AI SEO: Writing That’s Specific May Get Cited More via @sejournal, @martinibuster
Someone posted on social media about their experience writing deep and insightful articles last year and was pleasantly surprised to see that AI was leaning on their articles and even referencing them. Their secret was to choose highly specific topics, which is a good idea.
SEO And Natural Language AI
SEOs like to write articles based on keywords, and that’s actually how people did it in the relative caveman days of SEO, well over 25 years ago. Natural language processing has come a long way, and LLMs are now able to understand topics and questions in a conversational manner. So it’s truly outdated to proceed with SEO by focusing on keywords.
User behavior and what other sites and people are saying about a site or product are increasingly important. The best way to influence that is with content that’s insightful and gives users what they’re looking for and a lot of it, as often as possible.
It’s Not Just About Being Insightful
The person who started the discussion pointed out that they chose a “specific enough topic” and wrote something insightful about it. That’s a deceptively simple tip, but it is one of the key points about writing for an audience of humans and machines that interpret content as if they were humans.
Choosing a specific enough topic is about keeping the article focused on a topic and not allowing it to stray. One of the hallmarks of good writing is the willingness to remove the bits that tend to wander off topic. This is an American style of writing, although Europeans as far back as Charles Dickens knew the value of staying on topic so that the effect is a constant stream of interesting sentences that pull a reader all the way to the end of the page.
Writing is an art, like painting and composing music. But you don’t have to have a literature or journalism degree to engage users with text.
How Someone Got Lots Of Love From Claude AI
Bluesky user @danabra.mov posted about their experience writing an insightful article that subsequently began getting referred to by Claude AI.
“If you write an insightful blog post on a specific enough topic, and people link to it, you have a real chance at influencing everyone’s LLM output in a year or so. it’s a bit wild.
I wrote some articles last year that I thought nobody would read because they’re super long. And now I see Claude regurgitating what I wrote in those articles in a perfectly condensed way (and occasionally explicitly referring to the posts). they took away exactly what I wanted the reader to take!
For me it’s a relief because i was worried about falling interest to longform blogs and declining readership. but in a sense maybe it has significantly expanded! It’s just that my reader is now infinitely patient and really wants to hear the entire thing.”
Others Agree That Being Specific Is Key To Success With AI Citations
The response to Dan’s post was overwhelmingly positive, with one person commenting that it gave them hope.
One person named Tyler shared that they had a similar experience with content they published that was specific.
“I’ve seen a couple of mine, not even that insightful, just specific, get pulled into them and used within like 6 months. Wild.”
The person who started the discussion, Dan, agreed:
“I mean yeah but I think being specific by itself is enough…”
Why Is Being Specific Enough?
Based on my well over forty years of writing experience, including writing poems, short stories, one novel, blog posts, and articles for Search Engine Journal, my opinion on the matter is that focusing on being specific helps to keep a work focused in a way that matches the reader’s focus. The moment the article strays off topic is when the reader loses interest and jumps away.
Being insightful is not enough. Being witty or clever is nice in moderation, but in higher doses it becomes off topic and will, in my opinion, lose the reader. That’s why anyone who writes content must be willing to ruthlessly cut words out to keep it focused and specific (on topic).
What Google Said About The Topic
Google’s John Mueller reposted Dan’s post with the comment:
“Make more insightful & useful stuff.”
There was one skeptic in the crowd who argued that the economics remove the incentive to put in the work.
“Why on earth would anyone put in the effort required at this point only to have it immediately stolen, receive no compensation and no credit. It’s never been more hostile environment to be a creative. The economics DO NOT WORK.”
Yes, it’s true that today’s environment is hostile to creators because of AI. Yet there is always an opportunity for success by writing about the topics that interest you because they will be sure to be of interest to someone else.
Google Is Using Social Media Signals To Mask AI Search Click Loss via @sejournal, @TaylorDanRW
As you may already know, Google recently updated Search Console to let brands track how their social media and video posts perform in search results.
Most marketers view this update as a helpful gift. They believe Google wants to reward brands that build strong footprints across TikTok, YouTube, and X. And not wanting to be glass-half-full, I think this is the positive optics Google was hoping for.
If you look past the official announcements, a different picture comes into focus; this update is a clever trap. It serves as a shield to hide the traffic loss caused by artificial intelligence while positioning creators into further training Google’s AI models.
Redefining Success In The Era Of Click Loss
To understand this strategy, you must look at the crisis Google faces with web publishers.
Organic traffic to a company website was the main measure of marketing success, and a narrative we as an industry pinned to the mast for years as to whether or not we were justifying our budgets.
By tracking social media views inside Search Console, Google is trying to change the definition of success. If your website traffic drops by a third, Google can point to your social media data. They can show you that your TikTok videos received thousands of impressions on the search page; they want you to believe you are still winning, even if you do not get actual clicks.
It forces marketers to view Google as the central control room for all visibility, even when Google stops sending visitors to their websites, as they’re still providing visibility.
Outsourcing The Search Graph To Creators
The update also serves as a tool to train Google’s artificial intelligence and to power generative search, Google needs to understand the real world.
The engine maps relationships between people, brands, and topics. This process is called entity resolution.
Google needs to know who is an expert, what they write about, and whether they are a real person or just an automated spam site.
By encouraging you to verify your social accounts inside Search Console, Google makes you do their work. You hand over the exact connections they need, tell them that your website, your X profile, and your TikTok account are all the same entity.
Instead of Google guessing which profile belongs to which author, publishers hand-deliver verified identity maps. Google can then use this clean data to train its language models on who the true authorities are.
Having verified data is essential in the age of generative text.
Anyone can build a website, buy a drop domain, and programmatically generate thousands of articles with AI, and inflate third-party authority metrics.
Social profiles with real human engagement are the best proof of life. Real companies and real brands operate across the multiple channels and have a form of pulse and presence outside their single web domain.
Google uses these connections as a trust filter to separate real brands from synthetic spam. You are giving Google the exact blueprints it needs to verify content ownership. This helps Google decide which sources are reliable and which sources are junk.
Looking at this cynically, the ability to verify social profiles in Google Search Console is an optics masterclass in platform survival.
It somewhat pacifies publishers by giving them new vanity metrics to track, and at the same time, it creates a new network for those same publishers to map the entity relationships that Google needs to build its AI future.
How Google Get Social Content
Google pulls social media posts into search engine results pages through a combination of live data firehoses, standard web crawling, and dynamic JavaScript rendering. The process differs based on the specific platform and user privacy settings.
Some of these data pipelines have been around for almost a decade, with the X (then Twitter) firehose deal coming into play in 2015.
This doesn’t mean that fresh posts are the only ones considered. In my own Search Console profile, I’m seeing X posts receiving clicks on Google that I posted in October 2024.
LLMs behave in a similar manner, and because of this we need to look at a post deprecation strategy.
Reviewing pricing prompts for one of our clients, I found that a couple of LLMs were returning pricing information from an X post advertising a student only offer from July 2022. This isn’t only misinformation, but can lead to a negative brand experience when a user clicks through expecting to receive one price, but find one substantially different.
Your Audience, Google’s Platform
The brands that win in this new landscape will not focus on these new Google metrics, but understand these are now another piece of the puzzle.
We need to stop treating Google as a neutral partner, as Google needs Search to bring people to the platform for Ads.
We should use our social channels to build a direct connection with your audience. Gather your community on platforms you control, rather than a search engine that wants to keep your visitors for itself.
That still means Google processes 2.92 billion clicks to the open web every day. It’s still a figure worth fighting for – particularly for publishers whose business models heavily rely on a click.
So let’s not totally lose sight of what matters in the here and now. And unique content certainly fits that mould.
I have reviewed a few previous patents (Google’s in-depth article patent explained and how Google ranks news sites), and it is not a thoroughly enjoyable experience. A granted patent protects an idea; it doesn’t prove deployment or real-world use cases – and it’s certainly not unlike big tech to claim ownership of something just so it can’t be used elsewhere.
Whether it has international filings? Yes, but with some caveats. US, China, ceased in Europe and worldwide, but extended in the US to 2039 very recently.
Whether Google has protected the ranking technology around the world? Yes, again with some caveats.
Does it broadly align with your understanding of the concept (in this case non-commodity content)? Very much so. As the rasping breaths of SEO-first, commodity content make even iron lungs work hard, it would be inconceivable for Google to not measure or evaluate uniqueness in some manner.
It is more likely to be used in some capacity.
TL;DR
Google has multiple public and leaked systems that appear to evaluate originality, effort, and unique contribution – see OriginalContentScore and ContentEffort.
The patent describes an information gain score (potentially in a 0 – 1 framing) that is assigned to a document based on how much new information it adds beyond documents a user has already seen on the same topic.
In my – and many others’ – opinion, Google’s systems reward originality in some way. Whether that’s directly through an information gain score and re-ranking system, a Bayesian predictive score, or indirectly through positive engagement signals, I couldn’t tell you.
Originality doesn’t mean an entirely different document. As little as a 10% difference could be the delineator between marketing success or failure.
How Does It Work In Practice?
This patent is not about the information gain applied to the current set of search results. It’s about the subsequent set of results – ranking the next set of search results based on wider user search behavior, personalization, and added document value.
It highlights that documents:
May be reranked.
May be excluded.
May be significantly demoted.
May no longer appear in results.
Based on the amount of novel, relevant information provided when compared to other similar documents.
For any tech SEO geeks out there, you’ll be well aware of the concept of preloading. In nerd circles, preloading tells browsers which resources should be prioritized to improve the page load speed and above-the-fold rendering.
I think this patent works in a similar manner, but with bloody unreliable people instead of machines. Maybe bfcache is a more apt comparison, but I haven’t really got stuck into technical SEO for a while, so forgive me for my appalling analogies.
Step-By-Step
A user reads a document about a certain topic, let’s say, growing an apple tree.
Google understands that the majority of users don’t stop at one page here. It’s a rich topic. When should I plant one? Where? What do I feed it?
With 13 months of click and engagement data to hand, Google knows – with, I imagine, an unerring level of accuracy – what piece of content each user should be shown and when based on goal fulfillment.
But new content is written every day. Pages are updated. So this isn’t a static corpus to work with. And maybe someone has a novel way of growing apple trees?
So pages are compared. A user reads a document (d1). Google then compares a new or updated article (d2) to the original.
If d2 generates a favorable information gain score, it will likely be shown to the user as part of their journey. If it doesn’t, it’s doomed.
“An information gain score for a given document is indicative of additional information that is included in the given document beyond information contained in other documents that were already presented to the user.”
Let’s say two documents are chosen based on a user’s search and search history. They’re represented as d1 or d2. D1 is an already-consumed document, and d2 is brand spanking new. Well, to the user at least. These documents can be represented as a vector (or some other semantic representation) to help the model fake understanding of the document and its position against similar documents.
Vector mapping is all about angles and positioning on a graph to quantify a scoring or positioning system (Image Credit: Harry Clarkson-Bennett)
The system provides a quantitative score to assess whether the user should also view d2 after having viewed d1. If the machine learning model generates an information gain score of document d2 over document d1, then d2 is likely to be shown – for future use cases, possibly at the expense of d1.
There are some incredibly practical implications here.
If a topic has been done to death, you have a more limited chance to rank and generate value without providing something extra. In a scenario where your article scores 0, the system has assessed it provides nothing extra, and a user who has seen d1 is less likely to see d2 – your article.
If nothing else, make sure you stand out above your closest competitors in some manner.
As with so many of these Google-led ideas or initiatives there are flaws. You don’t have to follow it to the letter. But E-E-A-T and “information gain” are sound principles. You have to be memorable. There is no alternative.
How Important Is It?
I think uniqueness and standing out are more important than ever. Strip the patent out of the conversation. People or brands who publish content won’t survive if they aren’t memorable to people and – by proxy – search engines.
So you’ve got to do something differently.
In Google’s case, I think it’s more about efficiency than anything else. If they know the information gain scores of two documents are virtually identical, then a user isn’t going to be shown both versions of the document. The second document will be deprioritized in favor of richer, more unique content.
Google has enough engagement data to go along with these proxy scores to understand what document should be shown and when. They can get a user closer to their goal by removing overly similar pages from a user’s SERP or AI response.
Which may be exactly why they’re thinning their index – the removal of non-value-add content. Well, that and all the AI slop you’re creating.
It is quite literally down to a) computational resources (money) and b) getting the user to the point of completion quicker. In the DOJ Antitrust trial, Pandu Nayak’s sworn testimony called Navboost “one of the important signals that we have.”
“…a shorter query session or fewer dialogue turns can provide a corresponding reduction in the resource demands of the system e.g. with respect to memory and/or power usage of the system.”
And the Quality Rater Guidelines make numerous references to effort, originality and talent. Frameworks like E-E-A-T and the product reviews update really highlight the importance of actually using products and showcasing the effort you have gone to. The amount of “effort” you put in is quite literally quantified (highly recommend Sean’s breakdown here). It is part of the Helpful Content update (booooooo) and the more difficult your page is to replicate, the better chance it has of success, all things being equal.
These are not stupid principles. They’re very good ones. The problem is, effort is expensive. The fewer clicks content produces, the less each article will generate.
In an attributable manner at least.
Google Is Building An Audience Loyalty Ecosystem
Don’t take my word for it, take Barry’s. Google has wanted to get rid of click-chasing churnalism for years. Now it can. And it is – in most cases, I think, a positive.
Publishers that can demonstrate they have an audience outside of SEO are being “rewarded.” Although I suspect you could replace rewarded with crushed a little more slowly.
You can follow your favorite publisher via Preferred Sources and as a Search Profile via the Discover feed (U.S.-only at the time of writing this), and badges like “highly cited” have been in play for some time. It doesn’t work very well, but they are trying to promote unique reporting.
You can now see how content from social and video platforms performs on Google Search if you meet the requirements. Your digital footprint and impact within the industry you’re in really matters. Particularly when you consider how prevalent social and creator accounts are in Discover.
I worry that this is completely impossible to explain what is happening to users. What is Preferred Sources vs. a Search Profile?
It’s tough to force people to follow you on platforms – maybe that’s the point. Which I kind of understand – but I think one of these would’ve sufficed.
If you want to know a little more about where Discover is heading, I made a short video about it:
[embedded content]
Does Information Density Matter?
Yes and no. Long articles are not necessarily more effective at satisfying the user.
Google has methods to normalize the length of an article to prevent additional keywords and semantically relevant phrases from ranking the document too highly. Factors like TF-IDF normalization prevent long documents with high word counts from artificially inflating their relevance scores just because they’re quote-unquote richer.
More detail may be the wrong phrasing here. Detail and rigor are typically positives. But it’s less important than answering the question and getting the user closer to their end goal.
User satisfaction is quantified through goal completions and Navboost data – it trumps everything else.
How Does It Affect AI Systems?
Well, traditional search ranking is still crucial in AI systems – whether that’s how effectively you rank for the primary search, your inclusion in the training data, RAG, or suite of fan-out searches run concurrently. And AI searches are extremely personalized – something that’s likely to only increase over time.
When Claude starts knowing what toilet paper I buy or selects a poorly chosen “Happy Mother’s Day” card for my mum’s birthday that showcases my lack of effort and empathy, it’s time to call it a day.
The ideas described in this patent map almost too neatly onto how modern AI search systems retrieve relevant information. Of the SGE. It helps anticipate the user’s next interest in an assistant-like context. Personalized, “helpful” and with extreme memory.
As Roger Montti pointed out, this may give a clearer indication of how AIOs use pages that the user in question may be interested in. Their entire job is to synthesize answers from multiple sources and searches to provide the perfect jumping-off point. I suspect this scoring system is an excellent way to avoid computationally expensive, unnecessary utilization of documents.
contentEffort – described as a ‘Large Language Model (LLM)-based effort estimation for article pages’ – estimates the amount of effort invested in creating an article. As slop makes up more than 50% of the internet, this is seemingly one of Google’s way of dealing with it.
How Can I Use This Effectively?
Make differentiated, non-commodity content. It’s really simple. Apply what we call information gain in this context to your own content – if you cannot add anything of value to the existing index, then don’t bother.
You can use this with:
Original data.
First-hand experience.
Interviews.
Real reporting.
Being first on the scene and developing the story as it happens.
Proprietary analysis.
You don’t need a big budget. You can do amazing things with a few free data sources, some creativity, and a bit of rope. Just make sure the article has an element of uniqueness.
I think this really helps frame whether content is still worth creating. If you’re doing something just for SEO reasons and you can’t add anything extra to the existing suite of information, kill it. If a document contributes very little new information, the patent suggests it’s a strong candidate to be deprioritized when selecting subsequent documents.
Still costs time and money to make, but is less and less likely to drive any real value. Stay in your lane, but drive a nicer car.
I have a feeling your indexation report in GSC is invaluable here. Beige content has a shelf life so low it’s in the running for the new UK Prime Minister. So check for any pages dropping out of the index at scale for more serious issues.
Google Says No SEO Penalty For Year-Long A/B Tests? via @sejournal, @martinibuster
Google’s John Mueller recently answered a question about A/B testing web pages for long durations, warning that an unintended consequence is that enabling variations to be indexed can result in uncertainty as to which will be visible in the search results.
A/B Testing Traffic From Live Search Results
A/B testing is when one or more versions of a web page is shown to users. The reason for doing this is generally for testing conversion rates and user responses.
The important takeaway from the guidelines is that A/B testing live web pages is the guidelines were created to minimize impact on search performance.
The guideline begins:
“This page covers how to ensure that testing variations in page content or page URLs has minimal impact on your Google Search performance.”
While Google does not explicitly forbid using A/B testing to test which page ranks better, the context of the guidelines itself is defined as protecting search performance; measuring search performance is not in the guidelines.
What Google’s document describes getting measured is consistently user behavior, not rankings.
On a side note, something that’s not in the guidelines is that there is no “right” button color and size for improving clicks on a call to action button. Longstanding SEO knowledge and experience about this is that large buttons and/or colors that contrast strongly against the web page backgrounds tend to get more clicks. This likely explains why Amazon’s Add To Cart button is a bright mustard color and Walmart’s version is bright blue contrasted against a solid white background.
Google’s Guidelines On A/B Testing
Google’s guidelines on A/B testing describe it as showing different versions of a website and collecting data on how users react to them. In terms of SEO performance it says not to expect any disruption but by allowing Google to index the slightly different pages once the testing is over the winning combination will be indexed much sooner.
There are two kinds of A/B testing:
A/B Testing Testing two or more changes to a web page. Google uses the example of testing different fonts on buttons.
Multivariate Testing This is a test of multiple changes all at once in order to identify which combination of factors work best together. Google uses the example of testing different combinations of different fonts on buttons and on the web page itself.
Four Considerations For A/B Testing
Google also recommends four best practices:
1. Use The rel=”canonical” Link Attribute This is probably the most important factor to consider. Using the rel=canonical link attribute enables site owners to put all kinds of variations of a web page online and still include a strong hint about which version of a web page is best.
2. Use 302 redirects If you’re randomly redirecting users to different versions of a web page you should be using a 302 redirect, not 301 redirects. 302 means that a resource (like a web page) has been temporarily moved. That’s different from a 301 redirect which means that a move or change in URL is permanent.
3. Don’t Cloak Cloaking is the practice of showing one thing to Google and something else to users. If you’re testing different web pages to see how users react when they click through from search then Google insists that site owners show the same thing to Google, even if the page elements are constantly changing.
4. Don’t A/B Test For A Long Time
Google warns site owners to limit how long A/B testing goes on. They warn that excessive testing could get a site in trouble:
“If we discover a site running an experiment for an unnecessarily long time, we may interpret this as an attempt to deceive search engines and take action accordingly. This is especially true if you’re serving one content variant to a large percentage of your users.”
That last warning relates directly to the question asked on the Bluesky social network.
Google Answers Question About Long-term A/B Testing
The person asking the question specifically wanted to know about how Google handles A/B testing that lasts for as long as a year.
“Hey @johnmu.com, As Google’s A/B testing guide suggests to avoid running same A/B test for long durations, I was wondering how does Google handle long term holdouts (eg. 10% for 6-12 months), especially for a large scale marketplace with 10s of millions of crawls to similar amount of pages.”
Google’s John Mueller answered:
“Depending on your setup, what might happen is that one or the other version is used for indexing. If they’re close enough, probably that doesn’t matter. If they’re significantly different, that could be visible in search results too.”
The person who asked the original question then followed up with an additional question that revealed more about how much the web pages are changing.
They asked:
“…what if it’s fully different like a redesigned page, and since Googlebot is getting alternative versions with each crawl (sometimes in a day). Can that rapid change in core HTML structure cause issues with indexing and lead to Google potentially dropping the pages from index?”
Mueller responded:
“We’d take the content into account the way that we crawl it for indexing. There’s no (as far as I know) “penalty” or “demotion” for having varying content (lots of sites have that), but it can make it harder for you to debug & monitor if the content constantly changes.”
The person asking the question wanted to know how Google handled long-term A/B testing. They did not ask how Google handles indexing, but that’s the question Mueller answered. That may explain why the person followed up with a second question that was more precise about the extent of their A/B testing and Mueller again focused on indexing.
No Penalty For Having Varying Content?
Mueller’s statement seems to contradict Google’s own guidance about long-term A/B experiments.
The relevant context of Google’s guidelines is:
It confirms that A/B testing is legitimate.
Normal experiments are reasonably assumed to be temporary.
Once enough data is collected to reach conclusions the A/B test it’s normal that it ends.
That’s where we get to the warning part of the guidance:
“If we discover a site running an experiment for an unnecessarily long time, we may interpret this as an attempt to deceive search engines and take action accordingly. This is especially true if you’re serving one content variant to a large percentage of your users.”
So the point of where things get fishy is when the experiment goes on longer than what seems reasonable and where one variation of the content becomes the prime version for most users as part of an attempt to “deceive search engines.”
The Web Is Eating Itself And Your Metrics Look Fine via @sejournal, @DuaneForrester
That is not a moral claim, and it is not a warning about getting caught. It is a description of a mechanism that several groups of researchers have now documented from different angles, and once you see how the pieces fit together, a good deal of confusing behavior in AI search stops being confusing. I am going to walk through it in the real terminology, because the real terminology is where the understanding actually lives, and then put each piece into plain language so it’s approachable for everyone.
Set two curves side by side before we go further, because together they are why this matters now rather than someday. On the supply side, more than half of newly published English-language web articles are already AI-generated, according to a Graphite analysis of tens of thousands of pages. On the demand side, the machines are about to do most of the asking: Microsoft’s Jordi Ribas, who runs Search and AI there, has floated that, within a few years, AI agents could fire off a thousand times more queries than all human search combined. The web is filling with machine-written pages at the very moment machine readers are set to become its dominant audience. Both ends of the pipe are turning synthetic at once.
One thing to note is that there is a good chance you’ve already heard about the things I’m suggesting you do at the end of this article. But I’m betting you haven’t heard why, or how the systems operate that will lead to the change I’m predicting. TL;DR – the humans win.
Now, let’s start with the part that surprised me most.
The Systems Have A Thumb On The Scale For Machine-Written Text
Machine-written text carries a detectable structural signature, a generation fingerprint, and the detection research treats that signature as probabilistic rather than certain, a strong tell rather than a stamp. Fine. What matters is not that the fingerprint exists, which we have assumed for a while, but what the retrieval systems do with it, and the answer is the opposite of what most people expect.
There is a growing body of peer-reviewed work on what researchers call source bias, named invisible relevance bias in one influential paper. In plain terms: the retrieval systems, the components that decide which pages get pulled in to build an answer, have a measurable preference for machine-written text. They reach for it first and rank it higher, even when a human-written page answers the question just as well. The SIGIR study that named the effect found retrieval models ranking AI-generated items above human ones with no relevance justification for the promotion, extending an earlier finding of the same bias in plain text search. The leading explanation is that machine-written text tends to be smoother and more statistically predictable word-to-word, a property measured by something called perplexity, which is no relation to the answer engine that shares the name, and the retrieval models appear to find that smoothness easier to trust. The cause is still being argued. The effect is replicated. Right now, the fingerprint is not a liability. It is an advantage.
In practice, that looks like this. Two pages answer the same question equally well, one written by a person and one produced by a model. Offered both, the retrieval system reaches for the generated one, not because it is more accurate but because its smooth, evenly predictable phrasing reads as more trustworthy to a system that was trained on an enormous amount of exactly that kind of text. The human page was not worse. It simply did not sound like what the machine has learned to expect a good answer to sound like, and that expectation is now a ranking advantage you did nothing to earn and your human competitor did nothing to lose.
A Little Synthetic In The Pool Becomes A Lot In The Answers
Now layer time onto that preference. A 2026 Web Conference paper modeled what happens as machine-written content keeps accumulating in the pool that answer engines draw from, and gave the failure mode a name: retrieval collapse. Their controlled experiment is worth following in its own terms. They began with real search results, then added machine-written, SEO-optimized pages round by round until synthetic content made up two-thirds of the available pool.
Here is the number that matters. At that two-thirds contamination of the pool, more than 80% of what actually got retrieved into answers was synthetic. Say it plainly: a modest majority of machine-written pages in the pool produced an overwhelming majority of machine-written sources in the finished answers, because those pages were built to trip the ranking signals and so they got selected far out of proportion to their share. The bias from the first section is the amplifier. A little synthetic in the pool becomes a lot of synthetic in the answers.
Picture that on a single question, say how long probiotics take to work. At the start, the ten sources an answer engine can reach for might be a clinician’s explainer, a university health page, a supplement maker, a long forum thread, and a couple of established health publishers, a real spread of origins and points of view. Twenty rounds of synthetic accumulation later, eight of those ten slots are near-identical machine-written articles that each paraphrase the same small set of claims, differing mainly in the logo at the top. The answer you receive still reads fine. It is now assembled almost entirely from copies of copies, and the disagreement and texture that used to live in that source list has simply gone quiet.
The Dial Everyone Watches Stays Green
This is the part that should have your attention. Through all of that contamination, answer accuracy barely moved, holding around 68% to 70%. The researchers call this a deceptively healthy state, and the plain-language version is the entire reason this piece exists: the answers still sound right, so from the outside nothing looks broken, while underneath, the sources feeding those answers have narrowed to mostly synthetic and real source diversity has collapsed. The system looks fine on the one dial most people watch, and is hollow on the dial almost nobody watches.
Concretely, here is the trap. A content team opens its AI-visibility dashboard and sees its citation rate steady, maybe ticking up. Everything on the screen is green. What the screen does not show is that the three or four sources appearing alongside them in those answers, which a year ago were eight or ten genuinely different outlets, are now a cluster of near-duplicates repeating the same claims in the same shape. The team is still cited, so the tool reports health. The information environment their citation sits inside has quietly narrowed to an echo. Presence held, diversity collapsed, and only one of those two things was ever on the dashboard.
That gap is the measurement lesson, and it is easy to get exactly backward. If you track how often an answer engine cites you, a healthy-looking number tells you that you are being surfaced on a given run. It tells you nothing about whether the pool around you is collapsing into sameness, and citation frequency across repeated prompts is a directional read on how you are represented, not a clean count of demand.
Why This Cannot Simply Settle Into A New Normal
So if the fingerprint is favored and the pool is homogenizing, why call it a poisoned well rather than a stable equilibrium? Because the system is drinking its own output, and we have strong evidence about what that does over time. The Nature research on model collapse showed that models trained on recursively generated data degrade across successive generations, the way a photocopy of a photocopy loses a little fidelity each pass until the image is mush. A retrieval layer that increasingly grounds its answers in machine-written sources, which those same models produced, is a slower turn of that loop. The systems have a survival reason to care, and the retrieval-collapse authors say so outright, recommending that organizations treat trusted, human-reviewed content as a strategic asset and begin tracking provenance and source diversity instead of accuracy alone.
And here’s a thought that’s important. Right now the platforms say they are neutral about how content is made. Google’s own guidance on its AI features states plainly that it cares whether content is helpful, not how it was produced. So three forces are pointing in different directions at once: a documented, present-tense bias that favors machine-written text, a stated platform neutrality that neither rewards nor punishes it, and a structural survival pressure that should eventually push these systems to privilege human-verified, diverse sources. I cannot tell you the date those forces resolve, or which one wins. I can tell you that betting a strategy on the current bias holding forever is betting against the one force the systems’ own continued function depends on. And my money? It’s on human-created content being more valuable over time.
What To Do About It
None of what follows here is generic content hygiene, and each move traces to a specific mechanism mentioned above.
Produce the thing a synthetic pool cannot reproduce. The one category of content a homogenizing, self-referential pool structurally cannot generate is original evidence: first-party data, primary research, firsthand testing, direct reporting. Everything a language model writes is derived from what already exists. Truly new information has to enter the system from outside it, carried in by someone who went and found it. That is not only a quality play; it is the exact material that preserves the source diversity the researchers say the system will come to need. In the probiotics example, the eight duplicate pages all recycle the same claims; the one that ran an actual test, or published real intake data, is the only source in the set that a copy could not have produced, which is precisely what makes it hard to displace.
Make your provenance legible. If the coming pressure is toward privileging human-verified sources, the practical near-term move is to be unmistakably identifiable as one: clear authorship, real credentials attached to real people, sourcing a reader or a machine can check, a track record that exists in public. You are working to be the kind of node that a provenance-aware system, once it arrives, can recognize and keep. The researchers name trusted human-reviewed content as the strategic asset. The task is making sure you are legibly inside that set before it matters.
Read your own numbers against the collapse. Hold citation frequency as directional rather than absolute, and watch specifically for the deceptively healthy gap: are you being cited into answers that are themselves narrowing to a handful of synthetic-leaning sources? A rising citation count inside a collapsing pool may not be the win it looks like. The teams that internalize this will be watching source diversity and provenance, not presence alone.
Do not optimize your way into the fingerprint. This is the uncomfortable one, because the same optimization that wins the retrieval preference today is what feeds the collapse tomorrow. I am not telling you to abandon structure or clarity. I am telling you that if your content is structurally indistinguishable from machine-generated filler, you have bet everything on a bias the system has a survival reason to reverse. The hedge is to be verifiably human where it counts, in the evidence, the authorship, and the judgment a model cannot manufacture.
The Bet
Here is where it nets out. The content that wins the answer engines today sits on a collision course with what those engines need in order to keep working at all. The practitioners who build the non-synthetic, provenance-clear, evidence-bearing node are not chasing the current bias. They are positioning for the correction that the system’s own survival requires. That is a slower game than optimizing for this quarter’s retrieval preference, and it is the one I would put my own money on.
Google’s New Merchant Listing Structured Data Improves SEO via @sejournal, @martinibuster
Google made a major change to their Merchant Listing structured data requirements in addition to adding clarification on how to use structured data to indicate how long a sale prices will last. In total, there are three new additions but the biggest change by far is the addition of a new Category property. While it’s not a “required” structured data property it’s still a recommended one.
New Category Property
In Schema.org structured data, a Type is a classification of an entity, to say what something is. A structured data Property is an attribute of the Type, it can say what kind of type or add other descriptive details about the Type.
Google added a new category property to the Product structured data, which enables merchants to more granularly classify products directly in markup rather than relying solely on feed attributes. It also gives merchants a way to tie the web page structured data to Google’s own product taxonomy, closing a gap between what’s marked up on the page and what’s submitted through the Merchant Center feed.
Google’s new category property accepts either plain text or a CategoryCode object.
Plain Text
Plain text works like the existing product_type attribute in product feeds. It’s a custom category label that merchants define themselves.
CategoryCode
CategoryCode is a structured object that lets you declare a Google Product Category (GPC) directly in markup, using inCodeSet to point to Google’s taxonomy and codeValue to specify the category, either by numeric ID or full path. The CategoryCode object directly corresponds to the merchant feed specific Google Product Category (GPC). CategoryCode enables merchants to put that same GPC value directly into their on-page structured data instead of it only appearing in the merchant feed.
Here is example structured data showing how it works:
Specifies the product’s categories. This property can accept an array of values, mixing plain text strings and CategoryCode objects.
Custom product types: Plain Text values represent your custom product category, similar to the product_type attribute in product feeds. We recommend keeping custom product types under the 750-character limit.
Google Product Category (GPC): To specify a GPC, similar to the google_product_category attribute in product feeds, use the CategoryCode type.
Set @type to CategoryCode.
Set inCodeSet to a Google Product Taxonomy URL (for example, “https://www.google.com/basepages/producttype/taxonomy-with-ids.en-US.txt”).
Set codeValue to the GPC ID (for example, “2271”) or the full category path (for example, “Apparel & Accessories > Clothing > Dresses”).
When using the path format, use > as the separator between levels. Each segment in the path must contain at least one letter. Numeric IDs are also accepted. You can provide multiple category values. For example, you can include several GPC codes or paths and several custom product type strings.”
Sale Duration Structured Data
Google also added a new section to the Merchant Listing structured data documentation that enables merchants to express how long a sale will last. It adds new documentation about three properties:
priceValidUntil
validFrom
validThrough
Here are the new explanations:
“priceValidUntil
Date
The date and time after which the price will no longer be available, in ISO 8601 format. Your listing may not display if the priceValidUntil property indicates a past date. For details and markup examples, see Sale duration.
validFrom DateTime or Date
The start date and time when the price is valid, in ISO 8601 format. For details and markup examples, see Sale duration.
validThrough DateTime or Date
The end date and time when the price is valid, in ISO 8601 format. For details and markup examples, see Sale duration.”
Sale Duration
Lastly there is an entirely new section about Sale Duration. Sale duration is just the date that corresponds to the three structured data properties, priceValidUntil, validFrom, and validThrough. It tells Google exactly when a sale price starts and ends. It’s meant to keep sale pricing accurate in search results, so a listing doesn’t keep showing a deal after it’s expired.
The new documentation explains:
“Sale duration To specify the period when a sale price is active, use the following schema.org properties in ISO 8601 format (for example, 2025-12-31T23:59:59+01:00):
Start date and time: Use the validFrom property. End date and time: Use either the validThrough property or the priceValidUntil property.
Best practices: Provide both a start and an end date/time to clearly define the sale period. Ensure the start date/time (from the validFrom property) is earlier than or equal to the end date/time (from the validThrough property or the priceValidUntil property).
We recommend including the time and timezone in the ISO 8601 format for accuracy in Google systems.
Where to place the properties: On the Offer node: You can add the validFrom property and (the validThrough property or the priceValidUntil property) directly to the Offer node. These dates apply when the price property on the Offer node represents the current active sale price.
On a PriceSpecification node: If the sale price is defined within a PriceSpecification node (typically one without the priceType property when a StrikethroughPrice value is also present), add the validFrom property and the validThrough property to that specific PriceSpecification node. Note that the priceValidUntil property isn’t applicable to the PriceSpecification type.”
How It Benefits Merchants
Google’s new documentation for the Merchant Listing Structured Data enables merchants to express category and sale pricing details directly in structured data, which helps Google display accurate product information in the search results. The new structured data properties create unity between the Schema.org structured data and the Merchant Feed Google Product Category (GPC) data. Category now matches product_type and google_product_category from Merchant Center feeds, and Sale Duration matches sale_price_effective_date, so merchants have a page-level way to express them instead of relying solely on the feed.
Google Search Hits All-Time Usage Record During World Cup via @sejournal, @MattGSouthern
Nick Fox, Google’s senior vice president of Knowledge & Information, said that Google Search reached its highest usage ever on July 7, coinciding with Argentina’s comeback victory over Egypt at the World Cup.
In the post, Fox wrote: “Google Search broke all prior usage records and saw its highest usage in history right after Argentina scored their winning goal in yesterday’s match!”
Robby Stein, vice president of product for Google Search, amplified the post, writing that Search “hit all time high in usage yesterday.”
Fox didn’t share any specific figures or methods for the record, and Google hasn’t released a blog post or data about it.
The statement is consistent with Google’s public messaging this year. During its Q1 2026 earnings call, Pichai mentioned that “Search queries are at an all-time high,” though specific numbers weren’t shared.
Google has pointed to a World Cup traffic record before. In the 2022 final, Pichai stated that Search reached its highest traffic in 25 years, again without providing exact figures.
Argentina defeated Egypt 3-2 in the Round of 16 on July 7, overturning a two-goal deficit with Enzo Fernández scoring a stoppage-time winning header. Their quarterfinal against Switzerland is next, which tracks with Fox’s line about looking ahead to “the semis and final.”
Why This Matters
There’s been a lot of talk recently about whether AI answers are changing the way we search. A record day, if it holds up, is a reminder that Google is still where people turn the moment something happens live.
Keep in mind that ‘record usage’ and ‘record queries’ refer to Google’s side, not clicks to publisher sites. It’s possible for search usage to increase even when outbound clicks to your pages remain low.
Fox also didn’t define how “usage” is counted, or say whether the figure excludes bots. Imperva, which sells bot-management tools, estimated that automated traffic made up more than 53% of web traffic in 2025, up from 51% in 2024. None of that shows Google’s record was driven by bots. Without a clear methodology, it’s hard for external readers to understand exactly how Google counts automated traffic.
Looking Ahead
Google put no figures behind the 2022 claim and none behind this one, so whether it backs the record with data is the thing to watch. Argentina’s quarterfinal is next, and another usage spike is possible if the run continues.
AI Search Is Exposing SEO’s Risk Of Losing Ownership Of GEO Outcomes via @sejournal, @martinibuster
Tom Critchlow, a longtime search marketer with deep experience, recently shared his opinions of where the SEO industry is today, saying that AI Search is changing business priorities in a way that exposes the weaknesses inherent in SEO today. This transformation means that search marketing professionals need to evaluate the services they offer in order to align better with what is useful for today’s modern search surfaces.
Brand Marketing: The Hidden Pillar Of SEO
Google’s algorithms have long relied on user behavior signals. Google’s founders said that PageRank could “be thought of as a model of user behavior,” showing that user behavior relative to content was important to Google at the very dawn of Google.
What people respond to most online are brands. People could be said to be hardwired to respond to products and service providers they are already familiar with. This phenomenon is called Familiarity Bias, a tendency to prefer things one is already familiar with. Making potential site visitors familiar with a brand is a powerful marketing activity, and that approach aligns perfectly with what we know about Google’s algorithms relative to Navboost and branded search.
SEO Fundamentals Are A Foundation
In an interview with Ross Hudgens, Critchlow observed that the foundations of SEO remain the same in AEO/GEO. Google consistently says that the fundamentals of SEO remain the same. Critchlow’s view of AI Search goes beyond that by showing that SEO is more like a foundation.
Critchlow explained:
“And it points to something very important, which is I think that GEO, AI Search, is much more like brand marketing than it is SEO, in my opinion.
Right now, there is an underpinning, obviously, of the technical foundations and crawling and indexing that is kind of the same, or the same kind of discipline, right?
That is equally important before and after.”
It is at this point that Critchlow develops the idea that what is built on top of that foundation goes beyond just classic SEO, with the implication being that failing to anticipate this change could pose a career risk.
People Who Drive Outcomes Are Not SEO
Critchlow continues his thoughts, building on the idea of SEO being a starting point and going further by saying that the outcomes in AI Search are not driven by SEO. He describes this as contrarian, which is someone who holds an opinion contrary to what is commonly accepted. But as you’ll see, Critchlow’s ideas are founded on a more practical view of what drives ranking in both classic and AI Search.
Here Critchlow considers the questions that all SEOs need to be asking as the industry transitions to a post-Search AI-driven environment:
“But a lot of what you do, back to that question of like, okay, you put in a prompt and you say, do you recommend brand A or brand B?
And it says your competitor. What do you do about that, right?
And so like, and this, I’m a little contrarian, so forgive me, but like this was true in classical SEO and I think is increasingly true in the GEO world.
The people that drive SEO outcomes are not SEO professionals, by and large.
It’s painting with a broad brush and there are exceptions. …Both in the old SEO world and in the GEO world, the people that drive out the outcomes are the brand, product, PR and editorial teams, not the SEO teams.
And that was true in a classical SEO world. And I think it’s going to be increasingly true in a GEO world.
If I’m a CEO and I’m sat looking at my organization and I’m like, who’s going to do this GEO thing for me?
Is it the SEO team?
Or is it the brand team?
Or is it the product team?
And your answer to that question is going to depend a little bit on what kind of business it is and what industry you’re in, but there’s a real risk for the SEO industry, which was also a risk in classical SEO days…
…Because again, SEO has done a great job of being like, we’ve got to produce great content. We’ve got to have a good brand. We’ve got to have like strong branded search. We’ve got to be mentioned in all these places. We’ve got to have like positive reputation.
But does an SEO team do any of those things? In most organizations, the answer is no.
In most organizations, those outcomes are owned by other teams. That’s a real, I think of that as a career risk.”
Takeaway
Critchlow’s observations raise many questions that SEOs need to consider today:
Who drives SEO outcomes today in AI Search?
Is there a risk for the SEO industry as GEO becomes more important?
What does SEO emphasize organizations should do, and does SEO actually own those activities?
Who owns the outcomes that matter in most organizations, and how should SEO fit into that?
If SEO doesn’t drive the outcomes that matter, is there a career risk, or should SEO transform to encompass more?
Looked at another way, it could be we are in a liminal state where we are neither here nor there, where what was SEO is transforming and becoming something else.
Reclaiming Brand Sovereignty In The AI Era via @sejournal, @billhunt
For more than two decades, digital strategy has revolved around a deceptively simple objective: Drive people to webpages. Search engines rewarded documents. Analytics rewarded pageviews. Marketing rewarded engagement. As organizations matured, they invested heavily in designing increasingly sophisticated digital experiences that guided customers through carefully orchestrated buying journeys. Information was intentionally distributed across dozens, sometimes hundreds, of interconnected pages, each optimized for a different stage of consideration.
Consider how a company such as Ford presents the F-150, one of the best-selling vehicles in America. Rather than offering a single comprehensive representation of the vehicle, Ford brilliantly guides prospective buyers through an emotional journey spread across seven distinct viewports. The homepage establishes the lifestyle. Model pages introduce trim levels. Interactive configurators allow customers to visualize ownership. Feature pages explain towing capacity, off-road performance, and technology packages. Galleries reinforce the brand’s identity, while technical specifications are located deeper within the site, alongside regional offers and financing options.
For people, this architecture works remarkably well. Every page serves a purpose. Every interaction builds confidence. Every transition moves the customer toward a purchase decision. It is an outstanding human experience. For AI, however, the same architecture introduces friction.
The Quiet Crisis Of AI Disintermediation
The AI labs frequently tell enterprise leaders that their large language models (LLMs) are smart enough to crawl any messy web architecture, synthesize the data, and deliver accurate answers regardless of how that information is organized. That message oversimplifies reality and how AI retrieval actually works.
When data is deliberately fractured across multiple pages to serve human emotions, the AI’s synthesis engine breaks. Because the machine lacks an emotional context window, it searches for a high-density, low-latency semantic payload. When it cannot find that payload natively on an official corporate domain, it looks elsewhere. It then assembles the most complete answer it can from whichever sources are easiest to retrieve, reconcile, and trust. The consequences are already visible.
A straightforward query such as [ford f-150 Raptor gas mileage] produces a Google AI Overview that draws information from Reddit discussions, automotive publishers, and a local dealership rather than Ford itself.
Screenshot from search for [ford f-150 Raptor gas mileage], Google, July 2026
Ford already has the answer to nearly every conceivable question. The issue isn’t that the information doesn’t exist. The issue is that Google found it easier to assemble an answer from Reddit, an automotive publisher, and a dealership than from Ford itself. When that happens, the discussion is no longer about rankings or citations. It is about who controls the authoritative representation of your brand.
This is no longer simply an SEO problem. It is a content governance problem.
The issue is that AI has simply exposed a structural weakness that has existed for years. Enterprises organized their digital presence around webpages because search rewarded webpages. In many ways, search became the detour. Organizations optimized for ranking documents and triggering an emotional reaction rather than organizing knowledge. That approach worked because search engines retrieved pages. AI assistants attempt to synthesize a coherent representation of the organization. In doing so, they expose every inconsistency, every missing relationship, and every gap in the underlying architecture.
The organizations struggling today are rarely missing information. They possess enormous knowledge of their products, services, policies, and expertise. The problem is that the knowledge has been fragmented across webpages, content management systems, product databases, marketing campaigns, PDFs, support portals, and countless disconnected repositories. Humans can navigate those silos. Machines increasingly cannot.
AI did not create this problem. It simply made it impossible to ignore.
Brand Sovereignty Becomes An Executive Responsibility
Years ago, I had the opportunity to consult for Dell, where Michael Dell demonstrated an approach to digital leadership that feels even more relevant today than it did then. He regularly tested both Google Search and Dell’s internal search experience himself, not because he wanted to micromanage marketing or technology teams, but because he understood something many executives overlooked: the interface through which customers discover your products ultimately shapes how they perceive your company.
If he or a customer searched for a product and failed to find the right answer, Michael Dell did not see an isolated technology issue. He saw an organizational failure. That mindset has become even more important in the AI era.
I think of this as brand sovereignty: an organization’s ability to remain the authoritative source for information about its own products, services, and expertise, regardless of where those answers are ultimately delivered. For years, digital success was measured by how effectively organizations attracted visitors to their websites. Increasingly, a more important question will be whether AI systems consistently recognize the organization itself as the best source of that information.
This isn’t something marketing, SEO, or technology can solve on their own because none of those teams owns the complete picture. Product information, documentation, customer support, legal policies, and commerce all contribute to how an organization is represented digitally. Reclaiming brand sovereignty, therefore, becomes less about publishing more content and more about organizing organizational knowledge so that those pieces reinforce one another rather than compete.
From Pages To Knowledge
Most organizations didn’t set out to fragment their knowledge. It happened gradually. Every project added another page, another microsite, another content repository, or another system designed to solve a specific business problem. Over time, product information, marketing content, customer support, policies, and commerce evolved independently while the corporate website became responsible for stitching everything together into a coherent customer experience.
That approach worked because the web rewarded navigation. Customers could move between pages, and search engines could retrieve the most relevant document. Neither required organizations to explicitly connect the relationships between their products, services, policies, and expertise.
AI exposes the limitations of that model. Large language models are not attempting to navigate websites in the way people do. They are attempting to understand organizations by reconstructing the relationships between products, services, documentation, policies, locations, expertise, and supporting evidence. Every answer generated by an AI assistant represents an attempt to assemble that understanding from the information available to it. When those relationships remain implicit, distributed across hundreds of webpages, databases, and disconnected repositories, the resulting representation becomes incomplete or inconsistent.
Rather than treating products, services, documentation, policies, reviews, offers, support resources, and locations as independent publishing assets, organizations should begin managing them as interconnected business objects within a Unified Object Graph. Each object maintains its own identity while explicitly connecting to every related object throughout the enterprise. A product connects to its technical documentation, compatible accessories, warranty information, inventory, customer reviews, dealerships, and service locations. The webpage becomes one expression of those relationships rather than the place where those relationships are created.
One of the questions I hear most often is whether this requires replacing existing systems. In most cases, it doesn’t. Organizations have already invested heavily in product information systems, content management systems, commerce platforms, digital asset management, and customer support tools. Those systems continue to serve important purposes and should remain the systems of record for the information they manage best. The challenge is that none of them represents the organization as a whole.
Instead of trying to consolidate everything into a single platform, organizations should focus on creating a machine-readable knowledge layer that brings those pieces together. Product information, documentation, policies, reviews, marketing content, and commerce data continue to live where they belong, but they are aggregated into a single, machine-readable representation that explicitly describes the entities and relationships across the business.
Once that layer exists, the conversation changes. Publishing to a website, exposing an API, generating structured data, supporting an MCP endpoint, or adopting whatever protocol comes next all become different ways of expressing the same underlying knowledge rather than separate implementation projects.
This is the architectural shift that AI is exposing. For years we managed channels independently and treated the website as the place where everything came together. Increasingly, organizations will manage knowledge centrally while allowing every interface to consume the same authoritative representation. Websites, customer support portals, AI assistants, commerce platforms, and future interfaces all become consumers of the same knowledge rather than maintaining their own versions.
That shift also changes how content is created. Most organizations still separate technical accuracy from marketing language because different teams own different parts of the story. Product Information Management systems manage specifications, creative teams develop messaging, SEO teams research customer language, and customer support documents common questions. Each group adds value, but very little of that knowledge remains connected once it leaves the team that created it.
Consumers, however, do not separate facts from feelings when making decisions. A customer searching for [the safest family SUV], [a truck that feels unstoppable off-road], or [a quiet hotel for remote work] combines objective requirements with subjective expectations in the same question. Increasingly, AI systems are expected to interpret those blended expressions of intent in much the same way.
At Bisan Digital, we call this emotifacts (where feeling and fact are inseparable), and they become valuable to the process because they combine factual product attributes with the emotional language customers naturally use to describe, discover, and ultimately choose products or services. Rather than treating emotional messaging as creative copy layered onto technical specifications, both are treated as part of the same reusable knowledge object.
If marketing positions the Ford Raptor around freedom, confidence, and rugged independence, those ideas should be explicitly connected to the engineering evidence that supports them: suspension travel, approach angles, locking differentials, horsepower, towing capacity, and terrain management systems. The emotional promise and the technical proof reinforce one another because they originate from the same underlying object. The same principle extends well beyond the automotive industry. A luxury hotel should connect its promise of tranquility to room location, sound insulation, wellness amenities, and guest reviews. A healthcare provider should connect claims of clinical expertise to physician credentials, treatment outcomes, published guidelines, and patient education. In each case, trust is strengthened because the emotional narrative and the supporting evidence are inseparable.
This represents the broader transition from digital publishing to knowledge architecture. Machines can infer many things, but they should not be expected to infer the relationships that organizations already know to be true. Increasingly, competitive advantage will belong to the organizations that explicitly declare those relationships, govern them consistently, and make them available across every interface through which customers and intelligent systems engage with the business.
Building For Adaptability Rather Than Standards
Once knowledge becomes independent from presentation, exposing it to both people and machines becomes significantly easier. This is where much of today’s conversation around AI interoperability is focused, and understandably so. New protocols, APIs, and discovery mechanisms are emerging almost monthly as organizations race to determine how AI assistants should access trusted enterprise information.
Emerging standards such as MCP represent an important shift toward explicit machine interfaces. Today’s protocol may be MCP. Tomorrow it may be another widely adopted standard. The objective is not to predict which protocol will win but to organize knowledge so it can be exposed through whichever standards ultimately become dominant.
The same principle applies to commerce. Emerging initiatives such as Google’s Universal Commerce Protocol (UCP) illustrate how structured product knowledge can flow directly into AI-assisted purchasing experiences. Whether UCP becomes the dominant protocol is less important than ensuring the underlying knowledge is structured well enough to participate in whichever transactional ecosystem emerges.
This distinction between architecture and implementation has always mattered, but it has rarely been as visible as it is today. Organizations that continue to treat their website as the primary repository of business knowledge will find themselves repeatedly adapting to new interfaces, new protocols, and new retrieval models. Organizations that instead invest in well-governed, reusable knowledge assets will discover that supporting new delivery mechanisms becomes an incremental engineering exercise rather than a fundamental organizational transformation.
The conversation, therefore, should not begin with MCP, UCP, or any other emerging specification. It should begin with a more fundamental question: Does the organization possess a coherent, authoritative representation of its own knowledge independent of the interfaces through which that knowledge is delivered? Every protocol introduced over the coming decade will simply become another window through which that knowledge can be expressed.
The New Measure Of Digital Success
For much of the web’s history, digital success was measured by a familiar collection of metrics: rankings, website traffic, pageviews, engagement, and conversions. Those measures remain valuable because websites will continue to play an important role in how organizations communicate with customers. They are no longer, however, the only measure of digital effectiveness.
As AI assistants increasingly become intermediaries between organizations and consumers, a new question emerges. When an intelligent system answers a question about your company, your products, or your expertise, does that answer originate from your organization’s knowledge, or from someone else’s interpretation of it? That distinction defines brand sovereignty.
The organizations that succeed during the next decade will not necessarily publish more content than their competitors, nor will they build the most sophisticated websites. They will recognize that digital strategy is no longer centered on documents but on knowledge itself. Their webpages, mobile applications, customer support experiences, AI assistants, commerce platforms, and technologies yet to be invented will all become distinct expressions of the same authoritative foundation.
Search taught organizations how to build better webpages. The AI era is teaching them how to build better knowledge.
The organizations that win the AI era will not be the ones with the most webpages. They will be the ones with the best-organized knowledge. Your website is no longer your digital asset. Your knowledge is. The website is simply one way of expressing it.
An Easy Digital PR Strategy For AI SEO via @sejournal, @martinibuster
I had a conversation with an old friend from my WebmasterWorld Forum days about PR marketing for AI search. The friend had contacted me to hear my thoughts about it. The discussion seemed useful, so I rewrote it into an article.
Digital PR Outreach Because Links Matter Less
The friend I had this conversation with is Alistair White (LinkedIn profile), a search marketing professional based in Australia who has decades of experience.
White asked me:
“I was wondering if you have any thoughts on performance based digital?”
My response was, yes, I have a load of thoughts on the topic. The following is one of them. In the future, I will do a follow-up on more ideas.
I used to do PR outreach slash brand marketing for a B2B starting around 2004. But I scaled it up for another company around 2013 because I saw the writing on the wall that links were already on the decline. So my approach grew out of link building, but my intuition was that links did not matter. It was about putting the company in front of ten thousand, twenty thousand, sixty thousand potential customers and doing it in a way that makes it clear that this company solves the problem that these professionals have.
Strategy: Outreach Directly To Potential Customers
What I did was narrowly focus on a specific demographic that strictly lined up with their target customer. So, one typical customer was the head of IT and IT workers at a large corporation. Another demographic was the department manager. Two different demographics that both needed the same solution. So the campaign was split into two parts, one for each demographic.
It was essentially a PR campaign that was focused on identifying associations and organizations. Virtually every industry has an association of professionals. So, what I did was first target the organizations at the national level. The reason is that once you do a project with the national level, getting similar projects done at the state level was ten times easier. You just show them the national level article that featured the company, and the state-level organizations would almost always say yes.
Once you got that state-level project done, getting to yes with the individual chapters at the regional or county level became ten times easier. Each time I got a project done, it put the client in front of thousands of potential customers. Eventually, everyone knew who my client was and getting projects done became easier.
What were these projects?
Newsletters
Organization magazines
Website articles
Interviews
Every organization was different. So I would click around and see what they were up to and create the pitch to fit with what they were publishing.
Attribution Is Not Always Possible
This was not about building links, it was about building customers, making money.
And that’s not something that any SEO thirteen years ago would ever consider doing because there isn’t a clear way to track that the client spent X this month and earned Z the next month because of that activity. An SEO would never consider it because there’s no way to directly track the ROI.
You can track some of it with the “how did you hear about us” question. But how will you know if a customer heard of the company because a colleague at a conference who saw the client’s propaganda told them about it?
Not everything can be tracked. That’s why everyone else in SEO did not pursue these opportunities because they were like, where is the link, where is the attribution? Well, now the secret’s out. It’s infinitely adaptable, too.
Both companies that I did this for experienced year-over-year steady growth, and both were eventually acquired and made a lot of money for the founders. And how did I know it worked? Because this is how I promoted some of my websites, by building top-of-mind awareness, the kind that makes people type a domain name into the search box.
Google has algorithms that track things like branded navigation. You can’t build that kind of user behavior with links. You cannot build branded navigation with SEO. And yet, these are things that have been a part of Google’s algorithms since 2004 with the Navboost algorithm and in 2012 with Google’s branded navigation.
Brand Marketing And PR… And SEO?
SEOs have historically been about five to ten years behind the actual algorithm developments at Google. And I get it that ideas that a five year old can understand, like “adding EEAT” to articles, that’s easy to understand. Everyone else is doing it. But you know, everyone who did that knows now it was a grand waste of time.
And it’s not that I am a contrarian. It’s just that most of the time SEOs chase these ephemeral tactics with an SEO hammer, going bang, bang, bang. But it’s becoming clearer now that SEO for AI search is not the nail that building links used to be.
Like, how are you going to encourage people to do a branded search on Google? How are you going to get people to know about your brand in the first place? How are you going to target the office manager that makes the decisions at a company? Well, I shared an idea about that, right?
Those are the kinds of things that are going to trigger a positive ranking factor at Google, and yet none of those are a part of the SEO toolbox.
Back in 2015 I raised the idea that content is king misses the point of being successful online.
“If content is king, how come the top Internet businesses sites are not in the content business? What about Netflix? You think that’s content you are paying a monthly subscription for? Or is it convenience?
Netflix is not in the content business. They are in the convenience business. Anyone can provide content but nobody delivers convenience the way Netflix does. That’s because their focus is and always has been the user experience. Convenience, the user experience, is why customers pay Netflix. If Netflix had followed the Content is King strategy it would have been Blockbuster.
…Don’t focus on cranking out content. Focus on understanding what the user wants and your content strategy follows.
I cannot overstate the importance of understanding that the user experience underlies many of Google’s important decisions related to its algorithm.”
These are not new ideas that I’m presenting, but they were ahead of their time, maybe still are. Yet, they are as relevant today as they were in 2015 or 2004. Some are saying they are more relevant today because of AI Search.
