Google research suggests AI spam may be easier to detect by identifying originating networks instead of analyzing content one at a time.
The post Google Research Shows How AI Spam Can Be Detected appeared first on Search Engine Journal.
USA Today Co. is using AI-assisted shell files to publish breaking sports coverage faster. The strategy is designed to capture search traffic before Google’s AI Overviews summarize the news.
The publisher tested the approach during the 2026 Winter Olympics and is now using it for coverage of the 2026 FIFA World Cup, Digiday reported.
USA Today pre-writes breaking stories. The USA Today network, which includes the flagship site and more than 200 local publications, creates automated shell files for likely breaking news events. AI pulls subheads, photos, and links from the publisher’s archive. Editors turn that material into ready-to-publish files, allowing reporters to add new details, update the headline, and publish quickly.
The search window is shrinking. Publishers have long pre-written stories to move faster in Google Search. AI Overviews have increased the pressure.
Olympics coverage drove 116 million views. USA Today Co. said its national and local network generated 116 million page views from Winter Olympics coverage between Jan. 1 and Feb. 28. The flagship USA Today site drew 91 million page views, up 82% from the 2022 Winter Olympics.
Why we care. AI Overviews can compress breaking news into answers within hours. Publishing first improves your chances of capturing search demand before Google answers the query itself.
World Cup gets the playbook. USA Today Co. is now using the system for World Cup coverage, with five shell files ready each day. The publisher is also investing in original reporting. It has reporters in all 16 host cities and a dedicated World Cup hub.
Traffic may still fall short. USA Today Co. has 40 million monthly unique visitors to its sports content and expects a World Cup traffic boost, especially with the U.S. co-hosting the tournament. DelGallo said USA Today still expects “massive audience” spikes from the World Cup. But she said AI Overviews have likely lowered the traffic ceiling compared with a year ago.
The report. How USA Today Co. is trying to beat AI Overviews on World Cup news
AI is changing how Americans search for information. A new Pew Research Center report found that 60% read AI-generated summaries at the top of search results, and about 40% use chatbots to find information.
AI-generated answers now appear across both traditional search results and dedicated chatbot platforms, including tools like ChatGPT, Gemini, and Copilot, Pew found.
AI summaries reach most searchers. Six in 10 U.S. adults said they’ve read AI summaries at the top of search results, Pew found. Three in 10 said they haven’t.
Chatbots are search tools. About half of U.S. adults now use AI chatbots, up from about one-third in 2024. Roughly one in four adults use them daily.
ChatGPT dominates. ChatGPT remains the most widely used chatbot by a wide margin. Pew found that 44% of U.S. adults now use ChatGPT, up from 34% last year and more than double the share measured in 2023.
Why we care. People now find information through traditional results, AI summaries, and chatbot answers. A traditional search ranking may not reflect every place people now find answers.
Dig deeper. AI search adoption rises as consumer trust declines: Study
About the data. Pew Research Center surveyed 5,119 U.S. adults from Feb. 17-23, 2026, through its nationally representative American Trends Panel. The full-sample margin of error was plus or minus 1.6 percentage points.
The report. Americans and AI 2026: Chatbots, Smart Devices and Views on Impact
A coalition including Google, Microsoft, and GitHub published Agentic Resource Discovery, an open draft spec for how AI agents find and verify tools online.
The post Google, Microsoft Back Draft AI Agent Discovery Spec appeared first on Search Engine Journal.
Google AI Overviews cited self-promotional “best” listicles while excluding the brands behind them from recommendations in 69% of cases, according to a new analysis of B2B software queries by Lily Ray.
Brands have used self-serving listicles to influence AI search results, but Ray found Google often cited those pages while recommending competitors instead.
By the numbers. Ray analyzed 100 B2B “best [category] software” queries in Google AI Overviews across three dates: April 15, May 15, and June 8.
Competitors get recommended. Ray documented several cases where Google cited a brand’s “best” listicle while recommending better-known competitors.
Stronger brands still appeared. Brands that already led their categories, were widely mentioned by third-party sources, and had stronger link profiles were more likely to appear in AI Overview recommendations, according to Ray.
Organic visibility fell. Ray also reported organic search declines for many sites that relied heavily on self-promotional listicles.
Review sites gained citations. Ray found Google relied heavily on third-party and user-generated-content sites for “best” queries, with Reddit citations increasing sharply in recent months.
Why we care. A citation is not a recommendation. Your content can appear in an AI answer while helping competitors capture the visibility that matters most.
Catch up quick. Search Engine Land previously reported that some SaaS and B2B brands lost 30% to 50% of their visibility after relying heavily on self-ranked “best” pages, based on earlier research from Ray.
About the data. Using Ahrefs Brand Radar, Ray collected AI Overview answer text and cited sources for 100 B2B “best [category] software” queries at three checkpoints between April and June. The analysis measured two outcomes: whether a self-promotional listicle was cited and whether the brand behind it was recommended.
The report. Why Calling Yourself the Best Could Be Helping Your Competitors Win in AI Search
Adobe announced a new solution to help businesses ensure their brands are visible, trusted, and chosen across AI surfaces.
Called Adobe Brand Visibility, the product is part of Adobe CX Enterprise, an agentic AI system designed to simplify customer lifecycle management — from acquisition and prospect engagement to conversion and long-term loyalty.
AI traffic is exploding. The use of LLMs to identify and research products and services marks a significant shift for both marketers and consumers. Alongside the announcement, Adobe released data showing substantial growth in LLM usage. AI traffic to U.S. retail sites surged 1,324% between October 2024 and May 2026. In the travel sector, AI traffic increased 2,215% over the same period.
Measuring brand visibility in AI search. Adobe Brand Visibility is Adobe’s first generative engine optimization (GEO) product since its acquisition of Semrush in May. It combines Adobe LLM Optimizer with Semrush’s AI Optimization tool.
Adobe Brand Visibility draws on nearly 300 million real-world AI search prompts, which Adobe says is the largest global database of its kind, helping teams identify which prompts they’re winning or losing.
Combined with Adobe’s first-party signals from owned channels, the platform gives marketers a view of how their brands appear across ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics include mention frequency, audience reach, competitive share of voice, and content gaps. AI agents then surface prioritized recommendations, enabling teams to deploy updates quickly and measure their impact directly in the platform.
Competitive intelligence. Adobe Brand Visibility includes competitive brand comparison tools that let marketers benchmark against competitors, identify where their brands are cited, track brand mentions, and analyze historical trends.
The platform also includes SEO intelligence, reflecting the continued importance of SEO fundamentals in AI search visibility. Powered by Semrush data spanning 28.5 billion keywords and 43 trillion backlinks collected over 17 years, it shows where existing search authority should be generating AI citations and where content investments can close gaps across both channels.
There’s still much to learn about how LLMs work and how brands can improve visibility, but Star is confident Adobe is well positioned to lead in this space.
Ask ChatGPT or Gemini to “review my on-page SEO,” and you’ll get a perfectly reasonable answer.
Reasonable. Generic. Boring. Uninspired. And almost identical to the answer your competitors get when they ask the same question.
That’s the problem with AI out of the box. It’s a generalist. It knows a little about everything and nothing about you — your business, your customers, your market, or the way you do SEO. The questions are loosely framed and inevitably come back with general answers.
The good news is that’s also the opportunity. The same tools that produce generic answers can become specialist assistants that encode your knowledge, process, and standards. No code required.
Building one is simpler than most people think. With tools like GPTs, Gems, and Claude Projects, you can package your SEO process into a reusable assistant that helps identify opportunities, automate repetitive tasks, and apply your expertise consistently.
You don’t need a computer science degree here, but a basic understanding of how AI works helps explain the benefits of this approach.
Large language models are prediction engines. They’ve been trained on a huge slice of the internet and human knowledge, and when you ask a question, they predict the most plausible response based on everything they’ve seen.
In other words, by default, you get something close to the internet’s average opinion on a topic.
For SEO, the internet’s average opinion is … fine. It’s the same advice repeated across a million articles. Check your title tags. Improve your content. Build some links. Blah.
What the model doesn’t know is anything about your specific situation:
The output is only as contextual as the input. Give it nothing, and you get the average. Give it your knowledge, and you get something far more useful.
It’s a computing problem as old as computing itself: garbage in, garbage out (GIGO).
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
There are a few ways to add that missing context, in increasing order of effort:
The great thing is that the jump from a “big prompt” to a “simple app” is smaller than it sounds.
The skill is the same: clearly describing the job, the process, and the standards. If you can write a good brief for a junior team member, or a standard operating procedure (SOP), you can absolutely build one of these.
This is an important point because most people assume this is far more complicated than it is, and that assumption is holding them back. You don’t need to be a developer to do this.
The development of custom tools is no longer a heavily technical job. It’s becoming more of a creative endeavor enabled by these new AI tools and the simple, descriptive way of building apps.
If you can document your process, you can build an AI app.
A quick tour of the main options for building simple AI apps:
For most SEO and marketing professionals dealing with day-to-day optimization work, the sweet spot is the first tier: GPTs, Gems, or Claude Projects. They take minutes to build, require no code, and capture 80% of the value.
I’ll use Gemini Gems for the worked example below, as it’s the closest to home for those of us who live in Google’s world. The principles transfer directly to GPTs and Claude, and if you want to build something a little more advanced, have a play with Replit.
Standard SEO tools are brilliant at what they do — crawling, rank tracking, and link data. I use them every day. But they share a weakness: They’re generic by design, while your business is totally unique (or at least it should be). They have to work for every business in every industry, so they can’t know what matters to you. Everyone sees the same scores, the same recommendations, and the same “issues,” many of which don’t matter for your situation.
The tools are also largely focused on analysis and opportunity. The kinds of tools you can build with AI are more focused on the actual work.
Vanilla AI has the same problem from a different direction. Hugely capable, zero context.
The strength of building your own simple AI tools is personalization:
That last point is the big one. After 30 years of doing this, my honest take is that the value isn’t the AI. The value is the knowledge and process you encode into it. Your experience is what matters — the AI is just your superpower.
A simple rule: Automate repetitive tasks. Good candidates are tasks that are:
Reviewing Search Console data ticks all three boxes. So do first-pass on-page reviews, log file triage, internal link analysis, and monthly reporting prep.
What you don’t automate is judgment: strategy, prioritization against business goals, and the final call on what actually ships. The AI does the legwork and surfaces the candidates. You decide.
Let’s build a simple tool to help you mine Google Search Console for content ideas and easy wins.
I wrote “How to unlock easy wins in Google Search Console” two years ago, covering the creaky old human way of doing it. Let’s automate it to free up time for the really valuable creative work.
Note: This is a purposely simple example that’s ideal for AI and automation because the task is repetitive and the data is free.
Write one sentence describing what the tool does:
Simple enough.
This is the important bit, and it’s where you have to think about the process.
What do you actually do here? What process do you follow? What easy wins and opportunities are you looking for?
For each of these, also note the thresholds and judgment calls. What counts as “meaningful impressions” — 100? 500? What CTR is “low” for position 3 versus position 8?
This is your experience being made explicit, possibly for the first time.
Now open Gemini, create a new Gem, and translate that process into instructions. A solid structure is:
Here’s an abridged example to adapt:
Role: You are an experienced SEO analyst. You are methodical, skeptical, and prioritize commercial impact over vanity metrics.
Task: I will provide an export of Google Search Console performance data (queries and/or pages, with clicks, impressions, CTR, and position). Review it and identify quick-win opportunities.
Process: Check for, in priority order:
- Striking-distance queries — average position 5–15 with 100+ impressions.
- High-impression, low-CTR queries — flag where CTR is significantly below what you’d expect for that position.
- Pages or queries declining versus the comparison period.
- Multiple pages ranking for the same query.
Output: A prioritized table with opportunity, query/page, current metrics, recommended action, and expected impact (high/medium/low). Maximum 15 rows. Quality over quantity.
Below the table, provide a short plain-English summary of the three actions I should take first.
Guardrails: Only use the data provided. Never invent queries, pages, or metrics. If the data is insufficient to assess something, say so. Ask clarifying questions if the export format is unclear.
That guardrails section matters more than people realize. “Only use the data provided” is your main defense against the AI confidently inventing things.
Gems can reference uploaded knowledge files. This is where you fine-tune things and add depth without bloating the instructions.
Examples include:
This lets the Gem prioritize opportunities that matter, not just opportunities that exist. That’s especially important when reviewing Search Console data, as most sites show up for a wide range of searches that aren’t aligned with the client’s core goals.
It really is that simple. Hit save, and you’ve created an AI app.
Export your performance data from Search Console (Performance report > Export, or via the API or Sheets if you want more rows), then start a chat with your Gem and upload the file.
Browse to the Performance report and click Export in the upper-right corner. In this example, I use Google Sheets to keep everything in the Google ecosystem.
Then upload the file and ask for the output you want.
The Gem’s output — a prioritized quick-wins table for a real site
If at first you don’t succeed: The first output here wasn’t terribly useful for this site.
The recommendations didn’t align with the client’s goals. I had to revisit my third knowledge file regarding the business’s commercial goals and priorities.
After refining that document and running the analysis again, the suggestions became much more useful.
The first version will get things wrong. That’s expected, and it’s actually the useful part.
A bad recommendation is a way to identify what could be improved. Whatever your answer is, that’s a rule that was missing from the instructions. Add it, and the Gem gets a little closer to working the way you do.
Treat it like a new team member. Review its work, correct it, and update the brief. After a few rounds, you’ll have something that delivers a genuinely useful first pass in seconds — and a documented process that’s valuable in its own right.
Some honesty before you let this loose on client work:
Once you’ve built one, the pattern repeats. Same recipe — role, task, process, output, guardrails, and knowledge files — different job.
Any manual task you do repeatedly is a good candidate for this type of tooling. Examples include:
Each of these is an afternoon’s work.
The constraint isn’t technical. It’s whether you’ve documented your process clearly enough to hand it over. If not, this is a good opportunity to systemize your business and accelerate the work with a simple app.
You can also apply the same approach across digital marketing: building personas, improving your homepage for AI-driven search, comparing SEO, PPC, and AI strategies, or tackling whatever else falls under the modern marketer’s remit.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
The AI was never the valuable part. Anyone can open Gemini. What they can’t do is replicate the process you’ve built over years of doing the work.
Your knowledge, experience, and process are the product. AI helps you apply them at scale.
Tools come and go. Knowledge compounds. Write yours down, encode it, and let the machines do the boring bits.
Content operations can run on instinct at a small scale. With a strong editorial team, a handful of trusted writers, and an understanding of voice, there’s usually enough discipline to keep the calendar moving.
But some businesses aren’t built that way. For media rollups, large affiliate networks, entertainment properties, sports brands, and other content-led businesses, publishing at triple-digit volumes per day makes sense.
In some cases, it’s necessary to survive because content is the operating model rather than a marketing function, as it is in many B2B organizations.
At that scale, content strategies don’t break because of content. More often, they break because economics, systems, and editorial judgment stop speaking to each other.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
That B2B distinction is important. If you sell a niche manufacturing ERP, you simply don’t need that scale of content. There’s not enough to publish. You’d be burning cash and operating outside the market.
Some categories have the depth and audience appetite required to sustain hundreds of daily articles. Sports is an obvious example. There are games, trades, injuries, recaps, rankings, interviews, opinion pieces, explainers, storylines, and the list goes on.
A business like The Athletic can support significant publishing volume because audience demand is real, while the revenue model includes subscriptions, direct sales, programmatic display, affiliate revenue, and likely other sources under the hood.
In Q2 2025, The Athletic generated $54 million in revenue, according to its last standalone financial report. Of that, 64% came from subscriptions, 26% from advertising, and 10% from affiliate and licensing revenue.
When most revenue comes from people actively choosing to pay, editorial quality is no longer a judgment call. It’s the most important commercial requirement. Economics, systems, and editorial judgment are forced to speak the same language.
Other models are more fragile. The clearest example is when monetization is driven primarily by programmatic display measured by RPM (say, more than 70% of revenue), with content rewritten from existing coverage or produced around short-term search and social opportunities, where margins require high output and very low production costs.
The formula is simple:
So if a website earns 4,000 pageviews per article at a $16 RPM, it generates $64 in revenue.
Subtract production costs. The margin gets thin fast.
To generate meaningful profit, the organization has little choice but to publish hundreds of articles per day while doing everything it can to maintain quality, discoverability, and audience trust.
That’s where these content strategies break.
More content can look like more revenue. But the spreadsheet tells only a fraction of the story.
Numbers don’t show editorial quality, whether thinner work is being produced to feed the machine, or whether monetization decisions are inadvertently weakening the asset.
Data surfaces where that drift starts. Points captured within a CMS include:
Cross-referenced with sessions, pageviews, pageviews per session, session duration, RPM, source/medium, and other metrics.
That lets analysts drill into content types by source, category, and tag, while providing visibility into top performers, opportunities to optimize the ad stack by content type, and more.
Here are some simple scenarios that highlight what that looks like in practice:
Fairly simple stuff on the surface. However, this is where judgment becomes the difference between a healthy operation and one that’s quietly eating itself.
Scaling these operations past 100 writers is mainly a question of whether the business has the systems, data, and judgment required to keep the operation from collapsing under its own volume.
It’s worth noting that 100 writers is rarely just 100 writers. For many of these businesses, it’s 100 writers across a dozen properties, which is actually more than 1,000 writers when you account for the full footprint.
Independent publishers don’t typically hit that scale because the infrastructure requires a level of investment they most likely don’t have access to.
That infrastructure includes clearly defined communication structures for editors, project management ownership, and comprehensive guides covering writing, linking, imagery, social, and CMS usage.
Without them, standards can degrade unpredictably across properties, and editors lose the ability to diagnose why or quickly point people toward resources when putting out fires.
On the data side, granularity is a must. Without consistent tagging and categorization built into the CMS from the start, analytics can become too fuzzy to act on.
Performance needs to be attributable at every level, rolled up into a P&L for each property, and then rolled up again across the conglomerate.
Technical infrastructure is essential as well, often in ways editorial teams wouldn’t expect.
If you consider how to get images into Google Discover, for example, it requires CDN delivery within specific guidelines. That’s more of an engineering problem than an editorial one. User roles and permissions across CMS and revenue dashboards are another example, along with the development resources required to implement the CMS architecture needed for data capture and reporting in the first place.
Proprietary systems can also be beneficial depending on a business’s scale. If you’re a rollup with a dozen properties operating on one or two CMS templates, it’s much easier to make bulk optimizations or accelerate the integration of newly acquired properties.
Channel distribution isn’t static either. Platform value to publishers shifts. Think about when Facebook stopped sharing news links in Canada. It changes the economics of whether a platform is worth optimizing for. Consistent monitoring and testing need to be built in.
The systems above create favorable conditions, but they don’t guarantee sound judgment.
Let’s revisit one of the examples above:
If you’re looking only at the spreadsheet, you’d favor doing as much of that as possible. That’s tempting, especially if employers incentivize target RPMs or sessions per article as KPIs tied to bonus compensation.
However, thin content at volume isn’t ideal for organic visibility. Once readers and search engines encounter too much low-quality output, the traffic disappears.
You’d essentially optimize for short-term yield, reinforce that behavior through employee bonuses, and damage the asset in the process.
Or another example:
The problem is that doing it at scale without substantive edits and strict guidelines may create distrust. That’s the judgment call.
Three things need to be held in tension: economic logic, infrastructure and systems, and the judgment not to sacrifice long-term gains for short-term wins.
While that sounds like common sense, these responsibilities are often owned by different people who don’t speak the same language.
Finding a way to bridge that gap is the most important challenge in a scaled content operation. Diversified revenue streams like The Athletic’s help enforce that alignment.
Otherwise, your content strategy will probably fail when you scale past 100 writers. And the examples above are just two of hundreds of scenarios where the spreadsheet points one way, and the right decision points another.
Get it right, and you can scale to 1,000 writers.
For the past two years, the SEO industry has been asking Google for two things: more visibility into AI traffic and more control over how content appears in AI experiences.
Last week, Google started delivering both.
They announced new controls that allow site owners to opt out of AI-powered experiences (AI Overviews, AI Mode, etc.) and introduced new AI reporting within Google Search Console. (Note that both of these are in early beta and are not yet available for everyone.)
On paper, this is a victory for things moving in the right direction for publishers.
Instead, the conversation immediately split into camps. Some focused on the new reporting. Others focused on the new controls and began debating whether to opt out of AI altogether.
What caught my attention wasn’t the announcement itself. It was how quickly the conversation shifted from gaining visibility to voluntarily giving it up.
Before we go any further, let’s clear up what Google actually announced.
The new controls do not turn off AI Overviews, stop people from using AI Mode, or slow AI adoption. Users are still going to search and ask questions, and increasingly do so through AI-powered experiences.
Google introduced a way for publishers to have more control over whether their content can be surfaced in those experiences. (Was this the plan all along, or was it exclusively because of the UK Competition and Markets Authority demanding it?)
That’s an important distinction because many people are treating this as a decision about AI itself. It isn’t.
The user experience remains largely unchanged. The only thing that changes is which brands are eligible to appear.
If Expedia opted out tomorrow, people wouldn’t stop planning vacations. If NerdWallet opted out tomorrow (like I did their stock), people wouldn’t stop researching credit cards. Google would simply surface someone else in its place.
This isn’t a decision about whether AI succeeds or fails. It’s a decision about whether your brand is present when customers choose to use it.
I understand the appeal. Publishers are worried about losing more clicks, frustrated by changing search behavior, and concerned about how AI systems use their content.
Those concerns are beyond valid.
Where I disagree is with the assumption that opting out changes user behavior.
It doesn’t.
Users aren’t deciding whether to use AI based on your participation. They’re deciding whether AI helps them get answers faster. For a growing number of searches, it does.
That’s why opting out of AI inclusion and opting users out of AI experiences are two different things.
A publisher can choose not to participate. Users can still use AI Mode. Google can still answer the question. The only thing that changes is which brands are eligible to appear.
That’s the trap.
The practical outcome isn’t less AI. It’s more visibility for your competitors. They gain citations, exposure, and the opportunity to become the trusted answer, while your brand becomes less visible.
If the concern is that AI is changing how customers discover information, disappearing from AI-powered experiences feels like a pretty dumb move.
The challenge isn’t finding ways to be less visible. It’s finding ways to remain visible as search behavior continues to evolve.
The other part of Google’s announcement that received less attention was the reporting.
For years, the industry has been asking for more visibility into AI-driven search experiences. We wanted better attribution, better reporting, and a clearer understanding of how users interact with AI-powered search.
Now Google is beginning to provide some of that visibility, and almost immediately the conversation shifted to why it isn’t enough. Note that many of these screenshots are illustrative and are even from industry friends and well-respected search practitioners in our space. No shade intended to any one individual, simply wanting to illustrate the movement.
Maybe that’s true. The data isn’t perfect. The reporting doesn’t answer every question. I’d love more visibility into citations, AI Mode interactions, and better any sort of attribution modeling.
I especially agree with Dan’s post above, but waiting for perfect data has never been a winning strategy.
SEO has always operated with imperfect data. We’ve spent years making decisions based on estimated search volume, incomplete attribution, and reporting limitations. Some of the biggest wins in my career came from acting on directional signals rather than perfect certainty.
The same applies here.
The mistake is treating every reporting enhancement as either perfect or useless. We’re getting more visibility than we had six months ago, and we’ll likely have more six months from now.
Part of the reason this debate exists is that many teams are still measuring success through a traditional SEO lens.
Traditional reporting focuses on clicks, rankings (ewww), traffic, and conversions. Those metrics still matter, and I don’t see them disappearing anytime soon. The problem is that they’re no longer telling the entire story.
Users are discovering brands across more surfaces than ever before, especially outside of the Google ecosystem. Traditional organic search still matters, but so do AI Overviews, AI Mode, ChatGPT, Perplexity, Bing Copilot, Reddit, YouTube, and a growing list of ecosystems users rely on in their purchasing journey.
That’s why I’ve started thinking about reporting as “SEO+” rather than just SEO. (Yeah, I’m lazy and used the streaming naming convention “+” because… yeah, lazy.)
The goal isn’t to abandon traditional metrics. The goal is to expand what we’re measuring. Alongside traffic and conversions, I want to understand where brands are being cited, how often they’re being mentioned, how many unique URLs are being cited, whether branded search demand is increasing, how AI platforms reference them, and whether visibility is expanding even when attribution remains borked.
This is where I think many organizations are making the same mistake they made with content years ago.
With one of my clients, a lot of our content influences revenue months before a customer converts. Looking only at last-click reporting dramatically understates the impact. That’s why I started reporting on “content assists” as a key metric in their reporting. AI visibility is creating a similar challenge. A customer might first encounter your brand through an AI Overview, revisit you through traditional search, and ultimately convert through a completely different channel (probably a paid channel… ‘cause everyone loves ROAS).
The influence is real even when the attribution path is messy.
That’s why I’m less interested in measuring traffic alone and more interested in measuring discoverability. The brands that consistently appear across search, AI, and recommendation platforms are building familiarity long before a conversion occurs.
Most of the discussion around Google’s announcement has centered on a single question:
Should I opt out of AI?
I think that’s the wrong question.
The better question is whether you can afford to be absent from the places where customers increasingly discover information, products, and brands.
Users aren’t waiting for the SEO industry to decide whether AI is good or bad. They’re already using it.
That’s why I view Google’s announcement less as an AI opt-out feature and more as a strategic decision point. Opting out doesn’t remove AI from the equation. It simply increases the likelihood that someone else becomes the answer instead.
Some brands will use it.
Their competitors are hoping they do
Will you lean into change, or will you be another person complaining that Google owes them free clicks?
This post first appeared on the author’s website and is republished here with permission.
Google updated its site move documentation to say that you should enter in all the domain variants in the Google change of address tool when doing a site move.
What Google wrote. Google posted the following new note in the document:
“For domain migrations: If you’re moving your site from one domain to another, make sure to submit Change of Address requests for all subdomains and the www and non-www variants of the old domain name (for example, from en.example.com, www.example.com, and example.com to new-example.net), even if you’re not actively using these variants. Ensure that you have all of these variants verified in Search Console.”
Domain variants. Domain variants are all the variations of your domains, including sub-domains, different TLDs, www versus non-www and so forth. These include en.example.com, www.example.com, and example.com to new-example.net, as an example.
Why we care. Google wrote that “domain migrations work best when all variants of a site are migrated properly,” which is why you should follow Google’s guidelines carefully when making site moves and domain migrations.
Site moves and domain migrations are scary tasks for SEOs and site owners, so having very specific steps and guidelines make the process a little less painful.
The change of address tool is there to help you speed up these migrations, so make sure to use it properly.
Google says a core assumption driving LLMs.txt adoption conflicts with the purpose its creators originally intended.
The post Google Exposes The Fundamental Flaw Of LLMs.txt appeared first on Search Engine Journal.
Google's updated documentation for domain migrations requires broader compliance demands for site owners.
The post Google Tightens Requirements For Domain Migrations appeared first on Search Engine Journal.
The UK’s Competition and Markets Authority (CMA) has ordered Google not just to give site owners a way to opt out of AI Overviews but also to explain how the search engine ranks its search results. Also, the CMA has ordered Google to allow users to port their search data to certain third-party services.
Transparency on search rankings. The CMA wants Google to “improve transparency and fairness in how search results are ranked,” and implement this within 6 months.
UK businesses told the CMA that Google’s “ranking practices are neither fair nor transparent,” adding “changes are made without sufficient notice, and when these changes impact their businesses, they do not have effective ways to raise concerns.”
Technically, yes, we cover Google search updates all the time. Google continues to adjust its ranking algorithms to (1) improve the relevancy of those search results and (2) to remove those who try to manipulate those results.
The CMA added that under this conduct requirement, Google must:
Data portability. The CMA also wants Google to “Allow users to port their search data to authorized third parties such as rewards platforms or companies offering personalized offers or discount codes” within 3 months.
“Third-party firms are keen to offer people new products and services based on their Google search data but need to be able to access it with confidence. Using this data would allow third parties to offer people more personalized features – like tailored travel suggestions, more relevant shopping deals, and rewards (including cashback and discounts),” the CMA wrote.
Why we care. I highly doubt Google will follow these orders, as doing so would put its most prized asset – the search ranking algorithm – in its competitors’ hands. It will also show all how rankings work, thus making it easier to manipulate and spam.
The CMA is not the first to ask for this and won’t be the last, but Google will no doubt vigorously fight these orders.
ChatGPT’s product recommendations changed 80.2% when search was enabled, according to a study of 20,000 responses by Jeff Oxford, founder and CEO of Visibility Labs.
Oxford tested 1,000 product-recommendation prompts 10 times each with ChatGPT search enabled and 10 times with search disabled.
Only 19.8% of products recommended without search also appeared in recommendations generated with search enabled.
Search changed top picks. Even products ChatGPT recommended most often without search rarely carried over. Among products that appeared in 100% of search-disabled responses, only 15.8% also appeared when search was enabled.
Source mentions tracked visibility. The study also examined whether products mentioned in ChatGPT’s cited sources appeared more often in its recommendations. Oxford reported a 0.4 Pearson correlation between cited-source mentions and recommendation frequency.
Search narrowed recommendations. ChatGPT responses with search enabled contained an average of 5.2 products, compared with 6.2 when search was disabled.
Why we care. Search changed which products ChatGPT recommended, including products it named every time when web access was disabled. The findings suggest products appearing in cited sources may receive greater visibility when search is enabled, though the study does not determine whether cited-source visibility matters more than broader web visibility.
About the data. Oxford analyzed 1,000 product-recommendation prompts, running each 10 times with search enabled and 10 times with search disabled. Product names were standardized so naming variations counted as the same product. Because the study was observational, it did not establish a causal relationship between cited-source mentions and recommendation frequency.
Dig deeper. AI recommendation lists repeat less than 1% of the time: Study
The UK's CMA introduced two new conduct requirements for Google Search, covering fair ranking of organic results including AI Overviews, and search data portability.
The post Google Must Give Notice Before Significant Ranking Changes appeared first on Search Engine Journal.
AI referrals to U.S. travel sites nearly tripled in May. AI visitors spent more time on site and bounced less than visitors from traditional sources, according to Adobe.
By the numbers. Traffic from AI sources to U.S. travel sites grew 194% year over year in May 2026, Adobe said. It was up 2,215% since October 2024, when Adobe began tracking AI traffic.
AI visitors showed stronger engagement. AI-referred travel visitors still converted 28% less than non-AI traffic. That gap has actually narrowed nearly 70% since October 2024, Adobe said.
Travel pages and AI readability. Adobe also measured how readable travel websites are to large language models. Its AI Content Visibility Checker scores how much page content AI systems can read.
Where travel sites scored best. Hotels led across several page types, including destination guides, activities, search results, customer service, and promotions pages.
Retail’s conversion advantage. AI referrals to U.S. retail sites also hit a record high in May, rising 138% year over year and 1,324% since October 2024.
Why we care. Adobe’s data suggests AI referrals are becoming more commercially valuable, especially in retail, while its visibility scores indicate many sites still leave significant content inaccessible to AI systems. If key content is blocked, buried, or poorly structured, you may lose visibility before a traveler or shopper reaches your site.
About the data. Adobe’s findings were based on more than 8 million visits to U.S. travel sites, more than 1 trillion visits to U.S. retail sites, and more than 100 million SKUs. The company also surveyed more than 5,000 U.S. consumers in March about how they use AI for shopping and travel planning.
AI search is changing at a pace none of us has experienced before in marketing.
The presentations I saw at Zero Click NY highlighted both how much AI search has changed over the past six months and the characteristics that may become lasting features of the landscape.
Of all the points covered, these seven stood out as the most important.
From the rise of the marketing engineer, to the differences between Claude and ChatGPT results, to Claude’s meteoric rise among businesses over the past 12 months, here are the most impactful takeaways I left with.
ChatGPT and Claude share only about 8% of their citations, per Profound data. Put differently, 92% of what ChatGPT cites wouldn’t be cited by Claude for the same query. A brand can own visibility in one engine and be virtually invisible in the other.
On top of that, they don’t just cite different websites. They prefer different kinds of content.
The relationship to traditional search splits the same way. About 64% of the websites Claude cites also appear in Google’s top 50 for the same query. For ChatGPT, it’s only 37%.
In other words, “just do the SEO work you’ve been doing” might work for Claude visibility, but likely won’t for ChatGPT.
Takeaway: It’s critical to communicate to stakeholders that “AI visibility” will inevitably vary by LLM, and you’ll have to prioritize them depending on whom you’re trying to reach (more on that later).
Track visibility by engine because the work that wins in one might do almost nothing in another. UGC and community seeding move ChatGPT, while listicles and traditional rankings move the needle on Claude.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
If you’ve seen the generative AI traffic-share charts, Claude looks like a rounding error.
But web traffic is the wrong chart. Roughly 85% of Anthropic’s revenue comes from enterprise and API usage that never shows up in consumer traffic data.
The right chart comes from Ramp’s AI Index, which tracks corporate card spend across tens of thousands of businesses.
A year ago, single digits of those businesses were paying for Anthropic. Today, it’s 34.4% — ahead of OpenAI at 32.3%. For the first time, more businesses pay Anthropic than OpenAI.
I came away from this presentation asking myself: If business users are increasingly living in Claude while consumers live in ChatGPT, shouldn’t your optimization priorities focus on where your audience is?
Should B2B brands prioritize Claude visibility first? Should B2C brands prioritize ChatGPT first?
Almost nobody is doing this because people aren’t really thinking about who uses ChatGPT, Gemini, or Claude. That will likely change.
The moment is here: Your competitors are buying visibility through ChatGPT ads. ChatGPT ads are live and self-serve, sitting directly inside the chat product.
The same two weeks brought GPT 5.5, citation chips turning into clickable hyperlinks (referral traffic jumped roughly 60% overnight, with homepage referral share leaping from roughly 3.5% to 24%), and Google moving AI Mode into its main search box.
None of that was an accident. The hyperlinks are the click-tracking rails an ads business needs. The analysis of more than 100,000 ad placements surfaced three things everyone should internalize.
Ads match on topic similarity, not intent. Only 14% of real user prompts carry commercial intent, but 20% of prompts trigger ads — a math problem can serve an ad.
The embedding analysis found that ad titles and descriptions are the single biggest drivers of which conversations you show up in. Your title and description are now targeting parameters, not just creative.
“Pay-to-play” is here. About one in five ad placements appears against a mention of a direct competitor, and the brand mentioned organically shows up as the advertiser only about 8% of the time.
Someone else is twice as likely to be the advertiser on your organic mention as you are.
Startup CRM Adia is already placing ads against prompts where Salesforce appears, and Salesforce is playing defense, showing paid placements 40% of the time, even when it’s already mentioned organically.
ChatGPT shows roughly one ad per conversation, the median conversation is three turns, only 30% of eligible users see ads at all, and CPMs/CPCs are running around four times Meta’s.
Expect that to change in predictable ways: more ad slots per answer, ads deeper into conversations, and follow-up suggestions engineered to create more turns, which means more inventory.
The lesson: Organic AEO and paid defense are now the same job. If you’re tracking your brand’s organic citations but not who’s advertising against them, you’re seeing half the board.
When Claude searches the web, it pulls from Brave. Not “influenced by” Brave. According to the talk I saw, it pulls directly from it.
In Profound’s latest testing, 79.2% of Claude’s citations came directly from Brave’s top 10 results for the equivalent search.
There’s no meaningful reshuffling or reranking. No other model trusts its search provider to anything like this.
That makes Claude the most directly optimizable model in AI search: a visible index, a checkable ranking, and (as we’ll see next) predictable retrieval behavior.
If takeaway 2 convinced you that Claude matters for B2B, this is the playbook: Figure out where you rank on Brave for your key prompts and treat that as your Claude visibility roadmap.
A window this transparent doesn’t stay open. Optimize for it while it exists.
Dig deeper. Claude visibility may depend heavily on Brave Search rankings, new data suggests
There’s a catch, and it’s a big one. ChatGPT triggers web search on roughly 95% of prompts. Claude searches only about a third of the time — likely because every search costs money (Brave’s public API pricing runs around $5 per thousand searches), so Claude has a real financial incentive to answer from its weights.
You can only optimize Claude when it actually retrieves.
The good news is that its search behavior is predictable. Recency-framed prompts (“best X in 2026”) trigger search about 81% of the time.
Ranking-oriented prompts (“top 10…”) trigger it 67% of the time, location-dependent prompts 55%, and comparisons 51%.
Definitional and procedural prompts — “what is a CRM?” and “how do I…” — mostly don’t trigger search at all, which makes them nearly worthless optimization targets for Claude.
The lesson: Before you invest in Claude visibility for a prompt category, test whether Claude actually searches for it.
Recency, rankings, locations, and comparisons are the surface areas where Brave rankings translate into Claude citations.
Everything else is answered from memory you can’t touch.
Two speakers described the same mechanism in almost opposite terms, and the tension between them is instructive.
Query fan-out is the set of synthetic queries an AI engine runs in the background to gather content before generating an answer.
Mike King of iPullRank framed it as a raffle: You can’t see or control the fan-out, so the job is to maximize your raffle tickets — more surface area across owned, earned, and shared properties, and, crucially, the right content formats.
Even if you rank for a fanned-out query, the wrong format makes you ineligible.
His research points to new measures of what wins retrieval — content-to-query cosine similarity and information gain both correlate strongly with AI search performance.
Josh Blyskal of Profound’s data tells a different story for Claude specifically: Its fan-outs are near-deterministic.
The same prompt produces the same fan-out string about 65% of the time, and 94% of Claude’s fan-outs are stamped with the current year (ChatGPT does this only 17% of the time).
ChatGPT’s fan-outs churn constantly. Claude barely moves. Both views may be right — for different engines.
Where fan-outs are stable, as in Claude, you can read them and build content targeted directly at them. The year-stamping behavior alone argues for putting the current year in your titles.
Where fan-outs are volatile, as in ChatGPT, King’s raffle logic applies: Buy more tickets through formats and surface area.
One mechanism, two strategies, chosen per engine. Which, again, may require you to prioritize one over the other.
It would be easy to dismiss “marketing engineer” as a vendor-manufactured job title. The hiring market says otherwise.
Google has hired its first marketing engineer. Figma posted the role at a $295,000 base salary. RBC and Autodesk have made hires.
It became a breakout search term on Google, and Google’s own AI marketing lead called marketing engineers “the hire for 2026.”
Who is the ideal candidate to become a marketing engineer? Is this a role where you start with an engineer and teach them marketing, or vice versa?
The emerging consensus profile is a marketer first — someone with channel experience and taste — who builds and maintains AI systems, reports to the head of marketing, and unblocks the rest of the team. A marketer who ships systems end to end.
The underlying logic is that most marketing work decomposes into pipelines: extract data, transform it, and load it somewhere useful. Agents can now run those pipelines on a loop.
Tasks that used to be “we’ll get to it someday” projects become an afternoon of agent building. The constraint stops being headcount and becomes creativity.
If your team doesn’t have someone in this role yet, there’s a good chance it will eventually.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
There still is no clear playbook for AI search. When that playbook does emerge, however, the first step may be to prioritize one LLM over another based on who you want to find you.
And in many cases, that “who” is going to be an agent. At the same time, we’ll have agents assisting us in the work we’re doing, and the demand for people who can engineer these systems will continue to grow.
A year ago, 82% of consumers said AI-powered search was more helpful than traditional search. By 2026, that number had dropped to 54%, a 28-point decline in sentiment over 12 months.
Consumers aren’t giving up on AI search, though. Seventy percent say they’re using AI tools for search more than they did last year.
How should search marketers adapt their GEO strategies? Where are we going wrong as we bring AI deeper into our workflows?
To find out, Fractl partnered with Search Engine Land to expand our 2025 research, surveying 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are evolving in the age of AI. (Disclosure: I’m the co-founder of Fractl.)
Here’s what the data means for your 2026 search strategy.
Seventy percent of consumers report increased use of AI tools for search over the past year. Just 3% say it’s decreased.
Surprisingly, baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically love AI and older generations are lagging behind. In reality, early adopters are signaling that while usage may be rising, trust still has to be earned.
That matters because the remaining competitive battle isn’t about adoption. It’s about trust, quality, and which brands consumers find credible when AI surfaces answers.
In 2025, the AI skeptic camp (consumers who found AI less helpful than traditional search) represented just 3% of respondents. In 2026, that segment grew to 17%, nearly six times larger than the year before.
The 54% who still find AI helpful are mostly hedging: 37% say it’s “somewhat more helpful,” compared with 17% who say it’s “much more helpful.” Enthusiasm has declined rapidly as hallucinations have become a more widely recognized challenge.
In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%.
For search marketers, the implication is significant. Scaling content output with AI is no longer a neutral operational decision.
Consumers are paying attention, and a substantial portion of your audience has an opinion about it. Publishing without disclosure, or publishing at scale without clear quality signals, is now a reputational variable.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use (44% vs. 34%).
The audience most likely to engage deeply with your brand online, share your content, and drive long-term organic visibility is also the audience with the lowest tolerance for AI-generated filler. Quality isn’t optional if Gen Z matters to your brand.
Across every content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. The percentage of respondents who strongly agree exceeds 50% in every category.
This isn’t a soft preference. It’s close to a mandate, and as we’ll cover in Part 2, most brands are nowhere near meeting it.
Sixty-four percent of consumers agree AI will replace traditional search engines within five years, essentially unchanged from 66% in 2025. The belief that AI will eventually dominate search remains intact, even as satisfaction scores decline.
What this tells search marketers is that the channel isn’t going away. But being present in AI results and being trusted in AI results are increasingly separate challenges. Optimize for both.
When consumers are making purchase decisions, 39% turn to Google first. Reddit comes in second at 15%, just ahead of AI tools at 14%. Review sites and friends and family each come in at 11%.
The trust consumers have built in Google hasn’t automatically extended to AI.
Google dominates five of six major search categories. For local businesses (74%), product research (58%), travel planning (57%), and health questions (55%), it’s the default first stop. However, YouTube overtakes Google for how-to content at 50%.
ChatGPT has become the second-most-used destination for health questions at 26%. It also ranks second or third for product research (19%), travel planning (18%), and how-to content (17%).
There’s no single AI search platform to optimize for. Each query category has its own preferred platform. Map your content strategy to where your audience actually goes for each topic.
Before making a purchase decision, the average consumer checks 2.4 platforms, and that behavior is consistent across generations: Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2.
Google remains the default authority for product recommendations, while Reddit and AI tools reinforce confidence.
In 2026, search optimization is no longer limited to page rankings. It’s built around cohesive content strategies that strengthen your entity authority while helping people learn, engage, and convert across multiple platforms.
A brand that appears in Google results but nowhere else loses to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has review content on third-party sites.
AI now touches 53% of marketing work on average, up from 38% in 2025. The equivalent of one full workday per week has shifted to AI-assisted workflows in 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it’s involved in three-quarters or more.
For SEO and content teams, this means your competitors are producing at a higher velocity. Volume advantages are increasingly commoditized. Accuracy, original insight, and brand credibility aren’t.
We’re in an operational pressure cooker: 55% of marketing roles report a 7:10 level of pressure to adopt AI. SEO and analytics roles feel the greatest pressure, but PR sits at 5.8. As AI commoditizes generic content, the advantage shifts to what AI can’t automate: human judgment, relationships, and trust.
Only 26% say AI made their work faster and better. Nearly half admit it made their work faster, but more generic. Seven percent report an outright decline in quality.
This is where your competitive advantage lives. If your peers are scaling AI slop while your team invests in original data, expert quotes, and earned brand mentions, you’re building assets that make your brand more visible, credible, and retrievable across search engines, social platforms, and LLMs.
How you apply AI to your workflows will separate the brands that scale entity authority and brand visibility from those that scale slop and fade into a sea of sameness.
About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct a legal or compliance review. Only 27% evaluate content for bias.
Nearly half of AI-generated content is entering the market without fact-checking, legal review, or plagiarism checks. Instead, most marketers are focusing on subjective, surface-level editorial review: Does it sound right? Is the tone appropriate? Are there typos?
In a year when consumers are already primed to distrust AI slop, your brand’s AI governance process is one of the cheapest gaps to close and one of the most expensive to ignore.
Heavy, generic AI use is now a brand-trust liability. Yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling, and the compliance gap is significant.
For search marketers producing content at scale with AI, this is an emerging trust and brand risk, not just an ethical concern. The takeaway isn’t to abandon AI. It’s to stop treating governance as optional. Every AI workflow needs clear checks for accuracy, transparency, and human review before content reaches your audience.
A year ago, only about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved, reaching 24%.
Meanwhile, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.
More brands have been misrepresented by AI than have a formal monitoring process.
That should concern people. If AI is summarizing your category, comparing your product, or explaining your brand incorrectly, that’s not just an SEO issue. It’s a reputation risk, a revenue risk, and a PR issue waiting to become a headline.
When AI misrepresents your brand, fixing the source matters more than disputing the output. Reach out to the publisher for an update, update owned profiles, and publish a correction page tied to your brand.
So yes, 50% of the marketers we surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI.
This is a prime example of traffic diversification. The real shift isn’t from Google to ChatGPT. It’s from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across multiple platforms and communities.
The same marketers reporting organic losses are often finding new ground elsewhere:
Your 2026 brand visibility strategy now depends on how effectively you build brand mentions and entity authority across platforms, not just on individual page rankings in Google.
Which strategies are marketers prioritizing to hedge against AI’s impact?
The good news is that teams are moving toward the right categories: community building, earned authority, owned audiences, expert content, and traffic diversification.
The most prioritized strategies for maintaining visibility in the AI era include building brand presence on social platforms (59%), GEO/AEO optimization (54%), and creating authoritative expert content (44%).
The least prioritized strategy is investing in original research and data, at 15%.
That’s a strategic inversion. Original, proprietary research is one of the hardest content assets for AI to replicate, synthesize, or commoditize. It generates citations, earns links, and builds topical authority in ways that FAQ pages and generic thought leadership can’t.
Teams investing here are building durable moats. Others are investing in areas where AI makes competition easier.
When we drilled into the specific GEO tactics marketers were using, most were content-led and easily replicated by AI systems. Long-tail FAQs matter for AI Overviews, but they’re easy to replicate. Schema helps, but it doesn’t build credibility.
Entity authority creates the strongest moat: proprietary data, expert perspectives, topical authority, and third-party validation. These brands create the source material that journalists, communities, search engines, and AI systems rely on.
It’s no surprise that only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results.
While that’s normal for a new channel, GEO is becoming a serious function. Visibility tracking, citation monitoring, and branded search lift need more attention. Building measurement infrastructure for AI search visibility is a competitive advantage. Teams that can prove GEO ROI can defend and grow investment.
The top barrier to deeper AI integration in marketing is team training and skill gaps (26%). Tool fragmentation comes second at 20%, followed by budget constraints (19%), unclear ROI (12%), and legal and compliance concerns (12%).
Leadership buy-in stands at just 2%, indicating that executive support is largely in place. The gap is execution capability. For search marketing teams specifically, investing in AI literacy, prompt strategy, content quality control, and GEO measurement skills is more valuable right now than adding new tools.
The data across both consumers and marketers tells a coherent story. Users are adopting AI search faster than they’re developing trust in it. Marketers are deploying AI faster than they’re governing it. For search professionals, both gaps create specific, actionable opportunities.
Brands have already been misrepresented in AI responses. Query your brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews. Document what’s accurate, what’s missing, and what’s wrong. Build a monitoring cadence before you’re in damage-control mode.
AI can’t generate proprietary survey data, original research, named expert perspectives, or verified brand facts. Marketers prioritizing original research are building assets that will become even more valuable as AI systems get better at rewarding genuine authority over generic content.
Consumers are checking 2.4 platforms before buying, and they’re doing it consistently across every generation. Google organic is necessary, but it isn’t sufficient.
Your brand needs a coherent, consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media. If a consumer asks ChatGPT about your category and you’re not mentioned, or you’re mentioned inaccurately, you’ve lost that decision before they ever reach a search results page.
Consumer demand for AI disclosure ranges from 84% to 91% across formats. Only 20% of brands always disclose. This disconnect is a reputational liability and, increasingly, a legal and regulatory one. Establish disclosure policies, fact-checking checkpoints, bias reviews, and hallucination escalation processes as operating standards.
If you can build attribution frameworks that connect AI-assisted search mentions to traffic, lead quality, and revenue, you’ll be able to prove ROI at a time when most teams can’t. That’s a budget and strategy advantage that compounds.
Proprietary data. Named experts. Human-verified claims. Transparent sourcing. Consistent brand voice at high quality. The brands that treat quality as a strategic differentiator in 2026 are the ones whose names will come up when consumers and AI systems go looking for answers.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026.
Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.
“Ultimate guides” were the undisputed heavyweight champions of SEO. They were built specifically to align with how Google’s algorithm measured content value.
The “skyscraper technique” helped cement a doctrine: length = depth.
But the web moved on. Search intent shifted toward fast answers, AI saturation destroyed length as a credibility signal, and Google’s systems began penalizing the one thing ultimate guides were engineered to produce: zero information gain.
So, what now?
The new content constraint is extractability, and it changes every structural decision downstream, from brief to publication.
AI engines like Gemini allocate approximately 380 words per webpage for query grounding, regardless of the article’s total length. It’s a retrieval constraint you have to adapt to.
The extraction data is precise:
Generative systems now answer many queries without requiring a click. The traffic those pages once captured no longer exists to be captured. The 4,000-word ultimate guide content marketing approach actively destroys generative search visibility.
What replaces the informational library is something structurally different and considerably more demanding to produce. Every sentence must earn its place by naming an entity, stating a relationship, preserving a condition, or making a citable claim.
Dig deeper: How to write for AI search: A playbook for machine-readable content
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
Be the brand AI recommends.
See your AI visibility
Traditional keyword targeting asked one question: “What are people searching for?”
Problem-first positioning asks a harder one: “What situation has produced this search, and what does a genuinely useful answer look like inside that situation?”
That’s where the padlock principle becomes useful. Your business is a lock that opens for multiple combinations, each representing a distinct problem for a distinct person.
For example, a car insurance provider targeting “car insurance” is a category. The same provider building separate pages for “an 18-year-old new driver declined by standard insurers” and “a courier using a vehicle for commercial work” is a solution.
The distinction sounds philosophical until you realize it affects every downstream structural decision. Andrew Holland is right: AI killed low-grade informational SEO. Here’s some tactical advice to shift your content approach.
Acknowledging that your solution works for teams of 100 or more but not for solo operators signals to a retrieval system that your content can be cited with confidence. Generic advice is the content AI already generates for free.
Constraint-aware, condition-specific guidance is what AI cannot replicate and therefore must source.
This logic collapses one of the most entrenched distinctions in digital marketing. The traditional separation between informational content and commercial landing pages was always somewhat artificial, but AI retrieval has made it structurally unsustainable.
What replaces the previous distinction is a fundamentally different content architecture: Every page is a document that knows exactly who it is for, states the problem it solves in the first sentence, and earns its keep by delivering a resolution specific enough to be cited but human enough to convert.
Marketers should start injecting problem-positioned, AI-readable answers directly into commercial pages rather than blogs. Low-grade information recaps like the “best tools for X” roundup and the “how-to” guide that adds nothing to existing knowledge have been absorbed by generative systems that now answer those queries without a click.
Dig deeper: How to keep your content fresh in the age of AI
Every sentence must be self-contained and able to survive alone. AI retrieval systems do not read your article the way a human does: sequentially, with accumulating context.
Instead, an LLM will lift sentences in a “send this to someone without context” type of way by extracting passages and evaluating sentences as independent semantic units.
If a sentence requires its neighbors to make sense, it cannot be extracted and evaluated as an independent semantic unit (i.e., it’s neither easily understood nor useful for a machine).
The three failure patterns and their fixes:
| Failure | Example | Fix |
| Unresolved pronoun | “It also includes unlimited storage.” | “The Dropbox Business Standard Plan includes 5TB of encrypted cloud storage.” |
| Stripped condition | “The price has dropped significantly.” | “The Asana Enterprise Plan costs $24.99 per user per month, down from $30.49 in Q1 2024.” |
| Vague claim | “Our platform makes team management easier.” | “The Asana Enterprise Plan streamlines cross-functional project tracking for teams of over 100 people.” |
If you want to write LLM-friendly content, no matter what content format you are creating, here’s my advice: look into semantic triples.
Because AI systems evaluate content using identical retrieval infrastructure regardless of page type, the semantic triples (subject, predicate, object, conditions preserved) apply equally to blog articles, product descriptions, and pricing pages.
Here’s a concrete application of semantic triples: Make your heading more explicit. Explicit headings placed directly above their corresponding paragraphs add mathematical relevance (i.e., they improve cosine similarity scores), which means that an AI is 17.54% more likely to select that passage if it has a good headline.
How do you keep content fresh in the age of AI?
First, accept that you’re optimizing paragraphs, not pages.
The citation-bait formula defines how to structure the paragraph blocks that sentences belong to.
No preamble. No “in this section we will explore.” The answer first, always. This block is what generative systems extract.
Expand without burying. Every additional sentence beyond two reduces the density of what came before.
A table, a numbered list, or a comparison. Something extractable in its own right, independent of the surrounding prose.
The H2 or H3 that follows must name the topic, intent, and scope of what just appeared. Not “Key takeaways.” Not “Overview.”
The heading must make complete sense when read entirely out of context, because in generative retrieval, it frequently will be.
The playbook for machine-readable content contains even more citation bait advice.
Adam Tanguay explains it very well: The authority layer compounds over time. This is why the citation bait formula works in both the short and long term.
Managing the tension between AI-readable structure and human persuasion is difficult. Like Shrek’s onion analogy, LLM-friendly content has more layers than most people realize. You don’t have to choose between the two. You have to layer them.
The AI inverted pyramid places machine-readable answer blocks at the opening of each section. Human storytelling — the anecdote, the constraint, the actual number/stat/finding — belongs immediately after, connected by a natural transition that moves the reader from optimized structure and into earned narrative.
Jessica Foster identified Dove’s “Real Beauty Stories” as a great example of this type of copywriting. Dove opens with structured how-tos that satisfy intent-driven retrieval, then anchors those tutorials to the lived experiences of real customers.
The machine gets a citable answer at the top of the block. The human gets a reason to believe it in the body. Neither layer compromises the other because they occupy different positions in the document.
Casey Nifong has a great audit workflow for existing content:
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
You now know good content no longer looks like a 4,000-word-long ultimate guide. Now it’s time to figure out what workflow produces said new good content.
Most articles on Search Engine Land describe the destination, not the road. That’s because you’re responsible for the journey. You need to build your editorial checklist, prompt structure (if you’re using LLMs to restructure existing content), and grounding budget calculation.
Go beyond theory and build an editorial system that consistently produces LLM-friendly content without sacrificing the human specificity no model can replicate.
The common advice is to build authority off your own site through digital PR, mentions, and high-authority links. Grow your third-party mentions, grow your AI visibility, right?
Right instinct. But the source-set that an AI relies upon differs for every topic, so your off-property authority building efforts must also be topic-driven.
This week:
AI search engines rebuild which sources to trust around the subject of the question. Ask about invoicing, and it leans on one set. Ask about starting a business, and it leans on a mostly different one.
This data is a sample set from an anonymous pool of clients, but it illustrates the gap is large. AI citations follow a source-type pattern specific to the topic.
In invoicing questions, competitor domains hold 33.5% of what AI cites. In starting-a-business questions, that same source type holds 7%. Same model, two topics, and the kind of source it reaches for nearly flips.
This small sample analysis confirms something I wrote about in “Operationalizing your topic-first SEO strategy”:
Meanwhile, video and social surfaces run on separate mechanics and deserve their own separate play in your visibility mix, cited at ~6.5% across this sample set.
YouTube remains an exception across LLMs, and UGC platforms like Reddit behave differently again. But this memo sets aside the video/social/UGC slice deliberately and covers the publisher, research, and expert sources that behave like earned media.
So where you build authority depends on the topic you want to win. Lift a PR plan from a neighboring topic or vertical, and you’re likely to aim at the wrong sources.
AI does not form a fresh opinion about your brand on every query. It reuses the trust already attached to the sources it pulls from, and it favors documents and entities it already associates with authority on that topic. (Which is why, yes, building topical authority still matters.)
Your owned blog/site is one input. It’s a crucial input, but it’s likely one of the weakest. The publications, analysts, experts, competitors, and communities that mention you carry significant weight.
That gap is why two brands with identical on-page optimization can get cited at different rates: off-property reputation the model already trusts.
Add to that, a named author with a byline appears to beat the same content published under a brand. We don’t have a clean dataset on this across platforms yet and it’s based on one-off qualitative findings via clients and industry chatter, so treat this as a working belief, not a measured finding.
LinkedIn’s own analysis of AI visibility factors reported that authorship and timestamps tracked with better performance: fresh, expert-authored, clearly time-stamped content earned the fastest visibility and citation gains in their testing.
“Our early testing showed meaningful lift in visibility and citations across the topics we focused on, with owned content delivering the fastest and most scalable gains so far…. Publishing authoritative, fresh content improves visibility. LLMs favor content that signals credibility and relevance, authored by real experts, clearly time-stamped, and written in a conversational, insight-driven style on platforms like LinkedIn.”
— “How LinkedIn Is Adapting to AI-Led Discovery”
The mechanism is reasonable: A human author with a track record (someone who has written on the topic across other sites, has an active social presence, holds a license, or sits on your executive team) gives the model an entity to attach authority to. A faceless brand post gives it less to anchor on.
More quality mentions should mean more citations. Authority does not pay evenly, though. It pays in steps.
Our analysis of 1,000 domains with Semrush showed this for backlinks: Authority Score is the strongest backlink predictor of AI mentions in the study at 0.65 Pearson, ahead of raw link count, and the curve bends rather than climbing straight.
Why it matters: A “little more” third-party authority in a crowded middle tier probably won’t change how often AI cites you. The thing that changes citation is getting into the top tier of authoritative sources for your topic.
Read alongside the topic finding, the move is clear. Depth in your topic’s top-tier sources beats spread. Three placements in a top-decile source move you more than a dozen scattered across low-authority sites. That’s where you invest.
One question stays open: whether citation rises smoothly with mentions or trips past a tipping point, a volume of in-topic mentions after which a brand gets cited consistently instead of occasionally. We don’t know yet. More data and time will tell.
Whatever the tipping-point answer turns out to be, building authority through authoritative third-party sources in your topic is not an open-ended ask to your executives. It’s a target you can budget for and measure against.
Not every third-party signal carries equal weight. A research publication picked up across high-authority industry blogs likely moves citation more than a few executive podcasts a year, while YouTube remains an exception across LLMs. (UGC will be tackled in a later post as part of this building authority series.)
Test these moves, in order.
They don’t need an existing audience, and they don’t need to be a founder or on the executive team. They need:
Give them a process (and permission) to develop sharp, branded content with a strong point of view, tailored to specific personas.
How-to guides and roundups together account for 62.3% of cited source rows (see chart below), so build your SME’s work in those shapes.
Run your highest-intent prompts through an audience research tool like SparkToro and record which domains, social accounts, YouTube channels, and named authors show up. Do the same across AI search engines, and note overlap or inconsistencies in who gets cited and when.
Then chase the people, not only the publications. Get your SME quoted by the same journalist, booked on the same webinar series, or co-authoring with the author the model and your audience already trust. Co-occurrence with a trusted entity pulls you into the candidate set faster than a standalone post.
Depth beats spread: 3 placements in a top-decile source move you more than a dozen scattered across low-authority sites. Rank your target set by authority and spend there first. Use tools like Qwoted and HARO to discover opportunities.
Don’t skip these. The Semrush link study found nofollow pulls almost the same weight as follow for AI mentions (0.509 Spearman vs 0.504). They are easier to earn, and the models still count them. Build a list of the nofollow-heavy sources your category links to and pitch them deliberately.
Publish original charts and infographics bylined to your SME and let other sites embed them with attribution. One chart can earn citations across dozens of pages you never pitched. The format matters: AI leans hard on answer-ready pages.
According to their own testing, named-author posts get indexed and surfaced quickly, and first-hand reports in our space describe brands entering AI answers within weeks of consistent publishing under a person rather than a page (sometimes days, depending on a built-in LinkedIn audience). Possible play: Partner with LinkedIn experts and influencers in your area of authority.
This post first appeared on the author’s website and is republished here with permission.
Search Console reported 51,000 URLs as "Indexed, though blocked by robots.txt," and Google says that's not necessarily a problem.
The post Google Explains Why URLs Blocked By Robots.txt Can Still Be Indexed appeared first on Search Engine Journal.
Google updated its guidance to strike a less discouraging tone about the use of LLMs.txt, special markup, and markdown for AI SEO.
The post Google’s Updated Guidance Now Says It’s “Fine” To Use LLMs.txt For AI SEO appeared first on Search Engine Journal.
Meta launched AI Mode in Facebook Search. AI Mode gives users AI-generated answers based on public content from Facebook Groups, Reels, and other Meta apps.
Instead of showing a standard list of search results, AI Mode uses Meta AI to answer questions directly within Facebook. Meta said responses are grounded in what people are publicly saying across its apps, including real experiences and recommendations.
AI answers in search. AI Mode supports both broad discovery and specific questions. Users can search or explore their Feed and receive responses from Meta AI within Facebook. This gives Facebook a new way to surface public social content.
Source selection is unclear. Meta said the feature delivers “real answers from real people.” But it didn’t explain how AI Mode selects which public posts, Groups, or Reels appear in responses. It also didn’t say whether brands, creators, or publishers will be able to see when their content is used.
Why we care. Facebook search is moving toward an AI answer experience built on public social content. That could change how people discover recommendations, local information, and brand-related conversations across Meta’s apps.
A familiar name. Obviously, Meta’s new AI Mode feature shares its name with Google’s AI Mode. Meta gets no points for creativity.
What Meta is saying. AI Mode is powered by Meta AI and Muse Spark. Meta didn’t explain how Muse Spark influences search ranking, source selection, or answer generation.
The announcement. New AI Tools to Help You Make Things Happen on Facebook
Microsoft is now officially releasing a preview of the new AI performance report within Bing Webmaster Tools that now includes Intents, Topics, Citation Share, and Compare. We saw Microsoft demo these features in late April but now it is actually starting to roll out to users.
As a reminder, Bing officially rolled out its AI performance report in February. Google didn’t roll out its AI reporting in Search Console until June, and it seemed forced.
What is new. “These new capabilities build on that foundation by helping publishers better understand why their content is being surfaced, which broader subject areas they are gaining visibility in, how their presence evolves relative to other cited sources, and how citation patterns change over time,” wrote Krishna Madhavan from Microsoft.
Intent: The new Intents feature in Bing Webmaster Tools now classifies the grounding queries in the AI Performance Report in broader categories, such as Informational, Commercial, Navigational, Learn and Solve, Research, Creation, Local, and more. This in a sense helps you understand the intent behind the prompt or query. “This helps publishers move beyond simply seeing which queries triggered citations and begin understanding the broader query context our systems associate with those citation appearances,” Krishna Madhavan wrote.
The example provided was that an e-commerce publisher may discover strong visibility in comparison-oriented or shopping-focused AI experiences, while an educational publisher may find that their content is frequently surfaced in research or learning-oriented interactions. These insights can help publishers better align content structure and depth with the types of experiences where AI systems are surfacing their content.
Topics: The Topics in the AI performance reports group related grounding queries into broader thematic clusters. AI systems reason across concepts and themes rather than isolated keywords, Microsoft explained. So by having topics, it will help publishers understand visibility in the same thematic structure that modern AI systems use to organize information.
So for example, queries such as “solar panels,” “solar energy efficiency,” and “residential solar installation,” for example, may all map into a broader topic cluster like Solar Energy. “This creates a more natural way to analyze AI visibility. Content teams and publishers often think in terms of themes, editorial areas, and audience interests rather than isolated keywords. Topics help bridge that gap by turning grounding query data into a more thematic view of AI engagement,” Microsoft wrote.
One note, “during the preview phase, some labels may still be broad – especially for highly specialized or niche domains – but the system is already beginning to reveal meaningful thematic patterns,” Microsoft wrote.
Citations. Microsoft also added citation share, which shows how much of the citation space your site receives for a specific grounding query. Citation share is calculated as the percentage of citations attributed to your site out of all citations shown across all sites for that same grounding query. “This helps publishers understand not just whether they were cited, but how much visibility they received within the full set of cited sources for that query,” Microsoft explained.
Microsoft added these points:
Compare. With all of that, you can also compare the changes over time. The compare feature allows you to overlay a previous time period directly onto the current reporting view.
“Compare is designed to help publishers observe changes over time. Citation activity can be influenced by many factors including evolving AI models, competing content, freshness signals, and shifts in user demand,” Microsoft wrote.
Here is what it looks like:
Why we care. While we still do not have click and click-through rate data, Microsoft keeps adding more and more to its AI performance reports.
I am hopful that one day we will get click data, but I am still not expecting to see that from Google or Microsoft any time soon.
Google penalties, also known as manual spam actions, are among the few events in search that can disrupt an otherwise healthy online business overnight.
For companies heavily dependent on organic traffic, the consequences often extend far beyond lost rankings. Revenue drops, customer acquisition costs rise, expansion plans stall, and the effects can linger long after the original policy violations have been remedied.
With a steady 90% market share, Google remains the primary traffic source for many publishers, ecommerce platforms, retailers, travel brands, affiliates, and lead generation businesses.
Direct traffic rarely compensates for a major visibility loss, and Bing seldom offsets the difference. As a result, a manual spam action carries serious operational implications, not merely SEO risks.
One point still misunderstood throughout the industry deserves clarification. Manual spam actions differ from algorithmic updates. They aren’t fluctuations caused by changes in relevance calculations or ranking system adjustments.
Google’s manual penalties involve direct enforcement after suspected violations against Google Search Essentials, formerly Google Webmaster Guidelines, have been identified and confirmed. The distinction matters because the response required is completely different.
A website affected by changing ranking systems requires analysis, adaptation, and recrawling. A website affected by a manual spam action requires remediation and applying for reconsideration. Those are separate situations entirely.
Google doesn’t issue manual spam actions casually. The process involves internal senior employee review cycles. Suspected violations must be investigated and confirmed first.
Google states clearly that manual actions are the consequence of proven policy transgressions. Despite frequent cries of foul, false positives are exceptionally rare. Once a manual action appears in Google Search Console, the enforcement is already in the production pipeline.
The operational problem is that many businesses fail to recognize how much unresolved policy exposure their web platforms have accumulated over time.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
The initial steps that ultimately lead to a manual penalty and a website’s drop in search visibility often begin inconspicuously, gradually eroding policy compliance.
The underlying patterns are remarkably similar across industries. In many cases, organic search visibility initially improves and may even generate measurable revenue gains attributable to the SEO initiative.
The short-term results reinforce the perception that the approach is working. However, as time passes, nobody revisits whether those earlier decisions remain aligned with evolving search quality standards and webmaster policies.
One reason manual spam actions create so much disruption is that policy violations often persist quietly for years before review. Many organizations incorrectly assume that questionable SEO tactics of the past lose their relevance over time.
Yet Google Search systems don’t forget historical footprints. Archived URLs remain crawlable. Legacy sections continue contributing content quality signals long after internal ownership was abandoned.
Most persistently, backlink patterns remain visible for decades. Large numbers of websites remain affected by backlinks generated through manipulative campaigns dating back many years.
Paid placements, article syndication networks, private blog networks, commercial keyword-heavy guest posting campaigns, expired domain backlinks, directory spam, and widget distribution schemes that once formed part of mainstream SEO activity are today’s liabilities.
Some of these practices continue to operate more or less openly for years, while enforcement may appear erratic or inconsistent. When left unaddressed, they represent an incalculable risk to the website publisher.
This becomes particularly important during acquisitions. Businesses purchasing established domains frequently inherit unresolved compliance exposure alongside rankings and traffic. Google evaluates the website’s condition, not which employee, agency, or previous owner introduced the violations.
Traffic growth alone doesn’t confirm compliance health. A domain generating millions of clicks may still carry unresolved risks tied to old link schemes, expired sponsorship arrangements, deceptive user-agent cloaking, manipulative redirects, or scaled low-quality content sections. Those issues often go unnoticed until they’re brought to the surface by a Google manual spam action notification.
The mechanics behind reputation abuse are straightforward. A trusted brand with an established web platform allows third parties to publish unrelated, often unsupervised content under the same domain name. In many cases, publishers integrated discount coupon sections, casino reviews, affiliate content, or commercially motivated informational pages directly into existing editorial systems.
The problem frequently worsened significantly because the content wasn’t properly segmented. The consequence is that the distinction between trusted editorial work and commercially motivated material became blurred.
Once confronted with a site-wide penalty, affected publishers experience broad visibility declines across the entire platform, not merely within the originally offending sections of the website. The damage to a brand that lends its reputation to a disreputable third party is often substantial.
Recovery efforts frequently prove time-consuming and costly. Removing isolated pages rarely resolves the problem. Many organizations require broader structural changes, including archive cleanup, internal link reviews, crawl management adjustments, sponsorship governance reforms, the removal of spammy redirects, stronger editorial oversight, and stricter technical segmentation.
In short, recovering from such a penalty takes time, costs significant amounts of money, and is often a painful process.
Google increasingly scrutinizes large-scale publishing systems that produce repetitive, low-value content without a unique selling proposition.
The issue isn’t maintaining many websites simultaneously. Large website portfolios have thrived in Google Search for years and continue to do so. The underlying problem involves quality control, editorial oversight, originality, and informational value.
Most organizations don’t cross into problematic territory intentionally. The transition usually occurs gradually, often unbeknownst to the decision-makers who rely on outdated or misleading recommendations.
The resulting manual spam action in Google Search Console, followed by a sharp decline in rankings, frequently occurs after a prolonged period of spam signal accumulation rather than during the apparent growth phase.
Many site owners approach reconsideration requests as if they were negotiating with Google. That puts them at a significant disadvantage from the outset.
The reconsideration process exists for one purpose only: to demonstrate that the website has been restored to full compliance with Google’s guidelines. It’s important to note that Google expects complete compliance before lifting a manual spam action.
This means the requirement extends beyond the specific violation highlighted in Google Search Console. A site owner who addresses only one known spam issue while leaving unrelated policy violations unresolved elsewhere on the website will typically face rejection.
A common testing approach, such as a publisher removing some problematic sponsored content while retaining similar affiliate arrangements elsewhere, will fail. Likewise, a business that disavows recent manipulative backlinks while ignoring historical paid link schemes is unlikely to convince Google of its genuine commitment to complying with Google’s policies going forward.
Similarly, a website network that cleans up one property while continuing identical publishing practices across related domains signals incomplete remediation rather than meaningful operational reform. As a result, it stands little chance of regaining Google’s trust.
Effective website recovery requires a comprehensive review rather than selective cleanup. Technical infrastructure, content quality, sponsorship structures, redirect behavior, link acquisition history, indexing patterns, archive sections, and ownership transparency all require examination during serious compliance recovery efforts.
The Google Search team expects compelling documentation detailing what has changed and how future violations will be prevented. Temporary cosmetic adjustments rarely persuade reviewers to lift a manual spam action.
Making matters worse, each rejection typically requires an even more comprehensive review and cleanup effort. At the same time, every reconsideration request that Google deems disingenuous further erodes Google’s trust in the publisher.
There’s no guaranteed turnaround time for reconsideration processing. Some reviews are completed within days. Others take weeks or months.
At the same time, large websites with extensive SEO legacies accumulated over many years often require longer assessment periods due to the substantial volumes of data that must be crawled and analyzed before changes can be evaluated.
For businesses that rely primarily on Google traffic, this uncertainty creates a potentially existential threat.
The operational risk becomes even greater when companies fail to build a strong brand capable of partially offsetting organic traffic declines through direct navigation or alternative revenue-generating channels. In this context, paid traffic is a poor substitute due to its associated costs.
In short, some online businesses can’t afford to be penalized in the first place.
The issue extends beyond SEO performance. Search visibility directly affects commercial expansion, investor confidence, company valuation, partnership negotiations, and revenue stability.
Penalty expiration represents another commonly misunderstood aspect. Google manual spam actions may expire after prolonged periods, often years. However, this is rarely a viable strategy for an affected business.
Waiting passively through an extended period of declining visibility seldom aligns with commercial realities. More importantly, expiration alone doesn’t guarantee recovery or renewed growth, as the penalty could be reapplied not too long after it expired.
Google’s search systems continue evaluating overall site quality independently of manual enforcement status. A website carrying unresolved spam signals across its content, technical infrastructure, or off-page profile may continue to struggle long after the manual action itself has been lifted.
Compliance reviews can’t be considered optional or a luxury. Organizations heavily dependent on organic Google visibility require ongoing operational review cycles focused specifically on comprehensive policy compliance.
These reviews shouldn’t be conducted internally. Even the most talented in-house SEO teams are often hard-pressed to diligently identify shortcomings that may reflect on their own work or that of their colleagues. Policy compliance requires external expertise, sufficient authority, and a proven track record.
Purely technical SEO audits, while indispensable, are insufficient if commercial partnerships bypass oversight. Editorial standards alone won’t suffice if historical link manipulation remains unresolved. Planned growth initiatives require evaluation against established compliance frameworks before deployment, not after traffic has become dependent on questionable practices.
Mature organizations increasingly integrate compliance reviews into their operational governance. Sponsorship structures undergo search compliance review before launch. Scaled publishing systems are assessed for quality before expansion. Historical content is evaluated on a recurring basis. Acquisition due diligence includes policy exposure analysis alongside financial review.
See where your brand appears, where it doesn’t, and exactly how to win more visibility across search, AI, local, social, and every channel that matters.
This level of discipline and vigilance matters because manual spam actions rarely arrive at convenient moments. More often than not, undesirable Google scrutiny coincides with critical periods: just before a long-planned commercial expansion, in the run-up to a migration project, ahead of an acquisition, as the peak retail season begins, or shortly before investor reporting deadlines.
This is hardly intentional. It’s simply a matter of unfortunate timing. Google doesn’t align search quality enforcement with business planning calendars. Google cares primarily about user experience. For every website that loses its top position, there is usually another capable of providing users with a similarly compelling experience.
Businesses that ignore unresolved policy exposure often discover the problem the hard way, only after search visibility has collapsed and online sales have followed suit. At that point, recovery becomes a far more prolonged, expensive, and operationally disruptive undertaking than ongoing compliance reviews would have been prior to penalization.
Nevertheless, the work must be done. The one silver lining is that, in many cases, the process proves cathartic. Once the penalty has been resolved and the website’s SEO signals have become more consistent, the removal of legacy issues often allows rankings not merely to recover, but to exceed their previous highs.
The idea that AI is killing advertising misses the bigger shift. As AI expands across search, assistants, productivity tools, and transactions, advertising is moving with it.
Ad density may be changing within AI experiences, but advertising opportunities are expanding across a growing number of surfaces.
At the same time, paid and organic are becoming harder to separate. The same AI systems increasingly power ad campaigns, search experiences, and brand visibility across Google’s ecosystem.
That changes how brands should think about visibility.
Paid and organic are no longer separate channels competing for the same click. They are increasingly different ways of influencing the same AI systems, which means the signals shaping organic visibility may also affect paid performance.
Google’s SERP was a finite surface: 10 organic blue links, a few ad slots, and a knowledge panel on the right. The user landed, scanned, and clicked.
Paid and organic teams operated on separate budgets, separate tools, and separate quarterly reports, and rarely talked to each other because manual Google Ads kept the paid specialist busy full time. Titles, descriptions, bids, and campaign structure were all chosen by hand and required constant attention, which is why the organic team had no part in any of it.
DSA changed that for me. It read my organic pages to decide which ads to run, who to show them to, when, at what bid, and what title to use. I controlled the descriptions. The engine decided everything else, and it did it better than I would’ve done manually because it was reading the same signals the organic side was already optimizing for.
When someone at Google in Singapore explained how PMax worked, I thought, “That’s exactly what I was doing.”
PMax took the DSA logic and extended it across every Google surface simultaneously: Search, YouTube, Gmail, Display, Maps, and Shopping, all in one campaign, with the engine making every placement decision from your assets and audience signals.
AI Max brought the same intelligence into Search campaigns, specifically, with Gemini underneath instead of rules. PMax and AI Max run on the same Gemini brain: one focused on Search, the other spread across every surface, applying the same funnel logic to different contexts with different signal layers on top.
And if Gemini’s understanding of your brand is thin, it fills those decisions with whatever it thinks will work, which isn’t necessarily your brand narrative, and you have no direct way to override it. You train it, or you lose control of your own ads.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
Gemini now sits inside every layer of the Google ecosystem:
That’s how many connected consumers spend most of their workday, and most of those surfaces either carry ads now or have the infrastructure to start carrying them.
Microsoft Advertising sits inside Copilot across Bing, Edge, Windows Consumer, Office Consumer, Teams Free, and GitHub.
OpenAI Ads launched in February for logged-in users on Free and Go tiers in the U.S., placing ads below ChatGPT responses and clearly labeling them as sponsored. By May, OpenAI had opened a self-serve Ads Manager and was expanding internationally.
The ads layer travels with the engine, the engine is everywhere, and ads therefore have the potential to be everywhere. Most brands still treat paid as a separate channel run by a separate team on a separate dashboard, which is a search-era inheritance that was never ideal but now needs to be dropped.
Performance Max already runs the auction across YouTube, Display, Search, Discover, Gmail, and Maps as one campaign type. Search is one surface among many, and the “ads are dying in AI search” narrative is measuring the wrong thing. It sees ad slots compress inside the assistive interface while ignoring that the surface base has multiplied by an order of magnitude.
The dominant narrative in 2026 is that ads are dying because AI is replacing search, and ads inside AI are a problem nobody has fully solved yet. That’s partially correct: Ad density per session drops as AI takes more control, and nobody – including Google – has yet figured out how to insert ads into the AI response itself without killing the experience that makes the AI valuable in the first place.
But this is the part the analysis gets wrong: This doesn’t add up to fewer ads overall.
Search ads are Google’s goose with the golden egg, and the goose may be slowing down — though nobody outside Google actually knows, because Google doesn’t break out search ad revenue from YouTube, Display, and the rest. That ambiguity is doing a lot of work.
What we do know is that total ad revenue has kept growing even as AI has taken over more of the search experience, which proves the flock is already working.
Kodak invented the digital camera and then buried it to protect film-processing revenue, and we know how that ended. Google appears to be doing what Kodak didn’t: building the replacement while the original is still profitable.
Every surface Gemini sits inside is a new bird in the flock, each laying a smaller egg that grows over time, and when Google finally cracks ads inside the AI response itself, that’s one more goose. The surface base has expanded faster than density has dropped, and the ad-density problem in Search and AI is temporary.
The more the user delegates decisions to the machine, the less room the machine has to surface a paid option. Search keeps the user in charge, so the engine surfaces ads the user might pick. Assistive narrows the options, so a sponsored slot still has a chance. Agentic executes the decision, so the ad has nobody to persuade. Ad density follows that delegation, mode by mode, with AI deciding which brands win at each mode.
Google is running two moves at once, and it seems most people have noticed only the first one. Gemini is taking over the recommendation, targeting, and auction logic on surfaces that have carried ads for years. And Google is adding ads to surfaces where they were previously absent, with AI Overviews now eligible for ads above, below, and within the answer, and AI Mode testing conversational ad formats.
The first move is AI taking over the existing ad business. The second is the ad business expanding into surfaces it never occupied. The net effect is more AI-driven ads across more of the stack than ever before.
The monetization model that works at consumer internet scale is simple: pay with money, or pay with attention.
I learned about freemium the hard way. When our children’s media company, Boowa & Kwala, survived the dot-com crash, we added a paid tier that removed the ads. Out of a million unique visitors a month, a few hundred paid. Almost nobody chose to pay.
The freemium contract — free access in exchange for ads — is the deal they actively prefer, and the numbers prove it. And for ad-driven businesses, pure volume makes the money. In Big Tech, Google has the clear advantage.
Every AI surface is in the process of landing on the same answer because there is no other answer.
What changes is how the ads appear. The classic SERP ad was clearly labeled and set off in a colored panel. The Gemini recommendation that surfaces a product inside a Gmail context, the Copilot suggestion that names a vendor inside a Word document, and the agent that picks a supplier on the user’s behalf are something else entirely.
The ad becomes ambient. It dissolves into the surface, and what advertising looks like becomes harder to identify as advertising. Gemini reads context and intent with enough precision that an ad placed in a meeting summary can feel useful rather than disruptive, which is a risk profile Google’s rules-based systems could never have accepted.
At Boowa & Kwala, when we scaled free ad-supported views from 100 million to 1 billion, revenue multiplied by roughly two, and costs rose by around 20%. Surface (a.k.a. pageviews) multiplied tenfold, revenue doubled, costs grew by a fifth, and we went from profitable to significantly more profitable.
The aim was never to push revenue up at the same rate as surface expansion. It was to keep expanding the surface, knowing the incremental delivery cost was negligible.
Google’s ratios at planetary scale differ from ours, but the structural shape almost certainly doesn’t: surface expansion plus near-zero incremental cost equals profit growth, regardless of whether revenue per surface keeps pace.
PMax, AI Max, AI Overviews, AI Mode — Gemini is driving all of them. The AI optimizing your paid campaigns is the same AI evaluating your organic content, reading the same user, in the same moment, with the same intent.
The engine reads three signals:
In paid, you declare all three explicitly when you structure your campaigns. In organic, the engine infers all three from behavior: clicks, dwell time, and return-to-search serve as proxies for the profit signal that is missing there. Google denied using behavioral signals for years. Its own court case documentation told a different story.
Which means the organic discipline the whole series has been building — the funnel query pathway, the entity home, and the corroboration stack — has always been pointing at one thing: engineer the page so precisely for the right cohort that the behavioral signal does the same job as a correctly structured PMax campaign. The user lands, stays, converts, and doesn’t go back and research the same thing again. Google reads that behavior and infers your profit tier.
My bet, and I want to be clear it’s a bet rather than a documented fact, is that Gemini can’t serve a paid ad in real time without grounding against current search results because the ad has to match the organic context it’s appearing in.
If it doesn’t ground, the ad is inconsistent with what the user sees organically, which breaks the experience and loses the click. So the grounding process for paid is the same process as for organic: same knowledge graph, same search index, same LLM.
That means training Gemini on your brand through organic improves your paid performance through the same mechanism. One training investment, two outputs. I’ll be proven right on this eventually, and this article is the timestamp.
You can’t directly target Gemini in AI surfaces. You can only train it.
Across AI-driven placements, Gemini decides everything: where to show your ad, what to show, how to show it, who to show it to, when, and at what bid. The advertiser feeds it information and sets the parameters, but Gemini makes every decision that matters.
What you’re buying when you spend on Google Ads in 2026 is the right to feed a recommendation system that analyzes your brand on its own terms. The explicit signals you declare in paid — cohort, intent, and profit — are a real advantage over organic, where the engine has to infer all three from behavior.
But your ability to dominate through pure campaign structure is vastly reduced when Gemini doesn’t understand or trust your brand. The control has shifted: you guide it through signal clarity, not through the settings dashboard, and that guidance works best when your organic foundation is solid.
In a correctly structured PMax or AI Max campaign, you declare cohort, intent, and profit margin explicitly: this audience, this goal, this margin, in the same campaign. You don’t mix a luxury hotel and a budget guesthouse in the same ad group because the cohort is different, the profit margin is different, and handing the engine a mixed signal makes it spend your budget resolving a contradiction you created.
Organic doesn’t let you declare profit directly. The engine infers it from who landed, who stayed, who converted, and who never came back to search for the same thing. That behavioral signal is the only proxy it has for the profit tier, and it’s a thin signal compared to the explicit declaration you make in paid.
The smartest move for any brand running both is to treat them as a single loop. Run paid to find which cohort-intent-profit combinations actually convert. Build the organic pages around those combinations, designed so precisely for the right cohort that the behavior on the page sends the engine the same signal the paid campaign explicitly declared.
The paid side becomes cheaper because organic pages provide the behavioral confirmation the engine needs. The organic side gets stronger because the paid data tells you exactly which pages to build and for whom, and then feeds the engine the same signal the paid campaign declared explicitly, for free.
Most travel sites serve the same page template to a budget traveler looking for a €30 guesthouse in Bangkok and a wealthy traveler looking for a €3,000 suite at the Peninsula. Same layout, same fields, same photo grid, same review format.
The engine has to infer which cohort the page serves mostly from behavior because the differentiation of the pages is limited. Build the page for the person rather than the query, and you hand the engine the cohort signal it’s currently having to guess. That’s not a UX decision. That’s your profit margin declaration to an engine that can’t see your margins any other way.
And you win on all three fronts simultaneously. A page built precisely for the right person converts better because it works better for the human.
Better conversion behavior sends cleaner implicit signals to the engine, which improves your organic ranking for that cohort. And cleaner organic signals reduce your paid CPC because the engine has less to guess about. Better pages, more organic, cheaper paid – the same work produces all three.
The three revenue taxes — the doubt tax, the ghost tax, and the invisibility tax — operate on the organic side. Because the engine powering your organic results is the same one powering your paid placements, you pay all three on both sides simultaneously.
Paid surfaces carry two additional taxes that don’t exist on the organic side, and one discount you earn when you get it right.
The taxes and discounts in AI-driven paid search include:
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
Google has all the cards: the model, the surfaces, and the ads platform, all owned and tuned together in absolute harmony. Microsoft has the surfaces but lacks the LLM to drive them at the same level.
OpenAI has the model and launched a real ads business in February 2026, but lacks the surfaces – no Gmail, no YouTube, no Maps, no Play – and without surfaces, an ads business can’t compound at scale. Only Google has all three working as one system.
Paid and organic are now inseparable. The goose is fading, but Google can afford to let it. They know it rises like a phoenix, and in the meantime, they’ve got the biggest gaggle.
This is the 18th piece in my AI authority series.
As AI tool usage has become more common, I’ve seen impressive examples of people building tools to automate complex processes that once required significant manual effort. I’ve also seen teams adopt AI simply because it’s available, often with little practical benefit.
My approach is to focus on AI applications that save time and solve real problems.
Recently, I needed to align the SEO architecture for more than a dozen websites across three separate businesses, eight regional domains, and multiple languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.
Historically, mapping thousands of URLs to create cohesive hreflang XML sitemaps would have required specialized software or days of spreadsheet work. Instead, I used Google Gemini to build a custom Python script that handled the heavy lifting.
Here’s how the project evolved from an initial prompt into a highly customized automation tool, and what it taught me about using AI for technical SEO.
I use AI primarily for practical, time-saving tasks, including:
The hreflang project discussed here falls into that final category.
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The challenge was clear: map thousands of URLs across more than a dozen multilingual websites into accurate hreflang XML sitemaps.
Rather than tackling the project manually, I used Google Gemini to help build a custom Python solution.
Here’s how the process unfolded.
A common pitfall when using generative AI for coding is asking it to sprint before it knows the route. If you simply type, “Write a Python script to create an hreflang sitemap,” you’ll get a generic, fragile piece of code that breaks the moment it encounters real-world data.
Instead, I started by asking for an approach. I explained the scenario: multiple regional domains, organic growth over several years resulting in mismatched URL slugs, translated subfolders, and appended revision years.
Gemini suggested a multi-step, data-driven approach:
Following the strategy, I used a crawler to spider all the regional websites. The goal was to generate a unified comma-separated values (CSV) file containing the live URLs, status codes, title tags, and H1s. Screaming Frog worked perfectly for this application.
A critical point: Your AI output is only as good as your crawl data (remember the old saying, “garbage in, garbage out”).
An AI script will fail to map an obvious “exact match” if the target URL is a 404 or a 301 redirect in your source data. You must filter your CSV to include only indexable content before feeding it to the script.
Dig deeper: International SEO in 2026: What still works, what no longer does, and why
Google Colab provides a free, cloud-based Jupyter notebook environment where you can write, paste, and execute Python code without worrying about local installations or environment variables. You can access it through Google Drive. I found the free version had enough capacity to handle this project.
I uploaded the CSV to Colab, and Gemini provided the initial Python script. The script used a domain-mapping routine to assign language codes, clean the URLs, and generate an XML tree. The initial output was far from perfect.
If you expect AI to deliver a flawless, edge-case-proof script on the first try, you’ll be disappointed. You’ve probably heard the comparison of AI tools to interns, meaning you need to check their work. That’s very true.
The real value of AI lies in the iteration. As we ran the script, we encountered several unmatched URLs, leaving pages orphaned rather than grouping them with their international counterparts.
Here’s how I iteratively trained the AI to handle the nuances of human-managed websites.
The directory flattening problem
The U.S. site had recently reorganized its blog into topical folders, while the Mexican and Italian sites hadn’t yet been reorganized.
I prompted Gemini with these specific mismatched examples. It responded by adding a URL flattener function to the script, which stripped the topical folders behind the scenes so the translated slugs could align cleanly.
The aggressive semantic trap
To prevent the AI from mixing up different topics, we implemented concept traps. Initially, they were too strict. A UK article about the manufacturing sector wouldn’t match an Italian article because the U.S. title was slightly more generic.
I instructed Gemini to loosen the traps for generic industries while keeping them strictly enforced for critical acronyms (such as “SEO” versus “SEM”). This gave the AI the breathing room it needed to match creative translations.
The translated slug epiphany
The biggest breakthrough came while auditing the Mexican blog orphans. For example, the Spanish URL /detras-de-escenas-historias... is a direct translation of the English /behind-the-scenes-stories... I pointed this out to Gemini.
Instead of forcing me to hard-code hundreds of manual matches, Gemini updated the script to create a “Combined Semantic Signature.” It dynamically translated core operational phrases in the slugs, effectively bridging the language gap for the semantic matching model and connecting dozens of orphaned pages almost instantly.
Dig deeper: Cultural SEO: A practical framework for Spanish markets in AI search
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The project reinforced a simple lesson: AI works best when it’s treated as a collaborator rather than a shortcut.
By the end of the project, we had a robust, highly customized Python script that could process a massive CSV and generate a cross-referenced hreflang XML sitemap in minutes.
AI isn’t going to replace technical SEOs anytime soon. However, SEOs who know how to collaborate with AI to build custom, scalable, and useful tools will have a significant advantage.
Dig deeper: How AI search defines market relevance beyond hreflang
Much of the GEO conversation focuses on how AI systems discover, extract, cite, and recommend content. That work matters. But visibility also depends on what the content contains once it’s found.
Next-question intent is a way to test whether a page provides enough information to support the user’s next decision, not just the initial query.
The first search is often only the starting point. Real decisions happen in the follow-up questions, comparisons, constraints, and objections that come next.
Content that helps answer those questions gives AI systems more useful material to summarize, compare, cite, and recommend.
Traditional search was built around a results page: a ranked set of links users could scan, compare, and interpret for themselves. AI search is increasingly built around a synthesized answer drawn from multiple sources.
That changes what content must do. A page can rank, index, and appear technically sound, yet still fail to provide the information needed to support an AI-generated answer. That’s where next-question intent matters.
Search intent asks, “What is this user trying to do?”
Next-question intent asks, “What will the user need to know next before they can trust, compare, choose, buy, book, or move on?”
That question is becoming increasingly important because AI systems don’t simply match queries to pages. They assemble answers, comparisons, qualifications, and recommendations.
In that environment, content must support the full answer path, not just the first query.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
A user’s first search is often broad, incomplete, or simply exploratory. It signals a direction. Real value appears in what comes next: the follow-up, the objection, the comparison, the constraint, the “practical anxiety,” the “Yes, but what about my very specific situation?” moment.
As the simplest example, someone searches “best CRM software for small business.” The first query becomes a doorway. But the actual buying process begins with the follow-up questions.
These queries aren’t add-on or side questions. They’re the actual decision path.
Otherwise competent content fails at this stage. It answers the query, but doesn’t help complete the conversation. A page can define the category, mention benefits, include a few keywords, and still omit information buyers need to make decisions.
In traditional search, the user might click a few results and assemble context manually. In AI search, the system will assemble it for them. If your content lacks that useful context, it gives the system less to work with and may appear less visible.
The risk with any new content framework is that it becomes a fresh label for familiar advice. Next-question intent should do more than remind you to “write better content.” It should help you test whether a page contains enough context to support the next step in a user’s decision.
In practical terms, next-question intent means asking whether the content is answer-ready.
Answer-ready content addresses the user’s initial need, anticipates the next layer of decision-making, and provides specific, verifiable, and contextual information to support a synthesized answer.
This distinction matters because AI search visibility isn’t exclusively about rankings. It’s also about citations, mentions, recommendations, and whether a brand is recognized as a trusted answer in a given context.
Those outcomes require something more than volume. They depend on whether the brand’s content provides the system with enough substance to understand what the brand does, who it serves, when it’s useful, why it’s trustworthy, and how it compares to alternatives.
Most brands have decent content that’s accurate, readable, and optimized around a keyword. There may even be an FAQ section, like a useful but decorative basket of afterthoughts.
In AI search, decent may not be enough.
AI systems need extractable clarity, but they also need context. They must understand what something is, who it’s for, when it’s useful (and when it’s not), what evidence supports the claim, and what the user should consider next.
This level of context is where many pages go thin.
As an example, a service page says, “We offer customized marketing strategies.” But what does customized mean?
The product page says “safe for families.” Safe for whom?
A software page says, “built for small businesses.” What business?
Broad claims offer humans little to trust and AI systems little to use. Specific, structured, evidence-backed content offers something better.
A next-question audit looks beyond keyword coverage and asks whether a page contains the information needed to support the next step in the user’s journey.
For every important page, you should ask:
The best inputs for the audit often come from inside the business, not from keyword tools alone. Customer reviews, comparison queries, demo questions, sales calls, support tickets, chat logs, internal site search, and objection patterns can all reveal the questions real people ask when making decisions.
That information is often closer to the buyer’s actual path than a neat spreadsheet of keywords.
For a local service business, next-question content might involve service areas, prices, appointment windows, insurance, reviews, emergency availability, or what happens after someone books.
B2B software may invest in next-question content that involves integrations, user roles, implementation times, costs for switching, security, support, or whether a lower-tier plan is useful.
For higher-trust categories like medical, financial, and legal, next-question content involves scope, credentials, risk, regulation, evidence, or when to speak with a qualified professional.
The point isn’t to stuff pages with every possible question. It’s to build content around how people actually decide.
Next-question intent helps you avoid one of the least useful responses to AI search: publishing more content because visibility feels uncertain. The better move is more specific, decision-ready content.
If your page says, “I/we help small businesses grow,” explain which small businesses, what kind of growth, what constraints, what proof, what trade-offs, and what alternatives.
For example:
In that same line of thought, if a page says “We’re eco-friendly,” explain the materials, sources, use cases, certifications, limitations, disposal issues, and even circumstances where that claim doesn’t apply.
If a page says “This is AI-powered,” explain what that AI tool actually does, what it automates, what remains human-led, what data it uses, and where users will still need judgment.
This isn’t writing for bots. It’s writing for real people whose decisions are increasingly being mediated by AI-generated answers. The goal is to make your expertise, relevance, and trustworthiness easier to understand and use.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
Traditional SEO asked whether a page could rank. AI search asks whether a page can contribute to the answer.
Any page can be indexed, optimized, and technically sound, yet still fail if it lacks substance. It might answer the initial query, but ignore the information users need to make a decision.
The opportunity isn’t to chase every new acronym or rebrand every content plan as a new discipline. It’s to build answer-ready content.
That means clearer definitions, stronger examples, honest comparisons, better proof, more precise positioning, and direct answers to the questions customers ask every day.
In traditional search, visibility belonged to the page that best matched the query. In AI search, it increasingly belongs to the content that helps people move forward.
Google says that markdown solves one problem while potentially creating others for search visibility.
The post Google Says Markdown For AI SEO Strips Away The Parts That Matter appeared first on Search Engine Journal.
Google updated its AI Search optimization guide to clarify that llms.txt files neither help nor hurt Google search rankings. It also confirmed that Google Search does not use llms.txt files.
What Google wrote. I bolded the portion that is new, where Google wrote that Google Search does not use AI text files, markup or Markdown files.
Google also added a new note that reads:
Here’s a screenshot of this section:
Dig deeper.
Why we care. There’s been a lot of confusion about how Google Search handles llms.txt, markdown, and other AI-related files. In short, Google Search may discover, crawl, and index these files, but it does not use them in any special way. Having them on your site won’t help your rankings, and it won’t hurt them either.
You’ve probably seen some version of these three claims:
We tested all three against 1,674,518 English editorial articles and 1,690,295 French articles from the 1492.vision Discover corpus (November 2025 to May 2026): about 3.4 million editorial articles with at least one capture across our fleet.
They share a deeper flaw than any of their numbers.
All three treat headline format as a cause — a lever you pull to gain visibility. But the data shows, layer after layer, that a format’s measured effect is almost entirely a proxy for something else: which publisher used it, for which audience, and on which Discover surface.
The headline is a symptom of those choices, not an independent driver.
The clearest demonstration is Simpson’s paradox. Once you see it, you find it throughout the dataset.
Our metric isn’t clicks from Discover; no third party has that data. It’s hits per article: how often an article appears across the 1492.vision fleet we observe, a proxy for visibility.
The corpus is limited to editorial articles. YouTube and X are excluded because their headlines follow different conventions. We’ll return to both at the end—they sharpen the point more than anything else.
A word on why the volume matters: the entire argument depends on being able to slice 3.4 million articles by publisher, Discover surface, topic, and language while still retaining enough data in each segment for meaningful comparisons. That’s the difference between a number and an insight — and between a real format effect and a statistical mirage.
Pool all publishers together, and a clean gradient emerges: quote-led headlines at the top, statements at the bottom.
| Lang | Format | Articles | Mean hits | Median | vs statement |
|---|---|---|---|---|---|
| EN | Quote-led | 38,044 | 13.0 | 4 | +37% |
| EN | Quote inside | 75,463 | 11.5 | 4 | +21% |
| EN | Question | 53,081 | 10.2 | 4 | +7% |
| EN | Statement | 1,674,518 | 9.5 | 3 | baseline |
| FR | Quote-led | 179,472 | 52.8 | 13 | +48% |
| FR | Quote inside | 223,052 | 49.9 | 12 | +40% |
| FR | Question | 103,117 | 41.3 | 11 | +16% |
| FR | Statement | 1,690,295 | 35.7 | 9 | baseline |
The commonly cited +29% is conservative for pure editorial articles: quote-led headlines show a +37% lift in English and +48% in French. Questions, far from underperforming, also outperform statements (+7% EN, +16% FR).
At this level of aggregation, claim 1 looks understated and claim 2 looks plainly wrong.
This is the level of aggregation where most headline advice is born. Hold onto that +37% figure — the rest of this piece is about what it’s actually measuring.
The aggregate can’t answer a crucial objection on its own: the publishers that use quotes aren’t the same publishers that don’t.
Celebrity media, regional dailies, and buzz-driven sites lean heavily on quotes and earn more Discover hits per article regardless of headline format. Pure-play publishers, wire services, and utility-focused sites favor declarative headlines and tend to sit lower.
The raw comparison, then, isn’t quote versus statement. It’s one publisher population versus another.
This is a textbook Simpson’s paradox: a strong trend in the aggregate that weakens, disappears, or reverses once you segment by group.
To get anywhere near the effect of headline format itself, the grouping variable has to be the publisher.
So make each publisher its own baseline: compare quote versus statement within the same site, holding audience and topic mix constant.
Across 324 English and 439 French publishers with enough of both formats — at least 50 quote and 200 statement articles each:
| Lang | Publishers | Quote wins (median site) | Quote wins (mean site) | Median within-publisher Δ |
|---|---|---|---|---|
| EN | 324 | 31.5% | 55.9% | +3.1% |
| FR | 439 | 47.6% | 57.4% | +5.5% |
In English, statements outperform quotes at 68% of publishers by the median; quote-led headlines hurt more often than they help. In French, the result is close to a coin flip.
That leaves the underlying format effect at roughly +3% to +5%—about five to nine times smaller than the aggregate figure.
(The mean is higher than the median because a minority of publishers see large gains from quotes. The median is the more reliable measure of the typical publisher.)
Stop here and the lesson sounds like “segment your data.” But the collapse points to something larger.
If three-quarters of a +37% effect was really a publisher effect, the obvious next question is: what else is the headline metric standing in for?
The rest of this article is a tour of those hidden variables. And by this point, the answer to claim 3 is already coming into view: the format itself isn’t the driver.
The conventional advice says questions underperform by roughly 24%. The aggregate view of our data says the opposite: questions outperform statements (+7% EN, +16% FR).
Both conclusions are wrong for the same reason. Question headlines are disproportionately used by high-engagement publishers, which inflates their aggregate performance.
Within publishers, the picture settles.
In English, question headlines show a modest real underperformance (-3.7%), winning at only 29.3% of sites. In French, the effect is essentially neutral (-0.5%), with questions outperforming at 46.2% of sites.
The conventional advice gets the direction roughly right in English and neutral in French, but its usual magnitude is about sixfold too large.
The question mark isn’t the cause. The kind of publisher using it is. Same hidden variable, opposite sign.
Even that modest within-publisher effect drifts from month to month.
In English, it peaks at +2.5% and turns negative in March 2026, while statements outperform questions at 55% to 60% of sites each month. In French, it ranges from +3% to +12% — strongest in December and February, weakest in March — with no clear trend.
A genuine causal lever shouldn’t wobble like this. A correlation tied to a shifting content mix should.
The +3-5% average hides a sharp, consistent split. In English:
French data follows the same pattern in a different market.
The pattern is editorial, not algorithmic. Quotes tend to work where the audience comes for commentary, reaction, and framing, and fail where the audience comes for facts.
A publisher built around “what someone said” benefits from a quoted headline. One built around “what just happened” usually doesn’t.
The convergence between English and French is the giveaway. This isn’t a language effect; it’s a reader-intent effect.
What looks like a headline-format effect is, in this case, an audience effect wearing the clothes of a headline.
Discover isn’t a single feed. It’s a collection of pipelines, each selecting articles in different ways:
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moonstone, mustntmiss).aura).paginationpanoptic, content).relatedcontentruby, userpersonascontent).First, rule out the obvious alternative explanation. Are quote-led articles simply being routed to higher-value Discover surfaces, making the apparent bonus a placement effect rather than a headline effect?
The data says no.
Comparing where quote and statement articles actually appear, the distributions are nearly identical. In English, the largest differences are small: content.f (+2.2 percentage points), aura.f (-1.9), and moonstone.f (+0.6).
The bonus isn’t about placement: quotes and statements appear on the same surfaces in the same proportions. It’s about intensity — how each format performs once it’s on a surface. There, the overall +3% to 5% breaks into a wide range: from +22% to -14% in EN and from +25% to -12% in FR.
Grouped into functional families, the pattern is readable:
| Pipeline family | EN | FR |
|---|---|---|
| Editorial curation (moonstone, mustntmiss, astria, news…) | +3.4% | +9.7% |
| Related reading / context (paginationpanoptic, content…) | +2.0% | +6.7% |
| Trends / freshness (deeptrends, freshvideos…) | +4.4% | +2.3% |
Main personalization (aura) | +0.6% | +1.8% |
| Similarity-based recommendation (relatedcontentruby, userpersonas…) | -1.6% | -1.9% |
Quote-led headlines win where multiple headlines compete for attention at once — curation carousels, news clusters, and other surfaces where the title carries a social signal: someone said this. They lose on similarity-based recommendations, where the surface sells continuity (“because you read X, you’ll read Y”) and a quote disrupts the topic-clear promise with an out-of-context citation.
The largest pipeline by volume, Aura, ranks on topic affinity and barely reacts to format at all, with gains of just +0.6% to +1.8%.
A single quote-led FR article doesn’t get one number; it gets a blend:
Integrate those and you land at +4% to +7% net. The curatorial gains are real but partly offset by recommendation losses, which is why the aggregate is nowhere near +29%. The same format is both an asset and a liability, depending entirely on the surface serving it.
And +4–7% overstates how much the format itself matters because each pipeline’s ranking is a compound of signals unrelated to the title: engagement, scroll depth, topic affinity, E-E-A-T, entities, reading history, location, timing, and prior interactions.
A quote in the headline is, at best, one weak signal competing with all of those. Long before an article reaches a feed, it’s largely swamped by everything else.
These are within-publisher medians (each publisher against itself), so they aren’t a crude artifact of FR using more questions. The format follows the same pipeline logic as quotes, but in a more polarized form:
astria.f, the same pipeline in both languages, runs +9% in FR and -1% in EN; FR mustntmiss.f is +14%, EN moonstone.f is -13%.relatedcontentruby.f FR -11.5% (306 publishers), EN -6.1% (119); itemitemcollaborativefiltering.f FR -14.5%.aura stays neutral in both (+3.5% FR, -0.6% EN).Two caveats point in the same direction:
Even the honest +3% to 5% comes with a caveat that outweighs its size. When a publisher writes a headline as a quote, they choose the best available quote for that story. So the within-publisher figure compares the best quote an editor selected with the average of all that publisher’s statements, not the same article written two ways.
It’s the subject-line A/B testing problem: a good alternative beats a bad one, but the average alternative doesn’t. Convert every headline to quote-led and you’d be writing average quotes, so most of the gain would disappear. The +3–5% is an upper bound on a selective practice, not the return from a blanket rule.
That’s the final reason “do it everywhere” fails:
relatedcontentruby and friends are how an article redeploys after its initial peak, the main mechanism for extending Discover lifetime. Optimizing the headline for the curation peak while breaking the promise on these surfaces can net negative.aura is 11% to 15% of FR captures and 7% to 9% of EN, with a +0.6% to 1.8% quote effect. A universal quote rule optimizes secondary surfaces while ignoring that the biggest one runs on topic affinity.We excluded YouTube and X from the main corpus, but their results are the clearest proof of the thesis. The same quote-led format produces opposite effects depending entirely on what the title is trying to do.
| Domain | Lang | Quote articles | Statement | Mean hits quote | Mean hits stmt | Δ |
|---|---|---|---|---|---|---|
| YouTube | EN | 43,476 | 734,986 | 11.6 | 10.2 | +14% |
| YouTube | FR | 16,509 | 93,912 | 59.0 | 29.1 | +103% |
| x.com | EN | 34,156 | 268,175 | 5.2 | 4.9 | +6% |
| x.com | FR | 32,201 | 114,914 | 21.4 | 24.6 | -13% |
On YouTube, the title is effectively a text thumbnail that has seconds to create curiosity. A quote serves as a content promise — “here’s the line worth hearing” — which helps explain the +103% result in French. On X, the title is the post itself, and a detected quote usually indicates that someone is repeating or responding to another person’s words, diluting the original message. That correlates with a -13% result.
Same characters. Same regex. Opposite outcome. The format didn’t change; the job it was doing did.
(Methodological footnote: a naive audit that folded YouTube into the editorial corpus would inflate the overall quote bonus by 20–30 points, while one that folded in X would dilute it. Any serious headline study has to isolate editorial articles before measuring headline effects.)
Put the layers together. Three-quarters of the +37% raw bonus was explained by publisher differences. What remained split again by audience, then by Discover surface, then by which quote the editor selected, and finally reversed entirely when the title served a different function on another platform. At every step, removing context shrank or flipped the apparent format effect.
There’s no clean residue at the bottom where the headline acts independently. The effect is inseparable from the context that creates it.
That’s not a measurement failure; it’s the finding. We just saw the mechanism. Headline format is one weak signal among many stronger ones, all moving through pipelines that often pull in opposite directions.
The consequence is the point. An article’s visibility is the running score of that entire contest, not the verdict of any headline rule. A number measured across publishers is downstream of everything that travels with the format: who published it, what topic it covers, what the audience expects, the newsroom’s style and habits, and the conventions of the language itself.
So when an aggregate reports “+29% for quotes,” it isn’t isolating the quotation marks. It’s measuring a correlation with that whole bundle of factors and quietly relabeling it as causation.
None of this means aggregate data is the enemy. Everything above comes from aggregate data, just analyzed at the right level.
The trap is narrower: treating a single cosmetic variable, averaged across publishers that don’t belong in the same category, as a causal lever.
The same index that exposes that mistake also reveals the signals that genuinely drive Discover: which topics a publisher wins on, which entities are accelerating, who dominates a given surface, and what’s trending before it peaks. Those signals aren’t cosmetic, and they aren’t drowned out by stronger forces. They’re the underlying demand that headline format only weakly approximates.
The lesson isn’t “ignore the data.” It’s “stop averaging the wrong variable across the wrong population.”
This is why no cross-publisher average, corrected or not, converts into a rule for your site:
The only test that answers your question is the one you run on your own site, with your own audience. Know who you’re writing for, then measure them. Slice the data by your audience, your topics, and your surfaces — not by a single number averaged across everyone.
Each is real as a correlation and useless as a cause:
The honest version, if you want one sentence to keep:
A quote-led headline can earn roughly +3% to 7% additional Discover visibility for audiences that value commentary and framing (general news, magazines, regional press), especially on curation surfaces, and lose for factual audiences (sports, tech, utility) and on similarity-based recommendation surfaces. There is no universal gain from quotation marks; the popular ~+29% figure overstates the format effect by roughly an order of magnitude. The useful question isn’t “Should I use a quote?” but “Who am I writing for, and which Discover surface drives my traffic?” The only place to answer that is with your own site, not anyone else’s average.
AI Overviews and Google AI Mode now dominate conversations across the SEO community. One trend already stands out: Search is evolving from an information retrieval tool to a recommendation tool.
For travel brands, this changes the rules of online discovery. The challenge is no longer just helping search engines understand your website. It’s helping AI systems understand when your business should be recommended.
Many users now spend substantial time every week interacting with large language models (LLMs). With LLMs, they can organize conversations by project and create folders for upcoming trips. They can also build on previous chats that already recognize their interests, travel preferences, and demographic profiles.
This marks a departure from the traditional search process. Historically, travel planning started with Google searches for topics like:
Today, this process is far more conversational.
Rather than typing a series of disconnected searches, a traveler might create a new folder called “Summer 2026” in ChatGPT and start with a broad question that gradually evolves into a complete itinerary. For example:
What follows is an ongoing conversation that might expand into restaurant recommendations, attractions, accommodation options, transportation advice, and day-by-day planning.
When travelers ask AI assistants these questions, they aren’t looking for a list of websites. Instead, they’re looking for a recommendation.
The SEO toolkit you know, plus the AI visibility data you need.
AI Overviews synthesize information from multiple sources and present users with curated recommendations rather than a collection of links. As a result, trust, consistency, and contextual understanding become critical visibility factors.
A hotel may influence a traveler’s decision through an AI-generated response without leading to an immediate website visit. The traveler’s next action may be a branded search, a visit to a travel review site, or a booking through an online travel agency (OTA).
To earn recommendations from AI models, your brand first needs to be clearly defined. AI must have confidence in who you are, what you offer, who you serve, and when your brand is relevant.
To do this, choose one primary category and one clear position for your brand. Invest in digital PR and earn mentions beyond your own website. Aim to be included in travel articles that cover topics relevant to your category.
Most importantly, ensure your business information is accurate, consistent, and easy to interpret across your website, Google Business Profile, TripAdvisor, OTA listings, and social media platforms.
The way we measure search performance is changing. Traditional SEO metrics still matter. However, travel marketers should start expanding how they measure visibility.
One of the biggest mistakes is assuming that fewer clicks mean less visibility.
A traveler may discover your property through an AI-generated response, search for it later, visit a TripAdvisor profile, or book through another channel.
This is why branded search growth is becoming a valuable signal of AI visibility. Travel marketers should also monitor AI mentions, citations, and assisted conversions.
Assisted conversions reveal the channels and touchpoints that influence a booking, even if they aren’t the final source of the conversion. You can monitor these conversions in Google Analytics 4 by navigating to Advertising > Attribution > Conversion Paths and Attribution Reports.
TripAdvisor has become much more than a review platform. OTAs have become more than booking platforms.
When a user asks for recommendations, AI systems rarely rely on a single source. Instead, they build understanding by combining information from multiple platforms.
Your website is only one part of the ecosystem.
AI systems build confidence in recommendations by validating information across sources. What others say about your brand in reviews, travel guides, media mentions, OTA listings, or local citations increasingly matters. In many ways, this is simply online reputation at scale.
This additional context helps AI models determine when a property is relevant for specific traveler needs, such as:
A family-friendly hotel should consistently highlight family rooms, kids’ activities, children’s pools, and family-focused reviews. A romantic hotel should reinforce signals like couples’ stays, intimate atmospheres, spa experiences, and special-occasion packages.
Likewise, a business hotel should emphasize meeting rooms, workspaces, fast Wi-Fi, and proximity to business districts. A restaurant known for exceptional dining should earn reviews, media mentions, and third-party recommendations that consistently reference its food, chef, or culinary experience.
Many businesses naturally fit into more than one category. However, the clearer your primary positioning is, the easier it becomes for generative search engines to identify when your brand is relevant and should earn a recommendation.
The same principle applies to destinations. Generative search engines rely on signals across review platforms, travel guides, local listings, and publisher content when recommending where travelers should stay, visit, or explore.
As AI systems become more reliant on entities rather than individual webpages, travel businesses need to focus on creating a clear and consistent digital footprint.
Structured data helps search engines and AI models interpret key business information. For travel brands, this type of data includes accommodation types, amenities, locations, and other business details.
Highlight the attributes that differentiate your property. That might include family-friendly facilities, wellness experiences, exceptional dining, pet-friendly accommodation, or proximity to major attractions.
The clearer and more structured your information is, the easier it becomes for AI-powered experiences to surface your business in relevant recommendations.
Review what third-party sources say about your brand across the web. Look for conflicting information. AI search experiences pull information from multiple sources, and inconsistencies can reduce confidence in your brand.
Imagine a hotel with different phone numbers, outdated descriptions, inconsistent categories, or conflicting amenity information across various platforms. This is exactly the kind of ambiguity AI systems struggle with.
Maintain consistent information across your website, Google Business Profile, TripAdvisor listings, and OTA profiles to reduce ambiguity and strengthen confidence in your business data.
Start by auditing your existing customer reviews.
Constructive feedback is a fast and easy way to identify what truly differentiates your brand from competitors. Details like amenities, accessibility features, opening hours, parking, and pet policies help AI systems answer specific travel queries.
Google Business Profile is another critical source of operational information. The categories, attributes, amenities, and opening hours listed on your profile help AI models answer travel queries with greater confidence.
If you need to provide additional context, Google Business Profile allows you to publish posts linking back to relevant content on your website. Regularly publishing Google Business Profile posts can help drive engagement, profile visits, and customer interactions while keeping your listing updated with fresh content about your products, services, events, and offers.
Track, optimize, and win in Google and AI search from one platform.
Generative search is more democratic than traditional search. AI models recommend businesses, not websites. Visibility is no longer shaped exclusively by what happens on your website. It’s shaped by the broader digital footprint your brand has built across the web.
For travel brands, that means thinking beyond rankings and clicks. Reviews, OTA listings, travel guides, media mentions, and business profiles all contribute to how AI systems understand your brand and when they recommend it.
It’s time to be creative, experiment, and build partnerships with complementary businesses. Most importantly, it’s time to build the signals AI systems trust.
One topic that’s come up frequently in SEO circles is the difference between creating content for information retrieval and creating content that earns citations from large language models (LLMs) such as Claude, ChatGPT, and Google AI Overviews.
As AI search evolves, that distinction is reshaping content strategy. Content that delivers the best user experience and meets people where they are is more likely to earn citations and be recognized as a trusted source.
More importantly, we need to think beyond our own websites and consider third-party platforms as well. As algorithmic marketers, our goal is to keep our brand and messaging consistent so machines clearly understand what we do, who we serve, and when to surface our company and information.
For LLMs in particular, it’s important to stop thinking about interactive search as SEO. Instead, focus on the users you want to attract through citations, or those for whom you want information about your brand to surface.
Some SEO fundamentals apply, but LLMs and AI Overviews are looking to provide customized experiences based on users’ preferences. Your content marketing, both on your website and externally, should keep this in mind rather than focusing on creating content for citations and retrieval.
I’ll start with an example of this customization to show the difference between SEO and generative engine optimization (GEO) or AI Overview approaches, then jump into actionable items you can take.
On a team call this week, I pointed out that the client’s CEO and I are very similar. We’re both around the same age, in the same geographic region, have executive job titles, are very similar demographically, and both like to drink red wine.
However, if we both asked an LLM to make recommendations for a new wine to try, and both said we were looking for a wine with dark fruit notes that was dry and had a big, bold mouth splash, it’s almost certain that we wouldn’t get the same recommendation, even if we were using the same LLM. Why? Because he likes Italian wines, and I prefer Napa Valley wineries.
Google, functioning as a search engine, may know what a big red wine is, but LLM systems know more about our buyer personas because of how we engage with them. They remember who we are, while Google does not. From LLMs, I’ll likely get a recommendation for a Cabernet from California, while he may get an Amarone from Italy.
The LLM and Google AI Overview may both source products to recommend from retailers like Total Wine & More or Binny’s, and use publications like Food & Wine, Wine Spectator, and Vivino for knowledge, but that’s where the similarities end.
LLMs know what we like in a result and what we engage with, so they show us different varietals that better match our preferences when we ask more in-depth questions. Google and traditional search engines, meanwhile, will show more general options for big, bold, red wines.
That said, Google appears to be moving toward more personalized results, so expect a more LLM-style approach in the future. Apply this approach to content on your own platforms and anywhere you can influence the narrative on third-party sites.
Shifting your content from retrieval-based to citation-based starts with understanding how LLM and AI Overview results are generated, how personalized those results are becoming, and how retrieval methods combine with trust signals from traditional SEO results.
Retrieval-augmented generation (RAG) information sourcing requires trusted websites and resources to compile a reasonably factual result. When combined with a personal preference, it may favor one source over another while still using both.
If the wine suggestions above were to apply here and two retailers (say, one big-box store and one niche winery) were trying to get featured in the output, they’d need to approach the same publications differently.
Let’s look at an example of getting wines placed in listicle-style articles. The big-box retailer that carries both Italian and Napa wines will want to be featured under Italian reds with talking points that address the things that interest my colleague, my client’s CEO, while the Napa winery wouldn’t need to worry about making that list since it doesn’t produce Italian wines. However, both will want to be featured under Napa Cabernets since they both sell them, and both will want talking points that matter to my buyer persona.
Tip: Listicle placements are easiest to get through a media buy or advertorial, an affiliate program, or good old PR work for an earned placement.
For articles about varietals, the big-box retailer would want to focus on multiple articles and use talking points that matter to the CEO. For example, mentioning that the wine is produced on old vines, as these are more common in Europe than in the U.S. For the Napa wines I prefer, the winery would want to talk about how its wines feature a strong mouthfeel, have legs, and feature softer tannins.
Big-brand stores will want mass coverage and to have their products featured under many or most wine descriptions and types to help build relevance and be seen as experts on the topic of wine across the website since they carry wines from all countries and varietals.
The Napa Valley winery, on the other hand, wouldn’t need to worry about being cited across the entire site. Instead, it would want to focus on being featured in the Napa and California wine sections, in articles about grapes that are more common in California wines like Cabernet Sauvignon, Merlot, and Petit Verdot, and anything else directly related to the products and services it offers, like California wine tours and tastings.
If you’re an individual brand or a small business that sells women’s clothing, for example, you could use a similar, yet modified, version of the strategy above.
You’d also want to focus on getting featured in listicles, and when they mention you as one of the best retailers for women’s T-shirts, ensure your brand is present with some of the reasons why, then look for other lists about women’s fashion and clothing to be added to.
Whenever possible, especially once you’ve developed a relationship with the editor or contributor, have your differentiators present, whether it’s moisture-wicking materials, a patent you own, plus-size or petite sizing, signature colors, or being on trend. This builds the topical relevance of your brand mention and feature.
Most importantly, don’t stress over being included in every article across each media company. Focus on having your brand featured as a place to shop within the specific content that addresses the common issues your brand solves. After all, this is why your customers shop with you and how LLMs may learn who to show your brand and products to.
Non-shopping content that’s on topic, like a guide to materials or seasonal fashion trends that feature your brand and someone from it as a thought leader, may help as these systems become more advanced.
Right now, LLMs are using shopping lists as sources, but they’re looking for expertise as well. Being cited as an expert in niche themes and selling points across the sources LLMs already trust, and as a place where someone can purchase X, Y, and Z products, can help LLMs make the connection and build their knowledge bases about you. You’re not just a name anymore, but a trusted brand that sells X, Y, and Z to A, B, and C demographics.
When you keep getting mentioned more often in new content and are cited by a trusted resource, it may add credibility to your company as a retailer, service provider, or publication. That’s what we’re focusing on now with many of our content optimizations.
The goal here is to let LLMs and SEO algorithms know what you do and sell, and who the specific buyer persona is that shops your brand. Once they have a clear understanding of this, and if they trust your brand and your website or app enough, you may be able to show up in citations and recommendations more frequently and for the long run. And that bleeds into your website experience.
You’ll find out pretty fast that practices that have been considered bad in SEO for years still don’t work for GEO and AI Overviews. By this, I mean things like creating satellite pages, pages just for AI to index and find, hidden copy, content in schema, and similar tactics. They don’t work, and in the long run, they’re likely to tank your SEO, too.
The silver bullets and “strategies” we’re seeing now, and consider my tongue firmly in my cheek here, are the same things sold as SEO marketing years ago. The LLMs will catch up, your domain and brand will get penalized, and you’ll need to recover the losses while spending money you may not have on consultants and new team members.
Instead, focus your website experience on the actual customers and buyers who shop with you. This will naturally communicate what your products and services are to search engines and LLMs, your website visitors will know they’re in the right place, and you should see conversions increase if you’re better at meeting your visitors’ needs. How do you do this?
Each of the items above helps users on your website know what to buy, how to engage, and what meets their needs. This also helps define for LLMs what’s the best fit for your customer and the solutions your products and services naturally provide. That’s information search engines and AI can use to know when to surface your products and who to show your brand to.
That’s not to say SEO is going away. Traditional SEO practices still help LLMs understand what your website, products, services, content, and company offer and who they’re for. For example:
Creating content for citations and information retrieval isn’t just about technical optimization or the content on your page. It’s also about how third parties experience your brand and talk about it, which helps LLMs determine which users are the best fit for your company and content.
Focus on maintaining a consistent voice across every channel you control, make sure your pages are crawlable and easy to understand, and keep testing. LLMs are still new, and we all have an opportunity to learn and adapt as the technology evolves.
Trust in AI search is declining, consumers are validating information across more platforms, and AI visibility is increasingly tied to brand authority rather than traditional SEO metrics.
Those are among the key findings from new research by Fractl and Search Engine Land, presented by Fractl cofounder Kelsey Libert at SMX Advanced. The study offers a detailed look at how consumers evaluate AI-generated answers, which signals influence AI recommendations, and where brands are falling short on governance and disclosure.
After the conference, I spoke with Libert to dig deeper into the data. Our conversation covered the growing trust gap in AI search, the role of earned media and entity authority in AI visibility, and why many organizations are still unprepared for the operational challenges AI introduces.
The headline finding from Libert’s research is hard to ignore. In 2025, 82% of consumers found AI search more helpful than traditional search results. By 2026, that figure had dropped to 54%, a decline of 28 percentage points in a single year. The skeptic camp grew sixfold over the same period.
I asked Libert what she thinks is driving that erosion.
She isn’t entirely pessimistic about where things are headed, though.
That restabilization may come sooner than expected. Libert pointed to a June 5 CNN report covering Anthropic’s warnings that AI may soon be capable of improving itself without human intervention. Whether that accelerates the recovery of consumer trust or deepens concern about AI reliability remains to be seen.
In the meantime, consumers are hedging. The research found that buyers now check an average of 2.4 platforms before validating a purchase, a pattern consistent across Gen Z, millennials, and boomers.
Google still leads AI tools three to one for trusted product recommendations, commanding 39% of consumer trust versus 14% for AI tools. Reddit, at 15%, sits between them.
For SEOs worried about the erosion of organic traffic, Libert’s framing offers a more nuanced picture than the typical doom narrative. About 50% of marketers report traffic declines since AI Overviews launched, and 61% directly blame AI tools.
At the same time, 57% see traffic growth from social platforms, including TikTok, Reddit, and YouTube, and 40% see growth from AI assistants such as ChatGPT and Perplexity.
The channel map Libert presented at SMX is worth understanding.
Search isn’t disappearing. It’s fragmenting, and brands that optimize for only one of these channels are leaving significant visibility on the table.
Libert’s research categorized generative engine optimization tactics into three tiers, and the distinctions matter for how marketers should allocate effort.
The most commonly used tactic is FAQ optimization, employed by 49% of marketers. Libert calls this high risk, and the reason is straightforward.
FAQ strategies can be strengthened by layering in proprietary data, subject matter expertise, and unique insights, but on their own, they offer little defensibility.
Table-stakes tactics include building brand mentions (43%), establishing topical authority (36%), and implementing structured data (30%). These are necessary but not sufficient.
The moat, as Libert describes it, consists of original data and proprietary studies (35%) and digital PR (24%). These are harder for AI to replicate, which is precisely what makes them valuable.
The signal strength behind this approach is backed by Ahrefs research analyzing roughly 75,000 brands across ChatGPT, AI Mode, and AI Overviews.
Branded web mentions and YouTube impressions showed the strongest correlations with AI visibility, ranging from 0.50 to 0.74 on the Spearman scale.
Backlink count and ad spend fell in the weakest tier, below 0.30. The practical implication, as Libert put it, is that “AI systems reward brand presence and mentions more than traditional SEO scale metrics.”
I asked Libert what she would do first if building brand mentions from scratch. Her answer was tactical and specific:
One of the most striking data points in Libert’s presentation involves the gap between what consumers expect and what brands actually do.
Between 84% and 91% of consumers say they want AI labeling across all content formats, including text, video, audio, and images. Only 20% of organizations say they always disclose their AI use. One-third never disclose at all.
Libert doesn’t think consumers are simply opposed to AI in marketing. The concern is more specific.
She cited the research finding that nearly half of marketers admit to not fact-checking their AI-produced content and that 48% say AI makes their work faster but more average.
Dove came up as one of the few brands actively positioning against what Libert calls “AI-dominant marketing slop,” though she acknowledges the field is still mostly treating disclosure as a compliance checkbox rather than a brand signal.
There is also a deeper question about where AI assistance ends and human creativity begins, one the industry hasn’t settled.
The slide that drew the most reaction in the SMX room showed how marketers are allocating their human review time for AI-generated content. Editorial review claimed 72% of attention. Voice and tone review claimed 62%. Fact-checking fell to 54%, plagiarism and legal review to 42% each, and bias evaluation to just 27%.
Libert called this an “all of the above” problem, spanning training gaps, workflow fragmentation, and leadership priorities.
Her core argument here aligns with the most pointed line in her presentation: fix the infrastructure before you scale the output. AI won’t kill credibility. Untrained oversight will.
The closing argument of Libert’s SMX talk, that AI rewards brand equity rather than creating it, raises a fair concern for newer entrants who lack years of accumulated authority. I pushed her on this.
Her answer was more optimistic than the headline suggests. AI Overviews aren’t simply surfacing the top search results. They are surfacing brands that have built genuine authority around niche topics.
She also sees an opening created by the same corporate inertia that slows large-brand adaptation.
The caveat?
The 2026 AI search playbook that Libert presented at SMX distills to four imperatives:
None of this is particularly complicated in concept. The difficulty is organizational.
The brands that treat credibility as infrastructure rather than aesthetics are most likely to be cited, recommended, and trusted as AI search continues to mature.
Google's John Mueller said localized URL folders for a site's primary market offer little practical SEO advantage.
The post Google’s Unexpected Take On Site Folder Structure And SEO appeared first on Search Engine Journal.
Google publishes guidance after new law gives small businesses power to challenge lost visibility and deleted reviews.
The post Google Publishes Tennessee Search “Blacklist” Guidance appeared first on Search Engine Journal.
Looking to take the next step in your search marketing career?
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
(Provided to Search Engine Land by SEOjobs.com)
(Provided to Search Engine Land by PPCjobs.com)
Digital Paid Marketing Manager, IMA | Institute of Management Accountants (Remote)
Manager, Paid Search, NP Digital (Remote)
Digital Marketing Manager, Paid Social & PPC, 80Twenty (Hybrid, Newark, DE)
Search Engine Optimization Manager, Seer Interactive (Remote)
SEO Manager, Prosum (Dallas-Fort Worth Metroplex)
Senior Manager SEO/Gen AI (FTC), Jellyfish (Hybrid, New York, US)
SEO Marketing Manager (phone accessories), Velvet Caviar ($100,000 – $120,000)
Sr. Content Marketing Manager, Dayforce (Remote)
Paid Media Manager, Clients Blackbox, Inc. (Remote)
Senior Manager, Paid Search, Talkiatry (Hybrid, New York, NY)
Senior Paid Media Manager, Brightly Media Lab (Remote)
Paid Search Specialist, Maui Jim Sunglasses (Peoria, IL)
Note: We update this post weekly. So make sure to bookmark this page and check back.
Claude may be more directly tied to Brave Search rankings than other AI answer engines, according to information Jonathan Clark shared on LinkedIn from a Zero Click by Profound session.
Clark, managing partner at Moving Traffic Media, said the session’s key takeaway was that Claude “doesn’t re-rank search results” and instead appears to use Brave’s top 10 results directly in its answers.
Claude searched less often. Claude used web search in 36.6% of prompts, compared with about 90% for ChatGPT, according to Clark.
Brave rankings carried weight. Claude’s citations overlapped with ChatGPT’s in only 8% of cases when responding to the same prompts, according to Clark.
Some prompts stayed in memory. Prompts such as “how does,” “what is,” and “steps to” were less likely to trigger Claude to search the web. When Claude doesn’t search, it can’t cite web pages. Claude searched most often for prompts containing terms such as “best,” “top,” “near me,” and comparison-style queries, according to Clark.
Years showed up often. Clark also noted two patterns that could make Claude easier to test:
Why we care. Claude visibility appears to depend more heavily on ranking in the search results Claude uses. Clark’s takeaway was that Claude may be one of the most optimizable AI answer engines today because its search behavior appears more consistent and more closely tied to observable search rankings.
With 80% of AI traffic draining website infrastructure for model training, what can you do to fight back without affecting search visibility?
The post AI Bots Keep Overloading Servers. Should Website Owners Keep Paying? appeared first on Search Engine Journal.
