SEO Archives SEO

May 18 2026

Rethinking Audience Targeting In A Signal-Loss Era (With The R.E.M. Framework) via @sejournal, @SequinsNsearch

Do you know who your audience is and what they want?

Over the last 20 years or so, we used to rely almost purely on data to answer that question. But as cookie tracking and user signals declined and analytics shifted toward sampling (what we refer to as the “signal-loss era”), we’ve lost some of that superpower. On top of this, we’ve handed over control to hyper-personalized platforms with “black box” targeting algorithms to find our audiences, leaving us less able to truly understand what is going on. And in doing so, we have lost track of the user.

In a way, the abundance of data made us complacent: “Data-informed” became the standard, while “user-informed” strategies progressively faded.

The problem with that over-reliance on data is that it made it “okay” to forget we are fundamentally communicating with humans and creating connections. We focused on the outcome and lost the drive to know who we’re connecting with and what leads us to acquire certain users or lose some.

And while signal loss and AI targeting might be perceived as a constraint, in reality, it is actually a great opportunity to go back to basics of marketing. It means we can focus on really understanding the user as a person, and not as trace fragments of data they leave in our web analytics.

Ultimately, getting to know them means we can serve them better – and find stronger, long-lasting ways to connect.

The Opportunity: Understanding Users And How We Reach Them

Even if we still had the data we had before, would it even be enough? I don’t think so, because it assumes user behavior is limited to what we can observe. In reality, behavior is shaped by a series of small, automatic decisions that happen below the surface, often driving outcomes before any action is even initiated – let alone tracked.

On top of this, when we talk about “understanding the user,” this is often reduced to understanding their needs and a rough demographic, but that’s only part of the picture. Users are people, with unique needs and patterns of thoughts at every stage of their consideration journey.

Now more than ever, we need to truly know who we are talking to and interacting with. What makes them favor us over a competitor? What media and channels are they using so we can reach them? What emotional triggers are really relevant to them? What is important to them at every stage of the journey? Only after answering these questions can we claim to have at least scratched the surface.

I’ve said before that human decision-making is inherently imperfect, shaped by cognitive biases and heuristics that help us navigate complexity without analyzing every option in detail. And that’s the reason why knowing what they want is often not enough to get the full picture – you need to know how they make decisions too.

When we fully understand the user, we can shape our approach ahead of outcomes, inform testing and platform targeting, and even anticipate results before execution.

A Practical Alternative To Cookie-Based Strategies: The R.E.M. Framework

To make sure you can reach the right audience, even when data is scarce and tracking unreliable, you should work with three simple things to aim for: Being Relevant, Everywhere, and Memorable in your strategy, from creatives, messaging, and channel choices.

This is what I call the R.E.M. Framework.

1. Be Relevant (And Relatable)

Relevancy is the first gateway to attention. In a world saturated with competing stimuli, it’s one of the primary filters the brain uses to decide what deserves focus.

Think about it: You might be having a great conversation with a friend in a group full of other people talking, and pay attention only to what they say. And yet, if your name is mentioned by someone else in a conversation you are not listening to, it’s very likely that you will automatically start paying attention to that instead.

This is what is commonly referred to as “the cocktail party effect,” a great example of how stimuli that are relevant to our personal experience, context, and goals can automatically capture our attention even when we are engaged in another task – something that happens consistently on social media, for example.

Today, we often refer to attention as “marketing’s primary currency,” and for a good reason. In a market so saturated, we only have a few seconds to pique our users’ interest before they move on to the next thing. And any content that won’t result in early engagement is likely to be dropped by the algorithm, which won’t serve it to other users as deemed not a good fit for our audience.

This is known by the industry as “the three-second rule,” and might in fact even be optimistic for newer platforms where short-form video prevails, like TikTok videos and Instagram reels. Short-form videos tend to make people forget what they came to the platform for in the first place much faster than long-form videos, and it’s exceptionally easy to lose a viewer on these formats if the hook isn’t instantly strong enough.

But in order to understand how to capture interest early, we need to take a step back and understand how attention works.

As humans, we are consistently exposed to a lot of stimuli at the same time, and we don’t have the cognitive resources to process each one of them, so we select some of them for further processing while ignoring others. We do so via a process called “selective attention” that can be driven by internal motivations (“endogenous orienting”) or external drivers (“exogenous orienting”). In other words, we tend to allocate attention based on our own goals (for example, when we have a deadline and we need to focus on a deliverable) or on the perceptual features of the objects around us (for example, the sound of the phone ringing or a stand-out word in a sentence).

That means that we have two ways to engage someone’s attention: by connecting with their goals, or presenting them with something that stands out in a sea of other similar things.

We can argue that relevancy sits in between these processes and can engage them both. As a matter of fact, when we are researching something, we are already deciding to filter out all the results that seem relevant to our own goal. But it works the other way too: Something relevant to our needs, goals, and context will jump out when we are doomscrolling on socials, even when we are not engaged in a search.

So relevancy is a sort of “catch-all” for attention.

How do you make sure you are immediately relevant?

By identifying what your audience needs, and leading with the solution in the hook. Don’t waste time with obscure messaging or secondary angles that you can elaborate on once you’ve anchored attention.

Strong tests and creatives are the ones that don’t focus on the business, but focus on the user and what they are trying to solve instead. And hyper-personalized platforms make this even more layered. Make the audience see themselves in what you offer, and you’ll shorten the time it takes for them to recognize you as the right choice.

2. Be Everywhere (Your Audience Is)

But can you be relevant to everyone? Of course not. So it’s imperative you understand your audience and their motivations to capture existing demand. And beyond that, you need to be present where they can find you, with the message they’re looking for in that moment.

This is one of the main challenges, now that journeys are so scattered across different platforms and search experiences. There are so many channels people discover us by, that it’s virtually impossible to track where certain journeys even start from. We might get a user from an LLM query, or a social post, or a Google search. Most likely, it’s all of them. A consideration journey is not linear, and it’s in fact the result of a continuous loop of discovery and evaluation, something we know now as “The Messy Middle.” Even the best attribution models rarely capture this.

The image displays a representation of the concept of — The “Messy Middle” from Google’s 2020 consumer behavior report. (Screenshot by author, April 2026)

So, the solution is to work cross-functionally to cast a wide net across different channels, because visibility builds trust. “Out of sight, out of mind”: Our brain forms associations that strengthen with repeated exposure, and drops whatever is not used. If you’re consistently present where your audience is, with relevant content, you create the perception that you are indeed everywhere – without actually having to be.

And that matters, because repeated exposure is part of how we cast a choice in a sea of options. We call this “availability heuristic,” a decision-making shortcut that makes us favor what comes to mind easily: what we’ve seen often, recently, or remember clearly. Think about recommending a movie. You’re far more likely to mention something you’ve just watched, or keep seeing suggested, than something from years ago.

So, while relevance gets you noticed, presence keeps you top of mind. That means that when someone is ready to act, you’re already part of the consideration set, often before they even start a search.

Of course, going omnichannel is a beast in itself. Creatives and messages in one platform won’t work on another – you still need to test and iterate – but if you do it from a customer lens, your work is much simpler, and the benefits are two-fold: You can target different moments in the journey and stay top of mind.

But how do you prioritize channels when resources are limited?

You can rely on demographic research, personas, and early discovery data to establish a rough baseline, although that only gets you so far. Mapping who they are doesn’t tell you what they do when they make a choice, and how those behaviors shift across the journey. That’s the piece you have to find out for yourself: How do they make decisions? Who do they rely on for information, and where do they go to find it? And just as importantly, where are they when they’re not actively looking, and how can you meet them there?

And this is where personas fall short. They might tell you what people need and who they are, but not how they feel when making a decision. Often, what gets labeled as a bad strategy is simply incomplete research.

To really understand your audience, you need all of this information, which brings us to the next part.

3. Be Memorable

Being memorable is the one variable that still carries the most weight – yet is the hardest one to achieve. Why? Because it relies on creating a meaningful connection with the audience. And what that connection looks like can vary a lot across different individuals.

The general playbook to produce an emotion in marketing has often relied on the assumption that we share the same set of basic reactions, something that is based on Paul Ekman’s studies isolating fear, anger, happiness, surprise, disgust, and sadness as the “six basic emotions.”

And while it is true that some of these can be shared, the reality of the human emotional experience is much more nuanced and is often modulated by personal context, expectation, cultural values, and much more.

While attention works similarly across different individuals, memorability relies on personal context, values, and experiences. Think about an ad that stayed with you. What was the reason why you remember it so well? Chances are, it is because of the way it made you feel. Another reader of this article will have chosen a completely different ad.

Some brands, messages, or creatives stay with us because they elicit an emotional reaction. They make us laugh, they trigger nostalgia, sometimes they outrage us. But they all make us feel a certain way. And even when we choose employing rules of thumb like going for what we already know (“familiarity bias”) or what our peers suggest (“social proof”), it’s often because these are choices that are validated and make us feel safe.

We often hear that people make decisions emotionally, then they justify them rationally. This idea is reflected in early theories like Damasio’s “Somatic Marker Hypothesis,” which proposes that emotional signals influence decision-making, and is supported by neurophysiological evidence showing that physiological arousal varies between liked and disliked brands, pointing to the involvement of emotional processes in brand evaluation.

Bar chart comparing mean skin conductance (microsiemens) for liked vs. disliked brands. Disliked brands show higher mean skin conductance (3.583 µS) than liked brands (3.48 µS), with a statistically significant difference indicated by an asterisk. — Electrodermal activity to liked versus disliked brands. Disliked brands elicited significantly higher physiological arousal than liked brands, illustrating that emotional intensity can vary independently of stated preference (Walla et al., 2011).

And that’s important, because the way we feel about a brand determines not only our perceptions but it’s pervasive of the entire experience with them, including trust and willingness to engage with their messaging and offer. We remember experiences for how they made us feel; we connect with some brands and ethos, and we disconnect wildly from some. Once you gain that memorability with your audience, you have an easier time retaining it – as well as guiding them to choose you.

What does this mean for you? Get acquainted not only with what your user needs or what is most likely to catch their eye, but with their personal and cultural context, how they feel, and what their expectations and values are – because these are all aspects that influence the relationship between brand and consumer. A genuine connection will make the user bypass any intermediate evaluation, and make you stand out from competitors, looping us back to our R – the relevancy you aim for in the first step of this framework.

Takeaways

Catching attention isn’t the only metric of success in the signal loss and hyper-personalization era. You need to be everywhere, and to stay top of mind when your audience is looking for the solution you can offer. So it’s imperative you know your users, their motivations, and their emotional states to capture existing demand and connect with them, wherever they are.

Easy, right?

Not really, but here are some starting points:

Find what your audience needs by collating data that goes beyond search, and takes into account customer service logs, user interviews, and social scraping (both for your brands and your competitors), so that you can capture both the pre-purchase and post-purchase journeys. Use that data to inform your USP and messaging in your test and creatives. Make it all about them, not you.
Don’t take channels for granted, or ignore them just because they’re not useful to your immediate key performance indicators (KPIs). Visibility is often the result of compound actions and cross-functional collaboration. Map out your discoverability across different channels, content formats, and ways to consume content, so that you can target different moments in your audience’s journey. Let this be your guiding light when you pick your battles.
Get to know your audience at a granular level: What do they feel when they search? What are their values? What are their expectations? If they know us, how do they feel about us? Use those emotional drivers to understand what creatives, messaging, and format might be best to use as a gateway to create a meaningful connection.

In summary, start with finding your audience, learn how they decide and understand their underlying needs; all of this will inform your unique selling proposition (USP) and product value proposition, your messaging and creatives, as well as your distribution channels and the choice of formats.

It’s time we go beyond personas and start looking at the real people behind the screen.

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Featured Image: ImageFlow/Shutterstock

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May 18 2026

Anthropic’s Infrastructure Crisis – What It Means for Marketers & SEO Pros via @sejournal, @gregjarboe

On May 6, 2026, Anthropic CEO Dario Amodei walked out onto a stage at his company’s developer conference in San Francisco and said something you almost never hear from a tech CEO: Growth is the problem.

Anthropic had planned for a 10-fold expansion. What it got was 80-fold growth in Q1, on an annualized basis. Revenue has crossed $30 billion, up from $9 billion at the end of 2025. The company is weighing a funding round at a reported $900 billion valuation – which, if it closes at those terms, would likely surpass OpenAI’s most recent post-money valuation of $852 billion. And yet, as Amodei told the audience that day, “I hope that 80-times growth doesn’t continue because that’s just crazy and it’s too hard to handle.”

He wasn’t being falsely modest. Demand for Claude has already created what Anthropic described as “inevitable strain on our infrastructure,” hitting reliability and performance during peak hours. Hours before Amodei took the stage, the company announced a deal with SpaceX – which, earlier this year, merged with xAI, the company behind the Grok AI models, now rebranded SpaceXAI – to take over the entire compute capacity at the Colossus 1 data center in Memphis, giving it access to more than 300 megawatts of capacity and 220,000 Nvidia GPUs.

The detail worth noting: xAI and Anthropic are direct competitors at the model layer. The fact that Grok’s infrastructure is now running Claude’s workloads is the clearest signal yet of how constrained high-end compute capacity has become. That’s a bridge built under emergency conditions, not a planned expansion.

So, why should SEO professionals, content marketers, and entrepreneurs care about Anthropic’s infrastructure problems? Because this story is actually about something much bigger than one company scrambling for server capacity.

This Has Happened Before

In 2011, I read I’m Feeling Lucky: The Confessions of Google Employee Number 59 by Douglas Edwards, who was Google’s first director of marketing and brand management. That’s when I learned how close Google came to buckling under its own success in the early days.

In late 1999, Edwards wrote, “Google began accelerating its climb to market domination. The media started whispering about the first search engine that actually worked, and users began telling their friends to give Google a try. More users meant more queries, and that meant more machines.” Then the machines became impossible to get. A global shortage of RAM hit at the worst possible moment, and Google’s system, as Edwards put it, “started wheezing asthmatically.”

That infrastructure crisis drove decisions that shaped the web for the next two decades. Google started filtering duplicate content – even non-malicious versions like printer-friendly pages – because every redundant page required adding hardware without improving user experience. The constraint shaped the product. The product shaped SEO.

Anthropic’s compute crisis is the same dynamic, playing out 25 years later at a different scale. The question isn’t whether they’ll solve it. They will. The question is what decisions they’ll make under pressure, and how those decisions will reshape the products that millions of marketers depend on.

What The Data Actually Shows

When I went looking for what this growth moment means for practitioners, I found the headlines and the data pointing in surprisingly different directions.

Rand Fishkin recently shared findings from the Datos State of Search Q1 2026 report, which draws on clickstream data from tens of millions of real devices. His summary was pointed: AI is disrupting traditional search – no, the data doesn’t show that. AI tools are growing faster than traditional search in absolute terms – no, traditional search is still outpacing AI tool growth on an absolute basis. AI Mode in Google is huge – no, it’s still under 0.2% share, growing but still small. ChatGPT is pulling away from Claude – actually, no. Claude is closing the gap, Gemini holds the number two spot and is growing, and ChatGPT has plateaued since September 2025.

These are not the narratives that get clicks. They are, however, what the data says.

At the same time, I went to Think with Google and worked through its report, “The Rise of the Super-Empowered Consumer,” which tells a different part of the same story. Some of what’s in there deserves more attention than it’s getting. AI Overviews is used by over 2 billion people, and users report making decisions faster and with more confidence. AI Mode now has over 75 million daily active users, with nearly 1 in 6 queries using voice or images. Queries in AI Mode run three times longer than traditional searches, and sessions are becoming more conversational. Google Lens handles over 25 billion visual searches every month. Shoppers are 2.3 times more likely to use Google Search than ChatGPT for purchase decisions, and 40% of consumers who use Google AI Mode for shopping say they’re using ChatGPT less as a result.

Two different pictures of the same moment. Both accurate. Neither is complete on its own.

The Takeaway For Practitioners

The AI industry is generating a firehose of information, and most of it gets consumed at the headline level. A company announces 80-fold growth, and people read it as a story about AI winning. Fishkin publishes data showing traditional search still outpacing AI tools in absolute volume, and people read it as a story about AI losing. Google publishes a consumer report showing AI Overviews reaching 2 billion users, and people read it as confirmation that SEO is dead.

None of those readings are wrong. All of them are incomplete.

The strategic value isn’t in reading the news. It’s in following the thread further – downloading the Datos report, working through the Google consumer study, checking the CNBC article against the Cryptopolitan analysis of what the Anthropic-SpaceX deal actually signals about the infrastructure war playing out between the major AI companies.

Google’s early infrastructure crisis produced lasting decisions about duplicate content that practitioners are still navigating. Anthropic’s current one will produce decisions about rate limits, model availability, enterprise pricing, and compute allocation that will shape how Claude-powered tools perform for the marketers and developers using them. Those decisions are already being made.

The practitioners who understand the context those decisions come from will be better positioned than those who only read the headline.

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Featured Image: Anton Vierietin/Shutterstock

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May 17 2026

Google-Agent: The Web’s New Visitor Just Got An Identity via @sejournal, @slobodanmanic

On March 20, 2026, Google quietly added a new entry to its official list of web fetchers. Not a crawler. Not a training bot. An agent.

Google-Agent is the user agent string for AI systems running on Google infrastructure that browse websites on behalf of users. When someone asks an AI assistant to research a product, fill out a form, or compare options across websites, Google-Agent is the thing that actually visits the page. Project Mariner, Google’s experimental AI browsing tool, is the first product using it.

This is not Googlebot. Googlebot crawls the web continuously, indexing pages for search. Google-Agent only shows up when a human asks it to. That distinction changes everything about how it operates.

Robots.txt Does Not Apply

Google classifies Google-Agent as a user-triggered fetcher. The category includes tools like Google Read Aloud (text-to-speech), NotebookLM (document analysis), and Feedfetcher (RSS). All of them share one property: a human initiated the request. Google’s position is that user-triggered fetchers “generally ignore robots.txt rules” because the fetch was requested by a person.

The logic: If you type a URL into Chrome, the browser fetches the page regardless of what robots.txt says. Google-Agent operates on the same principle. The agent is the user’s proxy, not an autonomous crawler.

This is a meaningful departure from how OpenAI and Anthropic handle similar traffic. ChatGPT-User and Claude-User both function as user-triggered fetchers, but they respect robots.txt directives. If you block ChatGPT-User in robots.txt, ChatGPT won’t fetch your page when a user asks it to browse. Google made a different call.

Website owners who relied on robots.txt as a universal access control mechanism now have a gap. If you need to restrict access from Google-Agent, you’ll need server-side authentication or access controls. The same tools you’d use to block a human visitor.

Cryptographic Identity: Web Bot Auth

The more significant development is buried in a single line of Google’s documentation: Google-Agent is experimenting with the web-bot-auth protocol using the identity https://agent.bot.goog.

Web Bot Auth is an IETF draft standard that works like a digital passport for bots. Each agent holds a private key, publishes its public key in a directory, and cryptographically signs every HTTP request. The website verifies the signature and knows, with cryptographic certainty, that the visitor is who it claims to be.

User agent strings can be spoofed by anyone. Web Bot Auth cannot. Google adopting this protocol, even experimentally, signals where agent identity is heading. Akamai, Cloudflare, and Amazon (AgentCore Browser) already support it. Google brings the critical mass.

This matters because the web is about to have an identity problem. As agent traffic increases, websites need to distinguish between legitimate AI agents acting on behalf of real users and scrapers pretending to be agents. IP verification helps, but cryptographic signatures scale better and are harder to fake.

What This Means For Your Website

Google-Agent creates a three-tier visitor model for the web:

Human visitors browsing directly.
Crawlers indexing content for search and training (Googlebot, GPTBot, Google-Extended).
Agents acting on behalf of specific humans in real time (Google-Agent, ChatGPT-User, Claude-User).

Each tier has different access rules, different intentions, and different expectations. A crawler wants to index your content. An agent wants to complete a task. It might be reading a product page, comparing prices, filling out a contact form, or booking an appointment.

Here’s what to do now:

Monitor your logs. Google-Agent identifies itself with a user agent string containing compatible; Google-Agent. Google publishes IP ranges for verification. Start tracking how often agents visit, which pages they hit, and what they attempt to do.

Check your CDN and firewall rules. If your security tools aggressively block non-browser traffic, Google-Agent may be getting rejected before it reaches your server. Verify that Google’s published IP ranges are permitted.

Test your forms and flows. Google-Agent can submit forms and navigate multi-step processes. If your checkout, booking, or contact forms rely on JavaScript patterns that confuse automated systems, agent visitors will fail silently. Semantic HTML and clear labels remain the foundation.

Accept that robots.txt is no longer a complete access control tool. For content you genuinely need to restrict, use authentication. robots.txt was designed for crawlers. The agent era needs different boundaries.

The Hybrid Web Isn’t Coming. It’s Logged

A year ago, the idea that AI agents would browse websites alongside humans was a conference talk prediction. Today, it has a user agent string, published IP ranges, a cryptographic identity protocol, and an entry in Google’s official documentation.

The web didn’t split into human and machine. It merged. Every page you publish now serves both audiences simultaneously, and Google just made it possible to see exactly when the non-human audience shows up.

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This post was originally published on No Hacks.

Featured Image: Summit Art Creations/Shutterstock

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May 15 2026

Google’s New AI Search Guide Calls AEO And GEO ‘Still SEO’ via @sejournal, @MattGSouthern

Google published a new documentation page to help websites optimize for generative AI features in Search, including AI Overviews and AI Mode.

The page, “Optimizing your website for generative AI features on Google Search,” expands Google’s prior AI features documentation published in 2025. The earlier page explains how AI features work, how inclusion is controlled, and how performance is reported. The new guide focuses more directly on optimization advice and tactics Google says site owners can ignore.

Two sections are specifically worth highlighting. Google directly names popular optimization tactics it says aren’t necessary, and it redefines the AEO/GEO conversation as part of standard SEO.

Google Says AEO And GEO Are ‘Still SEO’

Google opens by confirming that foundational SEO best practices remain relevant for generative AI search. Its AI features are “rooted in our core Search ranking and quality systems” and rely on retrieval-augmented generation (RAG) and query fan-out to surface content from the Search index.

On the terminology debate, Google is direct. It defines “AEO” as “answer engine optimization” and “GEO” as “generative engine optimization,” then states:

“From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.”

This echoes positions Google employees have taken at conferences. Gary Illyes and Cherry Prommawin told Search Central Live attendees that GEO and AEO don’t require separate frameworks. The position now appears in Google’s published documentation, providing an official reference to cite.

What Google Says You Don’t Need To Do

The guide includes a “Mythbusting generative AI search” section listing tactics it calls unnecessary for Google Search. The guide is more explicit than Google’s prior AI features page, particularly in naming llms.txt, chunking, inauthentic mentions, and AEO/GEO directly.

The guide says site owners can ignore the following for Google Search.

On llms.txt files and other “special” markup, Google says you don’t need to create machine-readable files, AI text files, markup, or Markdown to appear in generative AI search. Google may discover and index many file types beyond HTML, but that doesn’t mean those files receive special treatment.

On “chunking” content, the guide says there’s no requirement to break content into small pieces for AI systems. Google’s systems “are able to understand the nuance of multiple topics on a page and show the relevant piece to users.” Danny Sullivan made similar comments in January 2026, saying he’d spoken with Google engineers who recommended against chunking.

On rewriting content for AI systems, Google says AI systems can understand synonyms and general meanings. Site owners don’t need to capture every long-tail keyword variation or write in a specific way for generative AI search.

On seeking inauthentic “mentions,” the guide acknowledges that AI features can surface what’s said about products and services across blogs, videos, and forums. But it says seeking inauthentic mentions “isn’t as helpful as it might seem” because core ranking systems focus on quality while other systems block spam.

On structured data, the guide says it isn’t required for generative AI search and there’s no special schema.org markup to add. It recommends continuing to use structured data as part of an overall SEO strategy for rich results eligibility.

Several recommendations run counter to advice that appears in some AI search optimization guides. Multiple GEO resources have promoted chunking and structured data as priorities for AI search visibility.

What Google Says To Focus On

The optimization advice follows familiar SEO territory, though Google contextualizes it for AI features.

Google puts particular emphasis on “non-commodity content.” It contrasts commodity content (“7 Tips for First-Time Homebuyers”) with a non-commodity alternative (“Why We Waived the Inspection & Saved Money: A Look Inside the Sewer Line”). The distinction is whether content provides unique insight beyond common knowledge.

On the technical side, pages must be indexed and eligible for snippets to appear in generative AI features. Google recommends following crawling best practices, using semantic HTML where possible, following JavaScript SEO best practices, providing good page experience, and reducing duplicate content.

Local and ecommerce optimization gets its own section. Google recommends Merchant Center feeds and Google Business Profiles for product and local business visibility in AI responses. It also mentions Business Agent, a conversational experience that lets customers chat with brands on Google Search.

Agentic Experiences Get Initial Guidance

A new section on agentic experiences describes AI agents as “autonomous systems that can perform tasks on behalf of people, such as booking a reservation or comparing product specifications.”

Google notes that browser agents may access websites by analyzing screenshots, inspecting the DOM, and interpreting the accessibility tree. The guide links to web.dev’s guide to agent-friendly website best practices and references the Universal Commerce Protocol (UCP) as an emerging protocol that “will allow Search agents to do more.”

Google announced UCP earlier this year, and Vidhya Srinivasan’s annual letter said it was co-developed with Shopify with more than 20 companies endorsing it.

Why This Matters

This guide gives Google’s most explicit guidance yet on what you should and shouldn’t do for generative AI features in Search. It consolidates positions that were previously scattered across conference talks, podcast appearances, and blog posts into a single reference.

The mythbusting section carries the most weight. Google is now telling you in its own documentation to skip tactics that a growing industry of AEO/GEO services has been promoting. That doesn’t settle the debate for non-Google AI platforms like ChatGPT or Perplexity, which may weight signals differently. But for Google’s own AI features, the guidance is now on record.

The agentic experiences section puts browser agents and UCP into Google’s official documentation for site owners. The guidance is early, and Google frames it as optional for businesses where agent access is relevant.

Looking Ahead

Google’s closing section says you don’t need to accomplish everything in the document to succeed. It notes that “plenty of content thrives in Google Search (including generative AI experiences) without any overt SEO at all.”

The agentic experiences guidance is labeled as something to explore “if this is something that’s relevant to your business and you have extra time.” That suggests Google sees agent optimization as forward-looking rather than urgent.

Featured Image: Anatolir/Shutterstock

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May 15 2026

GA4 Tracks AI Assistant Traffic, FAQ Results Gone – SEO Pulse via @sejournal, @MattGSouthern

Welcome to this week’s Pulse. The updates affect how you measure AI assistant traffic, what structured data does for visibility, and how a major publisher is planning for life after search.

Here’s what matters for you and your work.

Google Analytics Adds Native AI Assistant Channel

Google Analytics now assigns traffic from recognized AI chatbots to a dedicated “AI Assistant” default channel group. Custom channel groups with regex patterns are no longer the only way to separate AI assistant visits from referrals.

Key Facts

Sessions from recognized AI assistants now receive “ai-assistant” as the medium, route to a new “AI Assistant” default channel, and get a reserved “(ai-assistant)” campaign label. Google named ChatGPT, Gemini, and Claude as examples, but hasn’t published the full list of recognized referrers. All three changes happen automatically.

Why This Matters

Anyone running a custom channel group to isolate AI chatbot traffic can now compare their setup against Google’s native version. The custom regex patterns Google recommended last August still cover platforms outside the recognized referrer list. Both can run side by side.

The bigger question is what you do with the data once it’s visible. AI assistant traffic is now a distinct line item in acquisition, user, and channel reports. That makes it easier to compare conversion behavior, session quality, and volume against organic search without filtering or manual workarounds.

Google hasn’t said how quickly the recognized referrer list will expand as new platforms launch. If you track AI assistants beyond the three named examples, keep your custom groups running.

What Industry Professionals Are Saying

Kevin Indig, Growth Advisor at Growth Memo, commented on LinkedIn:

“Was about time! Literally complained about this on stage yesterday”

Johan Strand, Senior Digital Analyst and Partner at Ctrl Digital, wrote on LinkedIn:

“If you already have a Custom Channel Group set up to check for AI traffic, it´s probably a good idea to adapt it now.”

Read our full coverage: Google Analytics Adds AI Assistant As Default Channel Group

Google Completes FAQ Rich Results Deprecation

Google deprecated FAQ rich results, completing a removal that started a few years ago. The company added a notice to its FAQ structured data documentation without a blog post or separate explanation.

Key Facts

FAQ rich results stopped appearing in search results. Google will remove the FAQ search appearance filter in Search Console, the rich result report, and support for Rich Results Test in June. API support ends in August.

Why This Matters

If your reporting pipelines pull FAQ-specific data from the API, those API calls need to be updated before the August cutoff.

Leaving the markup in place shouldn’t create problems, but it no longer produces that visible result. Whether FAQ schema aids AI search is a separate question, and the deprecation doesn’t answer it.

Read our full coverage: Google Drops FAQ Rich Results From Search

Ahrefs Report: Adding Schema Didn’t Increase AI Citations

An Ahrefs report tracked 1,885 pages that added JSON-LD schema and found no meaningful increase in AI citations across Google AI Overviews, AI Mode, or ChatGPT.

Key Facts

Ahrefs matched each treated page against controls that never added schema and measured changes over 30-day windows. AI Overviews showed a 4.6% decline relative to controls, while AI Mode (+2.4%) and ChatGPT (+2.2%) showed changes too small to distinguish from noise.

Why This Matters

The correlation between schema and AI citations has been widely cited as evidence that structured data improves AI visibility. Ahrefs tested whether the relationship appeared causal and found no evidence of a meaningful lift, at least for pages already being cited. Sites with schema tend to also invest in better content, stronger authority, and more links. Those factors may explain the correlation better than the markup itself.

The report can’t say whether schema helps pages that aren’t yet visible to AI systems. That’s a different population that needs its own test. For pages already earning citations, though, adding JSON-LD is unlikely to be the unlock.

What SEO Professionals Are Saying

Chris Long, Co-founder of Nectiv, wrote on LinkedIn:

“this data is changing my viewpoint a bit on how effective it is at actually influencing AI citations.”

Read our full coverage: Schema Markup Didn’t Move AI Citations In Ahrefs Test

Condé Nast CEO: Plan As If Search Traffic Will Be Zero

Condé Nast CEO Roger Lynch said he told company teams to plan their businesses as if search traffic were zero. Lynch made the comments in an interview on TBPN, a tech talk show OpenAI acquired in April.

Key Facts

Lynch described three consecutive years in which internal forecasts underestimated the actual declines in search traffic. He expects search to settle at a single-digit percentage of total traffic, not literally zero.

Lynch pointed to a “barbell effect” in which large, authoritative brands and small, niche publications are performing well, while brands in the middle are most exposed. Condé Nast’s digital subscriptions grew 29% in revenue last year.

Why This Matters

Lynch is describing what the third-party data has been showing for months. Chartbeat reported a 60% decline in search referrals for small publishers over two years. The Reuters Institute found that media leaders expect search traffic to fall by more than 40% over three years. The difference is that a CEO running Vogue, The New Yorker, and GQ is now building budgets around those numbers.

The barbell observation is worth testing against your own client portfolio or publishing operation. Lynch’s argument is that brands without deep category authority or a strong niche focus lack a clear path forward. AI Overviews, commerce links, and sponsored results fill the page before organic listings appear.

What SEO Professionals Are Saying

Kevin Indig, Growth Advisor at Growth Memo, commented on LinkedIn:

“Makes sense, no escape hatch for publishers in AEO.”

Read our full coverage: Condé Nast CEO: Plan As If Search Traffic Will Be Zero

Theme Of The Week: The Measurement Is Catching Up To The Problem

The tools and signals that defined search visibility for years are being deprecated, questioned, or abandoned by the publishers who depended on them.

FAQ rich results are gone. Schema’s role in AI citations is weaker than the correlation suggested. A major publisher is planning as if search traffic won’t recover. Each story involves an environment where the old measurement infrastructure no longer matches the landscape.

The GA4 update is the other side of that coin. Google is building native tracking for the traffic source that’s growing while the traditional one contracts.

AI assistant traffic is a fraction of what search delivers. But it’s now visible by default, in the same reports, next to the channels it’s measured against.

Top Stories Of The Week:

More Resources:

Featured Image: PeopleImages/Shutterstock; Paulo Bobita/Search Engine Journal

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May 14 2026

Stop Treating AI Visibility As One Problem. It’s Actually Three, On Three Different Layers via @sejournal, @DuaneForrester

When a brand stops appearing in ChatGPT, or when its share of voice in Perplexity drops by half over a quarter, the typical response from the marketing org is to write more content. Sometimes a lot more. The thinking goes that if AI systems aren’t surfacing the brand, the fix is to feed them more material to work with. That instinct is a misdiagnosis. It’s a retrieval-layer fix being applied to what is increasingly a different kind of problem entirely, and the cost shows up as wasted budget, missed quarters, and a creeping sense that the work isn’t connecting to the outcomes anymore.

The mistake is treating AI visibility as a single problem when it isn’t. There are three structurally different layers between your brand and the answer a user receives, each with its own failure modes, its own fixes, and increasingly its own organizational owner. Diagnose the wrong layer, and the fix doesn’t land.

Where Most Of The Conversation Has Been Living

The first layer is retrieval. This is where the AI search optimization conversation has spent most of the last two years. The mechanics are familiar in shape if not in detail. When a model needs to answer a question grounded in real-world content, it pulls relevant material from external sources and uses that material to construct the response. The technical name is retrieval-augmented generation, or RAG, and the layer it operates on is the gateway between your content and the model’s output.

This is where crawlability, parseability, and chunk-friendliness do their work. If your content can’t be retrieved cleanly, nothing downstream matters. The visibility tracking platforms most marketing teams have evaluated this year measure outcomes that depend on this layer functioning, which is why they tend to reward the same disciplines that produced good results in classical search: structured content, schema markup, self-contained answers, clean technical implementation.

But retrieval has a structural limit, and Microsoft Research has been unusually direct about it. Plain RAG, in their words, struggles to connect the dots. It retrieves chunks of text that look relevant to the question, but it cannot reason about how those chunks relate to each other. When the answer requires synthesizing information across multiple sources, or when the question is broad enough that the right answer depends on understanding patterns across an entire dataset, retrieval alone breaks down. The model gets the chunks and has to guess at the relationships, and guessing is where hallucinations enter.

The discipline question this layer asks is straightforward. Can the model retrieve our content at all, and is it retrieving the right content for the right query? Most marketing teams have some version of this work in flight already, even if the specific tactics have shifted from classical SEO. But retrieval is only the gateway. Even when a model retrieves your content correctly, what it does with it depends on whether you exist as a recognized thing in the layer above.

Where Entity Recognition Does The Real Work

The second layer is the relationship layer, and the dominant structure on it is the knowledge graph. The major search infrastructures all maintain one. Google’s Knowledge Graph, Microsoft’s Satori, and the open knowledge graph built on Wikidata and schema.org collectively define how your brand is represented as an entity, what category you sit in, and which other entities you’re connected to.

This is the layer that decides whether AI Overviews and large language model responses treat you as a recognized member of your category, or as one fuzzy candidate string among many. Brands that exist as clean, well-defined entities get cited consistently. Brands that exist as undifferentiated tokens scattered across the open web get pattern-matched against fifty other candidates and lose more often than they win.

Knowledge graphs have been around long enough that the discipline is reasonably mature. Schema markup on owned properties, consistent naming and identifiers across the open web, structured presence on the high-trust nodes like Wikidata entries and review platforms, and the slow accumulation of brand mentions in contexts that the graph treats as authoritative. This is where the unlinked brand mentions conversation lives, because consistent contextual mentions strengthen the entity even without a hyperlink attached. The fix at this layer is structural rather than volume-based. Writing more content does almost nothing if the entity definition underneath it is fuzzy.

The discipline question here is harder than the retrieval-layer question. Are we a clean, defensible entity in our category, or are we still being pattern-matched against fifty other candidate strings? A brand that can’t answer that question affirmatively is going to lose ground in AI search, regardless of how much content it produces, because the second layer is where the model decides what your content is actually about.

The knowledge graph tells the model what your brand is. But increasingly, your brand has to function inside a third layer that most marketing teams haven’t met yet, where the model isn’t just understanding you, it’s being asked to reason about you on behalf of someone making a decision.

The Layer Enterprise Companies Are Quietly Building Right Now

The third layer is the context graph, and this one needs a careful introduction because most of the marketing conversation hasn’t reached it yet.

A context graph has the same structural shape as a knowledge graph, with entities, relationships, and typed connections, but it’s grounded differently. A knowledge graph models the world. It tells you what things are and how they relate in general. A context graph models a specific organization’s data, decisions, policies, and operational reality. The cleanest framing I’ve seen calls a knowledge graph the library and a context graph the operating manual written by the people who actually run the place. The library tells you what exists. The operating manual tells you what’s relevant, what’s authorized, and what to do about it right now. The library is read-only semantic infrastructure. The operating manual is a living operational layer that grows every time a business process executes.

What separates a context graph from anything that came before it is that governance lives inside the graph rather than alongside it. Policies, permissions, validity windows, and authorization rules are nodes the graph itself queries, not external documentation applied at the edges. When an agent retrieves something from a context graph, the result has already been filtered through what’s currently authorized, currently valid, and currently applicable. The graph is also continuously evolving, so what it knows about you this week is not necessarily what it knew last quarter. That’s where the word “governed” comes from when people in this space talk about governed retrieval. It isn’t a frame, but rather the architecture.

That architecture used to be invisible to anyone outside the organization that built it, which is why marketers haven’t had to think about it. That changed at Google Cloud Next ’26, when Google introduced the Knowledge Catalog inside its new Agentic Data Cloud. Google’s own description of the product, written in their own first-party blog content, says the Knowledge Catalog constructs a unified, dynamic context graph of your entire business, enabling you to ground agents in all of your business data and semantics. That sentence is the moment the term left the data-engineering blogs and entered enterprise procurement vocabulary.

The reason this matters for marketing is that context graphs are what’s going to power the next generation of agents inside your enterprise customers. Gartner projects that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. Procurement agents, competitive intelligence agents, content strategy agents, vendor evaluation agents. These agents won’t be reasoning about your brand from the open web. They’ll be reasoning about your brand from inside their company’s context graph, and what that graph says about you depends on what got ingested into it.

That ingestion is where the work for marketing lives. The brand that arrives at the context graph fragmented arrives weak. If your category positioning is inconsistent across owned and earned media, the graph picks up the contradictions and represents you ambiguously. If your entity data is fuzzy on the second layer, it stays fuzzy when it gets pulled into the third. If your third-party signal is thin or contradictory, the graph has nothing solid to anchor to. The work is upstream of the graph, but the consequences land downstream of it, inside an agent’s reasoning process that you’ll never see directly.

I think of this discipline as governed visibility. The practice of making sure your brand arrives at the context graph in a state that holds up under governed retrieval. Clean entity definition, consistent third-party representation, reliable structured data, and a category position that doesn’t fall apart when an agent traverses the relationships around it. Governed visibility isn’t a new tactic stack. It’s the result of doing the second-layer work well enough that the third layer has something solid to ingest.

The discipline question at this layer is the one most marketing teams haven’t started asking yet. When an agent inside our customer’s company is reasoning about us, what does it find, and is the version of us it finds the version we’d want it to act on?

Three layers, three different problems, three different fixes. But also three different responsibility zones, and that’s where most teams are quietly losing ground.

The Reason Most Teams Will Lose This Even Though They’re Working Hard

Each layer maps to a different organizational responsibility, and most marketing teams only own one of the three cleanly.

The retrieval layer is shared with web, dev, and sometimes IT. Marketing influences what gets published, but the infrastructure that makes content retrievable sits in someone else’s domain.
The knowledge graph layer is genuinely marketing’s territory. Schema discipline, entity definition, third-party signal, brand consistency, the slow structural work that compounds over years.
The context graph layer is where IT owns the infrastructure inside the customer’s organization, but marketing has to influence what gets ingested. The work is upstream, and the consequences land downstream, often invisibly.

The teams that win in 2026 are the ones that figured out how to operate across all three responsibility zones rather than perfecting their work on just one. Most teams I see are still optimizing their owned content, which is the retrieval layer, while losing ground on entity definition, which is the knowledge graph layer, and remaining completely absent from the context graph conversation, which is the layer where some enterprise businesses are quietly standing up right now.

The work isn’t writing more content. The work is figuring out which layer the problem actually lives on, and building the disciplines to operate on all three. Governed visibility is the third-layer discipline that marketing is going to have to develop, whether or not the term sticks. The brands that build it now will look prepared in eighteen months. The brands that don’t will be wondering why their content investments stopped producing the visibility they used to.

If any of this lands or contradicts what you’re seeing inside your own teams, I want to hear about it. Drop a comment about which layer your work has been concentrated on, where you’re seeing the gaps, or where the responsibility zones break down inside your organization. The patterns are still forming, and the conversations in the comments tend to be fresher than anything else.

A lot of the measurement frameworks for this kind of work sit in The Machine Layer, which expands the original 12 KPIs for the GenAI era into something teams can actually run against.

More Resources:

This was originally published on Duane Forrester Decodes.

Featured Image: Master1305/Shutterstock; Paulo Bobita/Search Engine Journal

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May 14 2026

Direct Traffic & Popularity – Correlation, Not Causation via @sejournal, @TaylorDanRW

Last week, Cyrus Shepard published an AI citation ranking factors study, and it created a lot of noise on X, LinkedIn, and a number of private WhatsApp groups I’m in. Not only the distinction between what is a factor, and what is a correlation, especially given a lot of studies in SEO and AI are multifarious and have high levels of imponderable complexity. To be clear, this isn’t a criticism of Cyrus’s work; the study is excellent, and the correlation/causation caveat is one he makes himself explicitly.

This led me to think about the parallels with other ranking factor studies done previously, which have implied direct traffic is a considerable traditional SEO ranking factor. At the time, these studies received a lot of negative feedback, and this was again discussed by many online after the documentation in Google’s DOJ trial revealed a “popularity” signal.

It makes sense for direct traffic to be a component of how popularity is measured through Chrome. Google uses Chrome data to find new websites. It also judges a page’s “quality” based on how users interact with it after clicking, but the atomic levels of how this is done, and how much weight the variables here carry, are not public knowledge.

Direct Traffic x Popularity Correlation

Direct traffic is widely considered a symptom of good performance, not a primary driver of search rankings.

Treating direct traffic as a ranking factor leads to a misinformation loop, which encourages superficial, low-effort tactics, such as purchasing bot traffic, in a misguided attempt to boost popularity, as it’s very possible to have high levels of direct traffic and poor SEO performance.

A wider view suggests that high direct traffic is typically an indicator of a strong brand, correlating with genuine ranking factors like numerous brand searches, high-quality backlinks, and strong social engagement.

These elements are the true causes of high ranking; the direct traffic merely serves as a quantifiable measure of the brand’s overall health and success, an “all ships rise in high tides” effect.

Spikes in direct traffic, don’t correlate with Organic Search traffic. (Image from author, May 2026)

If Chrome data were a direct factor, a sudden spike in browser activity on a specific URL would immediately push it up the SERPs, and this would be a gameable exploit.

This would also be something Google would pick up as it looks to stamp out obvious manipulations of search ranking, and this would have happened many years ago.

Other Insights From The DOJ Files

NavBoost and Glue are specialized systems within Google’s infrastructure that focus on user interaction signals rather than the raw volume of direct traffic.

NavBoost looks at historical clickstream data and user behavior on search results to identify which pages are most relevant for specific queries, effectively acting as a memory of what users have found helpful.

While NavBoost focuses on traditional organic results, Glue extends those same user interaction principles to all other SERP features: knowledge panels, video carousels, image packs, and featured snippets.

They allow Google to gauge a site’s authority based on how users interact with it in the search ecosystem, independent of the user’s traffic source.

So, What Is Popularity?

Based on what we know from various official (and unofficial) sources, research, and the general SEO hive mind, we can define popularity as a sign of brand strength characterized by user behaviors such as autocompletes and bookmarks.

It functions as a correlation to high rankings because it naturally aligns with the various signals that make a page rank.

Google may avoid using Chrome data directly as a ranking factor, choosing instead to use it as a dataset to train or validate its AI models. This we don’t know, and we likely won’t be able to prove or disprove through research.

Thank you to Ryan Jones, Mark Williams-Cook, Chris Green, Gerry White, Kristine Schachinger, Charlie Whitworth, Emina Demiri Watson, (and anyone else I’ve missed) for the fun weekend discussions on this topic.

More Resources:

Featured Image: PerfectWave/Shutterstock

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May 14 2026

How To Measure AI Search: Current KPIs You Need To Know [Webinar] via @sejournal, @hethr_campbell

If your organic traffic is down but your pipeline looks fine, you’re not imagining it. AI-generated answers are intercepting the journey earlier, meaning users are getting what they need from a citation or a recommendation before they ever hit your site. The click never happens. But the influence did.

That’s the measurement problem most marketing teams haven’t solved yet, and the KPIs they’re reporting on weren’t designed to catch it.

Your Brand Can Appear In 1,000 AI Responses & GA4 Shows Nothing

Citations, brand mentions, and AI recommendations don’t pass through your tag manager. They don’t fire an event in GA4 or register a session in your CRM. They happen in the interface of the AI tool, and by the time a user reaches your site, or doesn’t, the influence has already occurred.

Tracking these signals requires monitoring AI outputs directly: which queries surface your brand, in which tools, and with what frequency and context.

That’s a different data collection layer entirely from what most teams have in place.

Learn more in our upcoming SEO webinar.

Save Your Spot

Ways To Connect AI Signals To Business Outcomes Across Every Channel

Once you’re capturing AI visibility signals, the next problem is connecting them to outcomes.

Last-click and even multi-touch attribution models weren’t designed for journeys where the most influential touchpoint leaves no clickstream trace.

Learn: Incrementality testing, which lets you isolate the lift that AI visibility is actually driving by comparing performance across exposed and unexposed segments.

Learn: Media mix modeling, which takes a broader view, quantifying AI’s contribution alongside paid, organic, and direct channels in a single revenue model.

Used together, they give you a defensible number to bring into a budget conversation.

The Three-Layer Stack That Makes AI Search Defensible in a Budget Review

The stack works in sequence.

At the top, you’re monitoring AI visibility: citation rate, share of voice in AI responses, and brand mention frequency across tools like ChatGPT, Gemini, and Perplexity.

In the middle, incrementality and MMM translate that visibility into estimated conversion impact.

At the bottom, you’re tying those estimates to pipeline and revenue data so the whole chain holds up under scrutiny. The teams getting this right aren’t using one new metric. They’re connecting three existing disciplines, SEO, media measurement, and analytics, around a shared data model.

DAC’s Felicia Delvecchio, VP of Media, Vincent DeLuca, Director of SEO, and Gavin Bowick, Lead Web Analytics are running through exactly how that model is built in a free live session.

Save Your Spot

What This AI Search & Revenue Webinar Covers

How to track AI visibility signals: citations, mentions, and recommendations, across the full funnel
Which incrementality and cross-channel models connect AI influence to actual revenue outcomes
Which KPIs to retire in 2026 and which metrics reflect real performance across SEO, paid, and AI channels
How to build a reporting structure that aligns across SEO, media, and analytics teams, and holds up when you’re presenting to leadership

This one is worth showing up live for.

Save Your Spot

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May 13 2026

Condé Nast CEO: Plan As If Search Traffic Will Be Zero via @sejournal, @MattGSouthern

Condé Nast CEO Roger Lynch says he told company teams to plan their businesses as if search traffic were zero.

Lynch made the comments in an interview on TBPN, a tech talk show OpenAI acquired in April. He described three consecutive years in which internal budget forecasts underestimated actual declines in search traffic.

Lynch said:

“Each of the last three years, we would do our budgets, and we’d put forecasts in of search traffic declining… Because we’d seen the pattern of algorithm changes. And generally those algorithm changes were negative.”

“Every year, our search traffic was down more than we had forecast. So last year I told our teams, ‘Assume there’s no search.’ You have to have your businesses planned as if search is zero.”

Lynch told TBPN that Condé Nast doesn’t expect search traffic to literally reach zero. He expects it to settle at a single-digit percentage of total traffic.

What Changed

Lynch described how the search results page has changed, based on a comparison his team prepared for a recent board meeting. Lynch recalled:

“We took a snapshot of search results from seven or eight years ago. And what you saw were a few sponsored links, then the ten blue links.”

“Do the same search today, you get an AI overview, then you get rows and rows and rows of commerce links, then you get sponsored stuff.”

He noted that someone had recently asked him how search revenue could be up. “Have you done a search recently?” Lynch replied. “I basically have to go to the second page to get an organic result.”

Lynch acknowledged that changes in search traffic have affected Condé Nast’s business. The company has continued to grow revenue and profitability despite the decline, which he called a “headwind” rather than a crisis.

The Barbell Effect

Lynch described what he called a barbell effect across the Condé Nast portfolio. In his telling, large, authoritative brands and small niche publications with loyal audiences are performing well. Brands caught in the middle are the most exposed.

“Vogue has grown every year I’ve been at the company. It grows revenue, grows profitability every year,” Lynch said.

The New Yorker had its most successful year ever, he added. On the other end, Lynch pointed to Pitchfork, which represents about 1% of Condé Nast’s revenue but has a loyal audience in its category.

Lynch explained:

“If you try to be too broad, too large of an audience, this is not the era for that… You either need to be large and authoritative in a big category… or you need to be really nailing a specific niche where you have a loyal audience that’s willing to pay.”

Lynch added that brands in the middle of that barbell, those without deep authority in a category or strong enough niche focus, don’t have a clear path forward.

He added:

“If you don’t have really strong authoritative brands, or brands that have very strong niche in certain areas, or direct audiences, then you’re just going to be fighting that all the way down.”

Subscriptions As The Replacement

Condé Nast’s digital subscriptions grew 29% in revenue last year, according to Lynch. The company reported double-digit growth, which is continuing this year.

Lynch noted the company has raised subscription prices “fairly materially” over the past couple of years. He expected retention to decline with each increase. Instead, retention improved every year.

The company is also expanding subscriptions to smaller brands. Pitchfork and Tatler both launched paid digital subscriptions recently.

Why This Matters

Lynch’s comments are consistent with third-party measurements indicating that publisher search referrals are under pressure. Chartbeat data reported in March showed search referral traffic fell 60% for small publishers over two years. A Reuters Institute survey found media leaders expect search traffic to decline by more than 40% over three years.

Google’s VP of Search, Liz Reid, has reframed those losses as reductions in low-quality “bounce clicks.” Google hasn’t shared publisher-facing data to support that claim.

Lynch’s directive carries weight because of the portfolio behind it. Condé Nast operates Vogue, The New Yorker, GQ, Vanity Fair, Architectural Digest, Condé Nast Traveler, Wired, and Pitchfork, among others. When the CEO of a portfolio that includes those brands says teams should budget for zero search traffic, it gives industry data a concrete example from a major publisher.

The barbell observation matters for anyone managing a publisher caught between the two extremes. Lynch is describing a version of the pressure Chartbeat’s size-segmented data has tracked. Small and mid-tier publishers without deep category authority or direct audience relationships face the steepest declines.

Looking Ahead

Lynch told TBPN the company has started evaluating each brand’s plan for a low-search future. The company is prioritizing brands that can show a path forward without search traffic.

Lynch’s comments may put pressure on other large publishers to formalize similar planning. The trend data has been consistent enough that budgeting for search decline is already common. Budgeting for zero is a different level of preparation.

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May 13 2026

Why Your SEO Work Isn’t Getting Implemented (The IT Line Of Death) via @sejournal, @billhunt

I recently spoke with an SEO who, along with his entire team, had just been laid off. The company was rapidly losing organic traffic, leadership was frustrated, and from their perspective, nothing was being done to fix it. The SEO saw it very differently. They had submitted more than 1,400 tickets over the previous 18 months, each documenting an issue and outlining the importance of what needed to be done. The backlog was extensive, detailed, and, in their mind, proof that the SEO team was working hard to reverse the decline. The problem was that none of the requested actions had been implemented. Engineering time had been consistently redirected to CEO initiatives, product launches, and other internal priorities that always seemed to matter more. From the SEO’s point of view, the work existed. From the business’s point of view, nothing had changed. Traffic declined, visibility dropped, and eventually a decision was made to eliminate this underperforming team.

A backlog is not progress. It is an unimplemented intent.

This is the uncomfortable reality many practitioners struggle to accept. Submitting tickets is not the job. Getting them implemented is. If your recommendations never make it into production, they do not exist in any meaningful way. They do not drive traffic, they do not improve visibility, and they do not protect the business as Google continues to evolve. And right now, that evolution is accelerating, which makes the gap between activity and impact even more dangerous.

Align With What Already Matters

You can see how organizations are frantically responding to the pressure to perform in AI Search, albeit subtly. Work that sat untouched for months as “SEO improvements” suddenly gets prioritized when it is reframed as AI readiness, Generative Engine Optimization, or content structuring for AI discovery. Nothing about the underlying work changes, but the framing does, because it aligns with what leadership believes matters in that moment. It may feel frustrating, even cynical, but it reveals a deeper truth.

At IBM, we struggled to get many SEO initiatives prioritized. A report later flagged our site search experience as poor and negatively impacting sales of our own search product. The required improvements were largely the same as those we had been recommending for external SEO. By relabeling them as “site search fixes” under this new mandate, we were able to accelerate implementation and improve both internal and external search performance. Work is not prioritized because it is the right thing to do. It is prioritized because it aligns with the current narrative of impact and executive priorities. To understand why so much SEO work fails to cross that threshold, you have to look at where decisions are actually made.

The Line You Don’t See Until It Stops You

After selling my agency, I took on a project for a company that was already performing well in organic search. Then Google launched paid search, and everything shifted. Large advertisers began reallocating their budgets because buying search traffic directly from Google suddenly looked more efficient than advertising on websites that simply arbitrage organic traffic to generate the ad impressions they had purchased. The board’s response was immediate and direct. They wanted to dominate every aspect of their category and be in the top three across the board, and they were willing to provide me with whatever resources were necessary to make that happen.

So I went to engineering with my plan and list of activities for total domination, expecting complete alignment and momentum. Instead, the CTO walked me to a whiteboard and pointed to a faint dotted line. Anything above that line, he explained, might get implemented this fiscal year. Anything below it would not. There was no debate or negotiation. Every idea, no matter how strategically sound, had to either fit above that line or displace something already there. It was a simple constraint of available resources, and it made one thing clear: what was already there mattered. He told me that those initiatives were also blessed by the same executives who greenlit mine. These existing initiatives were tied directly to revenue, others to compliance or security, and some were simply protected by stakeholders with enough influence to keep them in place.

That was the moment the reality became clear. This line, invisible in every audit and absent from every SEO tool, determines what actually gets built. I call it the “IT line of death.” Your mission, as an SEO or GEO manager, is to find creative ways to get your activities into or to replace one of those above-the-line projects.

From Tasks To Contribution Value

Most SEO recommendations do not fail because they are wrong. They fail because they are not competitive within that resource allocation system. This means everything is a trade-off. Engineering does not evaluate your recommendation in isolation; they evaluate it against everything else competing for their time and resources. Revenue-driving features, compliance requirements, infrastructure improvements, and existing commitments all carry weight. And so does the requester. When SEO shows up as a collection of disconnected fixes, it struggles to compete because it lacks a clearly articulated cost, ownership, and relative impact.

That realization forces a shift in how SEO needs to be approached. It is no longer enough to identify issues. You have to justify why they deserve to exist above the line and are as important as or more important than another activity. That means translating work into effort, impact, and trade-offs. It means moving from tasks to contribution value. Audits, tickets, and backlogs describe activity, but engineering teams do not fund activity. They fund outcomes. If you cannot explain why your recommendation is worth more than another team’s request, it will not get done.

This is where many SEO programs stall. They are rich in insight but weak in prioritization, and that gap becomes even more visible when you look at how work actually gets implemented. It is often difficult to tie SEO activities directly to revenue or basket size, but that does not remove the responsibility to try.

Fix The Systems, Not The Symptoms

Once you understand your organization’s IT line of death, the question becomes practical. How do you get work implemented in an environment where everything is competing? The answer is not to push harder, but to work differently within the system. In most organizations, the fastest path to implementation is not to create new work but to align with work already in motion. Engineering teams are constantly updating templates, redesigning page structures, migrating platforms, or refactoring components. Those initiatives already sit above the line. They already have a budget, attention, and momentum. When SEO is introduced as a separate request, it has to fight for priority. When it is embedded into an existing initiative, it inherits that priority. Some of the most impactful SEO changes are implemented this way, folded into broader projects rather than introduced as standalone efforts.

This becomes even more effective when you focus on scale. Isolated fixes rarely justify prioritization, but changes that act as force multipliers do. Updating a template rather than a single page can affect thousands of URLs. Adjusting CMS logic can eliminate entire categories of issues. Fixing navigation or internal linking can reshape how the entire site is understood and crawled. These are the types of changes that connect relatively small effort to large-scale impact, which makes them far more competitive at the line.

Even then, success depends on understanding the problem at its source. One of the most common failure points in SEO is diagnosing symptoms instead of causes. Large numbers create urgency, but they can also mislead. Thousands of redirects, tens of thousands of 404 errors, and duplicate pages across a site often trigger large remediation efforts, yet they are frequently just the visible output of a much smaller issue.

I worked with a company that generated pages from a product feed daily, with URLs based on the product name and its first attribute. It seemed logical, but the attribute was not stable. Every time it changed, the URL changed with it. That single design decision created a cascade of problems. New pages were constantly being created, old URLs turned into 404s, and the site effectively churned its own index. The Search Console error log reflected this chaos, filled with tens of thousands of issues that needed fixing. But none of those issues was the real problem. The solution was not to clean up the errors; it was to stop creating them. By realigning the URL structure to a stable identifier such as a SKU, the entire system stabilized. The errors disappeared because the mechanism producing them was removed. One change replaced thousands of remediation tasks.

This is the difference between work that stays below the line and work that crosses it. The former treats symptoms, the latter resolves the system that generates them. This dynamic is not unique to a single company or a single moment in time. It shows up consistently across organizations, industries, and levels of Search maturity. Whether the constraint is engineering bandwidth, compliance requirements, or competing product priorities, the outcome is the same. Work that cannot justify itself at the line does not happen. We explored this further in a podcast episode, breaking down how this pattern repeats and why so many well-intentioned initiatives stall before they ever reach production. The conclusion was consistent. Most SEO work does not fail because it is wrong; it fails because it is not framed in a way the organization can act on.

Once you understand that, the role of SEO changes. You are no longer just identifying issues; you are shaping decisions. You are defining what is worth doing, why it matters now, and what impact it will have relative to everything else competing for attention. That is what moves work from backlog to implementation.

In the end, nothing gets done because it is best practice. It gets done because it is worth doing.

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