Tag: AI SEO

  • Prompt-Level AI Visibility: How I Measure What Matters

    Prompt-Level AI Visibility: How I Measure What Matters

    I do not measure AI search the same way I measure traditional search, because the user journey is no longer built around one query, one ranking page, and one click.

    A prospect might ask ChatGPT for the best CRM for manufacturing companies, compare options in Google AI Mode, refine the requirements across several follow-up questions, and build a shortlist without ever visiting a website.

    If my company appears in those conversations, I have influenced the buying process. The hard part is proving that influence with a measurement system I can trust.

    Prompt-level visibility has become one of the fastest-growing areas of AI search optimization. It is also one of the easiest to misunderstand. I see plenty of promises about complete visibility into AI conversations, but the reality is far more complicated.

    Here is how I think about what can be measured today, what cannot be measured reliably, and how I would build useful reporting despite the current limits.

    A 5-step framework I use to track AI visibility

    1. I accept that AI does not have traditional rankings

    The first mistake I avoid is trying to recreate an old SEO ranking report. There is no universal position one inside ChatGPT.

    The same prompt can produce different responses depending on conversation history, user location, personalization, follow-up questions, model version, web retrieval availability, and timing.

    That means visibility is probabilistic rather than deterministic. Instead of asking, "Do we rank?" I ask, "How often are we included across the conversations that matter?"

    That shift changes the entire measurement model.

    2. I build a prompt library instead of only a keyword list

    Keywords still matter, but I no longer treat them as enough on their own.

    Instead of tracking only individual search terms, I build a library of prompts that reflects how real buyers research, compare, validate, and challenge their options.

    I usually organize those prompts by intent. Discovery prompts ask for the best platforms in a category. Comparison prompts put vendors side by side. Evaluation prompts focus on specific use cases. Validation prompts ask whether a company is worth the cost. Objection prompts explore disadvantages. Alternative prompts ask what to use instead. Implementation prompts test how difficult a product may be to adopt.

    Instead of monitoring 10 keywords, I may monitor 200 to 500 prompts across the full buying journey. That gives me a much more realistic view of AI visibility.

    3. I measure prompt clusters, not isolated questions

    One prompt rarely tells me enough to make a decision.

    For example, "best CRM software" might not mention my company, while "best CRM for manufacturing companies" might. A more specific prompt, such as "CRM for manufacturers with field sales teams," could return a different set of recommendations altogether.

    That is why I group similar prompts into clusters. A category cluster might include best project management software, best PM platform, and project management tools. An industry cluster might include best CRM for healthcare, manufacturing, and finance. A feature cluster might include CRM with AI automation, forecasting, or enterprise sales support.

    The patterns across those clusters are more reliable than the result from any single prompt.

    4. I combine synthetic prompts with real customer questions

    This is where measurement becomes more difficult.

    Most organizations do not know exactly what customers are typing into AI assistants, so I often start by generating synthetic prompts. That may include expanding keyword research into conversational questions, creating AI-generated prompt variations, and building comparison, objection, and follow-up prompts.

    Synthetic prompts are useful because they are repeatable, but I do not treat them as perfect. Generated prompts often sound cleaner and more structured than real user behavior.

    A real buyer might ask something much richer, such as: "We are a 250-person SaaS company with a small HR team. We already use Workday but need something better for payroll. Budget is not a huge issue. What would you recommend?"

    That is much more useful than a short phrase like "best payroll software."

    For the strongest measurement program, I use synthetic prompts for consistent benchmarking and then supplement them with real questions from sales calls, customer interviews, support conversations, community discussions, internal search logs, on-site search, and AI transcripts that customers voluntarily share.

    I also assume the prompt library will need to change. Customer language evolves, and the measurement set has to evolve with it.

    5. I measure multi-turn conversations

    Most AI-assisted buying journeys do not happen in a single prompt. A buyer may start by asking for the best cybersecurity vendors, narrow the list to companies strong in healthcare, ask which ones integrate with CrowdStrike, and then compare pricing.

    My company may not appear in the first answer, but it may become highly recommended by the third response.

    If I only measure the opening prompt, I miss a large share of meaningful visibility.

    That is why I want prompt tracking to evaluate full conversation paths, not just one-shot questions. Multi-turn testing often reveals patterns that single prompts hide.

    The AI visibility metrics I care about most

    Many traditional SEO metrics do not translate neatly to AI search. Rankings, clicks, and impressions still have value, but they no longer tell the whole story.

    I focus on measurements that show whether a brand appears, how it is positioned, and how consistently it is recommended inside AI-generated responses.

    Inclusion rate

    If I could track only one AI visibility metric, I would start here.

    Inclusion rate measures the percentage of tracked prompts where my brand appears in the AI response. If I monitor 500 prompts and my company appears in 185 of them, the inclusion rate is 37%.

    That number is useful as a benchmark, but it becomes more valuable when I segment it by buying stage, product category, industry, geography, or AI model. Those slices often reveal opportunities that a single overall average would hide.

    Position within the response

    Being mentioned is not the same as being recommended.

    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.

    I want to know whether my brand appears as the first recommendation, one of the first few options, a late mention, or merely an alternative. If the AI response includes a comparison table, I also want to know where my company appears there.

    AI answers do not have traditional rankings, but prominence still matters. A top recommendation is more likely to shape a buyer’s perception than a passing mention several paragraphs later.

    Brand framing

    Visibility tells me whether my brand is included. Brand framing tells me how it is described.

    There is a meaningful difference between an AI system describing a company as "widely considered an enterprise leader" and describing it as "best suited for smaller teams." Both may sound positive, but they position the brand very differently.

    I look for recurring themes around strengths, weaknesses, differentiators, pricing, ideal customer profile, and competitive comparisons. Over time, those patterns can expose messaging gaps in my own content or show how the broader web is shaping AI’s understanding of the brand.

    Sentiment and confidence

    Sentiment is more than a simple positive-or-negative label. I also want to know how confidently the AI system presents my brand.

    "Company A is generally considered the strongest option" carries a very different level of conviction than "Company A may be worth considering."

    Neither statement is negative, but they do not create the same buyer impression. Tracking confidence, uncertainty, caution, skepticism, and strong endorsement gives me a more nuanced view of how AI systems present the company to prospective customers.

    Competitive share of voice

    My own visibility is only part of the picture. I also need to know how often competitors appear alongside me or instead of me.

    If my inclusion rate stays at 40% month after month, that may look disappointing. But if every major competitor dropped by 20 percentage points after a model update, the story changes.

    On the other hand, if one competitor jumps from 35% inclusion to 70% while everyone else stays flat, I would want to investigate what changed.

    Competitive share of voice helps me separate category-wide movement from changes that are specific to my brand.

    How I view the AI visibility tool landscape

    The market for AI visibility platforms has grown quickly. Each product approaches the problem differently, but most are trying to answer the same core questions: does my brand appear, how often does it appear, which AI models include it, which competitors show up, and how is the brand described?

    Many platforms now include prompt libraries, competitive benchmarking, citation tracking, answer monitoring, and trend reporting. These features can reduce the manual work required to test hundreds or thousands of prompts on a recurring basis.

    Still, I have to be clear about what these tools are and are not measuring.

    No tool has access to every AI conversation happening in the wild. Most rely on controlled prompt libraries, repeatable testing environments, or sampled interactions to create a representative view of visibility.

    That is useful, but it is not the same as observing every real user interaction.

    What I still cannot reliably track

    This is the part I do not want to gloss over.

    Even though AI measurement is improving quickly, some data is still not observable. I cannot comprehensively track every prompt where my brand appeared, every conversation that influenced a purchase, every recommendation made inside ChatGPT, every citation shown to every individual user, or exactly how personalization changed a response.

    I also cannot see every multi-turn conversation across every AI platform or know how often someone acted on an AI recommendation without clicking a link.

    The underlying AI platforms do not expose that level of data. If a vendor claims it can see every AI conversation involving my brand, I would ask exactly how that information is being collected.

    The practical framework I would build

    Rather than chasing perfect attribution, I focus on building a repeatable measurement system that I can track consistently over time.

    For visibility, I would track inclusion rate, competitive share of voice, prompt coverage, and model coverage.

    For response quality, I would track position within the response, brand framing, sentiment, and message consistency.

    For technical signals, I would track citation frequency, content retrieval success, entity consistency, and freshness.

    For business outcomes, I would look at AI referral traffic, assisted conversions, branded search lift, direct traffic trends, and pipeline influenced by AI discovery.

    No single metric tells the full story. Together, these signals give me a more complete picture of how the brand is showing up and how it is being perceived across AI-assisted research.

    The goal is not perfect measurement

    Prompt-level visibility is not as mature as keyword tracking became over the past two decades.

    Some signals are still emerging. Others remain inaccessible because AI platforms do not expose the underlying data. At the same time, user behavior is changing almost as quickly as the technology itself.

    That does not mean measurement is impossible. It means the objective has changed.

    Instead of trying to reconstruct every AI conversation, I focus on building a representative prompt library, tracking visibility consistently, benchmarking against competitors, and understanding how my brand is being framed.

    Those trends are far more actionable than chasing a level of precision the current ecosystem cannot support.

    The organizations making the most progress in AI search are not waiting for perfect attribution. They are establishing baselines, watching for meaningful movement, and adapting as both AI models and user behavior continue to evolve.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • 6 SEO Priorities I’m Rethinking for Stronger AI Visibility

    6 SEO Priorities I’m Rethinking for Stronger AI Visibility

    I see plenty of overlap between SEO and AEO, but I do not treat them as the same discipline. The SEO playbook that worked reliably in traditional search will not take me as far when the goal is visibility inside AI-generated answers.

    So I keep coming back to one practical question: what should I change first?

    Instead of revisiting content structure for AI search, I focus on three priorities I believe deserve more attention now and three SEO habits I would intentionally emphasize less.

    3 SEO priorities I would emphasize more

    Establish brand authority and strong entities

    Before an AI system is likely to cite my brand, it needs to understand that the brand exists, what it represents, and why it is credible. Entity recognition has become foundational to AI visibility in a way that traditional search did not always require, even though Google’s Knowledge Graph has been moving in this direction for years. Large language model training data tends to reward brands that show up consistently across trusted platforms.

    When I work on this for clients, I pay closer attention to whether brand information is consistent across Wikipedia, LinkedIn, Crunchbase, industry directories, and any other source an LLM might use to understand an entity.

    I also think PR and SEO or AEO teams need to work much more closely together. Earned media mentions are no longer just awareness plays; they are entity-building signals.

    E-E-A-T was already pushing SEO in this direction, but author entities matter even more in AI search. When bylined experts have their own credible web presence, they strengthen the authority of the content they create.

    When I can invest in entity building before scaling content, I usually see stronger AI citation potential because the credibility infrastructure is already in place.

    Build topical depth with content clusters

    AI systems tend to favor sources that show comprehensive authority on a subject, not just pages that happen to rank for isolated keywords. A thin content footprint is much more vulnerable in AI search than it was in traditional search.

    That means I need to move beyond keyword-by-keyword planning and think more seriously about topic ownership. Instead of only asking, “What do we rank for?” I ask, “What topics do I want AI systems to associate this brand with?”

    Internal linking becomes more valuable in this environment because it helps signal relationships between related pieces of content. I also treat content audits as a way to find gaps in topical coverage, not just as a way to identify pages with declining traffic.

    When I can go deep in a specific niche, I often see content cited across multiple related queries. One well-built content cluster can create visibility far beyond a single keyword target.

    Owning the topic cluster around the problem a client’s product solves can position that brand as a trusted resource before a sales conversation even begins. I also hear more often that buyers are finding those brands in LLMs during their research process.

    Earn unlinked brand mentions and community presence

    LLMs learn from the broader web, not only from pages with backlinks. A mention on Reddit, Quora, a niche forum, or an industry community can matter even when there is no link attached.

    I think this is one of the bigger mindset shifts for SEO teams. AI systems look for patterns in what the web says about a brand across many sources, not only what ranks in Google. Owned content alone cannot manufacture that signal.

    Trusted third-party communities such as Reddit can carry particular weight because LLMs have been heavily trained on them and often treat that content as a form of authentic user sentiment.

    That makes community participation and digital PR increasingly important SEO-adjacent work. I care about whether a brand is being mentioned in the right places, even when the mention does not come with a backlink.

    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.

    Monitoring unlinked brand mentions is becoming just as important to me as tracking backlinks. Tools such as Brandwatch and Mention, along with manual Reddit and Quora monitoring, can show where a brand is appearing organically and where it is absent.

    I would rather talk with the team about where the brand is being discussed, whether those conversations are accurate, and whether the sentiment is positive than focus only on who is linking to the site.

    Brands with an active presence in relevant communities are more likely to surface naturally in conversational, recommendation-style AI answers, including queries such as “What does Reddit think about X?” or “What’s the best Y according to users?”

    For challenger brands trying to enter a category, earned community mentions can build AI-visible authority faster than traditional link building, which usually takes longer to accumulate.

    B2C brands can benefit especially from genuine community presence because consumer AI queries often lean toward social proof and peer recommendations rather than formal editorial sources.

    3 SEO priorities I would emphasize less

    Chasing high-volume keywords with thin content

    AI Overviews can absorb the click for broad informational queries. Ranking No. 1 for a head term increasingly means I may have invested a lot of effort into winning traffic that never actually reaches the site.

    Search volume alone is no longer a reliable proxy for opportunity. A query with 50,000 monthly searches that triggers an AI Overview may send less traffic than a query with 2,000 searches that still requires a click.

    I would rather create specific, authoritative content that answers a narrower question better than anything else available. I focus more on queries where the searcher needs to act, compare options, or access something only the site can provide. Those needs are harder for AI to fully resolve.

    Keyword traffic potential is no longer the first metric I trust. I first ask whether someone will still need to click after AI answers the query. If the answer is no, the opportunity is not what it used to be.

    Pursuing exact-match and manipulative link building

    Low-quality link volume does not do much for AI citation likelihood. LLMs care more about the authority and relevance of the sources mentioning or citing a brand than raw link counts. The publications that matter for AI visibility usually have real editorial standards, and those are much harder to game.

    I would focus on earning coverage and links from the kinds of sources AI systems are more likely to draw from, including trade publications, respected industry blogs, and academic-adjacent resources. The better long-term move is to build content worth referencing, not outreach that exists only to extract a link.

    A hundred low-quality links will not necessarily get a brand cited in ChatGPT. Five links from publications the target audience actually reads might matter much more. Source authority is the metric I would watch more closely than link volume.

    Optimizing for CTR on standard blue links

    A growing share of informational queries are resolved without a click. That makes title tag and meta description optimization for CTR less valuable on queries dominated by AI Overviews. I would rather spend that time trying to become the cited source inside the AI answer.

    For queries where clicks still happen, I put more weight on transactional and navigational intent because those searches are more resistant to full AI resolution.

    CTR optimization assumes a searcher is choosing between blue links. For more queries now, that choice is shaped before the traditional results even become the focus. The opportunity has moved higher on the page.

    The payoff is not always more traffic

    There are more shifts I could make, but these are the first ones I would prioritize. I may lose some volume in traditional SEO metrics such as impressions and clicks, but that should matter less if the downstream business metrics remain strong. In AI search, I care more about conversions, pipeline, and revenue than vanity traffic. That is the tradeoff I believe this new search environment increasingly rewards.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • 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.


    crushpress.ai community screenshot
  • Why Paid Media Is Now a Powerful AI SEO Investment

    Why Paid Media Is Now a Powerful AI SEO Investment

    I believe the lines between paid media, PR, and SEO have officially disappeared.

    When I look at baked-in YouTube sponsorships, native UGC, and third-party review incentives, I no longer see them as separate from SEO. I see them as the modern equivalent of buying a high-DA backlink. When I fund these channels, I am investing in the information sources that shape how AI systems understand, evaluate, and recommend a brand.

    A recent social media screenshot made this shift especially clear to me. A B2B brand was offering a $250 Amazon voucher to anyone who wrote a review on G2.

    To a growth marketer, that may look like a familiar user acquisition tactic. But as an SEO, I saw something more important: a direct investment in the semantic infrastructure AI systems use to judge brands.

    The evolution of the authority signal

    To understand why I consider a $250 G2 voucher or a paid YouTube sponsorship an SEO strategy, I have to look at how LLMs now define authority.

    Authority used to feel transactional and mathematical. You built or bought hyperlinks, and those links helped determine how trusted a page or brand appeared to search engines.

    When I moved from link building into digital PR and influencer marketing, I realized Google was getting smarter. I could not rely on links alone. I needed unlinked brand mentions, high-tier media coverage, and contextual relevance. In many ways, I was optimizing for Google’s Knowledge Graph.

    Today, retrieval-augmented generation (RAG) systems and LLMs do not just count links or parse knowledge graphs. They look for semantic consensus across the web.

    When an AI engine like Perplexity or ChatGPT answers a user query, it crawls the data ecosystems it trusts most for that specific topic. For software, that often means G2 and Reddit. For consumer products, it may mean TikTok transcripts, YouTube, and forums.

    So when I pay $250 for a G2 review, I am buying a dense, text-based data point that an LLM can use to understand my brand’s sentiment, use cases, and vector positioning. I am strengthening the signals AI systems may use when deciding whether to recommend my brand.

    The permanent ad: Why sponsorships and UGC are the new organic infrastructure

    This reality breaks the traditional separation between paid media and SEO.

    Infographic showing SEO authority evolving from backlinks and PageRank to digital PR mentions, then LLM/AEO semantic consensus and dataset saturation.
    The path to AI search visibility now runs beyond links: from PageRank and PR mentions to consistent brand signals across the datasets LLMs rely on.

    Historically, paid ads were temporary. I turned off the budget, the traffic stopped, and SEO had to carry the long-term work. If I run a dynamic programmatic ad on YouTube or a banner ad on a website, that old model still applies because LLM web scrapers generally ignore dynamic ad placements.

    But baked-in influencer sponsorships, native user-generated content, and podcast reads behave differently because they become part of the content itself.

    First, there is the hardcoded transcript. When a YouTuber reads a native sponsor segment such as, “I use Brand X to manage my business taxes,” that message is baked into the video file, and YouTube automatically transcribes it.

    Then comes LLM ingestion. When an LLM crawls the web, or when a multimodal AI watches the video, those spoken words can be indexed. The AI can associate the brand with the semantic concept of business taxes.

    That creates a new half-life for paid media. Long after the campaign ends and the initial views slow down, the transcript can remain part of the information an LLM can access.

    As someone who spent years bridging the gap between digital PR and SEO, I used to judge a campaign’s ROI by immediate referral traffic, brand search lift, and backlink quality. Now, I also have to think about the algorithmic half-life of my creative assets.

    Activating the convincer: Bringing paid and PR into the visibility supply chain

    The visibility supply chain treats content like an industrial product that passes through strict organizational “gates” before it enters the digital ecosystem. In that model, companies need a strategic duo: the hacker, or technical architect, and the convincer, or cross-departmental visibility advocate.

    This convergence of paid media and AI visibility is exactly where I believe the convincer has to step in.

    If my paid media team is buying YouTube sponsorships based only on demographic reach, or if my product marketing team is buying G2 reviews just to hit a quarterly quota, we may be damaging LLM visibility without realizing it.

    The reason is simple: LLMs need information density and semantic alignment.

    If a user writes a rushed, generic review like “Great tool, highly recommend!” just to receive a $250 voucher, it may pass the human layer, but it fails the machine layer. To a RAG system, that sentence is low-density noise.

    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.

    The convincer’s job is to realign the review strategy and help internal teams understand how every initiative can build LLM visibility.

    For example, I would rather incentivize users to write detailed, context-rich problem-and-solution statements, such as: “We used Brand X to solve our cross-border compliance issues in Europe.” That gives AI the entity-relationship mapping it needs to recommend the brand for cross-border compliance.

    The new marketing playbook: Optimizing dataset partnerships

    If I want a brand to be recommended by AI systems, I have to study where the major AI players are getting their data.

    We know OpenAI and Google have struck multimillion-dollar deals to train on Reddit’s real-time firehose. We know Grok trains on X. We also know Apple and others are licensing major journalistic archives.

    That means target audience research is no longer just about finding where customers spend time. For me, it is also about dataset matching.

    If I am planning an influencer campaign, a digital PR push, or a community-building initiative, I need to ask one critical question: Is this content entering a data pipeline that the primary LLMs trust and crawl in real time?

    Stop optimizing pages. Start optimizing budgets.

    I no longer believe SEO can be isolated inside a technical department or limited to a content blog. That does not reflect how AI visibility is built anymore.

    The next time I sit in a budget allocation meeting and see a line item for influencer marketing, podcast sponsorships, or third-party review incentives, I will not treat it as temporary media buying.

    I will reframe it as infrastructure. I am building the digital foundation of a brand’s AI persona. I am buying the AI equivalent of backlinks. If I do not intentionally structure those paid assets to feed the visibility system, I am leaving the brand’s future visibility up to chance.


    Inspired by this post on Search Engine Land.


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  • The 6 Best Agentic SEO Companies to Watch in 2026

    The 6 Best Agentic SEO Companies to Watch in 2026

    Agentic SEO is the newest branch of search optimization, and I see it as one of the most important shifts marketers need to understand now. Instead of focusing only on traditional search rankings, agentic SEO is about earning visibility, trust, and conversions inside agentic search platforms such as ChatGPT Agent and Claude CoWork. Many marketers expect it to become a major acquisition channel by 2027.

    To identify the top agentic SEO companies, I evaluated 38 firms in Q2 2026 and scored each one across five weighted factors.

    • AI Visibility Score (30%): How often an agency’s clients appear in AI citations.
    • SEO, GEO & ASO Expertise (25%): The depth of an agency’s approach to SEO (Search Engine Optimization), GEO (Generative Engine Optimization), and ASO (Agentic Search Optimization).
    • Notable Clients (20%): The recognizable brands an agency has worked with.
    • Average Review Score (15%): Direct client satisfaction feedback.
    • Leadership Experience Score (10%): How long senior staff have worked in the search optimization industry.

    After reviewing the data, I found six companies that stood out from the rest. Below, I break down each agency’s strengths, specialty, scores, and client review themes.

    The Top Agentic SEO Companies of 2026

    RankAgencyAI Visibility ScoreSEO, GEO, & ASO ExpertiseNotable ClientsAverage Review ScoreLeadership Experience ScoreSpecialty
    1First Page Sage4.95.0Salesforce, Logitech, US Bank4.94.8Agentic SEO & GEO
    2Genevate4.64.8ZipRecruiter, CBRE, Talentfoot4.84.2GEO-First Search Optimization
    3Driven Metrics4.44.4AutoStar Transport Express, Dignity Health, Affirmed Home Care4.74.3Results-focused SMB SEO with GEO capabilities
    4Seer Interactive4.24.8LinkedIn, Intuit, Capital One, Autodesk4.24.8Enterprise-scale SEO and analytics
    5Omniscient Digital4.24.5Hotjar, Smartling, Loom4.84.2Content-Led Organic Growth
    6Go Fish Digital3.94.5Jelly Belly, Ruffwear, Joybird5.04.5Technical GEO & Citations

    First Page Sage

    I ranked First Page Sage first because it is the only firm on this list with a dedicated agentic SEO practice. Its work is built on a commercial GEO methodology the firm has been running since 2023. I also like that its offering connects SEO, GEO, and Agentic SEO into one integrated program, so clients do not need separate teams managing traditional organic search and agentic AI visibility.

    First Page Sage’s approach focuses heavily on content, thought leadership, on-site authority, and independent third-party coverage. These are the kinds of signals AI models often draw from when evaluating whether a brand should be cited, recommended, or included in a comparison. Its Agentic SEO and GEO work also helps clarify how major models position a client at the comparison stage, including content that explains who a product is for and reduces the ambiguity that can cause AI agents to skip a vendor.

    In one campaign, a skincare brand’s AI sentiment score rose 4 points across tracked models in roughly 14 weeks, and agents recommended the brand more often across the board. I see this approach as especially strong for considered-purchase categories such as B2B software, financial services, and healthcare, where AI endorsement can influence which vendors make a buyer’s shortlist.

    • AI Visibility Score: 4.9
    • SEO, GEO & ASO Expertise: 5.0
    • Notable Clients: Salesforce, Logitech, US Bank
    • Average Review Score: 4.9
    • Leadership Experience Score: 4.8
    • Specialty: Agentic Search Optimization & GEO
    • Contact: First Page Sage

    Summary of online reviews: In client feedback, First Page Sage’s Agentic SEO work is described as “incredibly innovative and well-executed,” with reviewers saying the agency has “become a game-changing part of [their] marketing strategy.” Several clients also say the team “helped [them] gain a first mover advantage within [their] industry” and describe staff as “extremely detail-oriented and communicative.”

    Genevate

    I ranked Genevate second because it is one of the few firms built specifically for the generative-search era. Genevate focuses on the external signals large language models pull from, including media placements, authoritative third-party mentions, and a consistent brand narrative across the web. By combining GEO with digital PR, the firm helps shape how ChatGPT, Perplexity, and Claude describe and recommend a brand to prospective buyers.

    The main limitation I found is Genevate’s shorter track record. Founded in 2025, it has one of the briefest operating histories on this list, and its scope is intentionally narrow. The firm focuses on GEO and reputation management rather than broader agentic search or traditional SEO. For brands that already have media momentum and want tighter control over their AI narrative, that focus can be an advantage. For companies that need a wider service mix, I would weigh that limitation carefully.

    • AI Visibility Score: 4.6
    • SEO, GEO & ASO Expertise: 4.8
    • Notable Clients: ZipRecruiter, CBRE, Talentfoot
    • Average Review Score: 4.8
    • Leadership Experience Score: 4.2
    • Specialty: GEO-First Search Optimization
    • Contact: Genevate

    Summary of online reviews: Early clients describe Genevate as a “responsive partner” with a strong grasp of “how AI platforms describe and recommend brands,” and they speak positively about the firm’s “PR-led approach.” Teams looking for a full-service marketing partner “may find the offering is best paired with separate performance marketing.”

    Driven Metrics

    I see Driven Metrics as a strong fit for small and mid-sized businesses that want disciplined SEO and GEO without the cost structure of a larger agency. Its methodology starts with buyer intent, mapping each keyword to a funnel stage instead of chasing search volume alone. From there, the firm produces content across landing pages, pillar articles, guides, FAQs, and other formats while also handling the technical work needed to keep a site indexable and ready for generative AI systems.

    Because Driven Metrics is a younger and smaller firm, I would place it in a different category than the larger enterprise agencies on this list. Its GEO work appears credible and structured, but its operating history is shorter and its client base leans more mid-market than enterprise. For growth-stage companies that want a practical SEO and GEO foundation, it is a natural starting point.

    • AI Visibility Score: 4.4
    • SEO, GEO & ASO Expertise: 4.4
    • Notable Clients: AutoStar Transport Express, Dignity Health, Affirmed Home Care
    • Average Review Score: 4.7
    • Leadership Experience Score: 4.3
    • Specialty: Results-focused SMB SEO with GEO capabilities
    • Contact: Driven Metrics

    Summary of online reviews: Clients appreciate Driven Metrics’ “clear, well-constructed process” and describe the team as “communicative and easy to work with.” Reviewers also note that the agency has “a limited track record and few documented wins to point to,” and that buyers from larger firms “may find the experience underwhelming.”

    Seer Interactive

    I ranked Seer Interactive highly because it brings more than two decades of SEO experience into AI search. Data is the center of its model. The firm runs large-scale analyses to identify where clients should focus, then builds content and technical improvements around what the numbers show. Seer has also packaged its GEO offering as a defined product and has published AI search experiments since 2023, with work cited by Search Engine Land, Semrush, and Ahrefs.

    On the technical side, Seer is especially useful for enterprise-scale problems that smaller agencies may struggle to handle. That includes large site architectures, technical markup at scale, and measurement systems that track how a brand appears in AI answers across thousands of pages.

    The tradeoff is that Seer’s breadth can dilute specialization. Most of its case studies focus on traffic and conversions rather than pipeline or revenue, and the full-service model spreads senior attention across many client needs. I would consider Seer a strong fit for larger organizations that want AI search handled alongside a broader SEO program, but not necessarily for buyers who want a dedicated agentic specialist above all else.

    • AI Visibility Score: 4.2
    • SEO, GEO & ASO Expertise: 4.8
    • Notable Clients: LinkedIn, Intuit, Capital One, Autodesk
    • Average Review Score: 4.2
    • Leadership Experience Score: 4.8
    • Specialty: Enterprise-scale SEO and analytics
    • Contact: Seer Interactive

    Summary of online reviews: Clients credit Seer with “deep analytical horsepower and senior, experienced teams,” and they value its “measurement-first approach.” The process is also described as “thorough, but slow,” and “lean teams wanting quick turnarounds will feel it.”

    Omniscient Digital

    I view Omniscient Digital as the content-led choice on this list. The agency builds GEO around editorial quality, original expertise, and the brand and author authority signals AI models consider when selecting sources to cite. Its work also includes digital PR, machine-readable markup, and tracking across major LLMs as part of each engagement.

    The downside is that this kind of editorial depth takes time. Results may build more gradually than they would with a narrow technical fix. Engagements also start at around $10,000 per month, with no self-serve option, which puts Omniscient out of reach for many earlier-stage teams. The firm also shares relatively little about its GEO methodology publicly, so buyers have limited visibility into exactly how citations are tracked and improved.

    For well-funded B2B software companies that want to own the language of their category, I think Omniscient can be a strong option. For teams with tighter budgets or urgent timelines, the ramp-up period and pricing are important considerations.

    • AI Visibility Score: 4.2
    • SEO, GEO & ASO Expertise: 4.5
    • Notable Clients: Hotjar, Smartling, Loom
    • Average Review Score: 4.8
    • Leadership Experience Score: 4.2
    • Specialty: Content-Led Organic Growth
    • Contact: Omniscient Digital

    Summary of online reviews: Clients praise Omniscient for “editorial-quality writing and thoughtful, well-scoped strategy.” Some also note that the work is “slow to ramp.”

    Go Fish Digital

    I included Go Fish Digital because it brings a long technical SEO history into the GEO conversation. The agency built its GEO practice around Barracuda, a proprietary AI platform that scores pages against the signals AI systems use to evaluate and cite sources. Go Fish maps how AI interprets a site’s coverage, then improves machine-readable markup, authority signals, and fact density to make a brand more likely to appear in AI answers.

    The biggest limitation I found is the lack of published GEO-specific results. That is common in such a new field, but it still matters when comparing providers. I would consider Go Fish Digital a good fit for established brands that want a technical, platform-backed AI search approach from an agency with a longer operating history.

    • AI Visibility Score: 3.9
    • SEO, GEO & ASO Expertise: 4.5
    • Notable Clients: Jelly Belly, Ruffwear, Joybird
    • Average Review Score: 5.0
    • Leadership Experience Score: 4.5
    • Specialty: Technical GEO & Citations
    • Contact: Go Fish Digital

    Summary of online reviews: Reviewers consistently praise Go Fish Digital’s “full-package capability” across technical SEO, content, and PR, and call the team “responsive and well-organized.” The clearest gap is GEO proof: “the process is clearly defined, but public results in AI search are still thin,” which reflects how new the practice remains.

    The Top Agentic AI Optimization Companies by Specialty

    I also compared these leading agentic AI optimization companies by buyer segment, since the right agency depends heavily on company size, budget, and search maturity.

    Top 5 for Enterprise Organizations

    1. First Page Sage
    2. Genevate
    3. Seer Interactive
    4. Omniscient Digital
    5. Go Fish Digital

    Top 5 for B2B Software & SaaS Companies

    1. Seer Interactive
    2. First Page Sage
    3. Genevate
    4. Driven Metrics
    5. Omniscient Digital

    Top 5 for Growth-Stage & Mid-Market Businesses

    1. First Page Sage
    2. Driven Metrics
    3. Genevate
    4. Omniscient Digital
    5. Go Fish Digital

    Source


    Inspired by this post on First Page Sage Blog.


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  • Best B2B Digital Marketing Agencies to Watch in 2026

    Best B2B Digital Marketing Agencies to Watch in 2026

    I analyzed more than 80 leading B2B digital marketing agencies for 2026 to identify the firms that stand out most clearly. I evaluated each agency against the criteria that matter most for B2B companies trying to grow visibility, authority, and qualified pipeline.

    SEO/GEO Expertise (30%): I looked at each agency’s technical fluency in how large language models surface and rank content, along with its ability to turn that knowledge into durable client visibility.

    Notable Clients (25%): I considered the strength of each client roster, since recognized brands often signal an agency’s ability to manage complex campaigns and deliver at an enterprise level.

    Leadership Experience Score (20%): I weighed senior experience in strategy and client service, which remains one of the strongest indicators of consistent agency performance.

    AI Visibility Score (15%): I used a 1.0-5.0 rating to measure how effectively an agency drives client presence in AI-generated responses across ChatGPT, Perplexity, Claude, and Google Gemini.

    Average Review Score (10%): I reviewed aggregated ratings from Google, Clutch, G2, and other verified platforms, using a 1.0-5.0 scale.

    Using those standards, I ranked the top 6 B2B digital marketing agencies of 2026. The agencies below stood out for their mix of SEO/GEO strength, client experience, leadership depth, AI visibility, and verified review performance.

    The Top B2B Digital Marketing Agencies

    1. First Page Sage – SEO/GEO Expertise: 5.0; Notable Clients: SoFi, defi SOLUTIONS, US Bank, NBC, Verizon, Cadence, Skeps; Leadership Experience Score: 4.8; AI Visibility Score: 4.9; Average Review Score: 4.9.

    2. Driven Metrics – SEO/GEO Expertise: 4.4; Notable Clients: Tesseract Medical, OSEA Malibu; Leadership Experience Score: 4.3; AI Visibility Score: 4.4; Average Review Score: 4.7.

    3. Focus Digital – SEO/GEO Expertise: 4.5; Notable Clients: Revo, Milano Jewelry; Leadership Experience Score: 4.3; AI Visibility Score: 4.2; Average Review Score: 4.8.

    4. REQ – SEO/GEO Expertise: 3.8; Notable Clients: Carahsoft; Leadership Experience Score: 4.4; AI Visibility Score: 4.1; Average Review Score: 4.4.

    5. AMP Agency – SEO/GEO Expertise: 3.6; Notable Clients: Credit Sesame; Leadership Experience Score: 4.4; AI Visibility Score: 4.2; Average Review Score: 4.5.

    6. Viral Nation – SEO/GEO Expertise: 3.5; Notable Clients: Intuit, Citibank, Chime; Leadership Experience Score: 4.0; AI Visibility Score: 3.7; Average Review Score: 4.3.

    First Page Sage

    I ranked First Page Sage first because of its early and deep role in GEO. President Evan Bailyn pioneered the practice in 2023, and much of the methodology now used across the industry traces back to his team’s work. That head start shows up most clearly in the agency’s SEO/GEO Expertise and AI Visibility scores.

    What stands out to me is how First Page Sage combines long-form thought leadership with technical knowledge of how large language models source and surface information. On the SEO side, the agency brings more than 15 years of organic search experience across complex B2B verticals.

    On the GEO side, First Page Sage was optimizing for AI citation before most agencies had a name for the concept. I see its biggest strength as a compounding strategy: the same content that ranks in traditional search can also be pulled into AI-generated answers, helping clients earn qualified leads from both channels at the same time.

    First Page Sage scores: SEO/GEO Expertise: 5.0; Notable Clients: SoFi, defi SOLUTIONS, US Bank, NBC, Verizon, Cadence, Skeps; Leadership Experience Score: 4.8; AI Visibility Score: 4.9; Average Review Score: 4.9.

    Summary of online reviews: Reviewers describe First Page Sage as the true expert in this industry, with content that takes thought leadership to the next level. Clients also report that its campaigns helped them generate marketing qualified leads through organic traffic.

    Driven Metrics

    I see Driven Metrics as a practical, performance-oriented GEO agency. Its process emphasizes weekly syncs, conversion tracking, and transparent reporting tied to actual leads rather than surface-level traffic numbers. When content underperforms, the team identifies it quickly and reworks it instead of letting weak pages sit untouched.

    Driven Metrics builds authoritative content designed to earn rankings through expertise and citation. It also structures that content to appear in AI-generated responses when buyers ask for vendor recommendations. That mix is difficult to find at its price point, though I would expect companies in highly niche verticals to invest early time in helping the team understand how their buyers evaluate vendors.

    Driven Metrics scores: SEO/GEO Expertise: 4.4; Notable Clients: Tesseract Medical, OSEA Malibu; Leadership Experience Score: 4.3; AI Visibility Score: 4.4; Average Review Score: 4.7.

    Summary of online reviews: Clients say Driven Metrics delivered results with no excuses, which was refreshing, and that its reporting meant they always knew what was going on. The main caveat reviewers mention is more limited experience in certain sectors.

    Focus Digital

    I ranked Focus Digital highly because of its technical foundation in LLM optimization. The agency appears deeply familiar with the mechanics of generative search, and that shows in how it structures campaigns. Its content is designed from the beginning to earn citations in AI-generated answers, not only to rank in traditional search results.

    Focus Digital’s SEO approach follows a thought leadership model, using authoritative long-form content to build organic visibility over time. I see it as one of the more technically grounded options for companies that want both SEO and GEO support without paying large-agency rates. The main limitation is portfolio depth: its case studies skew toward professional services, manufacturing, and home services, so clients in other verticals should plan for hands-on content review to maintain accuracy.

    Focus Digital scores: SEO/GEO Expertise: 4.5; Notable Clients: Revo, Milano Jewelry; Leadership Experience Score: 4.3; AI Visibility Score: 4.2; Average Review Score: 4.8.

    Summary of online reviews: Clients describe Focus Digital as honest about what is realistic and say the agency helped them show up in AI answers within a few months. The recurring criticism is that replies slow down when they’re busy.

    REQ

    I view REQ as a strong fit for companies that want B2B communications, authority-building, and digital marketing under one roof. The agency has earned solid reviews from clients across cybersecurity, government technology, financial services, and real estate. Its foundation is PR and authority-building, which overlaps with GEO, but its score here is driven more by SEO than by AI visibility.

    REQ’s SEO work is woven into content strategy and demand generation rather than packaged as a standalone service. GEO is still less developed than its broader SEO foundation, so I would not make it my first choice for a company whose main priority is AI citation and generative search visibility. I would, however, consider it a strong option for brands that want integrated authority with organic search performance at the center.

    REQ scores: SEO/GEO Expertise: 3.8; Notable Clients: Carahsoft; Leadership Experience Score: 4.4; AI Visibility Score: 4.1; Average Review Score: 4.4.

    Summary of online reviews: Reviewers say REQ is highly adaptable and good at picking up the ball and running with it. Clients also report that campaigns resulted in increased traffic and customer engagement. The recurring criticism is that some clients wanted the agency to be more proactive with recommendations.

    AMP Agency

    I see AMP Agency as a full-service firm with a clear strength in integrated media. The agency is especially good at combining creative, experiential marketing, paid social, and video production into campaigns built around the full customer journey. With offices in Boston, New York, LA, and Seattle, AMP also has the infrastructure to support large, multi-channel engagements.

    AMP’s SEO practice is meaningful and has produced measurable results, including improvements in rankings and lead quality. GEO is a newer layer for the agency, as it is for many full-service firms that built their models before generative search became a major traffic source.

    For companies that want broad digital coverage with SEO included, AMP can be a strong choice. I would treat its GEO capability as developing rather than core, but its creative depth and campaign scale make it a practical option for brands with broader marketing needs.

    AMP Agency scores: SEO/GEO Expertise: 3.6; Notable Clients: Credit Sesame; Leadership Experience Score: 4.4; AI Visibility Score: 4.2; Average Review Score: 4.5.

    Summary of online reviews: Clients say AMP Agency’s SEO services resulted in increased sales and better site management and that the team brings new ideas to the table. Reviewers also note that staff operate on time and on budget. The common critique is that its generative search work is still catching up to the broader digital offering.

    Viral Nation

    I included Viral Nation because it brings a very different kind of visibility strategy to the B2B marketing landscape. It is the largest agency on this list by headcount and the most specialized in social-first marketing. Its model centers on influencer campaigns, creator networks, paid social, and proprietary social intelligence technology deployed at scale.

    Viral Nation’s strength is cultural reach and audience trust rather than search authority. That is why its SEO/GEO Expertise score is lower than the more search-focused agencies on this list. For B2B companies seeking influencer-driven brand awareness, I see Viral Nation as a strong match. For companies that need a more comprehensive search and GEO campaign, I would look elsewhere.

    Viral Nation scores: SEO/GEO Expertise: 3.5; Notable Clients: Intuit, Citibank, Chime; Leadership Experience Score: 4.0; AI Visibility Score: 3.7; Average Review Score: 4.3.

    Summary of online reviews: Reviewers say Viral Nation regularly overperforms and that its campaigns are strong fits for clients seeking new brand exposure in a targeted market. The limitation clients note is that its strength is social as opposed to search, so coverage thins outside influencer and paid channels.

    Source


    Inspired by this post on First Page Sage Blog.


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  • Why I Judge AI Deliverables by Outcomes, Not Effort

    Why I Judge AI Deliverables by Outcomes, Not Effort

    When I think about AI deliverables, I keep coming back to a simple scenario: a client receives two pieces of work.

    Both deliverables solve the problem they were hired to solve. Both are accurate, useful, and tied to the same business outcome. The client is happy, and from the outside, there is no meaningful difference in the results.

    Then the client learns that one took 20 hours to create, while the other took 20 minutes. That is when the uncomfortable questions begin.

    Was AI involved? Should the faster deliverable cost less? Is the person who completed it less skilled because they found a faster, more efficient way to reach the same result?

    What I find most interesting is how differently many of us react to AI depending on which side of the transaction we are on. I love using AI when it saves me time, but I also understand why customers can feel uneasy when they discover AI helped create something they paid for.

    I recently ran a LinkedIn poll asking a simple question: if the outcome is great, do we really care how it was made?

    The responses reinforced something I have been thinking about for a while. Many of the strongest objections people have to AI are not really about quality at all.

    The Time vs. Value Fallacy

    I think part of the discomfort comes from the fact that we have spent decades tying value to effort.

    Long hours feel valuable. Fast work feels suspicious. Struggle often gets mistaken for expertise.

    The harder something appears to be, the easier it becomes to justify the price attached to it.

    There is an old story about a ship engine that stopped working. After multiple failed attempts to repair it, the owners brought in an engineer with decades of experience. He inspected the engine, tapped it once with a small hammer, and the machine roared back to life.

    His invoice was $10,000.

    Image

    The owners were furious and demanded an itemized bill. The response was simple: hammer tap, $2. Knowing where to tap, $9,998.

    People debate whether that story is true or just a useful tale for people like me who believe in value-based pricing. But whether it really happened almost does not matter. The lesson still holds.

    People are not paying for the tap. They are paying for the expertise behind it.

    That is what makes AI such an important topic for me. It forces us to confront a question many of us have avoided for years: are we paying for expertise, or are we paying for visible effort?

    Those are not always the same thing.

    The Objections That Actually Matter

    To be clear, I do not think every objection to AI is unreasonable. I have shared plenty of my own concerns, and some of them are serious.

    In fact, I think the strongest arguments against AI have very little to do with how quickly something was created.

    Risk matters. Hallucinations matter. Bad recommendations matter. Compliance, privacy, and security concerns matter. Accountability matters.

    Those are legitimate concerns. What stands out to me is that none of them has much to do with how long it took to create the deliverable.

    They are questions of trust.

    Can the output be trusted? Can the recommendation be defended? Can someone confidently stand behind the work if it is questioned six months from now?

    ```json
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```

    Because when something goes wrong, nobody gets to blame the AI. The employee is accountable. The consultant is accountable. The company is accountable.

    That is why I have always found the quality debate to be the least interesting part of the conversation. The more important question is not whether AI was involved. It is whether the outcome is trustworthy enough for someone to put their name behind it.

    The Outcome Test

    The more I think about AI, the less interested I become in whether it was used.

    Instead, I find myself asking a different set of questions. Was the outcome accurate? Was it useful? Was it better than the alternative? Would I be willing to stand behind it with my name, reputation, and credentials on the line?

    If the answer to all of those questions is yes, then I have a hard time arguing that the production method matters more than the result.

    I suspect this is where many people become uncomfortable because it shifts the conversation away from tools and back toward results.

    Ironically, this is also where humans become more important, not less.

    The future is not machines versus humans. I know, "The Terminator" and "I, Robot" movies will never feel the same. The real shift is humans using AI versus humans who refuse to adapt.

    The premium will not come from avoiding AI. It will come from judgment, taste, decision-making, communication, and accountability.

    AI can accelerate execution, but people still decide what should be built, what should be published, and what risks are acceptable. More importantly, people are still responsible for the outcome.

    The people who lose to AI will not be the ones using it. They will be the ones still evaluating effort while everyone else is measuring outcomes.

    This post first appeared on the author’s website and is republished here with permission.


    Inspired by this post on Search Engine Land.


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  • How AI Is Reshaping Search Demand Across 1M Keywords

    How AI Is Reshaping Search Demand Across 1M Keywords

    I do not see search demand disappearing. I see it moving. In this analysis, 29% of high-volume search demand is declining, while nearly the same amount is growing somewhere else. Overall demand is essentially flat because people are redistributing how and where they search instead of abandoning search altogether.

    That changes how I think about SEO strategy. I would not start by panicking over shrinking keywords. I would start by identifying which queries are losing volume, which ones are gaining momentum, and where a brand can build enough authority to appear in both traditional search results and AI-generated answers.

    This study looks at where search demand is shifting, which industries are seeing the sharpest changes, and what those patterns mean for SEO teams trying to adapt to AI-driven discovery.

    In 2024, Gartner predicted that traditional search engine volume would fall 25% by 2026 as consumers shifted to AI chatbots and virtual agents. Fractl and Search Engine Land set out to test that prediction. (Disclosure: I’m the co-founder of Fractl.)

    I worked from Semrush data covering 1,010,848 high-volume keywords, each with at least 10,000 monthly searches, across 379 brands in eight verticals. I also looked at survey responses from 1,004 U.S. consumers to better understand how AI is changing the way people search.

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    The analysis measured which keywords gained or lost search volume over the past year, how those shifts differed by industry, and how consumer behavior is evolving as AI tools become part of everyday discovery.

    The 29% search decline is real, but it depends on the vertical

    Across more than 1 million high-volume keywords, I found that 29% of search volume is in measurable decline. That is 4 percentage points above Gartner’s forecast. In a dataset representing 35.4 billion monthly searches, that difference represents a meaningful amount of search activity.

    The impact is not evenly distributed. FinTech showed the largest decline at -37.7%, while Lifestyle saw the smallest decline at -15.2%. Only three of the eight verticals, Insurance, SaaS, and Lifestyle, came in below Gartner’s 25% threshold. FinTech, HealthTech, and Wellness were well above it.

    The pattern makes sense when I look at how information-heavy each category is. When a chatbot can answer the question completely, such as summarizing drug interactions, explaining deductibles, or giving a quick overview of a fund, search volume is more likely to fall. When people need to compare prices, complete a transaction, or navigate to a specific site, search demand tends to hold up better.

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    That is why transactional verticals such as SaaS, Lifestyle, Insurance, and Travel are growing or staying close to flat. Information-heavy verticals such as HealthTech, FinTech, and Wellness are seeing the largest declines.

    Before reacting to broad claims about AI-driven search decline, I would benchmark these findings against the specific vertical in question. HealthTech and FinTech teams should expect more exposure than the overall 29% decline suggests. SaaS and Lifestyle teams have more reason to challenge the idea that search demand is simply collapsing.

    Search demand is being redistributed

    The headline number gets attention, but the offset is just as important. Demand did not vanish. It moved to a different set of words, and those are the terms I would want to understand first.

    Among the high-volume keywords tracked, 40.7% are in measurable decline, meaning they lost more than 15% of their volume over the past year. Within that group, the average decline is -41%, and 112,378 keywords lost more than 40% of their volume. For brands that depend on those terms, the impact is significant.

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    At the same time, 20.1% of keywords are growing by more than 15%. When I add up the volume on both sides, the decline and growth almost cancel each other out.

    The 285,489 declining keywords represent roughly 10.29 billion monthly searches. The 140,835 growing keywords represent roughly 10.31 billion monthly searches. Across the entire dataset, the net change is +16.8 million searches per month.

    Fewer keywords are growing than declining, but the growing keywords carry more volume each. That is why the totals balance out. In practical terms, I see demand relocating more than shrinking.

    The vertical-level growth-to-decline ratios show where that new demand is landing. Lifestyle leads at 2.6x, with 40% of keywords growing versus 15% declining. SaaS follows closely at 2.5x, with 48% growing versus 19% declining. HealthTech sits at the other end with an inverted ratio of 0.4x, making it the most disrupted vertical in the set.

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    The first audit I would run is simple: pull the tracked keyword set, filter it by year-over-year volume change, and see which side of the ledger the portfolio sits on.

    Non-branded queries are the most vulnerable

    I see non-branded queries as the easiest ones for AI chatbots to replace. When a search term does not include a brand name, the user is not necessarily trying to reach a specific site or source. The full exchange can happen inside the chat window.

    Across the dataset, 90% of all tracked search volume is non-branded. HealthTech, at 99.6%, and Wellness, at 98.5%, are the most exposed. Insurance, at 73.8%, and SaaS, at 82.0%, are less exposed, and both are growing overall. SaaS volume is up 48% year over year, while Lifestyle is up 40%.

    If I wanted to identify the content most at risk, I would start with keyword patterns. They offer one of the clearest signals in the study.

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    The reason SaaS and Lifestyle can be heavily touched by AI and still grow comes down to what happens after the AI answer. If AI recommends a project management platform or a couch, many people still search for the specific brand, retailer, review, or product page before buying. The AI answer creates a downstream search.

    HealthTech and FinTech often behave differently. A drug-interaction question or a “what is a deductible” query can be answered completely inside the chat window. There may be no next step that sends the user back to Google.

    If a category produces complete AI answers with no natural next click, I would treat AI visibility as a core strategy, not an SEO side project. In those cases, showing up in the answer may be the entire opportunity.

    70% of consumers use AI more, but only 17% use search less

    The keyword data shows what is happening in the index. The survey data shows what is happening in the minds of the people doing the searching.

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    Search behavior is spreading across more platforms. Many people are adding AI to their routines without giving up Google.

    Social platforms are also acting like search engines in a way they did not a few years ago. YouTube leads at 68%, followed by Reddit at 57%, Instagram at 42%, Facebook at 40%, and TikTok at 33%.

    If I had not already prioritized YouTube and Reddit, I would move them up the list. Both rank ahead of TikTok, Instagram, and Facebook as search destinations, and both can also surface in Google results, which gives visibility there a compounding effect.

    What has actually moved from Google to AI

    More than a third of respondents, 35%, say they have not replaced traditional search with AI for anything yet. Among those who have, how-to guides and tutorials have taken the biggest hit.

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    For purchase research, 47% of consumers start with a traditional search engine, tied with online retailers at 47%. Only 13% start with an AI chatbot, and shoppers check an average of three online sources before making a purchase.

    The number I would bring to a strategy meeting is this: nearly one in five consumers, 18%, have bought something based on an AI recommendation without checking it against a separate search.

    That creates a different kind of buyer journey. In that path, the brand may never receive a search-driven touchpoint. To be considered, the brand has to be one of the names the chatbot returns.

    Gen Z and millennials are 2.5x more likely than baby boomers to buy based on an unverified AI recommendation, at 20% versus 7%. Across all consumers, 59% say they are likely to visit a brand’s website after an AI chatbot mentions or recommends it.

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    That is the emerging conversion funnel I am watching closely. Brand mentions in AI answers are starting to function like rankings. Visits to a brand’s website after an AI mention are starting to look like the new click-throughs.

    Trust is still mixed. In the survey, 33% of consumers trust AI and traditional search equally, 46% trust search more, and 20% trust AI more.

    More than half of consumers, 56%, are at least somewhat skeptical of AI product recommendations. I read that as a sign that people are willing to let AI filter and shortlist options, but many still want to verify before they buy.

    The 5-year outlook: Google is not going away, but the shift matters

    When asked whether Google will still be their primary search tool in five years, 52% of consumers say yes, including 17% who say definitely and 35% who say probably. Another 27% are unsure, while 20% say probably or definitely not.

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    The top reasons people prefer AI over traditional search are better summaries across sources, at 21%; faster and more direct answers, at 20%; and the ability to ask conversational follow-up questions, at 19%. More personalized results and avoiding website click-throughs were much lower, at 6% and 4%.

    When asked what would bring them back to traditional search, the top answer was AI giving unreliable answers, at 35%. That means much of this shift depends on whether AI maintains trust as adoption scales. More accurate search results followed at 29%, then a preference for multiple source links at 22%, and privacy concerns at 20%.

    The 20% who expect to leave Google are not the majority, but I would not dismiss them. A strategy does not need to be rebuilt entirely around them today, but brands do need to appear where those users are already moving.

    What this means for content and SEO strategy

    I see Gartner’s 25% prediction as a useful directional warning. The real shift may be steeper, but calling it only a decline misses the more important story. Total search volume is basically flat. What has changed is which searches carry the demand.

    AI visibility is not just a threat to manage. I see it as a distribution channel. With 59% of consumers saying they are likely to visit a brand’s website after an AI mention, GEO has become a meaningful part of brand discovery.

    Earned media, credible third-party coverage, and strong entity signals all help brands appear in chatbot answers. That is why digital PR and GEO are becoming more closely connected.

    Search is moving, not disappearing.

    The brands that lose will be the ones still optimizing mainly for queries that AI now answers better. The brands that win will be the ones building enough authority to become the answer, whether that answer appears in Google or inside a chatbot.

    Methodology

    This study combined two data sources to test Gartner’s 2024 prediction that traditional search engine volume would fall 25% by 2026.

    Fractl and Search Engine Land analyzed Semrush search volume data for 1,010,848 high-volume keywords with 10,000 or more monthly searches each, covering 379 brands across eight verticals: FinTech, HealthTech, Wellness, Travel, Education, Insurance, SaaS, and Lifestyle. The dataset represented 35.4 billion in aggregate monthly search volume. Keyword-level year-over-year volume change was measured as of April 2026 and classified as declining, meaning more than 15% loss; stable, meaning within 15%; or growing, meaning more than 15% gain. Query pattern groupings, including “What is X,” “Best X for Y,” “X vs. Y,” and “How to X,” were applied at the keyword level.

    Fractl and Search Engine Land also surveyed 1,004 U.S. consumers about their search habits, AI tool adoption, and purchase research behavior. The sample was 52% women, 46% men, and 1% nonbinary, with 49% millennials, 26% Gen X, 16% Gen Z, and 9% boomers. The median respondent age was 41, with a range of 18 to 82.


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  • How I Turn Proprietary Data Into AI Citations

    How I Turn Proprietary Data Into AI Citations

    Why proprietary data is your most defensible AI citation asset - featured-image

    When I want a page to feel genuinely original, I start with original numbers. They are still one of the most reliable ways to make content stand apart, especially when those numbers come from the business itself instead of a one-off study created just to fill a content calendar.

    The old approach was to pay a PR or research firm for a loosely related survey, like a car insurance FinTech commissioning road-trip research to earn a mention in Yahoo. I see that play as increasingly outdated. Almost every product now creates data worth publishing, and extracting that data is easier than it has ever been.

    I do not need a full research department to compete here. The bar for standing out is lower than many teams assume.

    View embedded content

    First-party data: The strongest correlation of originality

    On-Page.ai’s recent information gain study scored 150 top-3 Google pages across 50 keywords and 10 verticals. The study looked at how much each page added beyond the rest of its ranking cohort, grading contribution from 0 to 100 by meaning rather than wording.

    The median page scored 52. More importantly, original data correlated with that score more strongly than any other page-level trait, including content length.

    Pages with at most 1 unique figure averaged an information gain score of 40.2. Pages with 15 or more unique figures averaged 62.1, and the score increased steadily at every step in between.

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    The good news is that the bar is not especially high. The study found that top organic results usually include only 4 unique data points on average. If I publish a page with more than 4 real original claims, figures, or answers, I create another lever for earning visibility in increasingly competitive organic search.

    The analysis also found that almost every search leaves adjacent questions unanswered. On-Page used synthetic reader questions, meaning plausible related questions generated for the study, and found room for new pages to answer those questions more completely. That immediately reminds me of query fan-out.

    I saw a similar pattern in an analysis of ChatGPT citations.

    “A single evergreen page covering 10+ query intents is worth more in AI citation reach than 10 single-intent pages. The ROI of comprehensive content is front-loaded: one well-built page compounds citation reach over time. The long tail exists, but the top 5% of pages capture a disproportionate share of ongoing citation activity.” – The science of how AI picks its sources

    That is why I believe high-intent prompts should be monitored across the full buyer journey. I would map them across the five stages from Reasoning Lift: Problem, Exploration, Comparison, Validation, and Selection. I would also use more accurate AI prompt tracking to understand where those questions emerge, then answer them with the kind of knowledge only the brand can provide.

    My main takeaway is simple: most pages are only middling on originality, genuinely original pages are still a minority, and scoring high enough to stand out is achievable without an extraordinary lift.

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    The limitation is just as important. This study focuses on classic search visibility and rankings, which makes sense because the SEO concept of information gain comes from Google patent language. It does not analyze AI citations or mentions, and it does not appear to include AI Mode or AI Overviews.

    Caveat: Being the primary source may not win the citation

    This is the part of proprietary data advice I think gets skipped too often. Everyone says to publish original research. Far fewer people test whether AI rewards the brand that created the number or the page that presents it in the clearest, most extractable way.

    More data analysis is still coming, but based on analyses completed at Growth Memo over the last year, I already see two patterns worth paying attention to.

    • The entity types that predict ChatGPT citations the most are DATE and NUMBER (from The science of what AI actually rewards). Highly cited pages tend to be dense with specific entities, such as a particular methodology, a precise statistic, or a named comparison. Even when another source picks up my proprietary findings and gets cited instead, those external third-party authority signals can still build over time.
    • Entity-richness and balanced sentiment matter (from The science of how AI pays attention). Generic advice is vague and risky. Specific entities are grounded and verifiable. Proprietary data can produce, verify, validate, and create entity-rich content at the same time. I can explain why a feature saves a certain percentage of dollars, how many hours clients save, or how performance compares with previous vendors. When I add balanced sentiment to the analysis and explanation, I get a stronger tactic from the same asset.

    If the hypothesis holds that first-party data is crucial in the era of AI search, then publishing proprietary data is necessary, but it is not enough. LLM extraction structure, along with the sites AI search engines already trust for a topic, helps decide who actually earns the citation, even when the brand owns the data.

    That is the frustrating part: an aggregator can repackage my benchmark into a cleaner, answer-ready page and collect the citation my research earned.

    • Who wins: Brands that already have proprietary product, usage, or pricing data and also structure that data for extraction while continuing to build organic brand authority. This connects directly to How to build an AI SEO strategy that outlasts tactics.
    • Who loses: Brands publishing opinion content that any tool can replicate, brands ignoring off-site authority, and primary sources that bury their own numbers inside narrative instead of surfacing them clearly.

    I do not yet know whether some verticals reward data content more than others. The science series found that citation signals vary sharply by vertical, so I would be surprised by a uniform payoff. Still, I would not claim a pattern without data.

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    How to structure data for extraction

    Owning the data gets me into the visibility race. How I structure that data may decide whether I win the citation.

    In an analysis of 18,012 verified ChatGPT citations, we found a ski-ramp distribution: 44.2% of all citations came from the first 30% of a page. The middle 30-70% earned 31.1%, and content buried deep in a long post was roughly 2.5x less likely to be cited.

    The follow-up analysis across 7 verticals made the target even clearer. The 10-20% band of a page is where AI reads hardest in every vertical, while the first 10% is usually navigation and intro filler that AI skips. The bottom 10% of any page earns only 2.4-4.4% of citations regardless of vertical.

    When I apply that to a data study, the structure becomes straightforward.

    • I lead with the headline statistic. My strongest number belongs in the first 30% of the page, ideally right after the title block where the 10-20% band begins. I want the number, the comparison, and the implication visible quickly.
    • I define the metric immediately. I include one sentence explaining what the number measures and which population it covers. An undefined statistic is harder to extract with confidence.
    • I box the methodology. I make the sample size, time window, and collection method easy to find in a short labeled block. Attribution confidence is part of what makes a number citable.
    • I front-load every secondary finding. I rank findings by strength, with the strongest first. A 20-paragraph narrative buildup may help human suspense, but it can cost machine citations.
    • I skip the suspense close. AI reads more like a busy editor than a patient student. The payoff-at-the-end structure that worked for ultimate guides often works against extraction.

    This post first appeared on the author’s website and is republished here with permission.


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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.

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