4 Sites That Recovered From Google’s December 2025 Core Update – What They Changed via @sejournal, @marie_haynes

The December 2025 core update had a significant impact on a large number of sites. Each of the sites below that have done well are either long term clients, past clients or sites that I have done a site review for. While we can never say with certainty what changed as the result of a change to Google’s core algorithms and systems, I’ll share some observations on what I think helped these sites improve.

1. Trust Matters Immensely

This first client, a medical eCommerce site, reached out to me in mid 2024 and we started on a long term engagement. A few days into our relationship they were strongly negatively impacted by the August 2024 core update. It was devastating.

When you are impacted by a core update, in most cases, you remain suppressed until another core update happens. It usually takes several core updates. And given that these only happen a few times a year, this site remained suppressed for quite some time.

We worked on a lot of things:

• Improving blog post quality so it was not “commodity content”.
• Improving page load time.
• Optimizing images.
• Improving FAQ content on product pages to help answer customer questions.
• Creating helpful guides.
• Improving product descriptions to better answer questions their customers have.
• Adding more information about the E-E-A-T of authors.
• Adding more authors with medical E-E-A-T.
• Getting more reviews from satisfied customers.

While I think that all of the above helped contribute to a better assessment of quality for this site, I actually think that what helped the most had very little to do with SEO, but rather, was the result of the business working hard to truly improve upon customer service.

Core updates are tightly connected to E-E-A-T. Google says that trust is the most important aspect of E-E-A-T. The quality rater guidelines, which serve as guidance to help Google’s quality raters who help train their AI systems to improve in producing high quality search algorithms, mention “trust” 191 times.

For online stores, the raters are told that reliable customer service is vitally important.

A few bad reviews aren’t likely to tank your rankings, but this business had previously had significant logistical problems with shipping. They had been working hard to rectify these. Yet, if I asked AI Mode to tell me about the reputation of this company compared to their competitors, it would always tell me that there were serious concerns.

Here’s an interesting prompt you can use in AI Mode:

Make a chart showing the perceived trust in [url or brand] over time.

You can see that finally in 2025 the overall trust in this brand improved.

My suspicion is that these trust issues were the main driver in their core update suppression. I can’t say whether it was the improvement in customer trust that made a difference, the improvements in quality we made, or perhaps both. But these results were so good to see.

They continue to improve. Google recommends them more often in Popular Products carousels, ranks them more highly for many important terms and more importantly, drives far more sales for them now.

2. Original Content Takes A Lot Of Work

The next site is another site that was impacted by a core update.

This site is an affiliate site that writes about a large ticket product. They have a lot of competition from some big players in their industry. When I reviewed their site, one thing was obvious to me. While they had a lot of content, most of it offered essentially the same value as everyone else. This was frustrating considering they actually did purchase and review these products. What they were writing about was mostly a collection of known facts on these products rather than their personal experience. And what was experiential was buried in massive walls of text that were difficult for readers to navigate.

Google’s guidance on core updates recommends that if you were impacted, you should consider rewriting or restructuring your content to make it easier for your audience to read and navigate the page.

This site put an incredible amount of work into improving their content quality:

• They purchased the products they reviewed and took detailed photos of everything they discussed. And videos. Really helpful videos.
• The blog posts were written by an expert in their field. This already was the case, but we worked on making it more clear what their expertise was and why it was helpful.
• We brainstormed with AI to help us come up with ideas for adding helpful unique information that was borne from their experience and not likely to be found on other sites.
• We used Microsoft Clarity to identify aspects on pages that were frustrating users and worked to improve them.
• We added interactive quizzes to help readers and drive engagement.
• We worked on improving freshness for every important post, ensuring they were up to date with the latest information.
• We worked to really get in the shoes of a searcher and understand what they wanted to see. We made sure that this information was easy to find even if a reader was skimming.
• We broke up large walls of text into chunks with good headings that were easy to skim and navigate.
• We noindexed pages that were talking on YMYL topics for which they lacked expertise.
• We worked on improving core web vitals. (Note: I don’t think this is a huge ranking factor, but in this case the largest contentful paint was taking forever and likely frustrated users.)

Once again, it took many months of tireless work before improvements were seen! Rankings improved to the first page for many important keywords and some moved from page 4 to position #1-3.

3. Work To Improve User Experience

This next site was not a long term client, but rather, a site review I did for an eCommerce site in an YMYL niche. The SEO working on this site applied many of my recommendations and made some other smart changes as well including:

• Improving site navigation and hierarchy.
• Improved UX. They have a nicer, more modern font. The site looks more professional.
• Improved customer checkout flow which improved checkout abandonment rates.
• Improved their About Us page to add more information to demonstrate the brand’s experience and history. Note: I don’t think this matters immensely to Google’s algorithms as most of their assessment of trust is made from off-site signals, but it may help users feel more comfortable with engaging.
• Produced content around some topics that were gaining public attention. This did help to truly earn some new links and mentions from authoritative sources.

After making these changes, the site was able to procure a knowledge panel for brand searches. And, search traffic is climbing.

4. First Hand Experience Can Really Help

This next site is another one that I did a site review for. It is a city guide that monetizes through affiliate links and sponsors. For every page I looked at I came to the same conclusion: There was nothing on this page that couldn’t be covered by an AI Overview. Almost every piece of information was essentially paraphrased from somewhere else on the web.

The most recent update to the rater guidelines increased the use of the word “paraphrased” from 3 mentions to 25. I think this applies to a lot of sites!

and

and also,

Yet, when I spoke with the site owner she shared to me that they had on-site writers who were truly writing from their experience.

While I don’t know specifically what changes this site owner has made, I looked at several pages that had seen nice improvements in conjunction with the core update and noticed the following improvements:

• They’ve added video to some posts – filmed by their team.
• There’s original photography from their team – not taken from elsewhere on the web. Not every photo is original, but quite a few of them are.
• Added information to help readers make their decision, like “This place is best for…” or, “Must try dishes include…”
• They wrote about their actual experiences. Rather than just sharing what dishes were available at a restaurant, they share which ones they tried and how they felt they stood out compared to other restaurants.
• They’ve worked to keep content updated and fresh.

This site saw some nice improvements. However, they still have ground to gain as they previously were doing much better in the days before the helpful content updates.

Some Thoughts For Sites That Have Not Done Well

The December 2025 core update had a devastating negative impact on many sites. If you were impacted, your answer is unlikely to lie in technical SEO fixes, disavowing links or building new links. Google’s ranking systems are a collection of AI systems that work together with one goal in mind – to present searchers with pages that they are likely to find helpful. Many components of the ranking systems are deep learning systems which means that they improve on these recommendations over time.

I’d recommend the following for you:

1. Consider Whether The Brand Has Trust Issues

You can try the AI Mode prompt I used above. A few bad reviews is not going to cause you a core update suppression. But, a prolonged history of repeated customer service frustrations, fraud or anything else that significantly impacts your reputation can seriously impact your ability to rank. This is especially true if you are writing on YMYL topics.

2. Look At How Your Content Is Structured

It is a helpful exercise to look at which pages Google’s algorithms are ranking for your queries. If they don’t seem to make sense to you, look at how quickly they get people to the answer they are trying to find. I have found that often sites that are impacted make their readers scroll through a lot of fluff or ads to get to the important bits. Improve your headings – not for search engines, but for readers who are skimming. Put the important parts at the top. Or, if that’s not feasible, make it really easy for people to find the “main content”.

Here’s a good exercise – Open up the rater guidelines. These are guidelines for human raters who help Google understand if the AI systems are producing good, helpful rankings. CTRL-F for “main content” and see what you can learn.

3. Really Ask Yourself Whether Your Content Is Mostly “Commodity Content”

Commodity content is information that is widely available in many places on the web. There was a time when a business could thrive by writing pages that aggregate known information on a topic. Now that Google has AI Overviews and AI Mode, this type of page is much less valuable. You will still see some pages cited in AI Overviews that essentially parrot what is already in the AIO. Usually these are authoritative sites which are helpful for readers who want to see information from an authority rather than an AI answer.

Liz Reid from Google said these interesting words in an interview with the WSJ:

“What people click on in AI Overviews is content that is richer and deeper. That surface level AI generated content, people don’t want that, because if they click on that they don’t actually learn that much more than they previously got. They don’t trust the result any more across the web. So what we see with AI Overviews is that we sort of surface these sites and get fewer, what we call bounced clicks. A bounced click is like, you click on this site and you’re like, “Ah, I didn’t want that” and you go back. And so AI Overviews give some content and then we get to surface sort of deeper, richer content, and we’ll look to continue to do that over time so that we really do get that creator content and not AI generated.”

Here is a good exercise to try on some of the pages that have declined with the core update. Give your url or copy your page’s content into your favourite LLM and use this prompt:

“What are 10 concepts that are discussed in this page? For each concept tell me whether this topic has been widely written about online. Does this content I am sharing with you add anything truly uniquely interesting and original to the body of knowledge that already exists? Your goal here is to be brutally honest and not just flatter me. I want to know if this page is likely to be considered commodity content or whether it truly is content that is richer and deeper than other pages available on the web.”

You can follow this up with this prompt:

“Give me 10 ideas that I can use to truly create content that goes deeper on these topics? How can I draw from my real world experience to produce this kind of content?”

Concluding Thoughts

I’ve been studying Google updates for a long time – since the early days of Panda and Penguin updates. I built a business on helping sites recover following Google update hits. However, over the years I have found it is increasingly more difficult for a site that is impacted by a Google update to recover. This is why today, although I do still love doing site reviews to give you ideas for improving, I generally decline doing work with sites that have been strongly impacted by Google updates. While recovery is possible, it generally takes a year or more of hard work and even then, recovery is not guaranteed as Google’s algorithms and people’s preferences are continually changing.

The sites that saw nice recovery with this Google update were sites that worked on things like:

• Truly improving the world’s perception of their customer service.
• Creating original and insightful content that was substantially better than other pages that exist.
• Using their own imagery and videos in many cases.
• Working hard to improve user experience.

If you missed it I recently published this video that talks about what we learned about the role of user satisfaction signals in Google’s algorithms. Traditional ranking factors create an initial pool of results. AI systems rerank them, working to predict what the searcher will find most helpful. And the quality raters as well as live users in live user tests help fine-tune these systems.

And here are some more blog posts that you may find helpful:

Ultimately, Google’s systems work to reward content that users are likely to find satisfying. Your goal is to be the most helpful result there is!

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Information Retrieval Part 3: Vectorization And Transformers (Not The Film)

Information retrieval systems are designed to satisfy a user. To make a user happy with the quality of their recall. It’s important we understand that. Every system and its inputs and outputs are designed to provide the best user experience.

From the training data to similarity scoring and the machine’s ability to “understand” our tired, sad bullshit – this is the third in a series I’ve titled, information retrieval for morons.

TL;DR

1. In the vector space model, the distance between vectors represents the relevance (similarity) between the documents or items.
2. Vectorization has allowed search engines to perform concept searching instead of word searching. It is the alignment of concepts, not letters or words.
3. Longer documents contain more similar terms. To combat this, document length is normalized, and relevance is prioritized.
4. Google has been doing this for over a decade. Maybe for over a decade, you have too.

Things You Should Know Before We Start

Some concepts and systems you should be aware of before we dive in.

I don’t remember all of these, and neither will you. Just try to enjoy yourself and hope that through osmosis and consistency, you vaguely remember things over time.

• TF-IDF stands for term frequency-inverse document frequency. It is a numerical statistic used in NLP and information retrieval to measure a term’s relevance within a document corpus.
• Cosine similarity measures the cosine of the angle between two vectors, ranging from -1 to 1. A smaller angle (closer to 1) implies higher similarity.
• The bag-of-words model is a way of representing text data when modelling text with machine learning algorithms.
• Feature extraction/encoding models are used to convert raw text into numerical representations that can be processed by machine learning models.
• Euclidean distance measures the straight-line distance between two points in vector space to calculate data similarity (or dissimilarity).
• Doc2Vec (an extension of Word2Vec), designed to represent the similarity (or lack of it) in documents as opposed to words.

What Is The Vector Space Model?

The vector space model (VSM) is an algebraic model that represents text documents or items as “vectors.” This representation allows systems to create a distance between each vector.

The distance calculates the similarity between terms or items.

Commonly used in information retrieval, document ranking, and keyword extraction, vector models create structure. This structured, high-dimensional numerical space enables the calculation of relevance via similarity measures like cosine similarity.

Terms are assigned values. If a term appears in the document, its value is non-zero. Worth noting that terms are not just individual keywords. They can be phrases, sentences, and entire documents.

Once queries, phrases, and sentences are assigned values, the document can be scored. It has a physical place in the vector space as chosen by the model.

Based on its score, documents can be compared to one another based on the inputted query. You generate similarity scores at scale. This is known as semantic similarity, where a set of documents is scored and positioned in the index based on their meaning.

Not just their lexical similarity.

I know this sounds a bit complicated, but think of it like this:

Words on a page can be manipulated. Keyword stuffed. They’re too simple. But if you can calculate meaning (of the document), you’re one step closer to a quality output.

Why Does It Work So Well?

Machines don’t just like structure. They bloody love it.

Fixed-length (or styled) inputs and outputs create predictable, accurate results. The more informative and compact a dataset, the better quality classification, extraction, and prediction you will get.

The problem with text is that it doesn’t have much structure. At least not in the eyes of a machine. It’s messy. This is why it has such an advantage over the classic Boolean Retrieval Model.

In Boolean Retrieval Models, documents are retrieved based on whether they satisfy the conditions of a query that uses Boolean logic. It treats each document as a set of words or terms and uses AND, OR, and NOT operators to return all results that fit the bill.

Its simplicity has its uses, but cannot interpret meaning.

Think of it more like data retrieval than identifying and interpreting information. We fall into the term frequency (TF) trap too often with more nuanced searches. Easy, but lazy in today’s world.

Whereas the vector space model interprets actual relevance to the query and doesn’t require exact match terms. That’s the beauty of it.

It’s this structure that creates much more precise recall.

The Transformer Revolution (Not Michael Bay)

Unlike Michael Bay’s series, the real transformer architecture replaced older, static embedding methods (like Word2Vec) with contextual embeddings.

While static models assign one vector to each word, transformers generate dynamic representations that change based on the surrounding words in a sentence.

And yes, Google has been doing this for some time. It’s not new. It’s not GEO. It’s just modern information retrieval that “understands” a page.

I mean, obviously not. But you, as a hopefully sentient, breathing being, understand what I mean. But transformers, well, they fake it:

1. Transformers weight input by data by significance.
2. The model pays more attention to words that demand or provide extra context.

Let me give you an example.

“The bat’s teeth flashed as it flew out of the cave.”

Bat is an ambiguous term. Ambiguity is bad in the age of AI.

But transformer architecture links bat with “teeth,” “flew,” and “cave,” signaling that bat is far more likely to be a bloodsucking rodent* than something a gentleman would use to caress the ball for a boundary in the world’s finest sport.

*No idea if a bat is a rodent, but it looks like a rat with wings.

BERT Strikes Back

BERT. Bidirectional Encoder Representations from Transformers. Shrugs.

This is how Google has worked for years. By applying this type of contextually aware understanding to the semantic relationships between words and documents. It’s a huge part of the reason why Google is so good at mapping and understanding intent and how it shifts over time.

BERT’s more recent updates (DeBERTa) allow words to be represented by two vectors – one for meaning and one for its position in the document. This is known as Disentangled Attention. It provides more accurate context.

Yep, sounds weird to me, too.

BERT processes the entire sequence of words simultaneously. This means context is applied from the entirety of the page content (not just the few surrounding terms).

Synonyms Baby

Launching in 2015, RankBrain was Google’s first deep learning system. Well, that I know of anyway. It was designed to help the search algorithm understand how words relate to concepts.

This was kind of the peak search era. Anyone could start a website about anything. Get it up and ranking. Make a load of money. Not need any kind of rigor.

Halcyon days.

With hindsight, these days weren’t great for the wider public. Getting advice on funeral planning and commercial waste management from a spotty 23-year-old’s bedroom in Halifax.

As new and evolving queries surged, RankBrain and the subsequent neural matching were vital.

Then there was MUM. Google’s ability to “understand” text, images and visual content across multiple languages simultenously.

Document length was an obvious problem 10 years ago. Maybe less. Longer articles, for better or worse, always did better. I remember writing 10,000-word articles on some nonsense about website builders and sticking them on a homepage.

Even then that was a rubbish idea…

In a world where queries and documents are mapped to numbers, you could be forgiven for thinking that longer documents will always be surfaced over shorter ones.

Remember 10-15 years ago when everyone was obsessed when every article being 2,000 words.

“That’s the optimal length for SEO.”

If you see another “What time is X” 2,000-word article, you have my permission to shoot me.

Longer documents will – as a result of containing more terms – have higher TF values. They also contain more distinct terms. These factors can conspire to raise the scores of longer documents

Hence why, for a while, they were the zenith of our crappy content production.

Longer documents can broadly be lumped into two categories:

1. Verbose documents that essentially repeat the same content (hello, keyword stuffing, my old friend).
2. Documents covering multiple topics, in which the search terms probably match small segments of the document, but not all of it.

To combat this obvious issue, a form of compensation for document length is used, known as Pivoted Document Length Normalization. This adjusts scores to counteract the natural bias longer documents have.

The cosine distance should be used because we do not want to favour longer (or shorter) documents, but to focus on relevance. Leveraging this normalization prioritizes relevance over term frequency.

It’s why cosine similarity is so valuable. It is robust to document length. A short and long answer can be seen as topically identical if they point in the same direction in the vector space.

Great question.

Well, no one’s expecting you to understand the intricacies of a vector database. You don’t really need to know that databases create specialized indices to find close neighbors without checking every single record.

This is just for companies like Google to strike the right balance between performance, cost, and operational simplicity.

Kevin Indig’s latest excellent research shows that 44.2% of all citations in ChatGPT originate from the first 30% of the text. The probability of citation drops significantly after this initial section, creating a “ski ramp” effect.

Even more reason not to mindlessly create massive documents because someone told you to.

In “AI search,” a lot of this comes down to tokens. According to Dan Petrovic’s always excellent work, each query has a fixed grounding budget of approximately 2,000 words total, distributed across sources by relevance rank.

In Google, at least. And your rank determines your score. So get SEO-ing.

Metehan’s study on what 200,000 Tokens Reveal About AEO/GEO really highlights how important this is. Or will be. Not just for our jobs, but biases and cultural implications.

As text is tokenized (compressed and converted into a sequence of integer IDs), this has cost and accuracy implications.

• Plain English prose is the most token-efficient format at 5.9 characters per token. Let’s call it 100% relative efficiency. A baseline.
• Turkish prose has just 3.6. This is 61% as efficient.
• Markdown tables 2.7. 46% as efficient.

Languages are not created equal. In an era where capital expenditures (CapEx) costs are soaring, and AI firms have struck deals I’m not sure they can cash, this matters.

Well, as Google has been doing this for some time, the same things should work across both interfaces.

1. Answer the flipping question. My god. Get to the point. I don’t care about anything other than what I want. Give it to me immediately (spoken as a human and a machine).
2. So frontload your important information. I have no attention span. Neither do transformer models.
3. Disambiguate. Entity optimization work. Connect the dots online. Claim your knowledge panel. Authors, social accounts, structured data, building brands and profiles.
4. Excellent E-E-A-T. Deliver trustworthy information in a manner that sets you apart from the competition.
5. Create keyword-rich internal links that help define what the page and content are about. Part disambiguation. Part just good UX.
6. If you want something focused on LLMs, be more efficient with your words.
   • Using structured lists can reduce token consumption by 20-40% because they remove fluff. Not because they’re more efficient*.
   • Use commonly known abbreviations to also save tokens.

*Interestingly, they are less efficient than traditional prose.

Almost all of this is about giving people what they want quickly and removing any ambiguity. In an internet full of crap, doing this really, really works.

Last Bits

There is some discussion around whether markdown for agents can help strip out the fluff from HTML on your site. So agents could bypass the cluttered HTML and get straight to the good stuff.

How much of this could be solved by having a less fucked up approach to semantic HTML, I don’t know. Anyway, one to watch.

Very SEO. Much AI.

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35-Year SEO Veteran: Great SEO Is Good GEO — But Not Everyone’s Been Doing Great SEO via @sejournal, @theshelleywalsh

As SEOs, we are used to being adaptable to changing algorithms, so LLM optimization should be a simple extension of that process.

To discuss the industry debates surrounding the differences between SEO and GEO and clarify whether they are the same or different, I spoke with SEO veteran Grant Simmons.

Grant has over 30 years of experience helping brands grow and has spent decades focused on meaning, intent, and topical authority long before LLMs entered the conversation.

I spoke with Grant about signal alignment, how Google’s latest continuation patents reveal the mechanics of LLM citations, and what SEOs are getting wrong about topical focus.

“We talk about writing for the machines, but we’re really writing for human need because it’s all driven by the prompt or the query.” – Grant Simmons

You can watch the full interview with Grant on IMHO below, or continue reading the article summary.

Great SEO Is Good GEO

At Google Search Live in December 2025, John Mueller said, “Good SEO is good GEO.”

I asked Grant what he thought were the differences between optimizing for search engines and for machines, and if he thought there were any overlaps.

Grant’s approach echoes what John Mueller said, but “Not everyone has been doing great SEO,” he explained. “Great SEO was always about building topical authority.”

He continued to say, “Essentially, machines (whether it’s Google or whether it’s an LLM) have to understand the underlying meaning of the content so they can present the best answer.

They have to understand the query or the prompt, then they have to send the best answer. So in that way, it’s very similar.”

Where Grant sees divergence is in how the systems evaluate content. Google has historically ranked pages, and even with passage ranking, it still considers the page and the site as a whole. LLMs operate differently.

“LLMs are looking more at that passage side, you know, something that’s easily extractable, something that has value semantically related to the query or the prompt. And so there’s that fundamental difference.”

Grant also stressed that great SEO has always been holistic, touching social media, PR, content, and brand messaging. Having brand awareness, brand visibility, and brand consistency across all channels is a significant factor in LLM representation. And this is exactly the kind of work that the best SEOs do.

“We’re marketers. We should make sure, not just from a standpoint of what we do in SEO and GEO for our clients, which is connecting a need and intent to the product or service that satisfies that intent, we’re also doing the same in our own marketing. We have to understand what our clients are looking for.

“[GEO] is the same [as SEO] if you’re doing it well. It’s not the same if you weren’t. And of course, there’s nuance.”

My thoughts are that SEOs who have been in the industry the longest are experiencing less disruption because they have seen it all before. They learned to be adaptable in the early years when there was so much flux as we progressed from multiple search engines to just one. Whereas for anyone new to the industry, they don’t have the same background points of reference.

Why Consensus Matters To Be Surfaced By LLMs

I went on to ask Grant about Google’s latest continuation patents, which describe two distinct systems that work together.

The first is what Grant describes as a response confidence engine. This system evaluates whether a passage can be corroborated, whether the information has consensus across the web.

“If they return a passage and they can corroborated that it is true, and when we say true, it’s true in the sense of more than one person is saying it, that doesn’t mean it’s true, but it means the consensus is there,” Grant explained. “The consensus generally wins out.”

The second system is what Grant calls a linkifying engine. Once a passage has been confirmed through consensus, this engine determines whether a specific sentence or sub-element within that passage, what Grant calls a “chunklet,” can be matched and linked to a source.

“Consensus decides whether it’s surfaced in the first place. The linkify engine actually decides whether it’s linkable, whether a citation is actually going to happen,” Grant said.

Getting mentioned by an LLM is one thing. Getting an actual link back to your content requires that the specific passage is both verifiable through consensus and uniquely attributable to your source.

Golden Knowledge Content Wins

So, what kind of content earns this kind of AI visibility? Grant described it as “golden knowledge,” content that is unique in some meaningful way.

“Generally, data-driven, your own data, your own opinion that’s proof-backed, evidence-backed. Taking a different view of things,” Grant said. “But in the same way of taking a different view, there still has to be some kind of consensus. If other people are agreeing with you, that is really important. Your content needs the uniqueness and the data-driven aspect, but it still has to align with the overall consensus on the web.”

Grant was also clear that while we often talk about writing for machines, the orientation should remain human-centered: “We talk about writing for the machines, but we’re really writing for human need because it’s all driven by the prompt or the query.”

This balance between uniqueness and consensus is perhaps the most actionable takeaway. Content that simply restates what everyone else is saying won’t stand out. But content that takes a position without corroboration elsewhere won’t pass the confidence threshold to be surfaced. The sweet spot is original, data-driven insight that others can and do validate.

The Biggest Mistakes SEOs Make With Topical Focus

When I asked Grant about the most common mistakes he sees with topical diversification on pages, his answer was clear: trying to be everything to everyone.

“When you think about intent, suddenly you understand that pages have a right to exist,” Grant said. “I call it path to satisfaction. Understanding who the audience is and what they need to find, you have to provide a path to that satisfaction.”

Grant pointed out that most SEOs inherit existing sites rather than building from scratch. The temptation is to focus on the surface-level optimizations, such as title tags, meta descriptions, and headers, without reviewing whether a page is actually focused on a specific intent or whether it has what he calls “drift.”

“What they won’t do is fundamentally review the page and understand whether that page is focused on a specific intent or whether it has this drift,” Grant explained. “Cleaning out those outliers, topics that you’re covering when you don’t really mean to, is essentially diffusing what the page means. Those are the things that I think SEOs miss out on.”

This ties directly back to LLM citability. If a page lacks clear topical focus, it becomes harder for AI systems to extract a self-contained passage that answers a specific query. Tightening that focus isn’t just good SEO; it’s the foundation of being visible in AI-generated responses.

Grant’s Strategy Recommendation For 2026

I finished by asking Grant what he’s recommending to his clients right now.

“Let’s double down on what’s working,” Grant said. “LLM traffic is so small today that optimizing for LLMs is important for the future but not for today’s metrics. Let’s improve our SEO. Let’s get to that great SEO level. And as we’re doing that, we are incorporating the elements that will help you show up for GEO, that will help show up on these other surfaces.”

His focus is on great content, topical authority, uniqueness, data-driven approaches, citations, and digital PR. In Grant’s words: “Getting content so good that LLMs can’t ignore you, Google can’t ignore you, and publications can’t ignore you.”

It’s the Steve Martin philosophy applied to SEO: “Be so good they can’t ignore you,” and, coincidence or not, the rule I have applied for the last 15 years in SEO.

Watch the full interview with Grant Simmons here:

Thank you to Grant Simmons for offering his insights and being my guest on IMHO.

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Featured Image: Shelley Walsh/Search Engine Journal

Are Citations In AI Search Affected By Google Organic Visibility Changes? via @sejournal, @lilyraynyc

I recently wrote about an unconfirmed Google algorithm update that rolled out in mid-January 2026, which negatively impacted the organic search visibility of dozens of major brands. For most of the impacted sites I analyzed, the impact was disproportionately targeted to the company’s blog, or another folder containing informational articles and resources.

That same organic trajectory has continued into mid-February for all of the subfolders I analyzed, using the Sistrix U.S. Visibility Index:

Zooming out, this is what the drops look like when you look at the visibility trends across the whole domains, not just the blogs:

Here is another example of the visibility impact on the company blog for the biggest company in the list (in terms of both ARR and organic visibility):

And this is what the impact looks like when you look at the company’s full domain’s visibility in organic search:

Needless to say, these recent organic visibility drops were extreme, relative to the sites’ overall SEO trajectories over the past few years. Drilling down into 11 of the sites that saw extreme declines over the last month, I wanted to see if this new data could help answer another question:

Do drops in Google organic search visibility coincide with similar drops in AI search citations?

My working hypothesis is that these drops are no longer just isolated to traditional search. Instead, I suspect we will find that, for most LLMs, AI search citation trends mirror what happens in Google’s organic search results, for two reasons:

1. The Direct Pipeline: Google’s AI Ecosystem

For Google’s own AI products – AI Mode and Gemini – the correlation should be strongest. Presumably, Google is using its own index and top-ranking search results to formulate AI search responses; therefore, dropping in organic rankings should logically cause those pages to be cited and referenced less frequently in generative answers.

2. The Downstream Effects: Third-Party LLMs (ChatGPT & Perplexity)

The link between Google organic rankings and third-party LLMs like ChatGPT and Perplexity is more nuanced, as we don’t know exactly which search engines these LLMs are surfacing for web search.

While there is a growing body of evidence (and industry reporting) suggesting that ChatGPT likely scrapes Google during live web searches, we still technically lack official confirmation from the source. Perplexity, on the other hand, is currently believed to utilize the Brave Search API as a core part of its retrieval process, alongside its own specialized “PerplexityBot” crawler.

To test this out, I wanted to drill down into the subfolders that saw substantial visibility drops on Google in recent weeks, to see whether the trend line for AI search citations followed suit.

To start, I honed in on a list of 11 sites whose subfolders saw substantial organic traffic drops between January 20, 2026 and February 16, 2026.

I used the Ahrefs MCP server with Claude Cowork to pull in estimated global monthly organic traffic numbers for each path (subfolder) in the list. Because most of the traffic declines started around January 21, 2026, I pulled the projected monthly organic numbers for January 20, 2026 and the most recent date, February 16, 2026.

I also redacted the site names, leaving the name of the subfolder and a brief, anonymized summary about the company type and the subfolder’s purpose:

These subfolders experienced anywhere from a -5.7% to -53.1% drop in estimated monthly organic search traffic since January 20th, 2026.

Using Ahrefs Brand Radar, you can drill down to see the number of AI search citations that a given subfolder has received across various LLMs over time. For example, here is the ChatGPT citation trend line for the first subfolder listed in the above table (U.S. data):

This is the corresponding chart showing the organic traffic trend for this same subfolder, which began dropping around January 21, 2026:

I used the Ahrefs MCP server with Claude Cowork to pull global traffic and citation data, and to analyze this same pattern for 11 of the subfolders that saw big drops.

Note on methodology: While 11 subfolders are a small sample size, I was specifically looking for a “clean” data set – subfolders experiencing a similar algorithmic demotion on Google during the unconfirmed January 2026 update. By narrowing the scope, I could better isolate whether a loss of traditional search visibility translates directly into a citation drop in AI search.

Below are the high-level summaries of how organic traffic and citation counts changed across Google and various LLMs, including AI Mode, ChatGPT, Perplexity, and Gemini:

Findings:

• The data shows a broad decline in both SEO traffic & AI search citations: Every subfolder in the study (11 of 11) experienced a drop in both Google organic traffic and total AI search citations, with a significant average citation decline of -22.5%.
• Google’s AI Mode (-23.8%) and ChatGPT (-27.8%) showed the most severe declines, closely mirroring the -26.7% average drop in organic traffic.
• While Gemini also saw broad declines (10 of 11 sites), Perplexity proved to be the most resilient, with only 4 of the 11 sites seeing a drop and a much milder average change of -2.9%.
  • This data supports the theory that Perplexity is primarily using non-Google search surfaces to generate its responses.

Looking at the changes in estimated organic traffic for each subfolder compared to total AI search citations between January 20 and February 16, 2026, the correlation is clear: Significant losses in organic search visibility are almost universally mirrored by a corresponding decline in AI search citations.

Drilling down into specific LLMs, including Google’s AI Mode, ChatGPT, Perplexity, and Gemini, shows how the decline was nearly universal for most platforms, whereas Perplexity frequently displayed a significant divergence, showing positive citation growth for the majority of the subfolders despite their organic traffic losses.

ChatGPT (green) consistently shows the deepest declines across almost every subfolder – often exceeding AI Mode and Gemini. This is intriguing because ChatGPT isn’t a Google product, yet it appears more sensitive to these organic ranking shifts than Google’s own Gemini.

This appears to be another clue that ChatGPT is reliant on Google’s search index during retrieval.

AI Mode and Gemini tend to move in the same direction but not the same magnitude. Despite both being Google products, AI Mode declines are generally steeper than Gemini’s. This could suggest they weight or source from Google’s organic index differently – perhaps AI Mode is more tightly coupled to live SERP rankings while Gemini draws from a broader or cached knowledge base.

The few sites where Perplexity did decline (e.g., Site J, Site K) are also the ones showing relatively smaller organic drops. So even in the cases where Perplexity tracked downward, it doesn’t appear to be correlated with the severity of the Google organic loss – further evidence that Perplexity is likely pulling from a different retrieval pipeline.

The below table shows all the organic search vs. AI search citation data in one place:

The table reveals a clear pattern: every subfolder that lost organic visibility on Google also saw a decline in total AI search citations, with an average drop of -22.5% across all LLMs.

ChatGPT was the most severely impacted platform, with citation declines reaching as high as -42.3% (Site E) and exceeding -34% for five of the eleven subfolders – often surpassing even the organic traffic loss itself.

Google’s AI Mode followed a similar trajectory, while Gemini showed more moderate declines across the board.

The most notable outlier is Perplexity, which actually showed citation growth for 7 of the 11 subfolders, reinforcing the theory that it retrieves from a non-Google search index.

Perhaps the most interesting finding is that ChatGPT – a non-Google product – appears more tightly coupled to Google’s organic rankings than Google’s own Gemini, suggesting that ChatGPT’s web retrieval pipeline is heavily dependent on Google’s search results.

One recommendation I’ve been making since AI search entered the SEO conversation is that you shouldn’t invest in AEO/GEO tactics that could be detrimental to SEO performance. For example, using hidden prompt injections, cloaking, or self-promotional listicles (tactics that some have advocated for to boost AI search visibility) might be temporarily beneficial for AI search, but could cause massive headaches with Google and Bing’s organic search ranking algorithms down the line.

Now, we have even more evidence that AI search is fundamentally connected to SEO performance: If you drop in organic search, you can likely expect a corresponding drop in citations not only from Google’s own AI search products, but from other LLMs like ChatGPT, which appear to also be heavily reliant on Google’s search results.

The one notable exception is Perplexity, which showed citation growth for the majority of subfolders hit by the recent algorithm update. That said, it’s important to weigh this against the scale of traffic and LLM usage at stake. According to a recent article by Similarweb, ChatGPT received 5.8 billion web visits in August 2025, compared to 148.2 million for Perplexity.

To add to this, when you factor in Google’s organic search traffic – which still dwarfs all the AI search platforms combined – the vast majority of your search-driven visibility across both search engines and AI chatbots is still flowing through a pipeline where Google’s rankings dictate the outcome.

For the past year, the SEO industry has been asking how closely traditional SEO and AEO/GEO are really tied together. I think this data helps answer that question: Not only is a strong SEO foundation critical for AI search visibility, but tactics that hurt your organic rankings can have a cascading negative impact on your AI search citations as well. In other words, the fastest way to lose visibility in AI search might be to lose it in Google first.

More Resources:

This post was originally published on Lily Ray NYC Substack.

Featured Image: PeopleImages/Shutterstock

Google Text Ad Click Share Rises Sharply In Some Verticals via @sejournal, @MattGSouthern

An analysis of 16,000 U.S. search queries found that text ads gained 7 to 13 percentage points of click share between January 2025 and January 2026.

SEO consultant Aleyda Solis used Similarweb clickstream estimates to measure click share across classic organic results, SERP features, text ads, PLAs, and zero-click behavior.

She also tracked how often AI Overviews appeared on the page, but the dataset doesn’t attribute clicks to AI Overviews directly.

What The Data Shows

Text ads gained between 7 and 13 percentage points of click share across every vertical Solis analyzed.

In the headphones vertical (top 5,000 US queries), classic organic click share fell from 73% to 50%. Text ads grew from 3% to 16%, and PLAs grew from 13% to 20%. Combined paid results now capture 36% of clicks in that category, up from 16% a year earlier.

Jeans followed a similar pattern. Classic organic dropped from 73% to 56%, while combined paid results rose from 18% to 34%.

The online games vertical saw text ads quadruple, from 3% to 13%, even though the category had historically had almost no ad presence.

In greeting cards, the only vertical where total clicks actually grew year over year, organic click share still fell from 88% to 75% as text ads nearly doubled.

The AI Overview presence on SERPs grew across all four verticals. Headphones saw AIO presence jump from 2.28% to 32.76%, and online games went from 0.38% to 29.80%. But the analysis measured how often AIOs appeared on the page, not how many clicks they captured or prevented.

Solis wrote:

“When I started this research, my hypothesis was that text ads and organic SERP features -not just AI Overviews- could be significant culprits behind declining organic clicks. The data confirmed this across all four verticals, and the scale of the text ad impact surprised me: they gained between +7 and +13 percentage points of click share in every vertical, making them the single biggest measurable driver of the organic decline.”

Independent Data Points To The Same Pattern

The SERP-level click data lines up with what advertisers are seeing from the other side.

Tinuiti’s Q4 2025 benchmark report found Google text ad clicks hit a 19-quarter high in Tinuiti’s benchmark dataset, growing 9% year over year. Overall Google search ad spend rose 13% in the quarter, up from 10% in Q3.

Google’s earnings tell a similar story. In its Q3 2025 report, Alphabet posted $102.3 billion in revenue, its first $100 billion quarter, with search ad revenue reaching $56.6 billion. CEO Sundar Pichai said AI features were expanding total query volume, including commercial queries.

More queries and more commercial intent create more ad inventory. The Similarweb data is consistent with more clicks shifting to paid placements in these verticals.

Why This Matters

The industry has spent much of the past year focused on AI Overviews as the explanation for declining organic clicks.

AIO presence is growing, and Google reported 1.5 billion monthly AIO users as of Q1 2025. This data indicates that text ads are an increasingly important factor to consider.

When diagnosing drops in organic traffic, it’s helpful to look at the SERP composition for your industry rather than assuming AI Overviews are the sole reason.

Looking Ahead

Data from different sources indicate that text ads are gaining click share.

Whether Google is actively expanding ad placements or advertisers are bidding more aggressively on existing inventory is unknown.

What you may consider doing now is tracking SERP composition changes in your own vertical using tools that measure click distribution rather than rankings alone.