Google Spam Update Rolls Out, AI Manipulation In Scope – SEO Pulse via @sejournal, @MattGSouthern
Welcome to the week’s Pulse: updates affect how Google measures its AI surfaces, what its spam rules cover, and where AI recommendations send traffic next.
Here’s what matters for you and your work.
Google Rolls Out June 2026 Spam Update
Google began rolling out the June spam update on June 24.
Key facts: Google announced the rollout on its Search Status Dashboard, noting it may take a few days to finish. This comes after Google clarified in May that its spam policies include efforts to manipulate generative AI responses in Search, such as buying or altering citations.
Why This Matters
Keep an eye on ranking changes throughout the rollout before drawing any conclusions, as spam updates can take a few days to settle. Trying to manipulate or buy citations for AI answers now falls under the same rules as older spam tactics. Tactics built to game AI Overviews or AI Mode can be treated as spam under the same policy.
What SEO Professionals Are Saying
Shushrita M., a freelance SEO consultant, cautioned against overreacting while the update settles:
A sudden decline does not automatically mean your content is “bad.” The right response is to identify which page types, queries and directories were affected, then look for a consistent pattern. SEO recovery starts with diagnosis, not panic.
Search Advocate John Mueller clarified how Google measures impressions in the updated generative AI report within Search Console.
Key facts: Mueller explained to Nicola Agius, Director of SEO and Discover at Reach PLC, that impressions indicate when links to your pages appear in AI Overviews or AI Mode. However, links that are hidden behind an expansion are only counted when a user opens them. The report currently doesn’t provide click data.
Why This Matters
AI impressions count as how many times links appear, not how often your content shaped an answer. If something is hidden behind a click-to-expand, it might be undercounted until it’s clicked on, so a low number doesn’t mean your content is missing.
Advanced Web Ranking’s latest benchmark data indicates that desktop click-through rates are increasing, while mobile click-through declined at the top position.
Key facts: Advanced Web Ranking, a rank-tracking company, published Q1 2026 click-through benchmark data showing desktop and mobile moving in opposite directions. Mobile’s top position dropped about 2.2 percentage points, while desktop gains showed up mostly below the third position.
Why This Matters
Focus on the device split here. Desktop gains alone don’t offset months of mobile softness, so this indicates a one-quarter divergence rather than a full reversal. Review your own desktop and mobile click-through rates separately before drawing conclusions from a combined figure.
Similarweb Ties AI Recommendations To Branded Search
According to Similarweb’s report, branded search captures most of the traffic that comes after a ChatGPT recommendation, highlighting the downstream impact of AI visibility.
Key facts: Similarweb, an analytics firm, reported that 55.9% of downstream traffic came via branded search after users were exposed to a ChatGPT recommendation. The data, sourced from a U.S. desktop panel across the finance, travel, and beauty sectors, measures branded search as the pathway through which AI suggestions lead to site visits.
Why This Matters
The report’s authors suggest that branded search is a way to gauge the impact of AI recommendations, making it worthwhile to track brand demand alongside rankings. When AI mentions your name, people usually search for you directly rather than clicking a link, making the volume of branded queries a useful indicator of your visibility.
What SEO Professionals Are Saying
Aleyda Solís, SEO and AI search consultant and founder of Orainti, pointed to the measurement blind spot in the data:
AI influence can happen without a click, and this is why measuring AI Search impact only through “AI referral traffic” is not enough.
She added that standard attribution misses it:
Our current attribution models have a blind spot: AI-influenced demand often arrives through Search and Direct, not through AI referrals.
Google Says It Doesn’t Evaluate Third-Party SEO Tools
Brendon Kraham from Google stated that effective SEO aligns with good GEO, and that Google doesn’t assess external SEO tools or vendors.
Key facts: Brendon Kraham, Google’s VP of Search and Commerce for Global Ads Solutions, said the work that drives search visibility carries into generative experiences. He added that Google doesn’t evaluate third-party SEO tools or vendors, and those tools have no access to Google’s internal metrics.
Why This Matters
Discount claims from any tool or vendor saying they have a special way into how Google ranks AI answers. Google has clarified that no such access is available. Focus on effective SEO strategies rather than looking for a separate GEO playbook.
What SEO Professionals Are Saying
Cyrus Shepard, who founded Zyppy SEO, agreed with the slogan but pushed on the reverse:
Google says, “good SEO is good GEO” I don’t disagree. But the same advice doesn’t always work in reverse. There are a whole lot of things AI-savvy SEOs do right now that they likely would never do if AI had never existed.
Theme Of The Week: The Rules And Meters Get Written Together
This week, Google shared more details on how AI search is evaluated and regulated, while independent data revealed what users are actually doing with it.
The spam update is gradually rolling out, aligning with Google’s policies that address AI-answer manipulation. Mueller explained how impressions are counted, while Kraham confirmed that no special vendor access is given to Google’s metrics. AWR and Similarweb complemented this information by providing insights into how clicks are divided between desktop and mobile, as well as showing how branded search captures traffic generated by AI recommendations.
The most reliable way to interpret any single number this week is to consider it as provisional.
Google Answers Question About SEO For AI Agents via @sejournal, @martinibuster
Google’s John Mueller responded to a question about whether Google’s search quality principles will change as AI agents increasingly browse websites on behalf of users. The answer may matter more to site owners than it first appears.
Question About Agentic Browsers And Search Quality Principles
An SEO asked Mueller on Bluesky whether Google’s guidance around satisfying user experiences, including principles around things like images and page design, would evolve as agentic AI tools gain the ability to navigate and retrieve information from websites autonomously. The question reflects a concern that has been growing in the SEO community as tools like Google Gemini become capable of browsing websites, completing tasks, and returning answers to users without the user ever directly visiting a page.
“Hi John – Given Computer use is now a built-in tool for Gemini 3.5 Flash, and as agentic becomes more of a “thing”, would you expect principles like “Images provide a satisfying experience” to evolve since the satisfying experience is an information agent? Curios on your thoughts.”
Websites Useful For Humans Will Generally Also Work For Agentic Browsers
Mueller explained that most of Google’s existing quality principles will remain in place. A website that is useful for human users will generally also be useful for agentic browsers.
He responded:
“I expect most principles will remain the same. A website that’s useful for users, will generally also be useful for agentic browsers.”
Mueller’s response means that it’s a good idea to keep making content useful for site visitors, which also means the site layout, navigation, and internal linking. AI agents do not change the fundamentals that Google’s algorithms are still picking up on external user signals for ranking purposes, especially signals that indicate site popularity with users.
Blindly Blocking Agentic Browsers Could Become An SEO Problem
Mueller’s answer also contained the observation that some details will evolve, and that site owners should avoid blindly blocking agentic browsers.
He said:
“Some details will undoubtedly evolve (and new basics – such as … not blindly blocking agentic browsers … will come into play), but in the end, it’s still users.”
Mueller’s response draws a line between content quality and technical accessibility. A site can meet Google’s quality standards and still create problems for itself if AI agents are blocked from accessing or interacting with its content.
In some ways this is similar to how nofollow links became an issue for some sites when it was introduced many years ago. Some site owners blocked off important sections of their websites in order to drive more PageRank to the pages they thought were important, giving zero priority to actually important parts of a website like the About Us pages.
The agentic browser situation may follow a similar pattern, where technical decisions made for one reason end up having unintended SEO consequences.
One way to think about it is that Google’s definition of a quality website is not being rewritten for the agentic era. The standards already in place were written for human users, and if AI agents are serving human users, then satisfying those agents is arguably the same job. What changes is the technical considerations of accommodating AI agents, not the underlying expectation of what a good website is.
The Accessibility Tree Is How AI Agents Read Your Site & It’s Breaking via @sejournal, @slobodanmanic
AI agents do not read your website the way you do. They do not see your layout, your hero image, or your brand color. They prefer reading the accessibility tree: a stripped-down structural model of the page, the same one that has powered screen readers for two decades.
Today, that matters more because the audience reading that way is now the majority.
For the week of May 30 to June 5, 2026, Cloudflare Radar measured 57.2% of HTTP requests to HTML content, the requests that represent web-page traffic, as automated bots, against 42.8% human. Cloudflare CEO Matthew Prince, who shared the data on June 3, had forecast that crossover for 2027. He got it wrong because it arrived over a year early.
Cloudflare Radar, Bot vs. Human distribution filtered to HTML content (web-page traffic), May 30 to June 5, 2026 (Image from Cloudflare Radar by author, June 2026)
Some of that automated traffic is scrapers you probably want gone. A large and rising share is AI agents reading pages for real people. And according to the accessibility data published this year, the structure those AI agents depend on is getting worse, for the first time in six years.
The Accessibility Tree Is A Structural Model The Browser Builds From Your DOM
The accessibility tree is a semantic version of your page that the browser computes from the DOM so non-visual software can understand it. The pipeline is short: HTML to DOM to accessibility tree to consumers (assistive technology, and now AI agents).
The W3C’s WAI-ARIA 1.2 defines it as a “tree of accessible objects that represents the structure of the user interface,” where each node “represents an element in the UI as exposed through the accessibility API.” The browser builds it from the DOM (the mapping is specified in Core-AAM 1.2) and exposes it through the operating system’s accessibility API, which, per the W3C, “can be used by any assistive technologies, such as screen readers.” MDN explains the pipeline this way: Browsers “create an accessibility tree based on the DOM tree.”
The accessibility tree discards most of the DOM. A page with several thousand nodes collapses to the meaningful, interactive set: headings, links, buttons, form fields, landmarks, images with their text alternatives. For software working inside a limited context window, it is that reduction that makes the tree usable at all.
Every node in the accessibility tree carries four properties:
Property
What it captures
Example
Role
What kind of element it is
Button, navigation region, list item
Name
How it is referred to
A link reading “Read more” is named “Read more.” An icon-only button with no label has no accessible name.
State
Its current condition
Checked, expanded, disabled, selected
Description
Any extra context beyond the name
A longer explanation, like a tooltip, that a screen reader can read aloud
The tree also records what can be done with a node: a link can be followed, a text input can be typed into. That is exactly the information an agent needs in order to act.
AI Agents Read The Accessibility Tree Because It Costs Less And Misleads Less Than Pixels
An agent driving a browser can understand a page three ways: read the raw HTML, look at a screenshot with a vision model, or read the accessibility tree. There is a real split in how today’s agents do it.
Purely relying on the accessibility tree. Microsoft’s Playwright MCP, a widely used tool for letting a model operate a browser, “uses Playwright’s accessibility tree, not pixel-based input,” with “no vision models needed, operates purely on structured data.” Its tool description tells the model an accessibility snapshot “is better than screenshot.”
Vision-first. OpenAI’s Computer-Using Agent, the model behind Operator, works primarily from screenshots. It is not reading your accessibility tree to decide what to click.
Hybrid. A third approach combines both: the structured accessibility tree for the bulk of the page, plus vision for the parts the tree cannot capture, like canvas-rendered apps and dense visual layouts.
Two forces push agents toward the accessibility tree:
Cost. A screenshot spends a large number of tokens encoding a picture the model then has to interpret. The accessibility tree is compact text.
Reliability. A vision model has to guess which pixels form a clickable control. The tree states this outright, with a role and a name for each.
The clearest signal of where this goes is the vendors’ own guidance. OpenAI’s Publishers and Developers FAQ says ChatGPT Atlas “uses ARIA tags, the same labels and roles that support screen readers, to interpret page structure and interactive elements,” and advises that making a website more accessible helps the agent understand it.
OpenAI’s Publishers and Developers FAQ (Image by author, June 2026)
OpenAI is the company behind Computer-Using Agent, the one that works by analyzing screenshots. They still recommend making websites more accessible. For the machine, accessibility and readability are the same problem. The full agent-by-agent breakdown is in a companion article on how AI agents see your website.
A Markdown Copy Is Not An Agent-Ready Page
A clean markdown version of a page is a good way to feed an agent your content, and providers like Cloudflare now generate one at the edge. For reading, extracting, and citing, markdown is fine, and often better than raw HTML.
But a markdown copy carries only the words. It cannot tell an agent that a control is a button, whether that button is disabled, or hand it something to click. It lets an agent read the page, not operate it.
It is also a separate copy of the page, and a separate copy can tell an agent one thing while the rendered page shows humans something else. A hand-maintained one also drifts from the real markup over time. The accessibility tree has neither problem. The browser builds it from the same page it renders to people, so there is nothing extra to maintain and nothing to cloak, and it carries the roles, states, and element references an agent needs to act. Which is why, for an agent that has to do something, one of the two is close to pointless, and the other is the whole point.
You Can See Your Own Accessibility Tree In About 2 Minutes
Every major browser shows you the exact tree an agent reads.
Open DevTools, select an element in the Elements panel, and open the Accessibility tab to see that element’s computed role, name, and state.
To view the whole page the way the tree does, turn on the “Show accessibility tree” toggle, which “replaces the DOM tree in the Elements panel with a full-page accessibility tree.”
For the same thing in code, Playwright’s ARIA snapshots produce “a YAML representation of the accessibility tree of a page,” capturing roles, accessible names, states, and nesting. Running an ARIA snapshot against your own URL returns almost exactly the structured text an agent like Playwright MCP receives.
Here’s an easy test you can run: For every important action on the page, does the tree show a node with the right role and a clear name? A “buy” button that appears in the tree as a generic element with no accessible name is a button your customers’ agents can see but cannot confidently use.
Run this on a few of your own pages, and the gaps will show up fast.
The 2026 Data Says The Web Is Getting Harder, Not Easier, For Machines To Read
The accessibility tree is only as good as the markup it is built from. In 2026, that markup got worse. Web accessibility regressed for the first time in six years, at the same moment agents became the majority of HTML traffic.
The WebAIM Million, the annual automated analysis of the top 1 million home pages, reported in its February 2026 edition:
95.9% of home pages had detectable WCAG failures, up from 94.8% the year before, which WebAIM describes as “reversing a trend of small improvements each of the previous 6 years.”
56.1 detected errors per home page, a 10.1% increase over the 51 found in 2025.
1,437 elements per home page, which WebAIM flags as “a 22.5% increase in only one year.”
A 22.5% jump in page complexity in a single year is not normal. More elements mean more places for structure to break, and the report shows exactly where it breaks.
The Most Common Failures Are The Ones That Blank Out The Accessibility Tree
The accessibility failures WebAIM finds most often are exactly the defects that strip meaning out of the tree an agent reads.
Failure
Home pages affected
What it does to the agent
Low-contrast text
83.9%
A visual failure for low-vision users and vision-based agents
Missing alt text
53.1%
The image contributes nothing to the agent’s understanding
Missing form labels
51%
An input the agent cannot map to a purpose, so it cannot fill it
Empty links
46.3%
A node with a role but no name: a door with no sign
Empty buttons
30.6%
A control the agent sees but cannot identify
Missing document language
13.5%
The wrong language model applied to the page
Nearly half of the top million home pages come with empty links. Almost a third have empty buttons. For the visitor class that now outnumbers humans, those are dead ends. To quote the report:
“Addressing just these few types of issues would significantly improve accessibility across the web.”
What WebAIM has measured every year for screen-reader users is the same thing that decides whether an AI agent can read and act on your page. They’re different audiences with identical broken structure.
WebAIM Ties The Rising Complexity To Frameworks And “Vibe Coding”
WebAIM attributes the rising complexity to “increased reliance on 3rd party frameworks and libraries and automated or AI-assisted coding practices (‘vibe coding’).”
This is the first WebAIM Million published well into the era of generating production websites by prompting a model. We have more code, shipped by more people, more pages deployed faster, more complexity stacked on complexity, with fewer humans in the loop asking whether an element needs to exist or whether a control exposes its name and role.
There is no way to prove a single cause for a one-year reversal across a million websites, and claiming one with certainty would be dishonest. But the timing is impossible to ignore, and the contradiction is the point: Humans are using AI to build a web that AI itself cannot reliably consume. Bloated DOMs, broken semantics, unnamed controls. The same defects that hurt humans and screen readers hurt the crawlers and the agents.
It’s tempting to think you should not worry, because the next model will be good enough to sort out the mess. That is a marketing line, not a strategy. The same products promising the model will handle anything also tell you, in fine print, that the assistant can make mistakes.
Independent measurements like the WebAIM Million are among the only objective signals we have about what is really happening to the web underneath that promise. Right now, the signal is that the web is getting harder to parse at the exact moment more of its traffic depends on parsing it cleanly.
The ARIA Paradox: Bolting On Attributes Makes It Worse
More ARIA correlates with more errors, not fewer. WebAIM found that home pages with ARIA present averaged 59.1 errors, against 42 on pages without it.
ARIA, short for Accessible Rich Internet Applications, is a set of attributes you add to HTML to hand the accessibility tree the roles, names, and states the native markup did not supply on its own.
The reason is simple. An empty or wrong attribute does not leave the accessibility tree blank. It fills the tree with confident, possibly incorrect information, which is worse for an agent than an honest gap, because the agent has no way to know it is being misled.
This is where the vendors and the standards body disagree:
OpenAI tells developers to add ARIA roles, labels, and states so agents understand a page.
The W3C’sFirst Rule of ARIA (first!) puts native HTML first: “If you can use a native HTML element … with the semantics and behavior you require already built in, instead of re-purposing an element and adding an ARIA role, state or property to make it accessible, then do so.”
Accessibility specialists have pushed back on the vendor framing directly. W3C contributor Adrian Roselli, responding to OpenAI’s guidance, argued it inverts the discipline, pointing teams toward bolt-on attributes when the durable fix is correct native markup.
The WebAIM data sides with the specialists: The pages reaching hardest for ARIA carry the most errors. You do not fix the accessibility tree by adding attributes. You fix it by … fixing it. By making the underlying markup mean what it says, and reserving ARIA for the genuine gaps native HTML cannot express.
Make The Markup Mean What It Says
The fixes are unglamorous and well understood, and they pay off twice: once for the humans using assistive technology, once for the agents that are now the majority of your traffic.
Use native HTML for native behavior. A <button> is a button in the tree with no extra work. A <div> with a click handler is an unnamed, roleless node an agent cannot trust. The same holds for <a href> and <select>.
Name every control. Use a real <label> on every form input. Accessible text on every link and button, including the icon-only ones. Empty links and empty buttons are the failures an agent hits first.
Server-render the content that matters. A price, a spec, or a primary action that only appears after client-side JavaScript runs may never reach the tree an agent reads.
Use ARIA for genuine gaps, not as a patch. Correct semantics first, attributes second, and only where native HTML cannot express the state. Remember the First Rule of ARIA?
Inspect the result. Run your key pages through the DevTools accessibility tree or a Playwright ARIA snapshot, and confirm every important action shows up with a clear role and name.
It is not too late to start, and none of this requires a redesign. The accessibility debt on most websites is real and years deep, and the 2026 numbers show it growing rather than shrinking. But the fixes are still small: markup-level changes you can make page by page, not a full rebuild that would take months. Start with your highest-traffic pages, check the accessibility tree, and fix the empty controls and unlabeled inputs first. Every one of these fixes serves a human visitor and a machine visitor in the same change.
Accessibility used to be a compliance checkbox; the thing reached after the redesign was launched. It is now the interface the majority of your visitors use to read your website. Teams that build their markup to mean what it says will be legible to the agents deciding what to recommend and what to buy. Teams betting that a future model will clean up the mess are wagering on someone else’s questionable roadmap. The web has now handed us a year of data on how that bet is going.
At the same time, the interest in web accessibility is at a five-year high.
Google Trends: worldwide search interest in “web accessibility,” past five years (Image by author, June 2026)
The interest was flat for years, then climbed through 2025 and spiked in 2026. The drivers are mixed, and worth being honest about: compliance deadlines like the ADA Title II web rule and the European Accessibility Act, a rising wave of accessibility lawsuits, and broader attention as AI changes how the web is built and read. No single one explains the whole curve, and claiming it does would be a guess.
But the direction is the whole point. The attention is arriving, the fixes are manageable, and the audience that depends on them is now the majority. The moment to fix the web is now.
You Can Finally Measure Content Alignment. That’s The Dangerous Part via @sejournal, @DuaneForrester
We have always been approximating relevance. Every keyword list, every TF-IDF score, every editorial judgment about whether a page “covers the topic” has been an attempt to answer a single question: is this content about the thing the user is looking for? The tools changed. The question did not. What changed, meaningfully, is the resolution of the instrument. Keyword research approximated relevance through lexical overlap: If the words match, the topics probably align. Vector-based semantic analysis approximates it through meaning overlap: If the concepts are close in embedding space, the content is probably relevant regardless of whether the exact terms appear. That is a genuine, material upgrade, but it is not a move from guessing to knowing.
The reason that distinction matters is that a significant portion of the SEO and content strategy community is right now treating it as if it were. They are looking at alignment scores, cosine similarity outputs, and semantic proximity metrics and reading them as ground truth. A high score means aligned. A low score means not aligned. Optimize until the number goes up. And the number, because it is a number, feels like it has settled the question that keyword research always left open. It hasn’t. It has given you a higher-resolution version of the same approximation, and the higher resolution is exactly what makes it dangerous, because it removes the humility that low resolution used to enforce.
Precision Is Not Accuracy
Gerard Salton’s SMART system at Cornell introduced the vector space model for document retrieval in the 1960s. The core insight then was the same insight powering today’s embedding models: represent both the query and the document as vectors, measure the angle between them, and use that angle as a proxy for relevance. What has changed across 60 years is the sophistication of how those vectors are constructed. Salton used term frequency. Modern embedding models use transformer-derived representations that encode semantic relationships, contextual meaning, and conceptual proximity across hundreds or thousands of dimensions. The measurement got dramatically better. But the thing being measured, the angular distance between two vector representations, is still a proxy for a relationship that exists outside the math.
This is where the Netflix research team landed in their 2024 study on cosine similarity in embedding models. Steck, Ekanadham, and Kallus demonstrated that cosine similarity applied to learned embeddings can produce results that are, in their framing, arbitrary. The way an embedding model is trained, the regularization applied, the data it saw, all shape the geometry of the space in ways that make a raw cosine score unreliable as an absolute measure of semantic similarity. A high score in one embedding space is not equivalent to a high score in another. The score is real. The similarity it claims to represent may not be.
For practitioners optimizing content, the implication is direct. When you score your content’s alignment to a query using an embedding model, you are measuring semantic proximity inside that specific model’s representation of language. You are not measuring how Google’s retrieval infrastructure or OpenAI’s RAG pipeline or Perplexity’s index would evaluate the same relationship. Those systems use their own embedding models, their own retrieval architectures, and their own reranking layers. A score of 0.92 in your measurement space might correspond to strong retrieval in one system, weak retrieval in another, and irrelevance in a third.
What Kind Of Wrong Are You?
This is the axis that matters, and it is not the one most practitioners are thinking about. The question is not whether keyword research or vector alignment is the better method. The question is what kind of error each method produces, because the error type determines whether you can correct for it.
Keyword research, for all its limitations, produces a known unknown. You know you are approximating. You know that matching terms to a page does not guarantee topical coverage, does not guarantee user satisfaction, and does not guarantee that a search engine will judge the page as relevant. The imprecision is visible, and because it is visible, it keeps you honest. Practitioners who grew up in keyword-driven optimization learned to over-cover, to build supporting content, to triangulate intent from multiple angles, precisely because they understood the instrument was blunt. The bluntness was a feature. It forced humility.
Vector alignment scoring, by contrast, can produce an unknown unknown. The number is precise. It has decimal places. It can be tracked over time, graphed, compared across content assets, and optimized against. And that precision creates a psychological trap: it feels like the question has been answered. The content is 0.89 aligned to the query. That must mean something definitive. But what it actually means is that in one specific embedding space, using one specific model’s learned representation, the angular distance between two vectors falls within a certain range. The score says nothing about whether the production retrieval system that will actually serve your content uses a compatible embedding space, applies the same tokenization, or weights semantic similarity the same way during reranking.
The MTEB benchmark leaderboard illustrates this concretely. The performance spread across current embedding models is not small. A content asset that scores well against one model’s embedding space may score materially differently against another, not because the content changed but because the geometry of the space changed. And the embedding model your scoring tool uses is almost certainly not the one any given AI platform uses in production. There is no public registry of which model powers which system’s retrieval layer. You are measuring in a space that is representative of the general problem but not identical to the specific system where your content will be evaluated.
That is not an argument against measuring. It is an argument against reading the measurement as settled fact. The distinction between a directional signal and a definitive answer is the entire discipline.
The Instrument Got Better. The Old One Is Not Enough
None of this rescues keyword-only optimization as a sufficient strategy. It is not sufficient, and the reasons are structural, not sentimental.
LLMs and AI retrieval systems operate in semantic space, not lexical space. They process meaning, not strings. A page can score perfectly against a keyword target list while being semantically adrift from the actual intent the query represents, because keyword presence and semantic coverage are different things. Conversely, a page can use none of the target keywords and still be strongly aligned semantically, because it covers the same conceptual territory through different vocabulary. The paraphrase and synonym space that LLMs operate in is structurally invisible to a keyword-based evaluation. You cannot see what you cannot measure, and keyword tools cannot measure semantic proximity.
Consider a practical case. Keyword research correctly identifies “customer churn prevention strategies” as a high-value target. The content team builds a thorough, intent-appropriate piece around it. It covers the topic, uses the target terms naturally, and would pass any keyword audit without issue. But an alignment score reveals that the content’s semantic center of gravity sits closer to “measuring churn” than to “preventing churn,” because the piece leans heavy on diagnostic framing, identifying at-risk accounts, calculating churn rates, segmenting by behavior, and lighter on intervention framing, what to actually do once you have identified the problem. Both treatments are on-topic. Both satisfy the keyword target. But the semantic distance between the content and the query as a retrieval system represents it is larger than the keyword coverage suggests, and keyword research has no instrument to surface that drift. The alignment score does. Not because the keyword research failed, but because it was never built to see at that resolution.
This is not a criticism of people who focus on keyword research. Those practitioners are not wrong. They are working at the resolution the available instruments allow. Intuiting alignment between content and query intent is a real skill, and the best keyword strategists are doing something genuinely sophisticated: they are approximating semantic relevance through lexical indicators, using editorial judgment to bridge the gap the tools could not cross. The tools can now cross a version of that gap. The editorial judgment still matters, but the gap it has to bridge is different.
The danger is the practitioner who decides that because keyword research is no longer sufficient, vector alignment scoring is the complete replacement. That practitioner has traded one approximation for a better one while losing the awareness that it is still an approximation. They have upgraded the instrument and downgraded the literacy, which is a net loss.
The Discipline Is Knowing What The Number Is Not Telling You
Goodhart’s Law, the observation that when a measure becomes a target, it ceases to be a good measure, is not just an aphorism for economists. It is the exact failure waiting for any team that treats an alignment score as a target to optimize against rather than a signal to interpret. The moment the score becomes the goal, the content starts drifting toward the score’s geometry and away from the actual relevance it was supposed to approximate. You start writing for the embedding model instead of the reader and the retrieval system, and the embedding model you are writing for is not the one any production system uses.
The real discipline, the one that did not exist when practitioners were navigating by keyword intuition alone, is understanding what an alignment measurement is and is not telling you. It is telling you that in a given embedding space, your content’s vector representation is geometrically close to a query’s vector representation. That is useful. That is more information than keyword presence gives you. It is telling you something about semantic coverage that lexical analysis cannot. But it is not telling you whether the production system’s embedding space has the same geometry. It is not telling you how reranking will treat the result. It is not telling you whether the LLM’s generation layer will interpret your content as authoritative, complete, or worth citing. Alignment is a retrieval-adjacent signal. It says nothing about interpretation.
The practitioner who can hold those two realities, the signal is real and the signal is incomplete, is the one operating with genuine literacy about the systems they are trying to influence. The one who collapses them, who reads a high alignment score as confirmation that the content is “optimized,” is operating with a more sophisticated version of the same overconfidence that made people think a keyword density of 3% meant their page was relevant. The number got better. The mistake is the same.
Representative, Not Identical
The honest framing is not “right space versus wrong space.” That binary invites paralysis: If no measurement space is the production space, why measure at all? The best framing, in my opinion, is a spectrum of representativeness. Some measurement spaces are closer to what production systems use than others. Some embedding models share more architectural DNA with the models powering major AI platforms than others. Some scoring methodologies account for the gap between measurement and production better than others. The question is not whether your measurement is perfect. It never will be. The question is how representative your measurement space is of the systems you actually care about, and whether you are treating the score with appropriate directional respect rather than absolute faith.
This is the actual work. Not chasing a number. Not abandoning measurement because it is imperfect. Building enough literacy about how these systems work to know which signals to take seriously, which to discount, and which to combine with other indicators before making a content decision. That literacy was optional when the only instrument was keyword research, because the instrument was so obviously blunt that nobody mistook it for truth. It is not optional now. The instruments are precise enough to fool you, and the cost of being fooled is optimizing content for a geometry that does not represent the system where your brand needs to be visible.
I wrote about a related dimension of this problem in the vector index hygiene piece last year, focusing on how the quality and maintenance of the index itself shape retrieval outcomes. This article is the other side of that coin: not the index, but the measurement you use to evaluate whether your content belongs in it. And both connect to a larger question I will return to in future work, which is a gap most people aren’t talking about yet.
Start With What You Can See
If you are still running keyword research as your primary content alignment method, you are working with a blunt instrument in an environment that now demands more resolution. If you are running vector alignment scoring and reading the output as settled truth, you have the resolution but not the literacy to use it safely. Both are correctable. The path forward is not choosing one over the other. It is layering them, understanding what each can and cannot tell you, and building the organizational capacity to treat precise measurements as what they are: directional signals produced inside a specific space that may or may not represent the systems where your content competes.
The gut feeling was never the enemy. The illusion that you have moved past the need for judgment is.
For a broader look at how AI search visibility is reshaping the work of being found, “The Machine Layer” covers the structural shifts that make this kind of measurement literacy essential.
For the past couple of years, AI has been moving search through a structural shift. Every software tool is embedding generative AI as a new product feature for default interface, and there seems to be a new AI measuring or optimization tool every couple of days.
But we’re seeing users react both positively and negatively to AI being seemingly thrust upon them. DuckDuckGo is reporting that visits to its No AI Search have tripled since Google announced Intelligent Search.
Screenshot from LinkedIn, June 2026
How Everyday Users Interact With AI
As an industry, we’re focused on this narrative of total disruption, and we are seeing disruption and movement away from what has been our norm, but research shows a fragmented adoption of AI, rather than blanket adoption.
For easy, low-risk tasks like finding a local plumber or brainstorming dinner ideas, people are happy to use AI.
The tripling of traffic to DuckDuckGo’s “No AI” search page is a direct reaction to users not having a choice.
When software forces AI on users without letting them turn it off, users feel trapped, especially if they’re not yet trusting of AI.
Instead of accepting it, they are actively switching to alternative search engines and browser extensions that offer the clean, link-based experience they prefer.
To understand this pushback, we have to look at how the human mind reacts to new technology (and a big thank you to Giulia Panozzo, who helped me source and research these studies).
The 5 Barriers To Trust
In a study published in Nature Human Behaviour, researchers De Freitas et al. (2023) looked at the psychological barriers that stop people from trusting AI.
There are two main reasons that stand out for search engines and AI.
First is “opacity,” which simply means the AI is a “black box.”
When a search engine gives a synthesized answer without showing its sources clearly, we cannot see how it got its information. Human minds naturally want transparency, especially when making important decisions.
Second is the threat to our “agency,” or our sense of control. When search engines force an AI chat onto users, it feels like our choice is being taken away. To regain control, users flee to alternative search engines that respect their independence.
Safety-First Thinking And Tech Anxiety
Research by Sapru (2026) in Technology in Society looks at why some people feel intense anxiety about AI.
The study divides users into two groups:
Promotion-focused people, who love trying new and exciting tools.
Prevention-focused people, who prioritize safety, accuracy, and keeping things simple.
For safety-first users, a search engine is just a basic tool to get things done, not a toy to play with. Forcing an AI layer onto these users makes them feel anxious.
They worry about being misled or having to learn a complicated new system, which drives them to look for “No-AI” options.
Recognition Doesn’t Equal Utilization
A study by Yin (2025) in Frontiers in Education shows that recognizing an AI tool is useful does not mean a person will actually use it.
The researchers mapped out a step-by-step path of how users actively avoid AI:
If they see a way to avoid the AI, they will take it. The sudden spike in DuckDuckGo’s traffic can be seen as people taking an available exit route to avoid the threat.
Outside Our Bubble, AI Adoption Is Happening, But We Shouldn’t Panic
It’s easy for SEOs and other tech-savvy professionals to assume the rest of the world is adopting AI at the same pace we are.
Microsoft’s Global AI Diffusion Report shows that despite billions of dollars spent on AI, the vast majority of the world has not adopted it.
Regular active use of generative AI sits at 17.8% of the global working-age population (15-64). That means more than four in five working-age adults worldwide are not regularly using generative AI tools.
This also means that a lot of our clients who are worried about audiences (with buying power) moving away from traditional search to AI alternatives, are in the majority not adopting AI on a regular basis.
A large number of users are still relying on the “traditional web” and methods of fulfilling their purpose of going online.
As an industry, we’re going through a lot of changes at a rapid rate, and users are going through the same changes and barrage of AI solutions to their problems. We need to be adaptive and forward-thinking with our approaches, but we’re not quite in panic mode yet.
Google CEO Sundar Pichai Is OK With AI Mode Replacing Classic Search via @sejournal, @martinibuster
In a recent interview, Google’s CEO Sundar Pichai confirmed that sources and links will always be a part of the AI answers and when asked how he feels about a decline in the use of Classic Search in favor of AI Search, he mentioned that Google will survive on a blend of subscriptions and advertising.
Google On The Future Of Links And Sources
Google’s CEO acknowledged that people still want to connect with what’s on the web. He also shared that Google is creating a seamless transition from classic search to AI Mode and that according to their internal metrics, people are satisfied with it.
In response to an interviewer question as to whether Google is at some point going to transition away from the ten blue links and classic search, he said that the process is a “continuum,” which means a gradual transition, not a “rip the band-aid” type change.
The question the interviewer asked:
“I think a lot of people expect that at some point, the kind of normal Google sort of classic search interface goes away. The 10 blue links maybe go away and you just kind of have this AI mode as the default.
…Do you think that goes away at any point that you sort of rip the band-aid off and just go full AI mode?”
Sundar Pichai confirmed that sources and links won’t always be a part of Search:
“You know, I think it’s important to bring users along the journey as well as making sure the product is working for their expectations.
So, you know, I try not to get ahead of that.
I think it is very clear as we evolve through these changes, people are responding positively. We can see it in the long-term metrics of the product in such a clear way. And so I think we understand that. But people want search to be fast.
I do think through search, people are looking to connect with what’s out there on the web, so that’s important to us. It’s all of that.
So I think you’re seeing us evolve the product.
And I think you’ll continue to see it be methodical, but we didn’t have AI Mode a year ago. But now a lot of people are experiencing it. I think we have made it more seamless to go there than before.
And so it’s a continuum.
But I don’t see…
Sources and links will always be there as part of it.”
Pichai says people want to connect with what is on the web and that sources and links remain part of the experience. But he also says AI Mode is becoming more seamless and widely used, which actually impacts referrals.
Visibility Is Not The Same As Referrals
The idea of evolving classic search so that it’s seamlessly transitioning to AI Mode is not going to be popular with publishers and SEOs. The reality of “links” and “sources” in AI Mode is that visibility is not the same thing as referral traffic. So when Pichai’s AI Mode offers visibility with one hand, it’s also diminishing referrals with the other.
This ties directly with the concept of Google Zero. Google Zero is the big brand calculation that referral traffic is dwindling to zero. So in order to survive, businesses must promote and monetize as if Google referrals will one day be zero, Google Zero.
The reality of AI Mode is that Google is preserving the appearance of attribution while diminishing the economic value of clicks.
Google Says Users Are Responding Positively
The other important point in his answer is that Pichai says Google can see positive user response to AI Mode in their long-term metrics. But people are increasingly concerned about data center use, the record amounts of water that they use, and the harm that does to the environment as well as to the cost of energy, which affects the cost of everything from the clothes you wear to the dinner on the table.
The interviewer even mentioned how college graduates across the United States booed at the very mention of AI. So with all of the negative public sentiment against AI, Pichai still insists that people are happy with it.
Google Is Okay With Replacing The Search Advertising Model
One of the two interviewers gestured to his co-interviewer and commented that he had told him that he hadn’t done a traditional Google Search in a year and asked Pichai if he’s okay with people abandoning search in favor of pure AI queries.
He asked Pichai if he’s okay with users who don’t use classic search :
“When you hear that, are you cool? Like, this is the kind of user that I want right now, or does it send you a little chill because the traditional search ad business is a pretty good one for you.”
Pichai’s answer seemed to suggest that there may in the future be a blending of subscription and advertising revenues.
He responded:
“Well, I think we will, if anything, in the AI mode, in an agentic… these things are going to do a lot more for you than what we were able to do for users 10 years ago.
I think the economic value is always a function of the total value you’re giving users. All of us would say over time, the value we are providing users increases, there’s more competition, there are more choices.
So I feel comfortable between a combination of subscription and ads that the right models will continue to be there.”
That’s maybe the first time someone at Google has mentioned a blend of subscriptions and advertising as a way of monetizing the AI web. Where does that leave publishers?
When asked about the negative economic future that many feel AI is bringing, he compared AI to the introduction of the spreadsheet and how that revolutionized financial analysis, to how it will make coding easier, and how it will enable doctors to spend more time with patients.
All of those analogies and comparisons sidestep the damage to the web ecosystem that non-referring visibility brings.
His answer:
“I think people are going to be more productive. They will have more time for leisure. All of that will simultaneously be true.”
Pichai is confident that the web ecosystem can subsist on visibility, and that Google will be fine if people stop using classic search. But where does that leave the web ecosystem? Pichai all but recommended eating cake.
Google Won’t Act On Spam Reports If They Contain Personal Information via @sejournal, @martinibuster
Google updated their spam reporting documentation to make it clearer that spam reports are not wholly confidential and that it’s possible for personal identifiable information to be shared with the sites receiving a manual action.
Change In Response To Feedback
Google’s changelog noted that they were updating the spam reporting form based on feedback they’d received about personal information contained in the spam report that is shared with spammy sites that receive a manual action (formerly known as a penalty).
The update contains a new notice that spam reports containing personal information will not be processed.
The changelog noted:
“Clarifying when and why we may take manual action based on spam reports What: Further clarified when and why we may take manual action based on spam reports. Why: To address feedback we received about the change on using spam reports to take manual action.”
Google removed the following from their documentation:
“If we issue a manual action, we send whatever you write in the submission report verbatim to the site owner to help them understand the context of the manual action. We don’t include any other identifying information when we notify the site owner; as long as you avoid including personal information in the open text field, the report remains anonymous.”
The above wording was replaced with the following:
“Don’t include any personally identifying information in your submission. To comply with regulations, we must send the submission text to the site owner to help them understand the context of a manual action, if one is issued.
Because of this, we won’t process your submission if we determine it contains personally identifying information to protect privacy. Not including such information fully ensures your information is safe and prevents your submission from being discarded.”
Action Moving Forward
On the one hand it’s good that Google won’t proceed with a manual action if the report contains personal information. This means that if you’re submitting spam reports to Google, don’t name your site, business name, personal name or anything else that you don’t want the affected spammer to know.
The Real Reason Your SEO Team Hasn’t Made The AI Transition Yet via @sejournal, @DuaneForrester
This series has spent five articles mapping what the AI search transition requires of your team, your content, your technical infrastructure, and your strategic framing. This piece addresses the question those five articles don’t answer: How do you actually make the organizational shift happen?
Most teams won’t fail here because they lack vision. The failure mode is execution, specifically the gap between knowing change is necessary and building the structure that makes it real.
The Transition Problem Is A People Problem, Not A Technology Problem
This isn’t a strategic failure. It’s a change management failure, and it has a predictable shape. Three stall patterns show up consistently.
Analysis paralysis is the team that has attended every conference session, read every report, and built a compelling internal case, but can’t commit to a starting point because the landscape keeps shifting. The logic feels defensible: Why restructure when the platform behavior might change next quarter? The answer is that waiting for stability in an unstable environment isn’t patience. It’s avoidance dressed up as diligence.
Pilot purgatory is more widespread than most leaders want to admit. A survey of 200 U.S. marketing leaders found that 82% of teams using AI for campaigns are still operating in pilot or experimental mode, with 61% using AI only at the individual level rather than building it into collaborative team workflows. The pilot never fails cleanly; it just never graduates to production.
Reorg fatigue is the subtlest of the three. Teams that have been through digital transformation cycles carry scar tissue. They’ve watched priority initiatives get announced, resourced, and quietly abandoned when the next priority arrived. When a VP announces a pivot to AI visibility, the team’s first internal question often isn’t how to do it; it’s how long until this one goes away, too. Credibility for this transition requires demonstrating that it’s structurally different from the previous three, which means visible commitment in budget, headcount, and KPI design, not just slide decks.
The Resistance Map
Not all resistance is the same, and treating it as a uniform problem produces uniform failure. Four distinct patterns appear in SEO and marketing teams, each requiring a different response.
Seniority-based resistance sounds like: I’ve been doing this for 15 years, and I know what works. This is often the hardest pattern to address because it’s partly legitimate. Senior practitioners have real pattern recognition that junior team members lack, and they’ve watched enough vendor-driven hype cycles to be appropriately skeptical of any new essential framework. The correct response isn’t to dismiss the experience; it’s to reframe the transition as an addition to what they know, not a replacement of it. As established in the context moat piece earlier in this series, the fundamentals of relevance and trust don’t disappear in an AI search environment. They compound. Senior practitioners who make that conceptual bridge become accelerants, not obstacles.
Skills-based anxiety is a different problem entirely. This person isn’t resisting because they distrust the framework; they’re resisting because they don’t know how to operate inside it. The language of vector indexes, structured data expansion, and retrieval architecture is genuinely foreign to someone who built their career on keyword clustering and link building. A useful diagnostic lens here comes from the ADKAR model, a change management framework developed by Prosci that identifies five sequential conditions an individual needs to reach for change to stick: Awareness, Desire, Knowledge, Ability, and Reinforcement. Skills-based anxiety is almost always a Knowledge or Ability gap, not a motivation problem. Treating it as motivation resistance wastes time and confirms the team member’s fear that leadership doesn’t understand what they’re actually being asked to do.
Political resistance is structural, not personal. If AI visibility expands SEO scope to include retrieval architecture, machine-facing content design, and cross-functional data coordination, someone’s budget conversation changes. Marketing ops, IT, and content teams all have a plausible claim on parts of that expanded scope. This resistance rarely surfaces as direct opposition; it shows up as slow approvals, ambiguous priorities, and repeated requests to align with stakeholders before anything moves. The response requires making budget and ownership decisions explicitly, not hoping that clarity emerges from collaboration.
Legitimate skepticism deserves its own category because it’s the resistance pattern most leaders mishandle. When someone asks to see the revenue connection, that isn’t obstruction; it’s the right question. The answer needs to be honest, which means acknowledging that the measurement infrastructure for AI visibility is still developing. Trying to manufacture certainty in response to legitimate skepticism destroys credibility faster than admitting the gap. Acknowledging where the data is incomplete while demonstrating directional progress is more durable.
Running Both Operations At Once
Most teams can’t switch from traditional SEO to AI visibility operations in a single reorg cycle, and the honest answer is that most won’t need to. The practical reality is a period of parallel operation, where traditional work continues while AI visibility capabilities are built alongside it, and for the majority of organizations, that parallel period won’t resolve into a clean new structure. It will simply become how the team operates. The most common near-term pattern is already visible: The existing SEO gets handed AEO responsibilities alongside their current work, budgets don’t expand to match the expanded scope, and the team figures it out. That state will persist for years in most organizations, and in many it will persist indefinitely. New dedicated roles will emerge at larger organizations and in more competitive verticals, but that’s the exception rather than the rule.
Ultimately, the right allocation isn’t a fixed ratio dropped in from outside your organization; it’s a function of where your current traffic and business value are coming from, and how fast that’s shifting. What research on enterprise AI adoption does confirm is a consistent structural principle: Organizations that successfully scale AI spend the majority of their transition effort on people and process, not on the technology layer itself. That inversion, most attention on tools and least on people, is the primary driver of the pilot purgatory pattern described above. Your capacity allocation decisions need to reflect that. Building a new AI visibility capability on inadequate team development produces a capability that exists on paper and stalls in practice.
Two operational principles matter during the parallel period. First, not all traditional SEO activities need equal intensity to maintain. Technical hygiene, crawl accessibility, and core structured data work protect your existing position and directly support AI retrieval; they aren’t legacy activities to deprioritize. High-volume tactical content production, by contrast, is where capacity can be reallocated toward AI-era work without meaningful risk to current performance. Second, the AI visibility workstream needs dedicated ownership, not shared bandwidth. Work that lives in everyone’s job description at the margin of their other responsibilities doesn’t graduate from pilot mode. Someone needs to own the new work as a primary accountability.
Sequencing The Role Transitions
Not all roles change at the same time, and trying to restructure everything simultaneously is how reorg fatigue gets manufactured. A phased sequence reduces disruption while building the internal momentum that carries later phases.
Phase one starts with content strategists, because the conceptual bridge is shortest. The move from “what does my audience search for” to “what context does a retrieval model need to surface my content accurately” is an extension of existing thinking, not a departure from it. As covered in the roles series, this is the capability layer with the most upskilling potential and the least new-hire dependency. Start here, build early wins, and let the internal success story carry credibility into subsequent phases.
Phase two moves to technical SEOs, who face a more demanding knowledge transition. Vector index hygiene, structured data expansion beyond standard schema implementations, and crawl accessibility for AI bots require genuine new technical literacy, and not every existing practitioner will choose to develop it. This is where the upskill-versus-hire question starts to get real, and more on that in the next section. The technical SEO role isn’t disappearing, but its scope is expanding in directions that require deliberate investment.
Phase three introduces roles that may not yet exist on your team: an AI visibility analyst responsible for monitoring retrieval inclusion and brand representation, and someone focused on machine-facing content architecture. These may start as partial responsibilities before they justify dedicated headcount, but they need to exist as named functions with owners before the measurement conversation in phase four can work.
Phase four restructures reporting lines and performance metrics to reflect the new operating model. Teams held accountable to AI visibility outcomes, while their performance reviews are built entirely around traditional organic traffic metrics, produce the behavior you’d expect: compliance theater. This phase shouldn’t wait until phase three is complete; it should be designed in phase one and communicated clearly so the team understands what the finish line looks like from the start.
The Training Investment Decision
Whether to upskill existing team members or hire new ones is often framed as a budget decision. It’s actually a knowledge gap assessment.
If the gap is conceptual, covering how retrieval works, how AI models use structured data, how community signals feed into model training as discussed in the community signals piece, invest in training. These are learnable frameworks, and experienced practitioners who understand the underlying logic of traditional SEO have strong transfer potential. Analysis of more than 10,000 SEO job postings shows a 21% year-over-year increase in AI-related skill requirements, which reflects real employer demand but also signals that the market expects existing practitioners to develop these capabilities, not that companies are replacing their teams wholesale.
If the gap is technical execution, building APIs, working directly with embedding architectures, constructing systems that require software engineering background, the calculus shifts toward hiring or contracting. This is specialized enough that the training timeline to bring an existing practitioner to production competency may exceed the cost and speed of hiring someone who already has it.
A practical diagnostic for each capability gap: ask whether a competent practitioner with your team’s existing background could reach working proficiency in 90 days with focused investment. If yes, train. If the honest answer is longer, or if the gap requires a completely different mental model of how software systems work, consider hiring. The important discipline here is answering honestly rather than answering in the direction of what’s cheaper.
Measuring The Transition Itself
The transition needs its own measurement framework, separate from the visibility metrics the transition is designed to improve. Without it, leadership has no way to distinguish between a team that is genuinely progressing and a team that is performing progress.
Leading indicators tell you whether the structural shift is actually happening: team fluency with retrieval concepts verified through practical exercises rather than self-reporting, the number of AI visibility experiments in active testing rather than sitting in a backlog, and cross-functional collaboration frequency between SEO, content, and technical teams on AI-era work.
Lagging indicators connect to the outcomes the transition is meant to produce: Brand citation share in AI-generated responses, retrieval inclusion rates across major platforms, and the accuracy of brand representation when your content is surfaced. The framework for approaching these metrics was laid out in the GenAI KPIs piece, and the methodology there applies directly to the lagging indicators here.
The honest acknowledgment is that standardized measurement infrastructure for AI visibility is still developing. The industry hasn’t produced the equivalent of what organic search has in terms of agreed-upon tracking methodology. That isn’t a reason to defer the transition; it’s a reason to document your own methodology consistently from the start, so you’re building a proprietary baseline as standards eventually emerge. Companies that begin measuring now, even imperfectly, will have comparative data that teams starting eighteen months from now won’t be able to reconstruct.
A 90-day scorecard for the transition itself should include: at least one role with formal AI visibility responsibilities assigned, a named owner for the dual operating model, at least two active retrieval experiments generating learning data, and a completed skills gap assessment for every team member against the phase three role definitions. None of those are visibility metrics. They’re execution metrics, and execution is where most transitions fail.
Who Wins?
The organizations that navigate this transition successfully won’t be the ones with the clearest vision of what AI search requires. They’ll be the ones that converted that vision into structure: named owners, phased timelines, honest skills assessments, and measurement that tracks the work before it tracks the outcomes. Vision is table stakes, and every team reading this already has it. The ones that pull ahead will be the ones that open Mondays with a plan.
Why Google Has Changed & Who’s Really Paying for It
Money, obviously. But it’s deeper than that.
Google’s market share has broadly held firm in the wake of everything AI. By held firm, I mean its share price has gone through the roof, and its AI offering is growing ever stronger.
Happy, happy shareholders. Sad, sad people. (Image Credit: Harry Clarkson-Bennett)
But I don’t think all is as rosy as it seems.
Google’s search product isn’t addictive – as much as they’re trying to change that. Nobody hangs out there except saddos like us. And audiences – particularly younger ones – have options.
They’re turning away from more traditional methods of information retrieval, and that’s a big problem. Even for Google.
Google’s worldwide audience share by age group (Image Credit: Harry Clarkson-Bennett)
Even the search engine giant isn’t immune.
Older audiences – those already ingrained in the system – are taking up a larger percentage of their audience. The younger ones have more exciting and addictive options, and best believe they’re using them to find stuff.
Worldwide Google engagement data broken down by age group (Image Credit: Harry Clarkson-Bennett)
Across every engagement metric, 18-24-year-olds have deteriorated faster than 65+ users over the same period. Shorter visit duration, fewer pages per visit, and a worse bounce rate. And it’s declining more rapidly with younger audiences.
Evolution for Google and the wider web is a necessity.
Although interesting to note that the 18-24 year old audience share has only suffered a small decline according to Similarweb data. The real losses were in the 25-34 cohort.
TL;DR
The publishing industry and Google have more in common than perhaps either of us cares to admit.
The changes Google has made are a very deliberate effort to engage with – and retain – younger audiences. Audiences who behave differently.
Engagement data on news websites (pages per visit, bounce rate, and time on site) declines with audience age. Exactly the same is true of Google.
AI Mode is Google’s attempt to create a “sticky” product. One aimed at younger audiences.
What’s Changed?
Well, the obvious:
Just look at the SERP for almost any term, particularly middle-of-the-funnel comparison ones.
You can’t move for video, which I sort of hate (Image Credit: Harry Clarkson-Bennett)
What people apparently want is not very publisher, or legacy-search-friendly. What they want is video.
Particularly the youth.
Right now, it’s feasible children spend almost four hours per day watching video on YouTube and TikTok. Four hours. That same group spends just four minutes on publisher websites.
The younger you are, the more time you spend watching, the less you spend reading. So the obvious counter (from a company that primarily organizes written content) is to saturate the market with video content.
Obviously, it’s very helpful if you own the market.
You could say that’s Google’s way of paying for AIOs – a far more expensive SERP to generate –due to the massive computational power and energy needed to run large language models (LLMs).
But I am not going to insinuate anything of the sort. It would be incomprehensible to me that the guys who earn the entire ad and search market would make the ad side of the business more expensive to run to pay for their search experiments.
Wait a minute…
Why Now?
I think this is a direct response to two things;
The 2023 Code Red Google sent out in response to OpenAI.
Younger audiences shifting information retrieval methods.
One is obvious.
OpenAI forced Google to move quicker than they would’ve liked. Hence, all the absolute trash in AI Overviews in the beginning. Well, and sort of now. It smacked of a product that hadn’t gone through the required amount of rigorous testing.
Two is more nuanced.
The youngest demographic spends less time on search (Image Credit: Harry Clarkson-Bennett)
This data correlates almost perfectly with the Similarweb data I pulled. In isolation, this may not be a problem. Could be as simple as saying younger audiences will grow into it.
But I don’t think that argument works. We see it in news and publishing. We are living through it, and we’re watching the decline in real time.
Younger audiences have the highest recorded screen time on record (globally, 7 hours 22 minutes), but are spending less and less time reading. More on far more visually engaging, stimulating, and addictive technologies.
Based on screen time alone, younger audiences should spend the most time on Google. But they don’t. I’m sure that is blatantly obvious to the Googlers.
The same principle is true of more traditional search.
At the risk of sounding a bit too AI-y, this is a really seismic shift. Ironically, not one driven by AI. Not entirely. One driven by a combination of big tech’s insatiable appetite for money, a lack of trust in more traditional brands, and the rise of the creator ecosystem.
And AI, obviously.
As someone in the comments said, Google is Unc. Maybe a little like news websites. Their ability to attract younger audiences has diminished.
Similarweb publisher data – last 24 months (using six major UK publishers) (Image Credit: Harry Clarkson-Bennett)
I think we can clearly correlate the changes Google has made to the reduction in the younger audience share for publishers. A generation less inclined to click.
One could argue that the traffic losses so many seem to have suffered are almost exclusively from younger audiences. I certainly am.
Audiences more likely to adopt new technologies – particularly flashy ones.
There Are Clear Parallels Between News And Search
Google has gotten richer, as has the AI bubble. All that money has to come from somewhere.
It’s everyone else who struggles.
These changes are designed to counter a younger generation’s shift toward people and ultra-engaging platforms that encourage passive or more incidental methods of information retrieval.
Either you browsed a news website (a real paper if you felt fancy) or you searched for it. But the discovery layer changed, and search – the engine that powered the volume-driven publishing model for two decades – is responding.
Responding to younger audiences’ shifting consumption habits. Just like publishers and websites will have to.
They expect you to just appear. Algorithmic consumption has reduced the need, want, and desire to actively seek something out. If what you serve isn’t delivered directly to their feed, you don’t exist.
Combine this with diminishing trust in more traditional brands, zero-click searches, and the rise of the creator, and you can see why publishers and Google are having to change.
There have been alternatives to Google when it comes to accessing and retrieving information – Instagram, Amazon, YouTube, et al., for years.
Really, this is, or has been, Search Everywhere Optimization. It has been around for a decade. It is also, IMO, why reframing SEO as GEO or some other BS because of LLMs is so moronic.
Views for The Washington Post’s YouTube channel dropped by 85% from its peak in April (54 million views) to 8.2 million views in September 2025, two months after Jorgenson’s exit. (Image Credit: Harry Clarkson-Bennett)
And now the individual has become the competition. The creator economy – soon to be worth $480 billion – has produced a new class of competitor: individuals with direct audience relationships, authentic voices, and none of the structural cost of a legacy newsroom.
And this is a problem for Google, too. People used to use their organizational skills to satisfy all of their needs. Now, it is so heavily navigational that it’s hard to know how much “new” stuff people really use it for.
Outside of news, at least, ironically.
Will This Work?
If it’s anything like news publishers, their primary concern is to continually generate new and engaged audiences with habitual products. AI Mode could absolutely be that product. Discover is their version of a social network. They are, in their own way, engaging products.
Although the low intent nature of Discover makes the advertising rubbish, and Google not really care about it. Sad, but true.
Like Google, the engagement data for publishers tells a pretty bleak story.
Similarweb publisher data (using six major UK publishers) (Image Credit: Harry Clarkson-Bennett)
If we isolate this to the youngest and oldest audience, it’s pretty clear what is going on.
(Image Credit: Harry Clarkson-Bennett)
Younger audiences:
Are far less engaged with the traditional news offering than older audiences.
Use these (and any) websites differently.
There’s no denying that younger audiences have more diverse and engaging options. This means they use websites like news publishers differently. To fact-check. To confirm something isn’t just spurious BS. To scan and skim.
The same is true of Google. Less of a discovery journey. More one of fact-checking and navigational searching.
Now, I’m not insinuating that older audiences get stuck with adverts and can’t use a menu. That can’t account for an extra 14 minutes of time spent on news websites.
But having watched my mother with a computer, it’s not impossible.
So, What’s The Answer?
To lean into what the new generation likes. Adapt and evolve.
Exec summary from WAN-FRA x the FT Strategies News Creator Project (Image Credit: Harry Clarkson-Bennett)
The same is true for search (internally and externally) and publishers. If you work for Google, it makes complete sense you would try to expand your video presence in the SERP and prioritize “quality” UGC.
The quality part is lacking as most of the internet – as we’re finding out – is a stinking pile of garbage.
But notoriously, the tide is tricky to swim against.
For publishers, it means working with creators, leveraging their audiences and ability to deliver things quickly. Differently. And creators can benefit from the trust associated with proper news organizations.
Is it that unreasonable to think Google should do the same?
Instead of abusing their position, they could start by giving people an idea of the impact of AIOs and AI Mode. I’m not a financial guru, but I reckon Google has enough money to build and foster creator and publisher programs that are not one-sided. That brings genuine value to people and the wider information retrieval ecosystem.
In this scenario, everyone benefits. When AI companies refuse to pay for publisher content, everyone loses.
LLMs lose because they have less unique, human-created, quality content to train on.
Publishers lose because they are forced to suppress their visibility and don’t get any money.
They’re as diverse and resilient as any publisher (Image Credit: Harry Clarkson-Bennett)
Final Thoughts
Unfortunately, I think the recent spate of job losses in the publishing industry is just the beginning. Bauer, the BBC, The Washington Post. It’s not UK or SEO-specific. 100,000 roles are becoming 70,000 ones. Teams are shrinking. And there are real-world ramifications.
We are not in a good moment. Some of this can be attributed to AI. But I think more of it is due to longer-term economic difficulties, audiences switching off from traditional news, and things like the Site Reputation Abuse update destroying much-needed revenue lines overnight.
It is hard to make these businesses profitable. Google doesn’t have that problem. But they’re not immune to changing behaviors and becoming yesterday’s news either.
