Google Says A New Wave Of AI Users Is Transforming Search via @sejournal, @martinibuster

Google’s Martin Splitt and Nikola Todorovic discussed the impact of AI on search, revealing that there’s a new wave of people that are doing things with Google search that is markedly different than in the past and that this is an upward trend.

Martin Splitt noted that AI in search is not new and that it had always been there behind the scenes assisting in the organic search results. It’s only recently that it’s been moved to the forefront where it is now assisting users with increasingly complex multimodal search queries. Funny thing about AI search is that whereas AI plays a role in the background of organic, organic search plays a role in the background of AI search.

Martin asked if AI Search is evolutionary or a revolutionary change:

“Yeah, because I think everyone is talking about AI in search as if it’s a new thing, but it has been there behind the scenes, so to speak, before that.

• So what makes these AI features that people are using now and that are progressively enhancing the search experience for them so different from the features we had before?
• Would you consider these new features revolutionary and completely different from what we’ve been doing so far?
• Or is it more like an evolution of what we have been doing in the past?”

Google’s Nikola Todorovic, Director of Software Engineering at Google Search, answered that it’s revolutionary and that search today is very different from what it was ten years ago. he also noted that current AI-driven search behavior is changing because users are becoming increasingly confident about the kinds of questions that Google is able to answer.

Todorovic replied:

“I think the way they are being used, and I think it is a revolution that they’re speaking of right now. But clearly in the whole process, there’s like small steps. But if you compare search now and search 10 years ago, it’s a very different product. So I would say yes, this is like a big step change and it is absolutely changing the way the users are searching.

So if you think about it, any feature is changing in some way. For example, if you bring like more images, videos, etc, then it is bringing this kind of experience. So people are going more to image search. For example, when we added what we call the image universal blocks on the main page. Now that this new wave is also changing the way the users are searching because they are uncovering that search can actually answer to more complex questions.

And for that reason, we do see that user queries or you call them prompts now, so they’re getting longer. They become more detailed and the average query length is growing.

So we do see the new traffic and this new wave of traffic is a consequence of users being able to see, aha, there is something new I can do over here. That’s from that perspective, it is revolution, but it is obviously a bunch of steps in between that happened and have been improving search all the time.”

Key insights about search behavior today:

• User queries are becoming longer and more detailed.
• Users are discovering new things they can do with search.

That last one is important and may partially explain where some of the traffic is going. People are doing more complex searches, plus as noted in a podcast interview of Liz Reid, people are using multiple AI chat services.

While some SEOs say that AI Search is longtail now, that’s not really what’s happening behind the scenes because Classic Search is still happening behind the scenes because the AI is splitting complex queries into simpler fan-out queries. “Keyword-ese” queries are still happening to a certain extent but now they’re components of a larger query that itself is longtail.

Takeaways

• AI in search is not new, but AI at the front of the search experience is changing how people use Google.
• Google says search today is a different product than it was ten years ago because users are asking longer, more detailed, and more complex questions.
• Users are discovering that search can handle questions they may not have tried before, which is creating new search behavior.
• AI Search may look like longtail search on the surface, but Google can break complex prompts into simpler fan-out queries behind the scenes.
• Classic Search still matters because AI Search depends on retrieval. Organic search has not disappeared. It has moved into the background of the AI experience.
• Keywords are not dead. They may now function as smaller pieces inside larger prompts and more complex search sessions.
• Content has to work at two levels: retrievable for classic search and useful for more complex AI Search behavior.

The important insight is not that users are writing longer queries, but that users are learning what search can do now. As AI Search solves more complex queries, SEO begins to feel more uncertain. It may be useful to consider that simpler fan-out queries are what is being optimized for. But also see the insights about Browsy Queries.

Listen to Search Off The Record

Featured Image by Shutterstock/takasu

Google: AI Makes Human Experience More Important For Content via @sejournal, @martinibuster

A recent Search Off The Record episode featuring Martin Splitt and Nikola Todorovic, Director of Software Engineering at Google Search, explored the revolutionary aspect of AI and how a new wave of users are crafting longer conversational queries. They pointed out that while AI has democratized access to information it has made experience-based insights more valuable, implying that this is a key to standing out in the AI search.

AI Makes Human Experience And Opinions More Important

While AI is making information more accessible, it’s making basic information less important because it’s something that AI can do. Something like the specs of Texas Instruments OPA1656 op-amps is something that is provided by Texas Instruments and data sheets available from sites like electronics warehouses like DigiKey and Mouser.

What AI can’t provide are opinions and experience with those electronic parts, like what is the sonic difference between using an OPA1656 and something else that is six times more expensive? This is something that an AI can’t provide and as a consequence human experience and opinion is the thing that is variously referred to as the “value” that makes one site useful and another site not useful.

Martin Splitt made this case in talking about how AI can bridge human experience and the basic type of information that’s found “on the box.”

Splitt explained:

“Some people have misunderstood whatever it was that they’re trying to accomplish or to provide to be these cumbersome bits and only these cumbersome bits, right?

But eventually that turned into…, how do I put this nicely, putting words around spec sheets from manufacturers. And that wasn’t really the value that I was looking for. I’m not interested in knowing how many gigahertz a certain new processor has because I can read that basically on the box. It says it on the box. You don’t have to tell me that this is now a 3 gigahertz processor. It says it on the box.

And I had a key moment when I was buying a joystick back in the days for a computer game. And I didn’t know what force feedback was. And that’s effectively you have a different resistance. And it might move and vibrate the device if there’s any shaking happening in the surroundings. And I didn’t know what that was. And it said on the box, it has force feedback.

And so I went to someone who worked at the shop, and I anticipated them to be like an expert on the topic. So I’m like, so this says force feedback. What does that mean? And he literally said to me, that means that this joystick has force feedback.

And this is funny, but I’m seeing this a lot in articles and on websites that they’re effectively not giving me any context. They’re just explaining what I can kind of glimpse and gather from the information that is right in front of me. And I think AI makes that easier. You don’t have to spend as much time to rattle off the spec sheets into a more readable human conversational form. But chat bots do that.”

Splitt followed up by saying that it’s no longer necessary for websites to focus on providing commonly available information. That’s still important but there is a higher level of information that based on human experience that websites can provide, even if it’s something as small as explaining what “force feedback” on a gaming joystick is.

Paradoxically, while information is now more widely available than at any point in human history, it’s also made human judgement and opinion more valuable because that’s something that an AI system cannot do.  And while there are many ways to approach content, it’s the subjective information that can be said to be the value add.

Splitt explained:

“So I think there is still enough space online for different outlets and people and opinions and experiences, but I think we have to increase the level of our content to be useful and interesting for humans, from humans to humans. And I don’t think AI is going to take that away. I think AI is going to bridge that.”

Martin Splitt insists that basic content is no substitute for expertise. He suggests that judgment and insights earned through experience are superior to surface-level content that can be found anywhere. Human experience is a key ingredient of high-value content.”

Content that only repeats widely available facts now has a weaker claim on attention because AI can make that same baseline information easier to reach. The stronger opportunity is content built from what a person notices, tests, prefers, questions, compares, and learns through use. That is where experience becomes editorial value, not as a decorative personal angle but as the part of the page that changes what the reader understands.

• Facts explain commonly known information.
• Experience explains what it means to a human.
• What it means turns information into guidance.
• Guidance is the value-add that makes a web page worth visiting.

What this means for SEO is that these kinds of considerations can be used for evaluating content and identifying reasons why it’s not being indexed, why it’s underperforming in search. And I know that for beginners a step-by-step approach feels useful but in real-life, optimizing for search engines, a checklist approach to optimizing only gets you to a shallow level of content and not to the higher standards necessary to stand out.

Listen to Search Off The Record here:

Featured Image by Shutterstock/ra2 studio

Google On Keyword Fragmentation And User Needs In AI Search via @sejournal, @martinibuster

Google’s Liz Reid explained on the Bloomberg Odd Lots podcast how AI Mode and AI Overviews are enabling detailed, need-based query patterns that create new challenges for Google. This points to a consequential change in search behavior that directly impacts how to approach SEO.

Keyword Fragmentation In AI Search

Liz Reid explained that users have always wanted to express longer natural language queries but were forced to narrow them down to keywords like “best restaurants in New York” even though what they really wanted may have been more specific like a restaurant with vegan options and an opening for a party of five.

For as long as I’ve been in SEO, and I’m near 30 years in the business, keyword research has been the foundation of digital marketing. You pick the keywords you want to rank for then create the content in a way that is optimized for that keyword. The problem with optimizing for a short keyword phrase is that there are hidden meanings within that keyword and that’s always been the case.

The way Google used the issue of latent meanings within keywords is to use things like clicks to better understand what users meant when they typed ambiguous keyword phrases like “restaurants in New York.” Some SEOs believe that the clicks were used for ranking websites but another use for clicks is understanding what people mean when they type ambiguous phrases. What Google has done for quite awhile now is to rank the most popular meaning of the keyword phrase first and no matter how many links a page received, if the content aligned with a less popular meaning the page wouldn’t rank.

Liz Reid said that people who use AI-based search are using longer queries that articulate what the problem or information need is, making it easier for Google fetch the information they’re looking for. That change gets to the heart of the problem with organic search that AI search is solving and the implications for SEO are profound.

Liz Reid begins:

“We have seen with AI overviews meaningfully longer queries. We see more natural language queries, but it’s also not even something as basic as that.

It can also be like you were searching for restaurants. We used to laugh about the like before I worked on search, I worked on maps and local, some of the intersection with search, and people would just be like, “restaurants New York.”

And you’re like, what do you want me to do with that query? Like, okay, the best restaurants in New York are going to take three months and 99.9% of the population can’t afford to go to them.

Okay, but like, are you picking 10 random ones, etc.?

But like, part of why people would do that is they had a much more complex– I want a restaurant in this location for five people. It can’t be too pricey. I have a vegan member. I also have kids. That was the question they had in their mind.

And in the old world of keyword-ese, that information would be spread throughout the web. And so you wouldn’t feel confident you could just put in the question.

And now with AI Overviews and AI Mode, you can start to actually, and you see people do this, they tell you the real problem, right?

They don’t take their need and translate it to what the computer understands. They try to give the computer their actual need and expect us to do the translation.”

The big ideas to unpack there are:

• A typical complex question asked in AI Search may not be solved by one web page.
• Complex questions may be one-off and rarely, if ever, repeated, which in many cases may lower the value of optimizing for those phrases, because the time used for crafting them could be more profitably spent doing something else.
• Given that a site will likely share the AI Overviews (AIO) space with another site it increases the need to optimize other factors such as brand icons that stand out in a positive way, use of images that are relevant, and even the use of videos to claim as much AIO space as possible.
• And yet, perhaps the bigger takeaway is that it’s not all longtail because Google breaks down the longtail phrases into smaller highly specific keyword phrases that reflect a portion of the information need, query fan-out, and fires those off to classic search. Google’s AI then picks from among the top three for each query and uses that to synthesize an answer.

So it’s not really that SEOs should optimize for long-tail queries because query fan-out uses Classic Search, bringing it all back to the specific queries that web pages are relevant and optimized for.

Addressing Real Needs

Reid didn’t go into detail about this point but it’s interesting anyway because she said that the process of breaking a complex natural language query into smaller queries becomes a quality issue. One of the problems with AI Search is that people aren’t searching with the same keyword phrases which means that Google can’t cache similar queries in the same way it can with organic search.

She explained:

“I think it means you have to do, it’s a harder job on quality, right?

You have to take this question, there’s many parts, and you have to figure out how you break it apart. And you have to do work to think about things like latency, because you can’t just, you know, if everyone uses the same keyword and it’s not personalized, then you can cache it all. If all of a sudden the queries get much more diverse, you know, it has consequences there.

But I think we just see that it’s very empowering people, right? That it takes some of the work out of searching.

A few years ago, they said, What more can you do with Google search? But if you actually ask them, Okay, when was the last time you spent 20 minutes searching when you would have preferred to spend 2? It’s actually not that hard for me. … And so it’s been kind of exciting to just… make people’s lives easier by helping them address their real need.”

On the surface, the idea of addressing user’s real needs sounds like one of those unhelpful “be awesome” or “content is king” type slogans. But it’s actually a way that every SEO should be auditing web pages. Rather than limiting their scope to keywords, headings, technical issues, take a look at how it’s filling some kind of need.

Someone today asked me to look at their website that was having trouble getting indexed. They suspected that it might be a technical issue. My response is that yeah, everyone hopes it’s a technical issue but in many cases, especially for this one I was looking at, the problem becomes apparent when looked at through the lens of asking, “what need is this page filling?” as well as by asking, “How is this not just different from some other page but different and better?”

Watch the Liz Reid interview here:

Google’s Liz Reid on Who Will Own Search in a World of AI

Featured Image by Shutterstock/TierneyMJ

Google On AI Search & Why Browsy Queries Favor Full SERPs via @sejournal, @martinibuster

Google’s Liz Reid recently discussed what goes on behind the scenes of AI Search, particularly with the fragmentation of complex queries into smaller ones and a relatively  new concept, Browsy Queries. Her feedback offers insights on what SEOs should be focusing on right now in order to perform better in AI search surfaces.

Search Behavior Is Varied, Not Monolithic

Host Joe Wazenthal asked Liz Reid about user behavior patterns in search, how users choose to use classic search or AI search, and what differences in queries result from choosing one platform over the other.

Liz Reid answered by first defining what she is talking about, linking classic search and AI Mode together as Search, then positioning Gemini as something else that is fundamentally different.

She also stated that there are a massive amount of users whose search behaviors are varies across all search surfaces, in essence saying that there isn’t a monolithic user behavior pattern in which people are doing the exact same searches, the patterns the interviewer was looking for in his question.

Liz Reid answered:

There’s sort of your main search page. There’s AI Mode. That’s part of search.

And then there’s the Gemini app.

And I would say there’s a lot of users, so their behavior varies across all of them.”

Search And AI Usage Patterns Are Complex

The SEO and publishing community often thinks about Search as Google but Liz Reid says that user behavior patterns point to a more complex search ecosystem where users are relying on multiple platforms.

She continued her answer:

“But there are some patterns. There’s plenty of people who co-use across them. There’s plenty of people that are actually using several AI products right now, just in general, not even just within Google.

Across Gemini and Search, the more informational ones… Like, if it’s an informational query, then the probability that they’re using Search or AI Mode is going to be higher.

If it’s a creative query, it’s like more of a productivity question like, please rewrite this to make it sound more formal, right? Those type questions are going to be more Gemini-oriented.

Between AI Mode and Search, the main search page, some people use AI Mode mostly via AI overviews. They start in AI overviews and they transition.

For those who go direct to AI Mode, they tend to do that for queries that they consider sort of more complex, longer questions, questions where they expect that they’re going to do more follow-ups, versus if you’re doing a very browsy query, you might choose to prefer all of the SERP.”

Browsy Queries And Browse Search Intent

When we think about search, it may be useful to consider that people not only search across platforms, but they do it for different reasons.

Takeaways About How People Use AI

• Co-Users
  People use multiple platforms simultaneously (co-use)
• Informational Queries
  These tend to happen on Classic Search and AI Mode
• Creative Queries
  These tend to happen on Gemini
• AI Mode Direct
  Queries that originate on AI Mode, where people navigate to AI Mode, tend to be complex, what was traditionally called longtail.
• Browsy Queries
  This is a relatively new phrase that Googlers apparently use.

What Are Browsy Queries?

The phrase “browsy queries” must be something that Googlers use internally and maybe is more familiar with people who do Pay Per Click advertising.  There aren’t really many instances of the phrase but here’s how Google uses it.

A software engineer formerly of DeepMind and Google describes in her LinkedIn Profile having created a machine learning model that identifies “browse intention” queries on Google Search, an invention that improved click-through rates by 5%.

She wrote:

“Built a machine learning model to identify ‘browse intention’ query on Google Search, which presents engaging content on search result pages for browsy queries (e.g. “best places to visit in Orlando”). Improved global search result click-through rate by 5%”

The phrase “browsy queries” is also used in a Google job description for a commerce software engineer, placing the phrase in the context of shopping queries.

“Commerce Retrieval researches and develops high-precision algorithms to reduce the search space for product queries by 8 orders of magnitude under tight latency and compute constraints. Our solutions are tailored to the unique complexities of the Shopping domain including browsy queries, a hierarchical schema, and short multimodal documents.”

It’s also used in the context of video ads in a Google support page for video ads:

“These new shoppable formats will be shown to potential customers in lower intent, more “browsy” Search placements earlier in their shopping journey.”

What Browsy Queries Means And How To Optimize For it

What’s consistent across all three uses is that “browsy queries” are defined by a discovery-level intent stage.

In each example, Google is identifying what the user keep the user exploring:

• The DeepMind example ties browsy queries to engaging content that a user wishes to browse through, not direct answers.
• The commerce job role positions browsy queries as a quality of commerce search.
• The ads example places browsy queries earlier in the shopping journey at about the discovery phase.

The useful takeaway is that Google treats these queries as exploration problems. What makes browsy queries complex is that they have under-specified user intent and are the result of consumers who may be looking for inspiration.

For an SEO or an online merchant, it means that a user has intent but hasn’t narrowed down what they want. That’s where contexts like “Stylish Outfits For Summer” come in handy. Broad keyword phrases are probably useful here. I like a pyramid structure where the deeper a user gets into a page, the more specific it may become.

Keyword Fragmentation In AI Search

Liz Reid explained that users have always wanted to express longer natural language queries but were forced to narrow them down to keywords like “best restaurants in New York” even though what they really wanted may have been more specific like a restaurant with vegan options and an opening for a party of five.

For as long as I’ve been in SEO, and I’m near 30 years in the business, keyword research has been the foundation of digital marketing. You pick the keywords you want to rank for then create the content in a way that is optimized for that keyword. The problem with optimizing for a short keyword phrase is that there are hidden meanings within that keyword and that’s always been the case.

The way Google used the issue of latent meanings within keywords is to use things like clicks to better understand what users meant when they typed ambiguous keyword phrases like “restaurants in New York.” Some SEOs believe that the clicks were used for ranking websites but another use for clicks is understanding what people mean when they type ambiguous phrases. What Google has done for quite awhile now is to rank the most popular meaning of the keyword phrase first and no matter how many links a page received, if the content aligned with a less popular meaning the page wouldn’t rank.

Liz Reid said that people who use AI-based search are using longer queries that articulate what the problem or information need is, making it easier for Google fetch the information they’re looking for. That change gets to the heart of the problem with organic search that AI search is solving and the implications for SEO are profound.

Liz Reid begins:

“We have seen with AI overviews meaningfully longer queries. We see more natural language queries, but it’s also not even something as basic as that.

It can also be like you were searching for restaurants. We used to laugh about the like before I worked on search, I worked on maps and local, some of the intersection with search, and people would just be like, “restaurants New York.”

And you’re like, what do you want me to do with that query? Like, okay, the best restaurants in New York are going to take three months and 99.9% of the population can’t afford to go to them.

Okay, but like, are you picking 10 random ones, etc.?

But like, part of why people would do that is they had a much more complex– I want a restaurant in this location for five people. It can’t be too pricey. I have a vegan member. I also have kids. That was the question they had in their mind.

And in the old world of keyword-ese, that information would be spread throughout the web. And so you wouldn’t feel confident you could just put in the question.

And now with AI Overviews and AI Mode, you can start to actually, and you see people do this, they tell you the real problem, right?

They don’t take their need and translate it to what the computer understands. They try to give the computer their actual need and expect us to do the translation.”

The big ideas to unpack there are:

• A typical complex question asked in AI Search may not be solved by one web page.
• Complex questions may be one-off and rarely, if ever, repeated, which in many cases may lower the value of optimizing for those phrases, because the time used for crafting them could be more profitably spent doing something else.
• Given that a site will likely share the AI Overviews (AIO) space with another site it increases the need to optimize other factors such as brand icons that stand out in a positive way, use of images that are relevant, and even the use of videos to claim as much AIO space as possible.
• And yet, perhaps the bigger takeaway is that it’s not all longtail because Google breaks down the longtail phrases into smaller highly specific keyword phrases that reflect a portion of the information need, query fan-out, and fires those off to classic search. Google’s AI then picks from among the top three for each query and uses that to synthesize an answer.

So it’s not really that SEOs should optimize for long-tail queries because query fan-out uses Classic Search, bringing it all back to the specific queries that web pages are relevant and optimized for.

Addressing Real Needs

Reid didn’t go into detail about this point but it’s interesting anyway because she said that the process of breaking a complex natural language query into smaller queries becomes a quality issue. One of the problems with AI Search is that people aren’t searching with the same keyword phrases which means that Google can’t cache similar queries in the same way it can with organic search.

She explained:

“I think it means you have to do, it’s a harder job on quality, right?

You have to take this question, there’s many parts, and you have to figure out how you break it apart. And you have to do work to think about things like latency, because you can’t just, you know, if everyone uses the same keyword and it’s not personalized, then you can cache it all. If all of a sudden the queries get much more diverse, you know, it has consequences there.

But I think we just see that it’s very empowering people, right? That it takes some of the work out of searching.

A few years ago, they said, What more can you do with Google search? But if you actually ask them, Okay, when was the last time you spent 20 minutes searching when you would have preferred to spend 2? It’s actually not that hard for me. … And so it’s been kind of exciting to just… make people’s lives easier by helping them address their real need.”

On the surface, the idea of addressing user’s real needs sounds like one of those unhelpful “be awesome” or “content is king” type slogans. But it’s actually a way that every SEO should be auditing web pages. Rather than limiting their scope to keywords, headings, technical issues, take a look at how it’s filling some kind of need.

Someone today asked me to look at their website that was having trouble getting indexed. They suspected that it might be a technical issue. My response is that yeah, everyone hopes it’s a technical issue but in many cases, especially for this one I was looking at, the problem becomes apparent when looked at through the lens of asking, “what need is this page filling?” as well as by asking, “How is this not just different from some other page but different and better?”

Watch the Liz Reid interview here:

Google’s Liz Reid on Who Will Own Search in a World of AI

Featured Image by Shutterstock/Summit Art Creations

Google Advises Using AI In Best Possible Way For AI Search via @sejournal, @martinibuster

Google’s Martin Splitt and Nikola Todorovic, Director of Software Engineering at Google Search, recently discussed how AI is changing Google and SEO. Todorovic encouraged SEOs and businesses to take advantage of AI to analyze data, research competition, and improve their ability to provide value.

AI And The Web Ecosystem

Google’s Martin Splitt asked a question that many SEOs and online businesses have on their minds related to what they should do for AI features like AI Mode and AI Overviews. Splitt and Todorovic both said that there are opportunities, especially with the use of AI within a narrow scope.

Martin asked:

“But one thing that we keep hearing from the ecosystem pretty much at every event we do and it’s everywhere is, how do we make sure that with AI features being part of Search now, that the ecosystem continues to thrive.

And I think that’s an interesting challenge, but also there are lots of opportunities thanks to AI features these days. And I know that we at Google try our best to go on this journey together with the ecosystem.

But how do you see it from your perspective? What is it that we do to make sure the ecosystem thrives with these new features?”

The question asked was specifically about what Google can do to assure that the web ecosystem thrives, but the answer wasn’t about what Google can do, but rather about what SEOs and businesses can do.

Todorovic acknowledged that this is a concern he’s also aware of, but he also said that there’s no “magic wand,” meaning there is no simple solution or a roadmap, and he did suggest that focusing on delivering value is a key way to adapt to the new AI search features.

He answered:

“This is clearly one of the key questions and you see them a lot on the social media as well. And I don’t think there is like a magic wand that can clearly give the guidance.
Okay, what do I do now? Like what would the SEO experts do now in the new system?

My kind of guiding principle or my like the way I see here is that the site owners, they do need to continue making sure that their products, that their websites, that their platforms are providing value to the user. Because ultimately, if you provide a particular value, then the users will continue coming to you and they will continue coming to you through Google as well.”

On the surface, this sounds like “content is king” or “be awesome” type of advice, but I think that would be missing a deeper point. One, there is so much that a Googler Engineer can say directly. But there is a lot that they can say indirectly, and I think that’s what Todorovic is doing here.

For example, if Google’s systems reward sites that users are actively looking for, then “providing value” is the kind of thing that’s going to ring bells in that kind of algorithm, where external signals generated by users play a role in what sites Google is ranking. I think it would be a mistake to conflate the advice to “provide value” as a platitude. Knowing what we know about Google’s external signals, the advice to provide value makes a lot of sense.

Todorovic continued his answer:

“So… for example, you’re selling something, you have like a product or like a platform, you have like some subscriptions, et cetera. …if you are providing value to your clients, they will continue coming to you.

In the AI centric or AI oriented system, …those kind of bringing the value still continues. …if you don’t provide value, nobody’s going to buy your newspaper or book or nobody’s going to listen to the radio or to the podcast.”

Master The Use Of AI To Provide Value

Todorovic next acknowledged that, as an employee at Google, he also faces questions of whether AI is going to take his job away, just like online businesses are worried about whether AI is going to replace them or make their businesses obsolete.

His answer is to adapt to AI and use it in a way that increases your value as an employee or as an online business.

Todorovic explained:

“So I think everybody, including all of us, there’s a lot of questions… Like, is AI going to take our jobs and so on. I think we all need to continue thinking, how do we provide value on top of all of this? And in many cases, this is about mastering the AI tools and being able to use them in the best possible way.

So this is one of my recommendations to all the SEO professionals, site owners, and the whole ecosystem, that they continue providing value, but then do not neglect the new technology and make sure you use it in the best possible way for you.

Now, obviously I don’t think we would …recommend the best possible way is to just multiply all the content and just generate because you know, it’s cheap and easy …it’s not going to provide a ton of value.

But if you’re using it to improve your grammar, to improve the style a little bit, make it kind of more interesting and so on, I don’t think that’s a wrong use of the technology. But then there’s plenty of ways, okay. Maybe AI can help you better understand your data. Maybe AI can help you understand the competition potentially better as well. So clearly this is something we can advise.”

My Example Of An AI Prompt For SEO

One of the ways you can use AI for SEO is to ask the AI to do a reverse knowledge search on your web page content. A reverse knowledge search is when an algorithm reviews content to extract the questions the web page is likely to answer. If you run this prompt for examining your web page, it will tell you what search queries your web page is likely to answer.

For example, I recently wrote an article about how Google uses clicks as part of the ranking process.

I uploaded a copy of the finished article to ChatGPT with the following prompt:

“Analyze the document and extract a list of questions that are directly and completely answered by full sentences in the text. Only include questions if the document contains a full sentence or contiguous sentences that clearly answers it. Do not include any questions that are answered only partially, implicitly, or by inference.

For each question, ensure that it is a clear and concise restatement of the exact information present. This is a reverse question generation task: only use the content already present in the document.

For each question, also include the exact sentences from the document that answer it. Only generate questions that have a complete, direct answer in the form of a full sentence or sentences in the document.”

The first question that ChatGPT said my article answers is: “What are clicks considered in the context of ranking signals?”

The following is a screenshot of ChatGPT’s response where it shows the question my article answers and a snippet of text from the article that answers that question.

Screenshot Of ChatGPT’s Answer

Query Ranks #1 In Google

I then took that question and entered it on Google and it ranks #1 for that question in the organic part of the search results.

Screenshot Of My Article Ranking #1 In Google

Query Ranks #1 In Bing

I then asked the same question in Bing and my web page content ranks in (1) the featured snippets, (2) Bing News, and (3) the top of Bing’s organic listing.

Screenshot Of Bing #1 Ranking

I didn’t use AI to create the article or to optimize it. I just wrote it based on all the different things that I know about clicks and Google’s algorithms, using a list of topics I wanted to cover. I have been doing SEO for 26+ years, so I don’t really need an AI to tell me how to optimize a web page, it’s second nature to me.

But I did use AI to check grammar.

The Reverse Knowledge Prompt is something anyone can use to test if their content is focused on the right topics, to check if the content is off-topic, or to understand what the web page is really about in order to clean it up if it’s not about what you hoped it would be.

It’s not a way to reverse-engineer search engines. It’s a way to reverse knowledge search your content with AI to see what it’s really about.

Hidden Gem Advice

I went to Google’s Search Central Live last year, and I was talking to an attorney who was attending the show, and he asked me what is an important thing to do for ranking better, and I said for you it would be branding your site’s offerings in the mind of potential site visitors with the services that you offer. Part of doing that is getting the word of mouth going so that potential clients will think of the law firm’s brand name when they need their specific service.

After the break, we went back into the auditorium, and Danny Sullivan started talking about how sites should try to be brands, and I looked over at the guy I had just been talking with, and he raised an eyebrow back at me.

The advice to provide value is a hidden gem type of advice, in my expert opinion.

Listen to the Search Off The Record Podcast here:

How AI Is Changing Google Search and SEO

Featured Image by Shutterstock/dee karen

Google’s March Core Update Shifted Visibility Away From Aggregators via @sejournal, @MattGSouthern

An analysis from Amsive found that aggregators and user-generated content platforms lost US search visibility after Google’s March core update, while first-party brand sites, government domains, and content originators gained.

Lily Ray of Amsive examined over 2,000 domains using SISTRIX Visibility Index data and categorized them with Google Product Taxonomy tags via the DataForSEO API. The analysis compared visibility on March 27 (rollout start) versus April 8 (completion).

Amsive sees this pattern as a correction for over-indexed UGC and aggregator content, favoring “the company that owns the thing” over “the platform people use to talk about the thing.”

For transparency, SISTRIX measures keyword visibility rather than organic traffic. Other factors can also influence visibility.

YouTube’s Drop Led All Losers

YouTube lost 567 visibility points, the largest single-domain decline in Amsive’s dataset. Ray notes this is roughly 30% larger than Wikipedia’s 435-point drop during the December core update.

She adds context that YouTube’s visibility dropped back to its level before the early March surge, not to a new low.

Reddit lost 64 points, Instagram lost 48, and X lost 46.

Category Patterns: Travel, Jobs, And Health

In travel, OTAs and aggregators lost ground while hotel chains gained. TripAdvisor fell 45 points, Yelp 33, Expedia 33. Hilton rose 4, Hotels.com 3.6, Trivago 3.2. NPS.gov gained 9.9, airport websites saw large gains.

In jobs and education, job board aggregators declined while employer career pages and government sites rose. Indeed lost 18, ZipRecruiter 13. BLS.gov gained 5.4, USAJobs.gov 16%, Disney Careers 59%, CVS Health Careers 45%.

Health showed a split, with GoodRx up 55% (9.5 points), NIH.gov +9.3, but the Cleveland Clinic dropped 12, WebMD 9, Mayo Clinic 6.

Google seems to favor authoritative sources over consumer health publishers, though this is interpretive.

Bounce-Backs Complicate The Loser Data

Ray notes some big losers recovered shortly after the update. Reddit and Indeed saw visibility bounce back, indicating the loser list shows the update window but not where domains settled.

Connection To Prior Research

The findings align with a Zyppy analysis of over 400 sites, published earlier this month. Cyrus Shepard’s analysis showed sites offering products or services that enable task completion tend to gain organic traffic.

Ray cites Shepard’s data as supporting, despite different methodologies: Shepard measured correlations with third-party traffic estimates, whereas Amsive tracked SISTRIX visibility during an update window.

A SISTRIX analysis of German data found similar results: online shops and utility sites lost ground, while official websites and brands were more resilient.

Why This Matters

The data doesn’t confirm what Google changed or why. What it shows is that across travel, jobs, health, finance, and entertainment, the same pattern appeared.

Platforms that aggregate, list, or comment on other people’s content lost visibility, while sites that created or owned the content gained visibility. That’s a pattern worth checking against your own data from the same window.

Looking Ahead

Google hasn’t detailed what changed in the March core update. The rollout window was March 27 to April 8, and Amsive’s data should be read as one visibility snapshot from that period.

Featured Image: Roman Samborskyi/Shutterstock