core SEO principles and foundations are still the same and have been throughout most changes in the past – including “the end of desktop” when mobiles became more dominant; and “the end of typing” when voice search started to grow with products such as Alexa, Google Home, and even Google Glass.

Is the emergence of AI going to render what I do as an SEO obsolete? No.

Technical SEO remains the same, and the attributes that agents look at are not dissimilar to what we would be optimizing if large language models (LLMs) weren’t around. Brand marketing remains the same. While the term “brand sentiment” is a term used more widely nowadays, it is something that should have always been a part of our online marketing strategies when it comes to authority, relevance, and perspective.

That being said, our native metrics have been devalued within two years, and those metrics will continue to shift alongside the changes that are yet to come as these platforms deliver more stability. This has already skewed year-over-year data and will continue to skew for the year ahead as more LLMs evolve. This, however, could be compared to events such as replacing granular organic keyword data with one (not provided) metric within Google Analytics, the deprecation of Yahoo! Site Explorer, or devaluation of benchmark data such as Alexa Rank and Google PageRank.

Revise Your Success Metric Considerations

Success metrics now have to go beyond the SERP into visibility and discoverability as a whole, through multiple channels. There are now several tools and platforms available that can analyze and report on AI-focused visibility metrics, such as Yoast AI Brand Insights, that can provide better insight into how your brand is interpreted by LLMs.

If you’re more technical, make use of MCPs (Model Context Protocol) to understand data more via natural language dialogs. MCP is an open-source standard that lets AI applications connect to external systems like databases, tools, or workflows (you can visualize this as a USB-C port for AI) so they can access information and perform tasks using a simple, unified connection. There are several MCPs you can work with already, including:

You can take this a step further by coupling this with a vibe coding tool such as Claude Code, where you can use it to create a reporting app that uses a combination of the above MCP servers in order to extract the best data and create visuals and interactive charts for you and your clients/stakeholders.

The Same But Different … But Still The Same

While the divergence between human and agentic experiences is increasing, the methods by which we, as an SEO, would optimize for them are not too dissimilar. Leverage both within your strategy – just as you did when mobile gained traction in the same way.

More Resources:

Featured Image: Vallabh Soni/Shutterstock

https://www.searchenginejournal.com/the-same-but-different-evolving-your-strategy-for-ai-driven-discovery/558619/

The French technology group Positive acquired Surfer, the popular content optimization tool. The acquisition helps Positive create a “full-funnel” brand visibility solution together with its marketing and CRM tools.

The acquisition of Surfer extends Positive’s reach from marketing software to AI-based brand visibility. Positive described the deal as part of a European AI strategy that supports jobs and protects data. Positive’s revenue has grown fivefold in the past five years, rising from €50 million to an expected €70 million in 2025.

Surfer SEO

Founded in 2017, Surfer developed SEO tools based on language models that help marketers improve visibility on both search engines and AI assistants, which have become a growing source of website traffic and customers.

Sign Of Broader Industry Trends

The acquisition shows that search optimization continues to be an important part of business marketing as AI search and chat play a larger role in how consumers learn about products, services, and brands. This deal enables Positive to offer AI-based visibility solutions alongside its CRM and automation products, expanding its technology portfolio.

What Acquisition Means For Customers

Positive Group, based in France, is a technology solutions company that develops digital tools for marketing, CRM, automation, and data management. It operates through several divisions: User (marketing and CRM), Signitic (email signatures), and now Surfer (AI search optimization). The company is majority-owned by its executives, employs about 400 people, and keeps its servers in France and Germany. Surfer, based in Poland, brings experience in AI content optimization and a strong presence in North America. Together, they combine infrastructure, market knowledge, and product development within one technology-focused group.

Lucjan Suski, CEO and co-founder of Surfer, commented:

“SEO is evolving fast, and it matters more than ever before. We help marketers win the AI SEO era. Positive helps them grow across every other part of their digital strategy. Together, we’ll give marketers the complete toolkit to lead across AI search, email marketing automation, and beyond.”

According to Mathieu Tarnus, Positive’s founding president, and Paul de Fombelle, its CEO:

“Artificial intelligence is at the heart of our value proposition. With the acquisition of Surfer, our customers are moving from optimizing their traditional SEO positioning to optimizing their brand presence in the responses provided by conversational AI assistants. Surfer stands out from established market players by directly integrating AI into content creation and optimization.”

The acquisition adds Surfer’s AI optimization capabilities to Positive’s product ecosystem, helping customers improve visibility in AI-generated answers. For both companies, the deal is an opportunity to expand their capabilities in AI-based brand visibility.

Featured Image by Shutterstock/GhoST RideR 98

https://www.searchenginejournal.com/surfer-seo-acquired-by-positive-group/558918/

I just got back from San Diego and Toronto, where I spoke at Ahrefs Evolve and SEO IRL – both of which were fantastic. A lot of people I met subscribed to the Growth Memo. Thank you all for coming out!

Image Credit: Kevin Indig

I also had the pleasure of facilitating a 3h mastermind with leaders from Redfin, Angi, Clickup, Glean and Ourplace in San Diego. If I’ll do more of these, I’ll let you know.

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Two weeks ago, we published the largest user behavior study of AI Mode and found groundbreaking results.

This week, I’m connecting the dots between 10 different studies, tests, and data sources to see what the research actually says about AI Mode – and to answer five questions everyone’s asking:

• How does AI Mode impact click-through rates and SEO traffic?
• Are people even using AI Mode?
• Are AI Mode responses accurate?
• How are AI Overviews and AI Mode similar? How are they different?
• Can brands still benefit from earning AI Mode visibility, even if clicks are scarce (if not zero)?

In this meta-analysis of the core data our industry produced about AI Mode in 2025, we’ll look at the aggregated research – all in one place. So, I’d bookmark this, as it’s likely a stakeholder is going to ask you for it soon, if they haven’t already.

(If I’m missing any big studies or tests here, send me a DM.)

Image Credit: Kevin Indig

Here’s what we do know for sure: AI Mode drastically reduces external clicks.

This is a corroborated finding across the research touchpoints I used for this meta-analysis (including studies, tests, and fresh data).

We live in this reality right now – and can’t afford to ignore it.

Organic traffic stagnation (or even traffic decline) despite ongoing organic growth efforts is the reality today … and will accelerate if/when AI Mode becomes the default Google search experience.

Image Credit: Kevin Indig

• Semrush’s AI Mode early‑adoption analysis of ~69 million U.S. Google sessions found that 92 – 94% of AI Mode sessions resulted in no external click, and only 6-8% produced any outbound traffic [1].
• The AI Mode user‑behavior study I published over the last two weeks, directed by Eric van Buskirk from Clickstream Solutions, corroborated this finding: 77.6% of our directed search task sessions had zero external visits, and median external clicks per task were zero.
• Eric and I also worked together on Propellic’s travel industry study, and it echoes the same sentiments even though it’s industry-specific. Our data showed that for some search tasks, users’ interactions never left AI Mode. Users found enough information from AI-generated answers and moved on, unless they needed to take a final booking step [2].

Traffic does flow in certain cases.

I reported last week in the Growth Memo AI Mode usability study part 2 that shopping prompts produced clicks nearly 100% of the time, while non‑transactional tasks produced almost none.

Likewise, Propellic’s travel study found that planning tasks kept users in AI Mode ( with ≈104 seconds of engagement), but once a decision was made they clicked out to book (spending about ≈38 seconds before the external click).

Keep in mind, AI Mode doesn’t just dramatically reduce clicks, it also shrinks searching sessions: Semrush saw AI Mode sessions average two to three queries versus ~5 in traditional search [3]. That means not only is there less of a chance for traffic, but there’s also likely less of a chance for visibility, too.

1. Expect massively lower click-through rates from AI Mode compared to classic blue‑link SERPs.
2. Instead of traffic, think in terms of brand visibility and user influence within AI Mode.
3. For performance metrics, shift your attention from CTR to brand mentions, dwell time, and conversion during the final, most high-intent step.

Research across our industry this year shows, at least for now, users are slow to adopt AI Mode.

However, Google seems hell-bent on training all searchers to head in that direction via including AI Mode buttons in Chrome and in AIOs on the SERP – even if that could mean less time for users in the SERPs or fewer ad clicks over time. In fact, the Growth Intelligence Brief #8, I reported:

Logan Kilpatrick, who leads the Google AI Studio and Gemini API product, shook the SEO world [on September 6] when he said AI Mode was going to become the default search experience.

Even though he qualified his statement shortly after, Sundar Pichai had already said the same thing on the Lex Fridman podcast back in June.

So get ready. All roads seem to lead to AI Mode, whether users like it or not.

iPullRank’s AI Mode UX study found that only 2-5% of participants used AI Mode across five tasks, while 30-47% engaged with AI Overviews.

Image Credit: Kevin Indig

And the month before they released their study, iPullRank received Similarweb data showing over 50% of users tried AI Mode once and then bounced [4].

When participants did use AI Mode in the iPullRank AI Mode study, they often consumed the answer, clicked nothing and moved on.

In addition, back in August, Aleyda Solis shared UK adoption of AI Mode slowed after user curiosity seemed to subside after the initial launch.

Image Credit: Kevin Indig

So, it makes sense why Google is slow to roll AI Mode out broadly – it doesn’t have product-market fit, yet.

In the Nielsen Norman Group’s (NNG) usability study – UX research that examined how people interacted with AI chats for search as a whole, including AI Mode – noted that after being introduced to AI chat, participants found it helpful for complex information‑seeking tasks. And that generative AI saved time for those tasks by synthesizing the data. [5]

However, participants in the NNG study still cross‑checked facts via classic search, indicating residual skepticism.

Other research highlights inaccuracies and gaps with AI Mode:

iPullRank participants searching for local news or health clinics found AI Mode results to be inaccurate or lacking specificity, so they relied on traditional sites or maps:

“In [the local sports and news headlines] search, many found the AIOs and AI Mode (as well as ChatGPT) to be inaccurate, less trustworthy, and not up to date, but the participants didn’t expect these sources to be timely or accurate in the first place, which is an issue in itself.”

In a small-scale experiment by Ahrefs, Patrick Stox created AI Mode‑generated articles on technical SEO topics that had contained factual errors (e.g., incorrect hreflang advice) and published them live to see if they could rank. The three test pages failed to appear for their target keywords, and the test suggests that AI Mode content may be insufficiently accurate for Google’s own EEAT guidelines.

1. Users generally trust AI Mode and AI answers on other platforms. Responses can be highly trusted for some high-intent shopping searches and informational queries, but they may contain inaccuracies or unwanted localization.
2. Users and marketers should treat AI answers as starting points, double‑checking critical information and considering brand authority and verification.
3. There’s a brand risk inherent to LLMs like AI Mode. Bad actors can use the still nascent and simple functionality of LLMs to spread lies about brands on the web and create bad brand sentiment. This is something you want to monitor with AI visibility trackers.

4. How Are AI Overviews And AI Mode Similar? How Are They Different?

Both AIOs and AI Mode produce synthesized answers drawn from multiple sources, and they both aim to keep users on Google. But users do interact with them differently.

While we found in our research that AI Overviews act more like fact sheets, where users skim to find quick information, AI Mode gets deeper engagement. Users spend on average twice as much time with AI Mode as with AI Overviews.

Image Credit: Kevin Indig

Interestingly, in iPullRank’s AI Mode study, users were confused about AI Mode vs. AI Overviews and mostly ignored the “dive deeper into AI Mode” button.

Setting aside the general user confusion, there are two core similarities seen across the 2025 AI Mode research and my analysis of 19 studies about the impact of AIOs:

1. Brand influence: Visibility in both AIOs and AI Mode depends on strong authority signals, like brand recognition, a quality link profile, and quality content.
2. Limited traffic: Both experiences reduce clicks. Studies on AIOs showed CTR declines, while AI Mode sessions are overwhelmingly zero‑click.

The differences?

• Citation patterns: SERanking found more sources (averaging 12.6 links per answer) with a mix of block and inline links in AI Mode, while AIOs often cite fewer sources. AI Mode and AIOs have low overlap with only 10.7% of URLs and 16% of domains overlapping between them.
• Content length and style: Semrush’s comparison study shows AI Mode produces longer answers (~300 words), similar to ChatGPT, and uses more unique domains (~7 per answer) than AI Overviews (~3).
• User interaction: AI Mode is accessed via a separate mode (or panel – at least, for now) and offers chat‑style follow‑up, product previews, local packs and business profile cards. AIOs appear inline within classic search and People Also Ask questions and rarely include interactive features (at the date of this writing, at least – we’re seeing more interactive features pop up that take users into AI Mode).
• Trigger frequency: AIOs aren’t triggered all the time, although Google has increased their rollout across queries over the last year. AI Mode can be invoked by the user or autopopulated for longer, conversational prompts.

5. Can Brands Still Benefit From AI Mode Visibility, Even If Clicks Are Scarce?

Yes – visibility inside AI Mode influences user decisions even without clicks. Here’s how I can answer this confidently: Several studies show that users read AI answers, examine citations and form opinions without leaving Google.

I get this question all the time from my clients: “If Google shows AI Mode and our clicks go away – how do we know whether what we’re doing works?”

Our AI Mode usability study found that participants spent 52-77 seconds reading AI answers per task and often concluded their research within the pane. Propellic’s travel research shows users spending ≈104 seconds planning inside AI Mode and then booking on an external site.

Image Credit: Kevin Indig

High trust scores (4.3/5) imply that brand mentions inside AI Mode transfer authority to those brands.

Participants looked at inline links, citations and product previews but rarely clicked out, unless they had a shopping task to complete.

We also found that brand familiarity meaningfully drives decisions.

Image Credit: Kevin Indig

In fact, recognized brands were chosen even when other options were available. Thus, being cited (even without a click) reinforces brand recall and can lead to direct visits later.

In short: Treat AI Mode as a branding channel. The goal is to be present where users read, not just where they click.

1. Attribution and tracking of decisions made in AI Mode is currently impossible, but we know from the research that it matters. If/when AI Mode becomes the default search experience, it will significantly change the way we think about Search.
2. The best we can do is track AI Mode visibility (how often, when and with what sentiment is our brand mentioned?) and self-reported attribution.
3. Ads in AI Mode will provide an extra layer of visibility that hopefully lets us quantify and prioritize optimization work.

I’m not taking swings at any of these studies and tests or the teams that developed them. We’re all benefiting from this expensive research these teams are working hard to distribute.

Across our sector, I’m seeing sharp experts and colleagues work diligently to widely and freely share information, and it makes me prouder than ever to be in growth marketing.

It truly feels like so many of us are doing this work together.

But the truth is, LLMs are a black box right now. And there’s so much more we need to know.

While the available studies offer valuable insights, they also come with limitations.

Below is my quick assessment. The intention of including this here is to inspire us all to further problem solve to crack upon these vaults of information.

This study uses a large dataset of 10,000 U.S. queries and repeats queries across three datasets to measure volatility. It analyzes link types and overlap with organic results, providing clear metrics.

But the study lacks qualitative user data and does not evaluate how often AI Mode appears.

This research includes real‑user think‑aloud sessions with 100 participants across multiple tasks. It also provides qualitative insights into user confusion between AI Mode and AI Overviews, which is meaningful.

Usage of AI Mode in this study was extremely low (2-5%), making some findings thin. So, while we received some good data here about how users are searching within Google right now with these new features available, we don’t get solid information about how people use AI Mode specifically.

The data for these two studies is very robust. But for the AI Mode comparison study specifically, I’d like to see research on an expanded view of search intents, other than the classic 4.

In Trust Still Lives in Blue Links – further analysis of the UX study of AIOs I published in May – I demonstrated a clear pattern of new ways users interact with LLM-based search features to validate AI outputs.

We all must expand our understanding of search intent, and having the data/research that more specifically parses out intent would help.

I would be very excited to see a combination of clickstream data with direct observations, broken down by vertical and over time for more AI Mode insights.

Growth Memo – User Behavior & SEO Impact (Parts 1 & 2)

I wouldn’t change anything about the research we’ve put out on AI Mode the last few weeks.

Just kidding.

Our results were specifically limited to the use of AI mode, so I caution against applying the insights from the study beyond the tasks or features tested. Participants knew they were in a study, which obviously can influence their behavior. We also select a broad range of tasks, which covers many intents and use cases but didn’t explore all of them in depth. I hope future research can focus exclusively on aspects like local search or shopping.

I’d also like to replicate the study type across industry types, larger sets of search tasks by search intent, and across LLMs.

Of course, this is an extremely small sample; results may not generalize. But, this is an interesting test of Google itself regardless.

I’d be curious to test more topics, including human‑written baselines.

This study looked at LLM interactions overall, and it wasn’t specific to AI Mode or Google; it also was a smaller sample of 10 participants.

Overall, it would be interesting to test each AI-chat-based search method, including AI Mode, specifically with a larger sample size and measure differences in trust and efficiency.

This was limited to travel vertical, although there are insights here that can be used regardless of industry.

Participants were prompted to use AI Mode, which may not reflect organic behavior – especially if people are naturally avoidant of the feature.

The study measures citations in LLMs, not click behavior or user satisfaction with those outputs or citations.

Overall, I’d love to see more information about AI Mode that includes broader geographic and multilingual datasets as it rolls out more globally, along with investigation into content accuracy and user satisfaction.

Our industry really needs increased sample size + diversity across these usability studies, but to be honest, it’s a huge, expensive undertaking.

Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/the-impact-of-ai-mode-on-seo-analysis-of-10-studies/558816/