Tag: Google AI Mode

  • Why I Think Meta AI Is Search’s Sleeping Giant Now

    Why I Think Meta AI Is Search’s Sleeping Giant Now

    I do not think enough people are treating Meta AI as a serious AI search contender.

    In SEO circles, I hear plenty about Google AI Mode, ChatGPT, Claude, Gemini, Perplexity, RAG, and every new answer engine worth testing. Those conversations matter. But I think Meta AI already has something most AI companies would spend years and billions trying to build: massive distribution.

    By May 2025, Meta AI had reached one billion monthly active users across Meta’s apps, according to Mark Zuckerberg.

    Zuckerberg has also made the direction clear. He wants Meta AI to become a leading personal AI, shaped around personalization, voice conversations, and entertainment, with monetization through paid recommendations or subscriptions already being considered.

    That is why I think Meta AI is becoming one of the most important AI search contenders to watch.

    Meta’s Advantage Is Distribution

    I think the AI search debate spends too much time on model quality and channel ownership. Which tool is smarter? Which answer engine cites better? Is this just SEO with a new label?

    Those questions matter, but distribution matters more than the search industry often wants to admit.

    Meta reported 3.56 billion family daily active people across its apps in March. In that same quarter, revenue reached $56.31 billion, up 33% year over year.

    WhatsApp passed 3 billion monthly users in 2025. Instagram reached 3 billion monthly active users in September 2025. Threads reached 500 million monthly active users in June.

    I know Facebook is not the cool platform anymore. The metaverse stumbled. Threads can still feel like a corporate response to Elon Musk running, or ruining, the artist formerly known as Twitter.

    But none of that changes the important point. Meta can put AI inside the apps where people already spend their time. In doing that, it can bring search-like behavior directly into the places where discovery already happens.

    I think that could push public AI adoption faster than almost anything else in the market.

    The First Search Is The Search That Matters

    Google’s historic power has always rested on a simple habit. When people wanted to know something, compare options, buy a product, find a local business, or settle an argument, they started with Google.

    That starting point became the most valuable real estate on the internet.

    AI search changes where that starting point can live. If someone sees a product on Instagram, they do not have to leave the app and search Google. They can ask Meta AI whether the product is any good, what alternatives exist, whether the brand is trustworthy, or where they can buy it.

    If a WhatsApp group is planning a weekend away, they do not need to switch to Google to compare hotels, restaurants, venues, or train times. Meta AI can sit inside the conversation at the exact moment intent appears.

    If someone is scrolling through a Facebook thread full of local recommendations, they can ask Meta AI to summarize what people are saying across Groups, Reels, and public posts.

    That is not traditional SEO. I see it as search behavior being absorbed into social platforms.

    The strategic question is no longer only, “Who ranks?” I think the better question is, “Where does the question begin?”

    Meta AI Is More Than Another Chatbot

    I think search marketers often approach AI through too narrow a lens. We find the chatbot, test a few brand queries, check which sources get cited, and decide we understand the platform.

    That is a mistake.

    Meta AI is becoming a layer across feeds, chats, search, content creation, recommendations, smart glasses, and social discovery. Meta says it is available across Facebook, Instagram, WhatsApp, and Messenger, including in feeds, chats, and search, giving users real-time information without leaving the app. The use cases include restaurant recommendations, travel planning, study help, and shopping inspiration.

    The standalone Meta AI app, launched in 2025, was designed around a more personal AI experience. Meta says it can use information people have chosen to share across Meta products, along with profile data and content engagement, to deliver more relevant answers in supported markets.

    I can see where this is heading. Meta AI could become the free AI tool that everyday consumers use without thinking much about it.

    How Meta AI Could Become Consumer AI

    ChatGPT and Claude still feel like work tools to me. They are excellent tools, but they are tools people deliberately open because they have decided to do something.

    Meta AI feels more like consumer AI. It is messier, more visual, more embedded, and less like launching a productivity suite. It feels more like finding an answer while doing what you were already doing.

    For many people outside tech, opening ChatGPT still feels like an intentional act. Asking a question inside WhatsApp or Instagram can feel almost frictionless.

    That is Meta’s advantage. It does not have to convince people to adopt AI from scratch. It can fold AI into existing habits.

    This is where it gets interesting. Meta AI is also a playground, and Meta gets to watch how people actually use it.

    I can imagine a 65-year-old grandmother using it to animate family photos and share them in a WhatsApp group.

    I can imagine a dog groomer using it to create short videos of clients’ pets and post them on Instagram.

    When AI becomes mainstream and easy to use, people will use it where they can reach other people. That gives Meta a powerful feedback loop. The more people play with Meta AI, the more Meta learns, improves, and adds features that fit real consumer behavior.

    AI Becomes Social, Visual, And Shoppable

    Then there is Meta AI Studio.

    Users can create AI characters built around their interests, work from templates, or start from scratch. They can build assistants for advice, captions, entertainment, and creator interactions.

    Futuristic SEO and AI search illustration showing old tools breaking apart as blue data streams lead to a glowing search platform and digital icons.
    Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

    Then there is Vibes. In September 2025, Meta introduced Vibes as a feed inside the Meta AI app and on Meta AI, where users can create, remix, and share short-form AI-generated videos, then distribute them through DMs, Instagram, Facebook Stories, and Reels.

    I will be honest: parts of this feel strange. Generative AI video on social platforms is a messy mix of creativity, novelty, nonsense, and low-quality output. But early weirdness is not the same as strategic irrelevance.

    I never expected AI to arrive as one perfect super-app that everyone understood immediately. Meta is putting new formats into users’ hands, watching what people do with them, and reshaping the product around that behavior.

    The Ad Machine Makes This A Google Problem

    Forecasts suggest Meta will reach $243.46 billion in net worldwide ad revenue in 2026, putting it ahead of Google at $239.54 billion. The same forecast has Meta capturing 26.8% of worldwide digital ad spend, compared with Google’s 26.4%.

    I think those numbers should get Google’s attention.

    If AI answers are monetized through paid recommendations, sponsored answers, shopping suggestions, or conversational ad units, the commercial value collects around the platform that owns the query. That platform does not always have to be the one with the best model.

    Meta has the audience, the ad graph, creator relationships, commerce signals, and behavioral data built from years of social, messaging, and content engagement. It can promote Meta AI inside its own products to billions of existing users.

    Google still has search intent, which is enormously powerful. But Meta has attention, habit, and context. Google is where people go when they have decided to search. Meta is where many people already are.

    Why “It’s Just SEO” Misses The Point

    The AI optimization debate keeps collapsing into the same comforting line: it is just SEO.

    Sometimes, I agree. Technical hygiene, crawlable content, authoritative pages, clear entities, strong brand signals, helpful content, and consistent information still matter.

    But I think the harder question is this: how exactly do you optimize for Meta AI?

    Facebook AI Mode makes the challenge obvious. In June, Meta introduced AI Mode as a Facebook search tab that uses Meta AI to surface answers rooted in public culture, opinions, and recommendations shared across Meta’s apps, rather than a traditional list of links. It draws on what people are posting publicly in Groups and Reels to provide perspectives instead of standard search results.

    That is a fundamentally different environment. If Meta AI pulls from public posts, Groups, Reels, creator content, user engagement, web information, social recommendations, product content, and eventually paid data, the standard SEO playbook is not enough.

    Your website may still matter. Your public social content may matter, too. Your creator strategy may matter. Your product feed may matter. Your reviews may matter. I think the point is clear: visibility is getting more complicated.

    Nobody can honestly say they know exactly how all of this works yet. Anyone who claims total certainty is probably selling a dashboard and a dream.

    The honest answer is frustrating: I do not think we know enough yet. But that is not a reason to ignore Meta AI.

    Google Is Being Attacked From Every Angle

    Google is still Google. I do not want to overstate the case. It remains central to search, commerce, publishing, advertising, and the open web.

    But Google is being pushed from every direction at once. ChatGPT is pressuring answers. Perplexity is pressuring research. Amazon is pressuring product search. TikTok and Instagram are pressuring discovery. Regulators are pressuring market power. Publishers are challenging AI content extraction. Meta is pressuring attention, ads, and AI-assisted discovery.

    In the UK, the Competition and Markets Authority imposed new conduct requirements on Google Search in June. Publishers will be able to opt out of having their content used to power AI features in Google Search, including AI Overviews. Google is also required to properly attribute publisher content with clear links in AI-generated results.

    I think this matters because AI search is not just another product feature. It changes the value exchange between users, publishers, platforms, and advertisers. While Google works through that challenge, Meta is quietly building AI into social behavior.

    What I Think Brands And SEOs Should Do Now

    I would not panic. Panic is rarely a strategy, even if it shows up in plenty of marketing meetings. But I would start testing now.

    I would run brand, category, product, local, and comparison queries in Meta AI. I would test Facebook, Instagram, WhatsApp, and the standalone app wherever possible, then compare the results with Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude.

    I would track whether my brand appears, whether answers cite or link to me, and whether public Meta content seems to shape responses. I would look closely at Facebook Groups, Reels, creator posts, Instagram content, product mentions, and recommendation language.

    If discovery moves into Meta’s AI layer, I want to understand what my brand needs in order to be visible there.

    That might mean stronger public social content, clearer product information across Meta surfaces, creator partnerships, better community management, more consistent entity signals, or paid social tests designed around AI visibility. It might also mean none of those things yet.

    Either way, I would rather have data than keep repeating “it’s just SEO” while the market reorganizes itself.

    The Sleeping Giant

    I do not think Meta AI has to beat Google at Google’s own version of search. It does not need to.

    It only needs to absorb enough search behavior into the places where people already spend their time.

    It needs to become the casual AI layer for people who may never deliberately open ChatGPT.

    It needs to make product discovery, recommendations, local advice, content creation, and shopping assistance feel native inside social apps.

    That is a serious threat. Meta AI may feel clunky right now, but so did much of the early web.

    I think the search industry should stop asking whether Meta AI looks like search. The better question is whether users care.

    If people start asking Meta before they ask Google, the game changes. That is how sleeping giants wake up.


    Inspired by this post on Search Engine Land.


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  • Remembering Bruce Clay: SEO Pioneer’s Final Lessons

    Remembering Bruce Clay: SEO Pioneer’s Final Lessons

    My heart sank when I learned that Bruce Clay had passed away. I knew he had been in the hospital, but my mind went straight to the two long conversations we had last fall: one simply to catch up, and one for what would become a deeply meaningful podcast interview.

    I first reached out to Bruce nearly 25 years ago. I had emailed him cold to ask whether I could republish some of his industry writing about ethics. He said yes. Somehow, the article I cited unintentionally ranked No. 2 on Google for “Bruce Clay” for years. I joked with him about that more than once, and he always seemed both amused and slightly annoyed, probably because I had done it with his own content and his own blessing.

    A few years later, I worked with Bruce and many other search professionals on the board of the Search Engine Marketing Professionals Organization, better known as SEMPO. It was a business nonprofit built to support and legitimize the then-new search industry. We promoted best practices, helped make the business case for search, and later became involved in U.S. Internet policy work in the early 2010s.

    SEMPO brought together board members from around the world, and in a very literal way, it took some of us around the world. That work is where I really got to know Bruce. Later, we would run into each other at conferences, sometimes even on the same panels. We were doing serious work, but we also had a great time doing it. The organization lasted about 15 years, and if I remember correctly, Bruce was one of its founding members around 2000 or 2001.

    One memory of Bruce has stayed with me vividly. A group of us from the SEMPO board were walking back to our hotel on the east side of Midtown Manhattan after dinner. A snowstorm had just begun, one that would leave several feet of snow by the next day. The usual roar of traffic had been softened by the weather and the empty streets. It was eerie, but almost joyously quiet. The city that never sleeps seemed to be taking a nap under a blanket of snow.

    Then something happened that I had never seen before, and have never seen since.

    As snow poured silently into the streets, a massive lightning strike hit just a few blocks away, over Bruce’s shoulder. I do not know whether he saw it directly. It felt like an explosion. We stood there for several minutes trying to understand the contrast: a shattering bolt of lightning between skyscrapers, in the middle of a torrent of snowflakes, with not a drop of rain.

    None of us knew what to call it. I believe Bruce called it “thunder snow,” and the name stuck. In that moment, his naming streak continued.

    Bruce was, and remains, the real deal in search. His legacy was never only about coining a term. He pushed the field forward, taught others generously, and stayed deeply connected to the people he cared about. Like many of the earliest professionals in search, he helped shape practices that still feel foundational today. Through his writing, interviews, books, tools, and hundreds of industry events, he became one of the people the industry looked to for clarity. For many who remember the beginning, and for many who still followed him closely, Bruce was the GOAT.

    I always felt that Bruce approached search intellectually. I do not think he saw it only as a job. It was exciting, unfinished, and new. Very few people get to help invent an entirely new discipline, and Bruce understood what that meant. He also recognized that AI is one of those moments now, and he approached it with the same curiosity, energy, and insight he brought to early search. Many people in the industry may only now be realizing that Bruce pioneered things they do every day. They feel obvious now, but they were not obvious then. Even the basics had to be debated and established.

    He was not only passionate about search. He was passionate and generous toward the people in search. If you cared about the work, you were part of his tribe. That was true for thousands of people in the industry, myself included.

    With Bruce, I could get deep into the weeds of the trade and still talk broadly about where everything was headed. He was an engineer with an MBA, and that combination came through in his leadership, expertise, and authority. He understood the work from top to bottom, and then back to the top again.

    He was also genuinely kind. He had friends around the world. In our last conversations, I sensed that he was content with his life and accomplishments, and that he felt blessed by the path life had given him. He had nothing left to prove.

    In the podcast interview, Bruce was as sharp and insightful as ever. He offered some of the most sensible thinking I have heard about where search is going in the world of LLMs. He was still innovating, just as he had been when search first began taking shape nearly 30 years ago.

    Because search is so closely tied to language, I have been especially interested in how we think about, and what we call, this “new” thing. Bruce’s perspective helped crystallize my own research. Over the last year, I have watched much of the industry move toward the same conclusion he shared in our discussion.

    If you are one of the many thousands of people who talked shop with Bruce over the years, I think you will recognize him in the ideas that follow. You may even relive some of your own conversations with him.

    As I reviewed the podcast transcript, I realized we had recorded hours of conversation beyond search, including cars and all kinds of other subjects. At the end of our first conversation, he said goodbye with great love and care. That was Bruce. Those words land differently with me now, and they always will.

    Rest in peace, Bruce. I miss you already.

    What Bruce taught me in our final industry conversation

    When I asked Bruce to talk about how he got started in the 1990s, he took us back to 1996. He had been working in corporate roles and wanted to become a consultant. His background was in math, programming, mainframes, PCs, networking, and optimization. When the Internet began moving into the mainstream, he saw something that matched both sides of his skill set: marketing and technical work.

    He started studying search engines because that was where the opportunity was. He experimented with what they wanted, adjusted web pages, and watched rankings appear. Then people began calling him and paying him. What he thought might become a one-person consulting business grew quickly into something global, with offices and work across Japan, Australia, Asia, Europe, India, and beyond. Bruce told me he never would have predicted it would take off the way it did.

    I reminded him how small the field was in those days. There were literally only tens of people doing this early on. Bruce was one of the first to build a legitimate service for businesses that needed to rank for their own brand names and for broader generic terms, while other corners of the field were still experimenting with black-hat tactics.

    Bruce pointed out that this was three years before Google. Search was a wild west. There were more than 20 major search engines, and many of them were taking data from one another. At the first SEO conference he remembered attending, all of the leading people in the field sat together at one round table in a bar. He joked that if a natural disaster had happened there, the whole industry might have disappeared.

    We talked about Danny Sullivan, Search Engine Watch, Search Engine Strategies, and the early vocabulary of the industry. Bruce had long been credited with helping coin the term “SEO,” though he was careful to say that no one can know who said something first. What he did know was that only a handful of people were in the room when the term started to take hold.

    At the time, other terms were in play, including “search engine positioning” and “ranking.” Bruce believed “optimization” won because it sounded technical, valuable, and precise. It was like fine-tuning a race engine. People could see themselves building a profession around it. Once the industry attached itself to that word, the term spread quickly around the world.

    That led us into the newer terms now being proposed around AI, including AIO, GEO, and AEO. I have been writing about how many of these terms still depend on the word “optimization.” Bruce’s view was clear: search engine optimization was never limited to organic blue links. It was about optimizing for anything a search engine produces that can drive business and traffic.

    In Bruce’s view, if AI appears inside search and influences discovery, citations, visibility, or traffic, then it belongs under SEO. GEO and AIO were not separate disciplines to him. They were extensions, just like link building or on-page optimization. He warned that many new terms are marketing labels more than practical new fields. If the work required to appear in AI results is still mentions, links, schema, authority, content structure, and rankings, then the work is still SEO.

    That point stayed with me. Bruce said that if someone claims you no longer need SEO and only need AI optimization, you should watch closely, because either they are going to do SEO under a different name or they do not understand what they are doing. He believed ranking in AI was possible, but the method was deeper and more complex than traditional SEO. To him, it was still SEO, just several levels more advanced.

    We also discussed whether AI feels like search did in the late 1990s. Bruce believed it does in important ways. AI depends heavily on search engines because search engines have spent decades fighting spam and building trust signals. AI systems do not yet have that same history, so they rely on what search engines have already learned to filter, evaluate, and rank.

    Bruce also believed AI could still be gamed at the content level. If enough pages repeat a false idea, an AI system may begin to treat it as true. He had already seen examples of people trying to influence AI answers by placing their names into “best SEO” lists across enough sources. To him, this was a sign that AI would need its own version of the spam fight search engines have been having for decades.

    One of the most important parts of our conversation was Bruce’s explanation of Google AI Mode and how it changes the way SEOs should think about structure. He described how a query can produce an overview, followed by sections and subsections that allow users to drill into narrower parts of a topic. When a user clicks into a section, the supporting sites can change to match that specific subtopic.

    That means content cannot simply be built around one broad keyword anymore. Bruce believed pages need to be structured so each section can stand on its own as an expert answer. A page should support a topic, but every H2-level section may need its own clarity, completeness, and internal logic. In his view, this raises the importance of siloing across a site and within a page.

    I framed this as a shift from keyword-led thinking to context-led thinking. Bruce agreed and connected it to entities, fan-outs, references, and cross-links. Keywords helped build the industry, but he believed the future depends on understanding entities in context. If content cannot answer the question clearly, it fails the core purpose of AI-assisted search.

    Bruce described the long-term target as something like the Star Trek computer: no matter what question someone asks, the system provides the answer. We are not there yet, but that is the direction. For websites, he believed the future architecture is question-centered, highly usable, structured into sub-silos, and able to answer and refer within a page while also fanning out to supporting pages.

    That naturally led us to content. Bruce said that for years SEO treated content like a stepchild, but now content is a peer. If SEO teams and content teams do not share the same goal, they will keep writing the way they did 20 years ago and fail in the AI search environment. He was already being hired to train content teams, even though he did not consider himself a “content guy” in the traditional sense.

    He believed the industry still suffers because SEO and content do not cross-pollinate enough. Content marketers may not attend SEO conferences, and SEOs may not spend enough time learning how content teams actually work. That separation matters more now because the structure of a page, the expertise of each section, and the way a topic is divided all affect visibility in AI-driven search experiences.

    Bruce’s advice was direct: stop spreading one keyword across a page and calling that optimization. Instead, build each section as if it were a standalone expert answer. If the sections belong to the same theme, they should support one another, but each needs to carry its own weight. In his words, the hierarchy is no longer only the page. The hierarchy is also the section of the page.

    When I asked Bruce about AI-generated content, he made an important distinction. AI is a tool, not a solution. He did not believe businesses should simply generate content, read it once, and publish it. Detection tools are inconsistent, and search engines may not reliably identify every AI-generated page. But that does not make low-effort AI content a good strategy.

    Bruce believed AI is strongest as a research assistant. His own Pre-Writer product was built around that idea: gather deep research and give a human writer a stronger starting point. The writer still finishes the work, adds style, voice, judgment, compliance, and business understanding. For Bruce, reducing a four- or five-hour writing project to two hours was a win. Replacing the writer entirely was not.

    He was especially clear that writers are artists. AI does not know a business the way its people do, and it does not bring the same finesse or judgment. The future, in Bruce’s view, requires writers, SEOs, and AI workflows to be integrated around shared goals. Without that maturity, teams will keep producing pages that look like they were built for search 10 years ago, and those pages will be ignored.

    We ended by talking about tools. Bruce reminded me that in the beginning, he wrote tools because none existed. He built one of the first page analyzers, including what he once called a keyword density analyzer. He later received a patent related to that kind of technology. His tools were never meant to replace large platforms like Semrush, Ahrefs, or Surfer. They were meant to extend them by analyzing things those platforms did not.

    Bruce pointed people to seotools.com and described the tools as inexpensive power tools, not products designed for the masses. Some users did not understand them at first, but came back later when they saw the value. He was still building, still solving problems, and still thinking about what the industry needed next.

    Near the end, Bruce mentioned a newer tool designed to show traffic loss through Search Console data over time, helping site owners see whether they had fallen off a cliff or declined gradually. It struck me as classic Bruce: while others complained that something should exist, he was building it.

    I thanked him for the conversation, and he answered with warmth: he was glad I had him on, and he loved talking with me. I hear those words differently now. I am grateful we had that final conversation, and I am grateful for everything Bruce gave to search, to this industry, and to the people inside it.

    Listen to the full episode

    Listen on Podbean

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    Inspired by this post on Search Engine Land.


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  • Google AI Mode Recipe Links Give Publishers a Boost

    Google AI Mode Recipe Links Give Publishers a Boost

    I’m seeing Google make recipe results in AI Mode more publisher friendly with a new visual treatment that gives recipe creators more visibility. For some recipe responses, Google is now showing details such as the creator name, recipe ratings, and the number of ingredients.

    What is new. Google’s Robby Stein said AI Mode now includes “prominent links at the top of responses with useful details and images,” including creator names, ratings, and ingredient counts. From my view, the key shift is that Google is trying to make recipe sources easier to recognize and visit directly from AI Mode.

    I also noticed that Google has been testing top stories carousels in AI Overviews, although that feature does not appear to be live yet.

    What it looks like. The new treatment places recipe links, images, and useful recipe details more prominently in the AI Mode experience, giving users a clearer path from the AI-generated response back to the original recipe page.

    Previously. Back in March, Robby Stein announced earlier changes to recipe results in AI Mode. At the time, he said Google had heard feedback and was making updates to better connect people with recipe creators across the web.

    Image

    I see this latest update as part of Google’s effort to address concerns around AI recipe slop and to make original recipe content more visible when people search for cooking ideas through AI-powered results.

    Why I care. Recipe bloggers, and content creators more broadly, have been frustrated that Google’s AI experiences often send less traffic than traditional search results. This update suggests Google is trying to encourage more searchers to click through from AI Mode to the publishers and creators behind the recipes.

    If Google continues adding more clickable link units into AI search experiences, I think it could help ease some of the tension between publishers and Google. The bigger question is whether these changes will drive enough meaningful traffic back to recipe sites and other content creators.


    Inspired by this post on Search Engine Land.


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  • Unveiling Google Search Console’s AI Controls and Reports

    Unveiling Google Search Console’s AI Controls and Reports

    As someone who eagerly follows Google’s updates, I was thrilled to learn about the latest developments in Google Search Console. Recently, Google has started to roll out new Search Generative AI performance reports. These reports, along with a feature to block your content in AI responses, are designed to give website owners more control.

    Currently, these features are being introduced to a select group of website owners in the UK, but there are plans to expand access in the near future. This gradual rollout allows us to get accustomed to these changes before they become widely available.

    Exploring the Search Generative AI Performance Report

    The new AI performance report in Google Search Console is something I’ve been anticipating. Although it doesn’t cover everything, it does provide some important insights into how our content is performing within AI responses, AI Mode, and AI Overviews on Google Search. The report includes data on impressions, pages, countries, devices, and dates. However, a notable omission is click data, so we’re left guessing about the exact number of searchers clicking through to our sites from AI responses.

    Google stated:

    – We’re rolling out new insights for website owners regarding their pages’ appearances in generative AI Search features. These insights include impressions metrics and information on which pages appear in AI responses and in which countries. We’re working closely with website owners to determine what insights would be most helpful and will expand the metrics available over time. 

    Additionally, Google shared more details about the metrics we can expect:

    Impressions: Frequency of your site’s URLs appearing in generative AI features in Search and Discover.

    Pages: Identifying URLs that appeared within AI features.

    Countries: Understanding visibility on a country basis.

    Devices: Identifying the devices used to view your website. Available for Search results.

    Dates: Monitoring performance with hourly, daily, weekly, and monthly granularity.

    I inquired about click data from a Google representative, who mentioned that they are exploring additional metrics that will help inform our strategies in the future.

    Initially, this report is available to a subset of users in the UK, with plans to expand globally in the future.

    If you want to explore more about this report, I recommend checking out the Google help center document.

    Introducing AI Blocking Controls

    Another exciting feature Google introduced is the ability to block your content from appearing in AI search features like AI Overviews, AI Mode, or AI Discover. Google described this as a “new toggle” within Google Search Console, allowing us to decide whether or not our site should be part of these AI search features.

    Google notes that opting out will prevent your site from receiving traffic or impressions from these features. Importantly, this control won’t affect your ranking in standard search results outside of generative AI Search features, so there’s no risk of negatively impacting core web search visibility.

    Again, like the performance report, this toggle is currently available to a subset of UK website owners, with plans to widen access as they complete further testing. Google had promised these controls after facing some backlash from the EU, and it’s promising to see them starting to roll out now.

    One study even showed that 1/3rd of SEOs are willing to block Google from showcasing their content in AI search features.

    Why It Matters

    As site owners and publishers, many of us have been asking for control over how and if our content appears in Google’s AI features. Now, we have just that. Although it’s initially limited, I’m hopeful these features will eventually be available to all.

    Moreover, we’ve been requesting AI Search reporting from Google from day one. With Google’s announcement following Bing’s release of its own AI performance report, we’re taking a significant step forward. While Google’s report currently targets UK site owners and lacks click data, it holds promise for a global rollout soon.


    Inspired by this post on Search Engine Land.


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  • How AI Search Engines Prefer Reddit, YouTube, and LinkedIn

    How AI Search Engines Prefer Reddit, YouTube, and LinkedIn

    AI citations

    During a recent study, I discovered that Reddit stands out as the most-cited domain in AI-generated answers. In fact, it’s ahead of heavyweights like YouTube and LinkedIn, thanks to an analysis of 30 million sources conducted by Peec AI, a tool specializing in AI search analytics.

    The findings: I’ve learned that Reddit claims the top spot across various AI platforms including ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. Top contenders YouTube, LinkedIn, Wikipedia, and Forbes are right behind. Platforms like Yelp and G2 frequently appear when searching for recommendations.

    As I delved deeper into the research, it became clear which domains the AI models tend to lean on:

    • ChatGPT values Wikipedia, Reddit, and editorial sites like Forbes.
    • Google shows preference for platforms such as Facebook and Yelp.
    • Perplexity favors Reddit, LinkedIn, and G2 for queries within the B2B realm.

    Why we care: The insight that resonated with me was the importance of having authority beyond just our own websites. Brands that consistently feature on reputable third-party platforms have a better chance of being cited by AI.

    Why these sources? It’s fascinating to see how AI systems are wired to prioritize both authority and authentic user input:

    • I’ve found that Reddit excels because it mirrors genuine user discussions.
    • YouTube shines in video citations, owing to their comprehensive transcripts and descriptions.
    • Wikipedia not only serves real-time data but also acts as a foundation for training datasets.

    About the data: The analysis spanned 30 million sources, providing a comprehensive look at how often domains are directly cited in AI answers, effectively revealing what shapes these responses.

    The study. For those interested in a deep dive, the full study is available here: Top domains cited by AI search: Analysis based on 30M sources

    Dig deeper. For more on citation research, check out these fascinating reads:


    Inspired by this post on Search Engine Land.


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  • Google Expands Shopping Perks with AI Integration Across 14 Nations

    Google Expands Shopping Perks with AI Integration Across 14 Nations

    I find it fascinating how Google is enhancing the way retailers promote their loyalty programs by embedding these perks directly into product listings. This major upgrade brings the benefits not only to a wider international audience but also into Google’s newest AI-powered shopping surfaces.

    Discover the Newest Features. As a merchant, you can now spotlight member pricing and exclusive shipping offers directly on your listings. The expansion of loyalty annotations to local inventory ads and regional Shopping ads means I can now easily promote in-store or region-specific perks.

    Why It Matters to Me. Personalizing an offer for shoppers is crucial. By embedding member perks right at the moment of purchase discovery, rather than relying on a separate app or webpage, these programs become more visible and are more likely to entice sign-ups from customers like me.

    Important Numbers. Google reports that some retailers have seen up to a 20% increase in click-through rates by showing tailored offers to loyalty program members, which is significant for any business.

    Taking a Broader View. The integration of loyalty benefits into Google’s AI-first surfaces, such as AI Mode and Gemini, introduces member offers at an entirely new layer within the search experience, reaching more potential customers during their shopping journey.

    ```json
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  "alt": "Three electric kettle listings with different offers and prices.",
  "caption": "Discover great deals on electric kettles! Save with a member offer, compare prices, and enjoy free shipping or timely delivery.",
  "description": "This image displays three side-by-side electric kettle listings. The first offers a kettle with blue accents for $49.99, boasting free shipping. The second listing shows a kettle with red accents priced at $34.99 with a $15 discount for members. The third lists the same red-accented kettle for $49.99, highlighting free delivery by 12/21. Each listing features star ratings and customer reviews, prompting users to 'Shop now'. Keywords include electric kettles, discounts, and offers."
}
```

    Where You Can Experience This. This exciting expansion is now available in 14 countries, including Australia, Brazil, Canada, France, Germany, India, Italy, Japan, Mexico, Netherlands, South Korea, Spain, the UK, and the US. This means a vast audience can benefit from these offerings.

    Getting Started Is Easy. Merchants can activate the loyalty add-on in Merchant Center, configure member tiers, and set up pricing and shipping attributes. To take full advantage, it’s necessary to connect Customer Match lists in Google Ads for displaying exclusive pricing and shipping perks to recognized members.

    An Opportunity Not to Miss. U.S. merchants are invited to apply for a pilot program that uses Customer Match as a relationship data source for free listings. This could expand the reach of loyalty programs without increasing ad expenditures.


    Inspired by this post on Search Engine Land.


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  • Uncovering AI’s Citation Preferences: Listicles Lead the Way

    Uncovering AI’s Citation Preferences: Listicles Lead the Way

    I recently delved into a fascinating study exploring how AI citations are significantly influenced by certain content formats. It turns out listicles, articles, and product pages are at the forefront, driving over 52% of mentions across various AI language models.

    The research, conducted by Wix Studio AI Search Lab, analyzed a whopping 75,000 AI answers and more than a million citations across platforms like ChatGPT, Google AI Mode, and Perplexity. It’s an exciting revelation that showcases the power of content structure in digital landscapes.

    The findings? Listicles claimed the top spot with 21.9% of citations, followed by articles at 16.7% and product pages at 13.7%. When combined, these formats make up a majority of the citations AI references.

    What’s interesting is that articles tend to dominate when it comes to informational queries, being cited 2.7 times more than other formats. Meanwhile, listicles capture nearly 40% of commercial-intent citations, almost double compared to any other type.

    The Why Behind Intent. It’s fascinating to see how query intent, rather than industry or AI model, is the strongest predictor of which content gets cited. This trend doesn’t shift much across different sectors, from SaaS to health industries.

    Informational queries skew towards articles (45.5%) and listicles (21.7%), while commercial queries are dominated by listicles (40.9%). Interestingly, transactional and navigational queries favor product and category pages, with those two formats comprising about 40% of the citations combined.

    The Impact for Us. This study is incredibly insightful, illustrating why aligning content types with user intent is more strategic than simply generating content. Articles serve to inform, listicles foster comparisons, and product pages drive conversions. Tailoring content to align with user goals might just be the key to snagging more AI citations and enhancing visibility.

    Not all listicles perform equally. In professional services, third-party listicles account for 80.9% of citations, showing a preference for neutral editorial comparisons over branded lists by large language models.

    Looking at Model Preferences. While all models have a penchant for listicles, their other preferences vary. ChatGPT leans heavily towards articles and informational content, Google AI Mode shows a balanced approach, and Perplexity stands out with 17% of its citations coming from discussions on platforms like Reddit and forums.

    Industry-Specific Trends. Though preferences shifted slightly among industries, there are notable trends. SaaS and professional services veer towards listicles, health sectors favor authoritative articles, and ecommerce spreads its citations across listicles, articles, and category pages. Interestingly, home repair maintains an even distribution across different formats.

    I’m intrigued to know more! The comprehensive research can be found here.


    Inspired by this post on Search Engine Land.


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  • Google’s AI Mode: Revolutionizing Ad Monetization

    Google’s AI Mode: Revolutionizing Ad Monetization

    As I explore the ever-evolving landscape of Google’s AI Mode, it’s fascinating to witness how ad formats, reporting, and control are taking shape. Google seems to have a master plan in place that competitors just can’t keep up with.

    I find myself intrigued by Google’s entry into this next phase of conversational search. It’s not just about user numbers but who can effectively monetize them. Google’s mature ad systems and extensive advertiser base offer a significant edge.

    The initial panic surrounding Google’s position is over. Google’s long-standing advantages and huge investments have leveled the playing field with ChatGPT in LLM search.

    Back in December 2025, when Google declared code red, it became clear that they were serious. Apple’s decision to partner with Google for its AI needs is indeed telling.

    Initially, it seemed plausible that Google would struggle against ChatGPT, but the market has since adjusted its views. The company’s valuation reflects renewed confidence, rivaling even Apple at a substantial $3.6 trillion.

    As I dive deeper into how monetization will shape this race, I’m struck by how Google’s recent advances have significantly boosted its valuation.

    ```json
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  "alt": "Alphabet Inc. (GOOG) stock performance chart over five years, showing growth of 190.88%.",
  "caption": "Alphabet Inc.'s (GOOG) stock chart reveals a significant upward trend over the past five years, with a marked growth of 190.88%.",
  "description": "This image displays a five-year stock performance chart for Alphabet Inc. (GOOG), highlighting a substantial gain of 190.88%. The chart features key stock prices at the market close on February 13, with a closing price of 306.02, reflecting a decrease of 1.08%. The after-hours price is 305.88, down by 0.05%. The chart tracks the stock's fluctuations, offering insights into significant trends and key events impacting performance in the NasdaqGS market."
}
```

    It’s clear that the visibility of financial projections plays a massive role in how the company is perceived financially. Google’s approach to shifts in user behavior is crucial in maintaining its robust business model.

    From my perspective, much of your digital advertising budget likely goes to Google. Its prominence demands attention, not just in search but also in emerging AI platforms like ChatGPT and Claude.

    The competition in LLM conversations is intriguing. Google and ChatGPT are vying for different monetization models, a fascinating case study of differing strategies.

    For those of us in advertising, it’s essential to monitor developments like ad formats, rollout pace, and public reception to ads within these platforms.

    OpenAI’s current monetization model is intriguing but still nascent, reliant on a small group of major advertisers. We’ll see how they expand and fine-tune this model over time.

    ```json
{
  "alt": "Weather forecast indicating rain in Sarasota on February 22, 2026, with a summary of rain chances over the next 14 days.",
  "caption": "Stay prepared, Sarasota! Rain is likely on February 22, with varying chances throughout the next two weeks. Know what's coming your way!",
  "description": "This image shows a weather forecast for Sarasota, highlighting expected rain on February 22, 2026, with a 40% to 70% chance of showers. The forecast includes a detailed 14-day rain outlook with additional chances of rain later in the week and into March. A summary table provides daily rain chances and expected conditions. A side panel lists various weather services providing localized forecasts."
}
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    Outsourcing inventory to programmatic partners is a smart move for OpenAI but highlights their early stage in building an ads business.

    For Google advertisers, the shift to AI Mode need not be alarming. I’m watching for the ways these LLM sessions are shaping user experiences and ad placements.

    One thing is for sure; the enhancements in AI Mode continue, promising more seamless and user-friendly interactions. The potential for ads remains, though their form is still evolving.

    Monitoring key areas like the extent of monetization, advertiser control, and campaign types becomes more important as we navigate this new landscape.

    Ultimately, the future of advertising in AI-driven search is one of adaptability and strategic planning, aligning closely with user and advertiser behaviors in this exciting yet challenging era.


    Inspired by this post on Search Engine Land.


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  • Google AI Mode: Ad-Free Personal Intelligence Experience

    Google AI Mode: Ad-Free Personal Intelligence Experience

    Recently, I’ve learned that Google’s AI Mode will continue to be ad-free for those of us who connect apps to enable Personal Intelligence. This remains true even as Google expands ad testing in its U.S. rollout of more personalized features.

    Although Google is experimenting with ads in AI Mode, those of us who have linked our apps for Personal Intelligence won’t see any ads — a feature confirmed by Google. This decision means our user experience remains focused and personal.

    What’s happening.

    Google has been testing ad placements within AI Mode in the U.S., and I’ve noticed how they describe these connections as “helpful” to users, which, according to Google, opens new opportunities to discover products and services.

    There is, however, an exception — no ads for those of us who opt into app-connected, highly personalized experiences.

    The details.

    Google has recently expanded Personal Intelligence in AI Mode as a beta for anyone in the U.S., allowing Gemini to create truly tailored responses. By linking data across Google services like Search, Gmail, and YouTube, our experiences become exponentially more personalized.

    By opting into Personal Intelligence, I’ve experienced that AI Mode remains ad-free.

    Why we care.

    With ads potentially moving into AI Mode, Google approaches carefully, especially where personal data is most sensitive. As it stands, Personal Intelligence experiences remain ad-free as Google finds the right balance.

    What Google is saying.

    A Google spokesperson shared:

    “There are no ads for us who connect our apps with AI Mode. That isn’t changing at this moment.”

    “Test results over the past months indicate that people find business connections helpful and uncover new opportunities for products and services.”

    “Future ads will operate similarly for us connecting apps. They will maintain relevance related to queries, response context, and user interests.”

    Bottom line.

    In embracing Personal Intelligence, I see Google’s Gemini app positioning itself as a more personalized assistant. This groundwork sets the stage for future ad experiences built on richer, cross-platform user context.


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  • How Google’s AI Mode Threatens Web Traffic: Insights from Yahoo CEO

    How Google’s AI Mode Threatens Web Traffic: Insights from Yahoo CEO

    As I delve into the evolving landscape of web traffic, I find Yahoo CEO Jim Lanzone’s insights on AI-powered search engines, particularly Google’s AI Mode, incredibly fascinating. He believes this technological evolution poses a significant threat to the web’s traditional traffic model.

    Jim highlights a major concern: “I think that the LLMs are one big reason they’re under threat, with AI Mode in Google being the biggest challenge.” This makes me ponder the impact on publishers who rely heavily on these traffic flows.

    I resonate with Jim’s view that publishers truly deserve this traffic. He articulates a fundamental truth: “Those publishers deserve [traffic], and we’re not going to have the content to consume to give great answers if publishers aren’t healthy.” This reflects the delicate balance required in the digital content ecosystem.

    Why I care. Many websites, mine included, are noticing a dip in traffic coming from answer engines such as Google and OpenAI. It feels like a looming concern that could worsen. Yahoo’s dedication to maintaining the “search sends traffic” model is reassuring, as Jim passionately explains: “We have very purposefully highlighted and linked very explicitly and bent over backwards to try to send more traffic downstream to the people who created the content.”

    Yahoo’s unique AI approach. Listening to Jim on the Decoder podcast, I learn that Yahoo is carving its own path with AI. Unlike the more conversational chatbot models, Yahoo isn’t pursuing to be an AI assistant: “Ours looks a lot more like traditional search and it is more paragraph-driven. It’s not a chatbot that’s trying to act like it’s a person and be your friend.” I see this as a move towards emphasizing informative search experiences.

    Moreover, “We’re not a large language model. We’re not going to be the place you come to code. We’ve really launched Scout as an answer engine.” This strategy, I believe, could provide a clearer, more reliable information source online.

    What’s next: Embracing personalization. In observing Yahoo’s strategy, I’m excited to see their efforts to evolve. They’re embedding AI across platforms: “You are very shortly going to see us get into very personalized results. You’re going to see us get into very agentic actions that you can take.” This indicates a future where user-specific solutions take precedence.

    For instance, Jim notes, “There’s a button in Yahoo Finance that does analysis of a given stock on the fly… It is in Yahoo Mail to help summarize and process emails.” Such tools could transform how I interact with content on various platforms.

    Yahoo vs. Google: A non-competition. Interestingly, Yahoo isn’t trying to directly outplay Google. Instead, as Jim points out, the focus is on existing users and enhancing their experience: “Nobody chooses, you will not be surprised, Yahoo over Google or somewhere else to search. The way that we get our search volume is because we have 250 million US users and 700 million global users in the Yahoo network at any given time. There’s a search box there. And infrequently, they use it.” It’s more about nurturing the loyalties of existing users.

    A word of caution. The conversation also shines a light on the potential pitfalls of heavily relying on AI platforms. Jim references past experiences with Google: “You are tempting fate by opening up a way for consumers to access your product within a large language model.” This analogy resonates with me deeply, remembering the cautionary tales in tech history.

    Yet, he warns: “The big bad wolf will come to your door and say everything’s cool.” It’s a timely reminder of the ever-competitive and unpredictable nature of tech alliances.

    The interview. For those intrigued by Yahoo’s journey, check out Yahoo CEO Jim Lanzone’s full interview on reviving the web’s homepage.


    Inspired by this post on Search Engine Land.


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