Google has fixed a bug that caused AI Mode search traffic to be reported as “direct traffic” instead of “organic traffic” in Google Analytics.
The problem started last week. Google was adding a special code (rel=”noopener noreferrer”) to links in its AI Mode search results. This code caused Google Analytics to incorrectly attribute traffic to websites, rather than from Google search.
Reports from Aleyda Solis, Founder at Orainti, and others in the SEO community confirm the issue is resolved.
Discovery of the Attribution Problem
Maga Sikora, an SEO director specializing in AI search, first identified the issue. She warned other marketers:
“Traffic from Google’s AI Mode is being tagged as direct in GA — not organic, as Google adds a rel=’noopener noreferrer’ to those links. Keep this in mind when reviewing your reports.”
The noreferrer code is typically used for security purposes. However, in this case, it was blocking Google Analytics from tracking the actual source of the traffic.
Google Acknowledges the Bug
John Mueller, Search Advocate at Google, quickly responded. He suggested it was a mistake on Google’s end, stating:
“My assumption is that this will be fixed; it looks like a bug on our side.”
Mueller also explained that Search Console doesn’t currently display AI Mode data, but it will be available soon.
He added:
“We’re updating the documentation to reflect this will be showing soon as part of the AI Mode rollout.”
“I don’t see the ‘noreferrer’ in Google’s AI Mode links anymore.”
She’s now seeing AI Mode data in her analytics and is verifying that traffic is correctly labeled as “organic” instead of “direct.”
Impact on SEO Reporting
The bug may have affected your traffic data for several days. If your site received AI Mode traffic during this period, some of your “direct” traffic may have been organic search traffic.
This misclassification could have:
Skewed conversion tracking
Affected budget decisions
Made SEO performance look worse than it was
Hidden the true impact of AI Mode on your site
What To Do Now
Here’s your action plan:
Audit recent traffic data – Check for unusual spikes in direct traffic from the past week
Document the issue – Note the affected dates for future reference
Adjust reporting – Consider adding notes to client reports about the temporary bug
Prepare for AI Mode tracking – Start planning how to measure this new traffic source
Google’s prompt response shows it understands the importance of accurate data for marketers.
Search is changing. I hate saying that (again) because it feels cliche at this point. But, cliche or not, it is true and it is seismic.
With the rollout of AI Overviews, Bing Copilot, and conversational search interfaces like ChatGPT and Perplexity, SEO is no longer just about traditional rankings; it’s about representation and visibility.
Instead of obsessing over page 1 and traffic numbers, WordPress site owners need to start focusing on whether they’re represented in the answers users actually see and if that visibility is resulting in revenue.
The old rankings system itself is mattering less and less because AI-driven search features aren’t just scraping a list of URLs. They’re synthesizing content, extracting key insights, and delivering summary answers.
If your content isn’t built for that kind of visibility, it may as well not exist.
Google doesn’t even look like Google anymore. Since the March core update, AI Overviews have more than doubled in appearance, and this trend shows no signs of slowing. This is our new reality, and it’s only going to accelerate.
WordPress is already a flexible, powerful platform, but out of the box, it’s not optimized for how AI-driven search works today.
In this guide, I’ll show you how to future-proof your WordPress site by aligning your structure, content, and technical setup with what large language models actually understand and cite.
Don’t Build Trash Content
Before we talk about how to do it right, let’s talk about the strategy that’s finally running out of road.
For literal decades, site owners have spun up content sites that were never designed for people, only for ad revenue. These sites weren’t meant to inform or help – just rank well enough to earn the click and display the ad.
Unfortunately, WordPress made this model wildly scalable. It almost instantly became the go-to tool for anyone who wanted to launch dozens (or hundreds) of sites fast, slap on some AdSense, and rake in passive income – money for nothing and your clicks for free.
That model worked very well for a very long time. But (thankfully), that time has come to an end.
AI Overviews and answer engines aren’t surfacing this kind of content anymore. Traffic is drying up. Cost per mille (CPM) is down. And trust – not volume – is the currency that search engines now prioritize.
Even if you’re trying to brute-force the model with paid placements or “citation strategies,” you’re competing with brands that have earned their authority over the years.
To be clear, WordPress is not and was never the problem. The problem is that people use it to scale the wrong kind of content.
If your content is created for algorithms instead of actual people, AI is going to pass you by. This new era of search doesn’t reward valueless content factories. It rewards clarity, “usefulness,” and trust.
Nothing in the rest of this article is going to fix that dying business model. If that’s what you’re here for, you’re already too late.
If, however, you’re focused on publishing something valuable – something worth reading, referencing, or citing – then please, keep reading.
Avoid themes and builders that generate “div soup.” Large language models rely on clean HTML to interpret relationships between elements. If your layout is a maze of
s and JavaScript, the model may miss the point entirely.
If the theme you love isn’t perfect, that’s fine. You can usually fix the markup with a child theme, custom template, or a little dev help. It’s worth the investment.
A Checklist For Optimizing WordPress Fundamentals
Use lightweight themes: e.g., GeneratePress, Astra, or Blocksy are all well-regarded by developers for their performance and clean markup.
Optimize image delivery: Large, uncompressed images are one of the biggest culprits behind slow load times. Reducing file sizes improves speed, performance scores, and user experience, especially on mobile.
Use caching and CDNs: These reduce server load and speed up delivery by storing content closer to your users. Better performance means faster indexing, higher satisfaction, and improved Core Web Vitals.
Delete unused plugins: Seriously. If it’s deactivated and collecting dust, it’s a liability. Every inactive plugin is an unpatched attack vector just waiting to be exploited.
Delete unused themes: Same issue as above. They can still pose security risks and bloat your site’s file structure. Keep only your active theme and a fallback default, like Twenty Twenty-Four.
Declutter Hidden Or Fragmented Content
Pop-ups, tabs, and accordions might be fine for user experience, but they can obscure content from LLMs and crawlers.
If the content isn’t easily accessible in the rendered HTML – without requiring clicks, hovers, or JavaScript triggers to reveal – it may not be indexed or understood properly.
This can mean key product specs, FAQs, or long-form content go unnoticed by AI-driven search systems.
Compounding the problem is clutter in the Document Object Model (DOM).
Even if something is visually hidden from users, it might still pollute your document structure with unnecessary markup.
Minimize noisy widgets, auto-playing carousels, script-heavy embeds, or bloated third-party integrations that distract from your core content.
These can dilute the signal-to-noise ratio for both search engines and users.
If your theme or page builder leans too heavily on these elements, consider simplifying the layout or reworking how key content is presented.
Replacing JavaScript-heavy tabs with inline content or anchor-linked jump sections is one simple, crawler-friendly improvement that preserves UX while supporting AI discoverability.
Use WordPress SEO Plugins That Help Structure For LLMs
WordPress SEO plugins are most often associated with schema, and schema markup is helpful, but its value has shifted in the era of AI-driven search.
Today’s large language models don’t need schema to understand your content. But that doesn’t mean schema is obsolete.
In fact, it can act as a helpful guidepost – especially on sites with less-than-perfect HTML structure (which, let’s be honest, describes most websites).
It helps surface key facts and relationships more reliably, and in some cases, makes the difference between getting cited and getting skipped.
Modern SEO tools do more than just generate structured data. They help you manage metadata, highlight cornerstone content, and surface author information – all of which play a role in how AI systems assess trust and authority.
Just don’t make the mistake of thinking you can “add E-E-A-T” with a plugin toggle. John Mueller has said as much at Search Central Live NYC in March of this year.
What author schema can do, however, is help search engines and LLMs connect your content to your wider body of work. That continuity is where E-E-A-T becomes real.
Finally, consider adding a WordPress SEO plugin that can generate a Table of Contents.
While it’s useful for readers, it also gives LLMs a clearer understanding of your page’s hierarchy, helping them extract, summarize, and cite your content more accurately.
Structure Your Content So AI Uses It
Whether you’re creating posts in the Block Editor, Classic Editor, or using a visual page builder like Elementor or Beaver Builder, the way you structure your content matters more than ever.
AI doesn’t crawl content like a bot. It digests it like a reader. To get cited in an AI Overview or answer box, your content needs to be easy to parse and ready to lift.
Start by using clear section headings (your H2s and H3s) and keeping each paragraph focused on a single idea.
If you’re explaining steps, use numbered lists. If you’re comparing options, try a table. The more predictable your structure, the easier it is for a language model to extract and summarize it.
And don’t bury your best insight in paragraph seven – put your core point near the top. LLMs are just like people: They get distracted. Leading with a clear summary or TL;DR increases your odds of inclusion.
Finally, don’t forget language cues. Words like “Step 1,” “Key takeaway,” or “In summary” help AI interpret your structure and purpose. These phrases aren’t just good writing; they’re machine-readable signals that highlight what matters.
Show AI You’re A Trusted Source
WordPress gives you powerful tools to communicate credibility – if you’re taking advantage of them.
E-E-A-T (which stands for experience, expertise, authoritativeness, and trustworthiness) isn’t just an acronym; it’s the bar AI systems use to decide whether your content is worth citing.
WordPress gives you plenty of opportunities to show you’re the real deal.
Start by making your authors visible. Include a bio, credentials, and a link to an author archive.
If your theme doesn’t support it, add a plugin or customize the layout.
Schema markup for authors helps, too, but remember, it doesn’t magically give you E-E-A-T. What it does is help LLMs connect your byline to your broader body of work across the web.
From there, build out internal signals of authority. Link your content together in meaningful ways.
Surface cornerstone pieces that demonstrate depth on a topic. These internal relationships show both users and machines that your site knows what it’s talking about.
Finally, keep it fresh. Outdated content is less likely to be included in AI answers.
Regular content audits, scheduled refreshes, and clear update timestamps all help signal to LLMs (and humans) that you’re active and credible.
Final Thoughts: Build For Understanding, Not Just Ranking
At this point, it should be clear that WordPress can absolutely thrive in an AI-first search environment – but only if you treat it like a platform, not a shortcut.
Success with AI Overviews, answer engines, and conversational search doesn’t come from tricking algorithms. It comes from helping language models truly understand what your content is about – and why you’re the one worth citing.
That means focusing on structure. On clarity. On authorship. On consistency. That means building not just for Google’s crawler, but for the models that generate answers people actually read.
So, yes, SEO has changed. If you’re using WordPress, you’re already holding the right tool. Now, it’s just a matter of wielding it well.
Google’s Sundar Pichai said in an interview that AI Overviews sends more traffic to a wider set of websites, insisting that Google cares about the web ecosystem and that he expects AI Mode to continue to send more traffic to websites, a claim that the interviewer challenged.
AI Agents Remove Customer Relationship Opportunities
There is a revolutionary change in how ecommerce that’s coming soon, where AI agents research and make purchase decisions on behalf of consumers. The interviewer brought up that some merchants have expressed concern that this will erode their ability to upsell or develop a customer relationship.
A customer relationship can be things like getting them to subscribe to an email or to receive text messages about sales, offer a coupon for a future purchase or to get them to come back and leave product reviews, all the ways that a human consumer interacts with a brand that an AI agent does not.
Sundar Pichai responded that AI agents present a good user experience and compared the AI agent in the middle between a customer and a merchant to a credit card company that sits in between the merchant and a customer, it’s a price that a merchant is willing to pay to increase business.
Pichai explained:
“I can literally see, envision 20 different ways this could work. Consumers could pay a subscription for agents, and their agents could rev share back. So you know, so that that is the CIO use case you’re talking about. That’s possible. We can’t rule that out. I don’t think we should underestimate, people may actually see more value participating in it.
I think this is, you know, it’s tough to predict, but I do think over time like you know like if you’re removing friction and improving user experience, it’s tough to bet against those in the long run, right? And so I think, in general if you’re lowering friction for it, you know, and and people are enjoying using it, somebody’s going to want to participate in it and grow their business.
And like would brands want to be in retailers? Why don’t they sell directly today? Why don’t they sell directly today? Why won’t they do that? Because retailers provide value in the middle.
Why do merchants take credit cards? There are many parts like and you find equilibrium because merchants take credit cards because they see more business as part of taking credit cards than not, right. And which justifies the increased cost of taking credit cards and may not be the perfect analogy. But I think there are all these kinds of effects going around.”
Pichai Claims That Web Ecosystem Is Growing
The interviewer began talking about the web ecosystem, calling attention to the the “downstream” effect of AI Search and AI search agents on information providers and other sites on the web.
Pichai started his answer by doing something he did in another interview about this same question where he deflected the question about web content by talking about video content.
He also made the claim that Google isn’t killing the web ecosystem and cited that the number of web pages in Google’s index has grown by 45% over the past two years, claiming it’s not AI generated content.
He said:
“I do think people are consuming a lot more information and the web is one specific format. So we should talk about the web, but zooming back out, …there are new platforms like YouTube and others too. So I think people are just consuming a lot more information, right? So it feels like like an expansionary moment. I think there are more creators. People are putting out more content, you know, and so people are generally doing a lot more. Maybe people have a little extra time in their hands. And so it’s a combination of all that.
On the web, look things have been interesting and you know we’ve had these conversations for a while, you know, obviously in 2015 there was this famous, the web is dead. You know, I always have it somewhere around, you know, which I look at it once in a while. Predictions, it’s existed for a while.
I think web is evolving pretty profoundly. When we crawl, when we look at the number of pages available to us, that number has gone up by 45% in the last two years alone. So that’s a staggering thing to think about.”
The interviewer challenged Pichai’s claim by asking if Google is detecting whether that increase in web pages is because they’re AI generated.
Pichai was caught by surprise by that question and struggled to find the answer and then finally responded that Google has many techniques for understanding the quality of web pages, including whether it was machine generated.
He doubled down on his statement that the web ecosystem is growing and then he started drifting off-topic, then he returned to the topic.
He continued:
“That doesn’t explain the trend we are seeing. So, generally there are more web pages. At an underlying level, so I think that’s an interesting phenomenom. I think everybody as a creator, like you do at The Verge, I think today if you’re doing stuff you have to do it in a cross-platform, cross-format way. So I think things are becoming more dynamic cross-format.
I think another thing people are underestimating with AI is AI will make it zero-friction to move from one format to another, because our models are multi-modal.
So I think this notion, the static moment of, you produce content by format, whereas I think machines can help translate it from, almost like different languages and they can go seamlessly between. I think it’s one of the incredible opportunities to be unlocked.
I think people are producing a lot of content, and I see consumers consuming a lot of content. We see it in our products. Others are seeing it too. So that’s probably how I would answer at the highest level.”
The interviewer asked Pichai what his response is to people who say that AI Overviews is crushing their business.
Pichai answered:
“AI mode is going to have sources and you know, we’re very committed as a direction, as a product direction, part of why people come to Google is to experience that breadth of the web and and go in the direction they want to, right?
So I view us as giving more context. Yes, there are certain questions which may get answers, but overall that’s the pattern we see today. And if anything over the last year, it’s clear to us the breadth of where we are sending people to is increasing. And, so I expect that to be true with AI Mode as well.”
The interviewer immediately responded by noting that if everything Pichai said was true, people would be less angry with him.
Pichai dismissed the question, saying:
“You’re always going to have areas where people are robustly debating value exchanges, etc. … No one sends traffic to the web the way we do.”
Google co-founder Sergey Brin says AI is transforming search from a process of retrieving links to one of synthesizing answers by analyzing thousands of results and conducting follow-up research. He explains that this shift enables AI to perform research tasks that would take a human days or weeks, changing how people interact with information online.
Machine Learning Models Are Converging
For those who are interested in how search works, another interesting insight he shared was that algorithms are converging into a single model. In the past, Googlers have described a search engine as multiple engines, multiple algorithms, thousands of little machines working together on different parts of search.
What Brin shared is that machine learning algorithms are converging into models that can do it all, where the learnings from specialist models are integrated into the more general model.
Brin explained:
“You know, things have been more converging. And, this is sort of broadly through across machine learning. I mean, you used to have all kinds of different kinds of models and whatever, convolutional networks for vision things. And you know, you had… RNN’s for text and speech and stuff. And, you know, all of this has shifted to Transformers basically.
And increasingly, it’s also just becoming one model.”
Google Integrates Specialized Model Learnings Into General Models
His answer continued, shifting to explaining how it’s the usual thing that Google does, integrating learnings from specialized models into more general ones.
Brin continued his answer:
“Now we do get a lot of oomph occasionally, we do specialized models. And it’s it’s definitely scientifically a good way to iterate when you have a particular target, you don’t have to, like, do everything in every language, handle whatever both images and video and audio in one go. But we are generally able to. After we do that, take those learnings and basically put that capability into a general model.”
Future Interfaces: Multimodal Interaction
Google has recently filed multiple patents around a new kind of visual and audio interface where Google’s AI can take what a user is seeing as input and provide answers about it. Brin admitted that their first attempt at doing that with Google Glasses was premature, that the technology for supporting that wasn’t mature. He says that they’ve made progress with that kind of searching but that they’re still working on battery life.
Brin shared:
“Yeah, I kind of messed that up. I’ll be honest. Got the timing totally wrong on that.
There are a bunch of things I wish I’d done differently, but honestly, it was just like the technology wasn’t ready for Google Glass.
But nowadays these things I think are more sensible. I mean, there’s still battery life issues, I think, that you know we and others need to overcome, but I think that’s a cool form factor.”
Predicting The Future Of AI Is Difficult
Sergey Brin declined to predict what the future will be like because technology is moving so fast.
He explained:
“I mean when you say 10 years though, you know a lot of people are saying, hey, the singularity is like, right, five years away. So your ability to see through that into the future, I mean, it’s very hard”
Improved Response Time and Voice Input Are Changing Habits
He agreed with the interviewers that improved response time to voice input are changing user habits, making real-time verbal interaction more viable. But he also said that voice mode isn’t always the best way to interface with AI and used the example of a person talking to a computer at work as a socially awkward application of voice input. This is interesting because we think of the Star Trek Computer voice method of interacting with a computer but what it would get quite loud and distracting if everyone in an office were interacting audibly with an AI.
He shared:
“Everything is getting better and faster and so for you know, smaller models are more capable. There are better ways to do inference on them that are faster.
We have the big open shared offices. So during work I can’t really use voice mode too much. I usually use it on the drive.
I don’t feel like I could, I mean, I would get its output in my headphones, but if I want to speak to it, then everybody’s listening to me. So I just think that would be socially awkward. …I do chat to the AI, but then it’s like audio in and audio out. Yeah, but I feel like I honestly, maybe it’s a good argument for a private office.”
AI Deep Research Can Synthesize Top 1,000 Search Results
Brin explained how AI’s ability to conduct deep research, such as analyzing massive amounts of search results and conducting follow-up research changes what it means to do search. He described a shift in search that changes the fundamental nature of search from retrieval (here are some links, look at them) to generating insights from the data (here’s a summary of what it all means, I did the work for you).
Brin contrasted what he can do manually with regular search and what AI can do at scale.
He said:
“To me, the exciting thing about AI, especially these days, I mean, it’s not like quite AGI yet as people are seeking or it’s not superhuman intelligence, but it’s pretty damn smart and can definitely surprise you.
So I think of the superpower is when it can do things in the volume that I cannot. So you know by default when you use some of our AI systems, you know, it’ll suck down whatever top ten search results and kind of pull out what you need out of them, something like that. But I could do that myself, to be honest, you know, maybe take me a little bit more time.
But if it sucks down the top, you know thousand results and then does follow-on searches for each of those and reads them deeply, like that’s, you know, a week of work for me like I can’t do that.”
AI With Advertising
Sergey Brin expressed enthusiasm for advertising within the context of the free tier of AI but his answer skipped over that, giving the indication that this wasn’t something they were planning for. He instead promoted the concept of providing a previous generation model for free while reserving the latest generation model for the paid tiers.
Sergey explained:
“Well, OK, it’s free today without ads on the side. You just got a certain number of the Top Model. I think we likely are going to have always now like sort of top models that we can’t supply infinitely to everyone right off the bat. But you know, wait three months and then the next generation.
I’m all for, you know, really good AI advertising. I don’t think we’re going to like necessarily… our latest and greatest models, which are you, know, take a lot of computation, I don’t think, we’re going to just be free to everybody right off the bat, but as we go to the next generation, you know, it’s like every time we’ve gone forward a generation, then the sort of the new free tier is usually as good as the previous pro tier and sometimes better.”
Watch the interview here:
Sergey Brin, Google Co-Founder | All-In Live from Miami
On March 29, 2025, the New York Yankees hit a franchise record nine home runs in one game versus the Milwaukee Brewers.
To accomplish this feat, they used a bat that will probably change baseball forever (or not).
They provided Google with the opportunity to oversell its AI abilities, hoping that no one would be familiar with baseball, torpedo bats, and Google Search – all at the same time.
I am that person.
The Background Google Didn’t Tell You
That same day, I was sitting at the very desk I am writing this article on, ordering my groceries online and watching Nestor Cortes pitch against his former team, the New York Yankees.
Cortes, a personal favorite of mine (and of all Yankees fans), picked up right where he left off in 2024 … giving up home runs (he gave a grand slam to the Dodgers in the World Series that I am still hurting from) – dinger (that’s a word for a home run) after dinger.
As I was watching the Yankees crush Cortes (my 7-year-old was on cloud nine), I noticed one of the players’ bats was oddly shaped (that player was Austin Wells, note this for later). I thought it must have been my eyes, but then, player after player, it was the same thing.
The shape of the bat was different. What the Yankees did was custom-load the bulk of the wood of the bat to where the player (per advanced analytics) makes contact most often (so that when they did make contact, it would be harder). Not every player, but a good chunk of the lineup, was using these bats.
You do marketing, not baseball. Why do you care?
Because, as the Yankees hit a franchise record number of home runs in this game, the entire baseball world went bonkers.
Is this bat legal? What is this thing? Who is using it? Since when?
If you are in the baseball world, you know this was an absolutely huge story.
If you’re not familiar with baseball, the term “torpedo bat” sounds entirely obscure and obtuse, which is what Google was counting on when it used this example at Google I/O 2025 to show how advanced its AI abilities are.
What Did Google Say Exactly?
Rajan Patel, who is the VP of Search Engineering at Google, got on stage at Google I/O and said he was a huge baseball fan.
Screenshot from Google I/O 2025 livestream, May 2025
So what?
Rajan used a query related to baseball to show how Google’s AI Mode was analyzing complex data.
Specifically, he ran the following query (prompt?): “Show the batting average and OBP for this season and last for notable players who currently use a torpedo bat.”
It seems really complex, which is exactly how Rajan packaged this to the audience, saying: “Think about it, there are so many parts to that question.”
It does seem like a really niche sort of topic that you’d have to have very specific knowledge about.
It’s got multiple layers to it. It’s got data and acronyms wrapped up in it. It’s got everything you need to think that this is a seriously complex question that only advanced AI could answer. It’s even asking for two years’ worth of data.
The reality is that you could find this information out in three very easy searches and never even have to click on a website to do it.
Shall we?
Why What Google Said Is Not ‘Advanced’
This whole “torpedo bats” thing seems extremely niche, which is, again, from an optics and perception point of view, this is exactly what Google wants you to think.
In reality, as I mentioned, this was a hot story in baseball for a good while.
Moreover, the question of who uses these bats was a big deal. People thought, initially, that these players were cheating.
It was a semi-scandal, which means there is a ton of content from big-name websites that specifically lists which players are using the type of bat.
Here you go, it’s right in the featured snippet:
Screenshot from search for [who uses torpedo bats], Google, May 2025
The last player mentioned above, Dansby Swanson, is who Google featured in its talk:
Screenshot from Google I/O 2025 livestream, May 2025
Compare the list Google shows to the one from Yahoo Sports.
Four out of the seven on Google lists are right there in the featured snippet (Austin Wells, Jazz Chisolm [Jr.], Anthony Volpe, and Dansby Swanson).
For the record, three out of those four play for the Yankees, and Wells was the first person we saw use the bat type in 2025 (some players experimented using it in 2024).
Not hard information to access. It’s right there – so are the players who are “notable.”
Rajan makes a point of saying Google needs to know who the notable players are to answer this “complex” question. Meaning, he portrayed this as being “complex.”
Screenshot from search for [best players mlb], Google, May 2025
Boop. Carousel of the best MLB players in the league right now (pretty good list too!)
So, the complex thing that Google’s AI Mode had to do was pull information from a Yahoo Sports page (or wherever) and combine that with information it’s been using in the Knowledge Graph for years?
If that’s hard for Google and complex for AI, then we have problems.
There were, in essence, three parts to Google’s “query” here:
Answering who in the league uses a torpedo bat.
Identifying notable current players.
Pulling the stats (this year’s and last year’s) of these notable players using this new bat.
The data part seems complex. Stats? Current stats? Seems like a lot.
There are two things you need to know:
The first thing is that baseball is famous for its stats. Fans and teams have been tracking stats for 100 years – number of home runs, batting averages, earned run averages, runs batted in, strikeout walks, doubles, and triples (we haven’t even gotten into spin rates and launch angles).
That’s correct, baseball teams track how many times the ball spun between when the pitcher threw the ball and when the catcher caught the ball.
Today, the league is dominated by advanced analytics.
Guess who powers it all?
I bet you guessed, Google.
Screenshot from search for [who powers mlb statcast], Google, May 2025
The second thing you need to know is that Google has been collecting stats on specific players in its Knowledge Graph for a good while.
Forget that the stats on specific players can be found on dozens upon dozens of websites; Google itself collects them.
Here’s a search for the league’s best player (no, he does not use a torpedo bat):
Screenshot from search for [aaron judge stats], Google, May 2025
Did you notice the stats? Of course, you did; it’s a tab in the Knowledge Panel.
It’s information that might seem incredibly vast or complex, but it’s literally stored by Google.
What I’m saying is, Google created a “complex” scenario that was nothing more than combining two things it stores in the Knowledge Graph with one thing that is spread all over the web (i.e., the list of players using this type of bat).
Is that really that complex for Google, or was it engineered to look complex for the optics?
What Is The Best Way To Talk About AI Products?
I love the graphs. Taking the data and the information and creating a custom graph with AI?
Love that. That’s amazing. That’s so useful.
Google, you don’t need to oversell it; it’s awesome without you doing that.
Google is not going to listen to me (it will read this article, but it will not listen to my advice). I’m not writing this for Google.
I am writing this for you. If you are a small or a medium-sized marketing team and you’re looking at how Google and other big brands market their AI products as a beacon for your own marketing … don’t.
Don’t feel you have to. Decide on your own what is the best way to talk about AI products.
Is the best way really to overstate the complexity? To try to “package” something as more than it really is?
I get the temptation, but people are not stupid. They will start to see through the smoke and mirrors.
It may take them time. It may take them more time than you might think – but it will happen.
I’ll end with a personal story.
My wife is a nurse. She was recently sent to a seminar where they talked about “what’s happening with all the AI stuff.”
My wife came home and was taken aback by what’s going on out there and how people are using AI, as well as how good the AI was (or wasn’t).
My wife is now a thousand times more skeptical about AI.
What happens if you’re following what these brands are doing and oversell AI when your target audience eventually has the experience my wife did?
Google has released new guidelines for website owners who want to excel in AI-powered search.
In a blog post, Search Advocate John Mueller shared tips for ranking in AI Overviews and AI Mode.
This guidance comes as Google moves beyond traditional “blue links” to offer more AI-driven search features.
Curious about AI in Search and your website? We just launched two new documentation pages and a blog post with some general tips on what to think about with regards to these new Search experiences. Check out the blog post at https://t.co/fYaVOfICmL
— Google Search Central (@googlesearchc) May 21, 2025
AI Is Changing Search Behavior
Google noted that users now ask longer questions and follow-up queries through these new interfaces, which creates challenges and opportunities for publishers.
Mueller writes:
“The underpinnings of what Google has long advised carries across to these new experiences. Focus on your visitors and provide them with unique, satisfying content.”
Content Quality Remains Paramount
Google says creating “unique, non-commodity content” is still the foundation for success in all search formats, including AI.
The company recommends focusing on content that meets user needs instead of trying to trick the algorithm.
Google points out that AI search users ask more specific questions and follow-ups. This suggests that thorough, detailed content works especially well in these new search environments.
Technical Requirements and Page Experience
Beyond good content, Google stressed the importance of technical access.
This includes ensuring that:
Googlebot isn’t blocked
Pages load correctly
Content can be indexed
Also focus on user experience factors like mobile-friendly design, fast loading speeds, and clear main content.
Mueller writes in the blog post:
“Even the best content can be disappointing to people if they arrive at a page that’s cluttered, difficult to navigate or makes it hard to find the main information they’re seeking. Ensure that you’re providing a good page experience for those who arrive either from classic or AI search results…”
Managing Content Visibility In AI Experiences
Google confirms that current content controls work for AI search.
Publishers can use the following tags to control how their content appears:
nosnippet
data-nosnippet
max-snippet
noindex
More restrictions will limit visibility in AI results.
Multimedia Content For Multimodal Search
Google’s blog post stressed the growing importance of images and videos as Google’s AI improves.
With multimodal search, you can upload images and ask questions about them. Google recommends adding high-quality visuals to support your text content.
Ecommerce businesses should keep their Merchant Center and Business Profile information updated for better performance in visual searches.
Rethinking Success Metrics
Google shared insights about user behavior with AI search results, suggesting publishers may need to reconsider how they measure success:
“We’ve seen that when people click to a website from search results pages with AI Overviews, these clicks are higher quality, where users are more likely to spend more time on the site.”
Google suggests AI results provide better context about topics, potentially sending more engaged website visitors.
Mueller encourages site owners to look beyond just clicks and focus on more meaningful metrics like sales, signups, and engagement.
What This Means
This guidance shows that while search looks different now, Google’s main ranking principles haven’t changed.
Unique content, technical quality, and user experience still define success, even as AI changes how people use search.
The key takeaways are:
Your website meets the technical requirements for Google Search
Optimize your images and videos
Review your meta directives
Rethink how you measure traffic quality from AI search rather than just counting clicks.
Google’s full guidance, along with additional resources on AI features and generative AI content, can be found on the Google Search Central blog.
Google has announced updates to its Gemini AI platform at Google I/O, introducing features that could transform how search and marketing professionals analyze data and interact with digital tools.
The new capabilities focus on enhanced reasoning, improved interface interactions, and more efficient workflows.
Gemini 2.5 Models Get Performance Upgrades
Google highlights that Gemini 2.5 Pro leads the WebDev Arena leaderboard with an ELO score of 1420. It ranks first in all categories on the LMArena leaderboard, which measures human preferences for AI models.
The model features a one-million-token context window for processing large content inputs, effectively supporting both long text analysis and video understanding.
Meanwhile, Gemini 2.5 Flash has been updated to enhance performance in reasoning, multimodality, code, and long context processing.
Google reports it now utilizes 20-30% fewer tokens than previous versions. The updated Flash model is currently available in the Gemini app and will be generally available for production in Google AI Studio and Vertex AI in early June.
Gemini Live: New Camera and Screen Sharing Capabilities
The expanded Gemini Live feature is a significant addition to the Gemini ecosystem, now available on Android and iOS devices.
Google reports that Gemini Live conversations are, on average, five times longer than text-based interactions.
Camera and screen sharing capabilities, allowing users to point their phones at objects for real-time visual help.
Integration with Google Maps, Calendar, Tasks, and Keep (coming in the next few weeks).
The ability to create calendar events directly from conversations.
These features enable marketers to demonstrate products, troubleshoot issues, and plan campaigns through natural conversations with AI assistance.
Deep Think: Enhanced Reasoning for Complex Problems
The experimental “Deep Think” mode for Gemini 2.5 Pro uses research techniques that enable the model to consider multiple solutions before responding.
Google is making Deep Think available to trusted testers through the Gemini API to gather feedback prior to a wider release.
New Developer Tools for Marketing Applications
Several enhancements to the developer experience include:
Thought Summaries: Both 2.5 Pro and Flash will now provide structured summaries of their reasoning process in the Gemini API and Vertex AI.
Thinking Budgets: This feature is expanding to 2.5 Pro, enabling developers to manage token usage for thinking prior to responses, which impacts costs and performance.
MCP Support: The introduction of native support for the Model Context Protocol in the Gemini API allows for integration with open-source tools.
Here are examples of what thought summaries and thinking budgets look like in the Gemini interface:
Image Credit: Google
Image Credit: Google
Gemini in Chrome & New Subscription Plans
Gemini is being integrated into Chrome, rolling out to Google AI subscribers in the U.S. This feature allows users to ask questions about content while browsing websites.
You can see an example of this capability in the image below:
Image Credit: Google
Google also announced two subscription plans: Google AI Pro and Google AI Ultra.
The Ultra plan costs $249.99/month (with 50% off the first three months for new users) and provides access to Google’s advanced models with higher usage limits and early access to experimental AI features.
Looking Ahead
These updates to Gemini signify notable advancements in AI that marketers can integrate into their analytical workflows.
As these features roll out in the coming months, SEO and marketing teams can assess how these tools fit with their current strategies and technical requirements.
The incorporation of AI into Chrome and the upgraded conversational abilities indicate ongoing evolution in how consumers engage with digital content, a trend that search and marketing professionals must monitor closely.
At its annual I/O developer conference, Google announced upgrades to its AI-powered Search tools, making features like AI Mode and AI Overviews available to more people.
These updates, which Search Engine Journal received an advanced look at during a preview event, show Google’s commitment to creating interactive search experiences.
Here’s what’s changing and what it means for digital marketers.
AI Overviews: Improved Accuracy, Global Reach
AI Overviews, launched last year, are now available in over 200 countries and more than 40 languages.
Google reports that this feature is transforming how people utilize Search, with a 10% increase in search activity for queries displaying AI Overviews in major markets like the U.S. and India.
At the news preview, Liz Reid, Google’s VP and Head of Search, addressed concerns regarding AI accuracy.
She acknowledged that there have been “edge cases” where AI Overviews provided incorrect or even harmful information. Reid explained that these issues were taken seriously, corrections were made, and continuous AI training has led to improved results over time.
Expect Google to continue enhancing how AI ensures accuracy and reliability.
AI Mode: Now Available to More Users
AI Mode is now rolling out to all users in the U.S. without the need to sign up for Search Labs.
Previously, only testers could try AI Mode. Now, anyone in the U.S. will see a new tab for AI Mode in Search and in the Google app search bar.
How AI Mode Works
AI Mode uses a “query fan-out” system that breaks big questions into smaller parts and runs many searches at once.
Users can also ask follow-up questions and get links to helpful sites within the search results.
Google is using AI Mode and AI Overviews as testing grounds for new features, like the improved Gemini 2.5 AI model. User feedback will help shape what becomes part of the main Search experience.
New Tools: Deep Search, Live Visual Search, and AI-Powered Agents
Deep Search: Research Made Easy
Deep Search in AI Mode helps users dig deeper. It can run hundreds of searches at once and build expert-level, fully-cited reports in minutes.
Image Credit: Google
Image Credit: Google
Live Visual Search With Project Astra
Google is updating how users can search visually. With Search Live, you can use your phone’s camera to talk with Search about what you see.
For example, point your camera at something, ask a question, and get quick answers and links. This feature can boost local searches, visual shopping, and on-the-go learning.
Image Credit: Google
AI Agents: Getting Tasks Done for You
Google is adding agentic features, which are AI tools capable of managing multi-step tasks.
Initially, AI Mode will assist users in purchasing event tickets, reserving restaurant tables, and scheduling appointments. The AI evaluates hundreds of options and completes forms, but users always finalize the purchase.
Partners such as Ticketmaster, StubHub, Resy, and Vagaro are already onboard.
Image Credit: Google
Image Credit: Google
Smarter Shopping: Try On Clothes and Buy With Confidence
AI Mode is enhancing the shopping experience. The new tools use Gemini and Google’s Shopping Graph and include:
Personalized Visuals: Product panels show items based on your style and needs.
Virtual Try-On: Upload a photo to see how clothing looks on you, powered by Google’s fashion AI.
Agentic Checkout: Track prices, get sale alerts, and let Google’s AI buy for you via Google Pay when the price drops.
Custom Charts: For sports and finance, AI Mode can build charts and graphs using live data.
Image Credit: Google
Personalization and Privacy Controls
Soon, AI Mode will offer more personalized results by using your past searches and, if you opt in, data from other Google apps like Gmail.
For example, if you’re planning a trip, AI Mode can suggest restaurants or events based on your bookings and interests. Google says you’ll always know when your personal info is used and can manage your privacy settings anytime.
Google’s View: Search Use Cases Are Growing
CEO Sundar Pichai addressed how AI is reshaping search during the preview event.
He described the current transformation as “far from a zero sum moment,” noting that the use cases for Search are “dramatically expanding.”
Pichai highlighted increasing user excitement and conveyed optimism, stating that “all of this will keep getting better” as AI capabilities mature.
Looking Ahead
Google’s latest announcements signal a continued push toward AI as the core of the search experience.
With AI Mode rolling out in the U.S. and global expansion of AI Overviews, marketers should proactively adapt their strategies to meet the evolving expectations of both users and Google’s algorithms.
The search landscape undergoes its biggest shift in a generation.
If you’ve been in SEO long enough to remember the glory days of the all-organic search engine results pages (SERP), you’ll know how much of this real estate has been gradually taken over by paid ads, other first-party products, and rich snippets.
Now, the most aggressive transition of all: AI Overviews (as well as search-based large language model platforms).
At BrightonSEO last month, I explored how this evolution is forcing us to rethink what SEO means and why discoverability, not just ranking, is the new north star.
The “Dawn” Of The Zero-Click Isn’t Just Over – It’s Now Assumed
We’ve been reading about the rise of zero-click searches for some time now, but this “takeover” has been much more noticeable over the past 12 months.
I recently searched [how to teach my child to tell the time], and after scrolling through a parade of paid product ads, Google-owned assets, and the AI Overview summaries, I scrolled a good three pages down the SERP.
Google and other search and discovery platforms want to keep users in their ecosystems. For SEO pros, this means traditional metrics such as click-through rate (CTR) are becoming less valuable by the day.
From Answer Engines To Assistant Engines
LLMs have changed not just the way a result is displayed to the user but also changed the traditional search flow born within the browser into a multi-step flow that the native SERP simply cannot support in the same way.
The research process is collapsing into a single, seamless exchange.
But as technology accelerates, our own curiosity and research skills are at risk of declining or disappearing completely as the evolution of technology exponentially grows.
Assistant engines and wider LLMs are the new gatekeepers between our content and the person discovering that content – our potential “new audience.”
They parse, consume, understand, and then synthesize content, which is the deciding factor in what it mentions to whom/what it interacts with.
Structured data is still crucial, as context, transparency, and sentiment matter more than ever.
Personal LLM agent flow diagram by Alain Schlesser, used with permission, May 2025
Challenges Are Different, But Also The Same
As an SEO, our challenges with this new behavior affect the way we do – and report on – our jobs.
In reality, many are just old headaches in shiny new wrappers:
Attribution is a mess: With AI Overviews and LLMs synthesizing content, it’s harder than ever to see where your traffic comes from – or if you’re getting any at all. There are some tools out there that do monitor, but we’re in the early days to see a standard. Even Google said they have no plans on adding insights on AIO within Search Console.
Traffic is fragmenting (again): We saw this with social media platforms at the beginning, where discovery happened outside the organic SERPs. Discovery is now happening everywhere, all at once. With attribution also harder to ascertain, this is a bigger challenge today.
Budgets are under scrutiny from fear, uncertainty, and doubt (FUD): The native SERP is changing too much, so some may assume there’s less (or no) value in doing SEO much anymore (untrue!).
The Shift Of Success Metrics
The days of our current success metrics are dwindling. The days of vanity-led metrics are coming to an end.
Similar to how our challenges are the same but different, this also applies to how we redefine success metrics:
Old Hat
New Hat
Content
Context + sentiment
Keywords
Intent
Brand
Brand + sentiment
Rankings
Mentions
Links from external sources
Citations across various channels
SERP monopoly
Share of voice
E-E-A-T
Still E-E-A-T
Structured data
Entities, knowledge graph & vector embeds
Answering
Assisting
What Can You Do About It?
Information can be aggregated, but personality can’t. This is why it’s still our responsibility to help “assist the assistant” to consider and include you as part of that aggregated information and synthesized answer.
Stick to the fundamentals: Never neglect SEO 101.
Third-party perspective is increasingly important, so ensure this is maintained and managed well to ensure positive brand sentiment.
Embrace structured data: Even if some say it’s becoming less crucial for LLMs to understand entities, structured data is being used right now inside major LLMs to output structured data within responses, giving them an established and standardised way to understand your content.
Educate stakeholders: Shift the conversation from rankings and clicks to discoverability and brand presence. The days of the branded unlinked mention suddenly have more value than “acquiring X followed non-branded anchor text links pcm.”
Experiment with your content: Try new ways to produce and market your content beyond the traditional word. Here, video is useful not only for humans but also for LLMs, who are now “watching” and understanding them to aid their response.
Create helpful, unique content: To add to the above, don’t produce for the sake of production.
LLMs.txt: The Potential To Be The New Standard
Keep an eye on emerging standards proposals, such as llms.txt, which is one way some are adapting and contributing to how LLMs ingest our content beyond our traditional approaches offered with robots.txt and XML sitemaps.
While some are skeptical about this standard, I believe it is still something worth implementing now, and I understand its true benefits for the future.
There is (virtually) non-existent risk in implementing something that doesn’t take too much time or resources to produce, so long as you’re doing so with a white hat approach.
Conclusion: Embrace Discoverability And New Metrics
SEO isn’t dead. It’s expanding, but at a rate we haven’t experienced before.
Discoverability is the new go-to success metric, but it’s not without flaws, especially as the way we search continues to change.
This is no longer about “ranking well” anymore. This is now about being understood, surfaced, trusted, and discovered across every platform and assistant that matters.
Embrace and adapt to the changes, as it’s going to continue for some time.
More Resources:
Featured Image: PeopleImages.com – Yuri A/Shutterstock
Search marketers assert that Google’s new long-form AI Overviews answers have become the very thing Google’s documentation advises publishers against: scraped content lacking originality or added value, at the expense of content creators who are seeing declining traffic.
Why put the effort into writing great content if it’s going to be rewritten into a complete answer that removes the incentive to click the cited source?
Rewriting Content And Plagiarism
Google previously showed Featured Snippets, which were excerpts from published content that users could click on to read the rest of the article. Google’s AI Overviews (AIO) expands on that by presenting entire articles that answer a user’s questions and sometimes anticipates follow-up questions and provides answers to those, too.
And it’s not an AI providing answers. It’s an AI repurposing published content. That action is called plagiarism when a student does the same thing by repurposing an existing essay without adding unique insight or analysis.
The thing about AI is that it is incapable of unique insight or analysis, so there is zero value-add in Google’s AIO, which in an academic setting would be called plagiarism.
Example Of Rewritten Content
Lily Ray recently published an article on LinkedIn drawing attention to a spam problem in Google’s AIO. Her article explains how SEOs discovered how to inject answers into AIO, taking advantage of the lack of fact checking.
Lily subsequently checked on Google, presumably to see if her article was ranking and discovered that Google had rewritten her entire article and was providing an answer that was almost as long as her original.
She tweeted:
“It re-wrote everything I wrote in a post that’s basically as long as my original post “
It re-wrote everything I wrote in a post that’s basically as long as my original post ☠️ pic.twitter.com/ucNCSMHsY4
An algorithm that search engines and LLMs may use to analyze content is to determine what questions the content answers. This way the content can be annotated according to what answers it provides, making it easier to match a query to a web page.
I used ChatGPT to analyze Lily’s content and also AIO’s answer. The number of questions answered by both documents were almost exactly the same, twelve. Lily’s article answered 13 questions while AIO provided answeredo twelve.
Both articles answered five similar questions:
Spam Problem In AI Overviews AIO: “s there a spam problem affecting Google AI Overviews? Lily Ray: What types of problems have been observed in Google’s AI Overviews?
Manipulation And Exploitation of AI Overviews AIO: How are spammers manipulating AI Overviews to promote low-quality content? Lily Ray: What new forms of SEO spam have emerged in response to AI Overviews?
Accuracy And Hallucination Concerns AIO: Can AI Overviews generate inaccurate or contradictory information? Lily Ray: Does Google currently fact-check or validate the sources used in AI Overviews?
Concern About AIO In The SEO Community AIO: What concerns do SEO professionals have about the impact of AI Overviews? Lily Ray: Why is the ability to manipulate AI Overviews so concerning?
Deviation From Principles of E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) AIO: What kind of content is Google prioritizing in response to these issues? Lily Ray: How does the quality of information in AI Overviews compare to Google’s traditional emphasis on E-E-A-T and trustworthy content?
Plagiarizing More Than One Document
Google’s AIO system is designed to answer follow-up and related questions, “synthesizing” answers from more than one original source and that’s the case with this specific answer.
Whereas Lily’s content argues that Google isn’t doing enough, AIO rewrote the content from another document to say that Google is taking action to prevent spam. Google’s AIO differs from Lily’s original by answering five additional questions with answers that are derived from another web page.
This gives the appearance that Google’s AIO answer for this specific query is “synthesizing” or “plagiarizing” from two documents to answer the question Lily Ray’s search query, “spam in ai overview google.”
Takeaways
Google’s AI Overviews is repurposing web content to create long-form content that lacks originality or added-value.
Google’s AIO answers mirror the content they summarize, copying the structure and ideas to answer identical questions inherent in the articles.
Google’s AIO arguably deviates from Google’s own quality standards, using rewritten content in a manner that mirrors Google’s own definitions of spam.
Google’s AIO features apparent plagiarism of multiple sources.
The quality and trustworthiness of AIO responses may not reach the quality levels set by Google’s principles of Experience, Expertise, Authoritativeness, and Trustworthiness because AI lacks experience and apparently there is no mechanism for fact-checking.
The fact that Google’s AIO system provides essay-length answers arguably removes any incentive for users to click through to the original source and may help explain why many in the search and publisher communities are seeing less traffic. The perception of AIO traffic is so bad that one search marketer quipped on X that ranking #1 on Google is the new place to hide a body, because nobody would ever find it there.
Google could be said to plagiarize content because AIO answers are rewrites of published articles that lack unique analysis or added value, placing AIO firmly within most people’s definition of a scraper spammer.
