A sharp-eyed search marketer discovered the reason why Google’s AI Overviews showed spammy web pages. The recent Memorandum Opinion in the Google antitrust case featured a passage that offers a clue as to why that happened and speculates how it reflects Google’s move away from links as a prominent ranking factor. Ryan Jones, founder of SERPrecon (LinkedIn profile), called attention to a passage in the recent Memorandum Opinion that shows how Google grounds its Gemini models. Grounding Generative AI Answers The passage occurs in a section about grounding answers with search data. Ordinarily, it’s fair to assume that links play a role in ranking the web pages that an AI model retrieves from a search query to an internal search engine. So when someone asks Google’s AI Overviews a question, the system queries Google Search and then creates a summary from those search results. But apparently, that’s not how it works at Google. Google has a separate algorithm that retrieves fewer web documents and does so at a faster rate. The passage reads: “To ground its Gemini models, Google uses a proprietary technology called FastSearch. Rem. Tr. at 3509:23–3511:4 (Reid). FastSearch is based on RankEmbed signals—a set of search ranking signals—and generates abbreviated, ranked web results that a model can use to produce a grounded response. Id. FastSearch delivers results more quickly than Search because it retrieves fewer documents, but the resulting quality is lower than Search’s fully ranked web results.” Ryan Jones shared these insights: “This is interesting and confirms both what many of us thought and what we were seeing in early tests. What does it mean? It means for grounding Google doesn’t use the same search algorithm. They need it to be faster but they also don’t care about as many signals. They just need text that backs up what they’re saying. …There’s probably a bunch of spam and quality signals that don’t get computed for fastsearch either. That would explain how/why in early versions we saw some spammy sites and even penalized sites showing up in AI overviews.” He goes on to share his opinion that links aren’t playing a role here because the grounding uses semantic relevance. What Is FastSearch? Elsewhere the Memorandum shares that FastSearch generates limited search results: “FastSearch is a technology that rapidly generates limited organic search results for certain use cases, such as grounding of LLMs, and is derived primarily from the RankEmbed model.” Now the question is, what’s the RankEmbed model? The Memorandum explains that RankEmbed is a deep-learning model. In simple terms, a deep-learning model identifies patterns in massive datasets and can, for example, identify semantic meanings and relationships. It does not understand anything in the same way that a human does; it is essentially identifying patterns and correlations. The Memorandum has a passage that explains: “At the other end of the spectrum are innovative deep-learning models, which are machine-learning models that discern complex patterns in large datasets. …(Allan) …Google has developed various “top-level” signals that are inputs to producing the final score for a web page. Id. at 2793:5–2794:9 (Allan) (discussing RDXD-20.018). Among Google’s top-level signals are those measuring a web page’s quality and popularity. Id.; RDX0041 at -001. Signals developed through deep-learning models, like RankEmbed, also are among Google’s top-level signals.” User-Side Data RankEmbed uses “user-side” data. The Memorandum, in a section about the kind of data Google should provide to competitors, describes RankEmbed (which FastSearch is based on) in this manner: “User-side Data used to train, build, or operate the RankEmbed model(s); “ Elsewhere it shares: “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: _____% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.” Then: “The RankEmbed model itself is an AI-based, deep-learning system that has strong natural-language understanding. This allows the model to more efficiently identify the best documents to retrieve, even if a query lacks certain terms. PXR0171 at -086 (“Embedding based retrieval is effective at semantic matching of docs and queries”); …RankEmbed is trained on 1/100th of the data used to train earlier ranking models yet provides higher quality search results. …RankEmbed particularly helped Google improve its answers to long-tail queries. …Among the underlying training data is information about the query, including the salient terms that Google has derived from the query, and the resultant web pages. …The data underlying RankEmbed models is a combination of click-and-query data and scoring of web pages by human raters. …RankEmbedBERT needs to be retrained to reflect fresh data…” A New Perspective On AI Search Is it true that links do not play a role in selecting web pages for AI Overviews? Google’s FastSearch prioritizes speed. Ryan Jones theorizes that it could mean Google uses multiple indexes, with one specific to FastSearch made up of sites that tend to get visits. That may be a reflection of the RankEmbed part of FastSearch, which is said to be a combination of “click-and-query data” and human rater data. Regarding human rater data, with billions or trillions of pages in an index, it would be impossible for raters to manually rate more than a tiny fraction. So it follows that the human rater data is used to provide quality-labeled examples for training. Labeled data are examples that a model is trained on so that the patterns inherent to identifying a high-quality page or low-quality page can become more apparent. Featured Image by Shutterstock/Cookie Studio All Images/Sources/Credit By -searchenginejournal.com

This post was sponsored by Writesonic. The opinions expressed in this article are the sponsor’s own. AI search now makes the first decision. When? Before a buyer hits your website. If you’re not part of the AI answer, you’re not part of the deal. In fact, 89% of B2B buyers use AI platforms like ChatGPT for research. Picture this: A founder at a 12-person SaaS asks, “best CRM for a 10-person B2B startup.” AI answer cites:a TechRadar roundup,a r/SaaS thread,a fresh comparison,Not you. Your brand is missing. They book demos with two rivals. You never hear about it. Here is why. AI search works on intent, not keywords. It reads content, then grounds answers with sources. It leans on third-party citations, community threads, and trusted publications. It trusts what others say about you more than what you say about yourself. Most Generative Engine Optimization (GEO) tools stop at the surface. They track mentions, list prompts you missed, and ship dashboards. They do not explain why you are invisible or what to fix. Brands get reports, not steps. We went hands-on. We analyzed millions of conversations and ran controlled tests. The result is a practical playbook: eight strategies that explain the why, give a quick diagnostic, and end with actions you can ship this week. In This GEO Guide 1. Find & Fix Your Citation Gaps 2. Engage In The Reddit & UGC Discussions That AI References 3. Study Which Topics Get Cited Most, Then Write Them 4. Update Content Regularly To Maintain AI Visibility 5. Create “X vs Y” And “X vs Y vs Z” Comparison Pages 6. Fix Robots.txt Blocking AI Crawlers 7. Fix Broken Pages For AI Crawlers 8. Avoid JavaScript For Main Content Off-Page Authority Builders For AI Search Visibility 1. Find & Fix Your Citation Gaps Citation gaps are the highest-leverage strategy most brands miss. Translation: This is an easy win for you. What Is A Citation Gap? A citation gap is when AI platforms cite web pages that mention your competitors but not you. These cited pages become the sources AI uses to generate its answers. Think of it like this: When someone asks ChatGPT about CRMs, it pulls information from specific web pages to craft its response. If those source pages mention your competitors but not you, AI recommends them instead of your brand. Finding and fixing these gaps means getting your brand mentioned on the exact pages AI already trusts and cites as sources. Why You Need Citations In Answer Engines If you’re not cited in an answer engine, you are essentially invisible. Let’s break this down. TechRadar publishes “21 Best Collaboration Tools for Remote Teams” mentioning: When users ask ChatGPT about remote project management, AI cites this TechRadar article. Your competitors appear in every response. You don’t. How To Fix Citation Gaps That TechRadar article gets cited for dozens of queries, including “best remote work tools,” “Monday alternatives,” “startup project management.” Get mentioned in that article, and you appear in all those AI responses. One placement creates visibility across multiple search variations. Contact the TechRadar author with genuine value, such as: Exclusive data about remote productivity. Unique use cases they missed. Updated features that change the comparison. The beauty? It’s completely scalable. Quick Win: Identify 50 high-authority articles where competitors are mentioned but you’re not. Get into even 10 of them, and your AI visibility multiplies exponentially. 2. Engage In The Reddit & UGC Discussions That AI References Image created by Writesonic, August 2025 AI trusts real user conversations over marketing content. Reddit citations in AI overviews surged from 1.3% to 7.15% in just three months, a 450% increase. User-generated content now makes up 21.74% of all AI citations. Why You Should Add Your Brand To Reddit & UGC Conversations Reddit, Quora, LinkedIn Pulse, and industry forums together, and you’ve found where AI gets most of its trusted information. If you show up as “trusted” information, your visibility increases. How To Inject Your Brand Into AI-Sourced Conversations Let’s say a Reddit thread titled “Best project management tool for a startup with 10 people?” gets cited whenever users ask about startup tools. Since AI already cites these, if you enter the conversation and include your thoughtful contribution, it will get included in future AI answers. Pro Tip #1: Don’t just promote your brand. Share genuine insights, such as: Hidden costs. Scaling challenges. Migration tips. Quick Win: Find and join the discussions AI seems to trust: Reddit threads with 50+ responses. High-upvote Quora answers in your industry. LinkedIn Pulse articles from recognized experts. Active forum discussions with detailed experiences. Pro Tip #2: Finding which articles get cited and which Reddit threads AI trusts takes forever manually. GEO platforms automate this discovery, showing you exactly which publications to pitch and which discussions to join. On-Page Optimization For GEO 3. Study Which Topics Get Cited Most, Then Write Them Something we’re discovering: when AI gives hundreds of citations for a topic, it’s not just citing one amazing article. Instead, AI pulls from multiple sites covering that same topic. If you haven’t written about that topic at all, you’re invisible while competitors win. Consider Topic Clusters To Get Cited Let’s say you’re performing a content gap analysis for GEO. You notice these articles all getting 100+ AI citations: “Best Project Management Software for Small Teams” “Top 10 Project Management Tools for Startups” “Project Management Software for Teams Under 20” Different titles, same intent: small teams need project management software. When users ask, “PM tool for my startup,” AI might cite 2-3 of these articles together for a comprehensive answer. Ask “affordable project management,” and AI pulls different ones. The point is that these topics cluster around the same user need. How To Outperform Competitors In AI Generated Search Answers Identify intent clusters for your topic and create one comprehensive piece on your own website so your own content gets cited. In this example, we’d suggest writing “Best Project Management Software for Small Teams (Under 50 People).” It

  Some of the critical questions that are top of mind for both SEOs and CMOs as we head into a multi-search world are: Where is search going to develop? Is ChatGPT a threat or an opportunity? Is optimizing for large language models (LLMs) the same as optimizing for search engines? In this two-part interview series, I try to answer these questions to provide some clear direction and focus to help navigate considerable change. What you will learn: Ecosystem Evolution: While it is still a Google-first world, learn where native AI search platforms are growing and what this means. Opportunity vs. Threat: Why AI platforms create unprecedented brand visibility opportunities while demanding new return on investment (ROI) thinking. LLM Optimization Strategy: Why SEO has become more vital than ever, regardless of the AI and Search platform, and where specific nuances to optimize for lie. CMO Priorities: Why authority and trust signals matter more than ever in AI-driven search. Organizational Alignment: Why CMOs need to integrate marketing, PR, and technical teams for cohesive AI-first search strategies. Where Do You Think The Current Search Ecosystem Might Develop In The Next 6 Months? To answer the first question, I think we are witnessing something really fascinating right now. The search landscape is undergoing a fundamental transformation that will accelerate significantly over the next six months. While Google still dominates with about 90% market share, AI-powered search platforms are experiencing explosive growth that is impossible to ignore. Let me put this in perspective. ChatGPT is showing 21% month-over-month growth and is on track to hit 700 million weekly active users. Claude and Perplexity are posting similar numbers at 21% and 19% growth, respectively. But here is what has caught my attention: Grok has seen over 1,000% month-over-month growth. Source BrightEdge Generative Parser and DataCube analysis, July 2025. Sure, it is starting from a tiny base, but that trajectory makes it the dark horse to watch. Meanwhile, DeepSeek continues its gradual decline following its January surge, which highlights the volatility in this emerging market. I will share more on that later. In A Google First World, User Behavior Is Also Evolving On Multiple AI Platforms What is particularly interesting is how user behavior is evolving. People are not just switching from Google to AI search — they are starting to mix and match platforms based on their specific needs. I am seeing users turn to: ChatGPT for deep research. Perplexity for quick facts. Claude, when they need reliable information. Google when they want comprehensive breadth. Image from BrightEdge, August 2025 The CMO AI And SEO Mindset Shift From a marketing perspective, this creates a massive change in thinking. SEO is not just about Google anymore – though that is still where most of the focus needs to be. Marketers will need to consider optimizing for multiple AI engines, each with its own distinct data ingestion pipelines. For ChatGPT and Claude, you need clear, structured, cited content that AI models can safely reuse. For Perplexity, timeliness, credibility, and brevity matter more than traditional keyword density. It is no longer about optimizing just for clicks; it is about optimizing for influence and citations and making sure you appear in the proper context at the right moment within all these distinct types of AI experiences. The Search Bot To AI User Agent Revolution ChatGPT and its ChatGPT-User agent are leading the charge. In July, BrightEdge’s analysis revealed that ChatGPT’s User Agent real-time page requests nearly doubled its activity. In other words, it shows that users relying on real-time web searches to answer questions almost doubled within just one month. For example, suppose you are looking to compare “Apple Watch vs. Fitbit” from current reviews. In that case, the ChatGPT user agent is acting as your browsing assistant and operating on your behalf, which is fundamentally different from traditional search engines and crawlers. Image from BrightEdge, August 2025 In summary, I believe the next six months will establish what I term a “multi-AI search world.” Users will become increasingly comfortable switching between platforms fluidly based on what they need in that moment. The opportunity here is massive for early adopters who figure out cross-platform optimization. Is The Rise Of AI Platforms Like ChatGPT An Opportunity Or A Threat That CMOs Need To Be Aware Of? It is all opportunity. Each AI platform is carving out its own distinct identity. Google is doubling down on AI Overviews and AI Mode. ChatGPT is making this fascinating transition from conversational Q&A into full web search integration. Perplexity is cementing itself as the premier “answer engine” with its citation-first, mobile-focused approach, and they are planning deeper integrations with news providers and real-time data. Claude is expanding beyond conversation into contextual search with superior fact-checking capabilities, while Microsoft’s Bing Copilot is positioning itself as this search-plus-productivity hybrid that seamlessly blends document generation with web search. The rise of AI platforms represents both a transformative opportunity and a strategic challenge that CMOs must navigate with sophistication and strategic foresight. Learn More: How Enterprise Search And AI Intelligence Reveal Market Pulse CMOs And The Shift From Ranking To Referencing And Citations And that brings me to a huge mindset shift: We are moving from “ranking” to “referencing.” AI summaries do not just display the top 10 links; they reference and attribute sites within the answer itself. Being cited within an AI summary can be more impactful than just ranking high in traditional blue links. So, CMOs need to start tracking not just where they rank, but where and how their content gets referenced and cited by AI everywhere. Technical Infrastructure Requirements And CMOs Leaning Into SEO Teams On the technical side, structured data and clear information architecture are no longer nice-to-haves – they are foundational. AI relies on this structure to surface accurate information, so schema.org markup, clean technical SEO, and machine-readable content formats are essential. Image from BrightEdge, August 2025 Brands, The CMO, And The Authority And Trust Premium Here is something that is

We’re more than halfway through 2025, and SEO has already changed names many times to take into account the new mission of optimizing for the rise of large language models (LLMs): We’ve seen GEO (Generative Engine Optimization) floating around, AEO (Answer Engine Optimization), and even LEO (LLM Engine Optimization) has made an apparition in industry conversations and job titles. However, while we are all busy finding new nomenclatures to factor in the machine part of the discovery journey, there is someone else in the equation that we risk forgetting about: the end beneficiary of our efforts, the user. Why Do You Need Behavioral Data In Search? Behavioral data is vital to understand what leads a user to a search journey, where they carry it out, and what potential points of friction might be blocking a conversion action, so that we can better cater to their needs. And if we learned anything from the documents leaked from the Google trial, it is that users’ signals might actually be one of the many factors that influence rankings, something that was never fully confirmed by the company’s spokespeople, but that’s also been uncovered by Mark Williams-Cook in his analysis of Google exploits and patents. With search becoming more and more personalized, and data about users becoming less transparent now that simple search queries are expanding into full funnel conversations on LLMs, it’s important to remember that – while individual needs and experiences might be harder to isolate and cater for – general patterns of behavior tend to stick across the same population, and we can use some rules of thumb to get the basics right. Humans often operate on a few basic principles aimed at preserving energy and resources, even in search: Minimizing effort: following the path of least resistance. Minimizing harm: avoiding threats. Maximizing gain: seeking opportunities that present the highest benefit or rewards. So while Google and other search channels might change the way we think about our daily job, the secret weapon we can use to future-proof our brands’ organic presence is to isolate some data about behavior, as it is, generally, much more predictable than algorithm changes. What Behavioral Data Do You Need To Improve Search Journeys? I would narrow it down to data that cover three main areas: discovery channel indicators, built-in mental shortcuts, and underlying users’ needs. 1. Discovery Channel Indicators The days of starting a search on Google are long gone. According to the Messy Middle research by Google, the exponential increase in information and new channels available has determined a shift from linear search behaviors to a loop of exploration and evaluation guiding our purchase decisions. And since users now have an overwhelming amount of channels that they can consult in order to research a product or a brand, it’s also harder to cut through the noise. So by knowing more about them, we can make sure our strategy is laser-focused across content and format alike. Discovery channel indicators give us information about: How users are finding us beyond traditional search channels. The demographic that we reach on some particular channels. What drives their search, and what they are mostly engaging with. The content and format that are best suited to capture and retain their attention in each one. For example, we know that TikTok tends to be consulted for inspiration and to validate experiences through user-generated content (UGC), and that Gen Z and Millennials on social apps are increasingly skeptical of traditional ads (with skipping rates of 99%, according to a report by Bulbshare). What they favor instead is authentic voices, so they will seek out first-hand experiences on online communities like Reddit. Knowing the different channels that users reach us through can inform organic and paid search strategy, while also giving us some data on audience demographics, helping us capture users that would otherwise be elusive. So, make sure your channel data is mapped to reflect these new discovery channels at hand, especially if you are relying on custom analytics. Not only will this ensure that you are rightfully attributed what you are owed for organic, but it will also be an indication of untapped potential you can lean into, as searches become less and less trackable. This data should be easily available to you via the referral and source fields in your analytics platform of choice, and you can also integrate a “How did you hear about us” survey for users who complete a transaction. And don’t forget about language models: With the recent rise in queries that start a search and complete an action directly on LLMs, it’s even harder to track all search journeys. This replaces our mission to be relevant for one specific query at a time, to be visible for every intent we can cover. This is even more important when we realize that everything contributes to the transactional power of a query, irrespective of how the search intent is traditionally labelled, since someone might decide to evaluate our offers and then drop out due to the lack of sufficient information about the brand. 2. Built-In Mental Shortcuts The human brain is an incredible organ that allows us to perform several tasks efficiently every day, but its cognitive resources are not infinite. This means that when we are carrying out a search, probably one of many of the day, while we are also engaged in other tasks, we can’t allocate all of our energy into finding the most perfect result among the infinite possibilities available. That’s why our attentional and decisional processes are often modulated by built-in mental shortcuts like cognitive biases and heuristics. These terms are sometimes used interchangeably to refer to imperfect, yet efficient decisions, but there is a difference between the two. Cognitive Biases Cognitive biases are systematic, mostly unconscious errors in thinking that affect the way we perceive the world around us and form judgments. They can distort the objective reality of an experience, and the way we are persuaded into an action.

Interaction to Next Paint (INP) is a meaningful Core Web Vitals metric because it represents how quickly a web page responds to user input. It is so important that the HTTPArchive has a comparison of INP across content management systems. The following are the top content management systems ranked by Interaction to Next Paint. What Is Interaction To Next Paint (INP)? INP measures how responsive a web page is to user interactions during a visit. Specifically, it measures interaction latency, which is the time between when a user clicks, taps, or presses a key and when the page visually responds. This is a more accurate measurement of responsiveness than the older metric it replaced, First Input Delay (FID), which only captured the first interaction. INP is more comprehensive because it evaluates all clicks, taps, and key presses on a page and then reports a representative value based on the longest meaningful latency. The INP score is representative of the page’s responsive performance. For that reason**,** extreme outliers are filtered out of the calculation so that the score reflects typical worst-case responsiveness. Web pages with poor INP scores create a frustrating user experience that increases the risk of page abandonment. Fast responsiveness enables a smoother experience that supports higher engagement and conversions. INP Scores Have Three Ratings: Good: Below or at 200 milliseconds Needs Improvement: Above 200 milliseconds and below or at 500 milliseconds Poor: Above 500 milliseconds Related: Get Ready For Google’s INP Metric With These 5 Tools Content Management System INP Champions The latest Interaction to Next Paint (INP) data shows that all major content management systems improved from June to July, but only by incremental improvements. Joomla posted the largest gain with a 1.12% increase in sites achieving a good score. WordPress followed with a 0.88% increase in the number of sites posting a good score, while Wix and Drupal improved by 0.70% and 0.64%. Duda and Squarespace also improved, though by smaller margins of 0.46% and 0.22%. Even small percentage changes can reflect real improvements in how users experience responsiveness on these platforms, so it’s encouraging that every publishing platform in this comparison is improving. CMS INP Ranking By Monthly Improvement Joomla: +1.12% WordPress: +0.88% Wix: +0.70% Drupal: +0.64% Duda: +0.46% Squarespace: +0.22% Which CMS Has The Best INP Scores? Month-to-month improvement shows who is doing better, but that’s not the same as which CMS is doing the best. The July INP results show a different ranking order of content management systems when viewed by overall INP scores. Squarespace leads with 96.07% of sites achieving a good INP score, followed by Duda at 93.81%. This is a big difference from the Core Web Vitals rankings, where Duda is consistently ranked number one. When it comes to arguably the most important Core Web Vital metric, Squarespace takes the lead as the number one ranked CMS for Interaction to Next Paint. Wix and WordPress are ranked in the middle with 87.52% and 86.77% of sites showing a good INP score, while Drupal, with a score of 86.14%, is ranked in fifth place, just a fraction behind WordPress. Ranking in sixth place in this comparison is Joomla, trailing the other five with a score of 84.47%. That score is not so bad considering that it’s only two to three percent behind Wix and WordPress. CMS INP Rankings for July 2025 Squarespace – 96.07% Duda: 93.81% Wix: 87.52% WordPress: 86.77% Drupal: 86.14% Joomla: 84.47% These rankings show that even platforms that lag in INP performance, like Joomla, are still improving, and it could be that Joomla’s performance may best the other platforms in the future if it keeps up its improvement. In contrast, Squarespace, which already performs well, posted the smallest gain. This indicates that performance improvement is uneven, with systems advancing at different speeds. Nevertheless, the latest Interaction to Next Paint (INP) data shows that all six content management systems in this comparison improved from June to July. That upward performance trend is a positive sign for publishers. What About Shopify’s INP Performance? Shopify has strong Core Web Vitals performance, but how well does it compare to these six content management systems? This might seem like an unfair comparison because shopping platforms require features, images, and videos that can slow a page down. But Duda, Squarespace, and Wix offer ecommerce solutions, so it’s actually a fair and reasonable comparison. We see that the rankings change when Shopify is added to the INP comparison: Shopify Versus Everyone Squarespace: 96.07% Duda: 93.81% Shopify: 89.58% Wix: 87.52% WordPress: 86.77% Drupal: 86.14% Joomla: 84.47% Shopify is ranked number three. Now look at what happens when we compare the three shopping platforms against each other: Top Ranked Shopping Platforms By INP BigCommerce: 95.29% Shopify: 89.58% WooCommerce: 87.99% BigCommerce is the number-one-ranked shopping platform for the important INP metric among the three in this comparison. Lastly, we compare the INP performance scores for all the platforms together, leading to a surprising comparison. CMS And Shopping Platforms Comparison Squarespace: 96.07% BigCommerce: 95.29% Duda: 93.81% Shopify: 89.58% WooCommerce: 87.99% Wix: 87.52% WordPress: 86.77% Drupal: 86.14% Joomla: 84.47% All three ecommerce platforms feature in the top five rankings of content management systems, which is remarkable because of the resource-intensive demands of ecommerce websites. WooCommerce, a WordPress-based shopping platform, ranks in position five, but it’s so close to Wix that they are virtually tied for position five. Takeaways INP measures the responsiveness of a web page, making it a meaningful indicator of user experience. The latest data shows that while every CMS is improving, Squarespace, BigCommerce, and Duda outperform all other content platforms in this comparison by meaningful margins. All of the platforms in this comparison show high percentages of good INP scores. The number four-ranked Shopify is only 6.49 percentage points behind the top-ranked Squarespace, and 84.47% of the sites published with the bottom-ranked Joomla show a good INP score. These results show that all platforms are delivering a quality experience for users View the results here (must