How AI Is Changing Google Search and SEO

0:10Hello and welcome to a new episode of Search of the Record, the podcast where we take you a little bit behind the scenes of Google Search and hopefully have some fun along the way. Well, you probably have seen AI features in Search and whenever I have to talk about AI features in search. I'm really, really happy that I got to see a presentation at Search Central Live in Zurich last year. And I think it's time to open this up to more people. So I invited a guest today. My guest today is Nikola Todorovic. And would you like to introduce yourself, Nikola? Yes. Thank you, Martin. So I have joined Google about 15 years ago over here in the Zurich office.

0:57and for all of that time I've been a part of the search organization what used to be called search quality nowadays is search intelligence and I've been a part of the of the team that's called safe search and for the last several years I've been I've been leading that team and also in the last couple of years I was more involved in the ecosystem work working together with you with the folks from Search Console, Google Trends, etc. And so have some more experience on that front as well. And we pushed you into the cold water of our stage in Zurich as well. And you had a really, really cool topic.

1:36You talked a bit more about AI and search. Would you like to tell us what led to that talk and what was the thinking behind it and what you want people to take away from that? Yeah, well, clearly AI is the topic that everybody's talking about right now. A lot of people are wondering how is search evolving and what will be the future of search, the future of AI, etc. And from that perspective, I think it was valuable to bring that particular presentation. Now, the presentation that you referred to has showed a lot more things before the new wave of AI came in. I think that was the context that I felt was helpful to present to the audience over here.

2:21Yeah, 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, right? 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? I think the way they are being used, and I think it is a revolution that we're speaking of right now, but clearly in the whole process there are small steps.

3:09but if you compare search now and search 10 years ago, it's a very different product. So I would say, yes, it's 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.

3:49And for that reason, we do see that user queries, or if you call them prompts now, 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. So from that perspective, it is revolution. But it is obviously a bunch of steps in between that happen and have been improving search all the time. Can you shed some light on the steps in between that you think are outstanding and probably have kind of paved the way for this?

4:32Before I jump into that, maybe it'll be interesting to tell you a little bit about the process, how the changes happen in search. Oh, yeah. And then I can add what are the particular changes that reflected this AI revolution. So in principles, Google Search is a huge product. It has a lot of different components. And you, Gary Lies, John Miller, and others have been talking about this. It all starts with the web, with the crawling, indexing, the ranking components, and so on, the new features on top, etc. So we have thousands of changes in Google search per year. I'm not sure how many, but it's certainly in thousands.

5:16We know that because we're tracking all of them and we are evaluating all of them. We're measuring because the key point is, yes, we have new technology. We have things that are, for example, we know problems that happen. Like very often, you know, the changes that come to search are either a consequence of the new technology that's coming up. And we say, oh, let's use this new technology because it certainly will bring us something, some improvements. Or alternatively, we see how there is a problem. I'm typing this query, but I'm getting this result. It's not optimal to see this. And when we do this, we as engineers on search, we are making a kind of an experimental version of Google search that has something new, that has something different compared to the production version of Google search.

6:07and we need some way to tell us, okay, what is better? Because we're not just launching these 5 ,000 changes because some engineer or some product manager has an intuition, ah, this probably will be better. So let me add this thing there, this thing there. No. So we have to start and see, I have to build the prototype of the new version. Thankfully, all the infrastructure at Google is really amazing. So it helped us run this very quickly once we have a good idea. So we can build a new version, run a comparison with the baseline, which is the production system. And we run those things called side-by-sides.

6:47So you're getting random user queries that will see a difference between the production and your experiment. And we have published the guidelines that help human raters review those changes, those differences between the baseline and the experiment. And out of these reviews, out of these human reviews, we're getting statistics. And this statistic is telling us the experiment is better than the baseline. And if it is, then, well, you would think, yeah, let's submit it and commit the changes and like go launch. No, we will have something called launch review. And that is a process where we are, where the engineers are talking to the leads who have the decision-making power in the end and make a call, yes, this is better.

7:38And sometimes it can be that your overall statistics look like improving, but you have some really bad pattern of losses in your experiment. And well, if there's a kind of reasonable way how to fix those patterns, we're going to bring the engineer back and let them fix those patterns and make an improvement. And so right now I'm just talking about the standard good old process of the launch reviews and the new experiments and everything that goes in search. And this process has been going on and is still there. So let me know if this, what I was just explaining, is clear. Do you have any sub-questions on that before I move into the kind of more AI territory?

8:22I'm just wondering if at some point we should break this out as a separate episode, because I think we've mentioned both the search quality radar guidelines and experiments beforehand, but I don't think we've ever gotten such a nice explanation of how the process works and how the different bits and pieces fit together. So that was really, really cool. But let's take it back to AI now. So I'm guessing the AI features underwent more or less the same process, right? Yeah, absolutely they do. And I have to say, yes, given that the world is obviously changing, the competitor landscape has changed as well.

8:59We also need to adapt to this new world. However, a lot of AI inside of Google has been developed for years before the generative AI came to play. As I mentioned in the beginning, I am responsible for the Safe Search Engineering team. And we're one of the first places where Google was able to comfortably apply artificial intelligence slash machine learning models directly in search. The reason why it was not so easy to just apply it everywhere is because these models function like a kind of a black box. You don't always understand what's happening underneath. It's a complex set of, for example, neural networks or even the older kind of simpler, even the linear models are kind of the easiest ones to understand and to debug, right?

9:48because it's not just you kind of can put your AI or ML system into search and, you know, you reap the most benefits from your side-by-side experiments that I just mentioned previously. And now you will, you know, get to something and launch it. But then you will have problems with that as well because obviously the systems evolve, the searches evolve and so on. And then you will need to debug this and replace it. And this kind of replacement and changes is complicated. So the more you can understand how these things work, what signals are you using, what signals are important for the relevance, for the quality, for the safety of the results.

10:27So you do need to understand the system. And kind of the more complex the AI or the ML systems are, then the more challenging it is. But SafeSearch has been one of the places where, you know, you could isolate outside of the main search ranking flow. You can isolate the systems that just do like process the images, process the videos, process the text, and just give you kind of a signal on its own how explicit, for example, a result can be. And then the kind of the understanding of, let's say, 10 years ago or 15, no, it's more like 12 years ago, when really the convolutional neural networks came in to help us understand the images better.

11:12And in many places, they were actually already doing things better than humans and understanding images. Then, you know, we could apply this as a kind of a standalone AI system that runs on a topic. And if we have problems, yes, the engineers in the SafeSearch team had the intuition and could run an iteration and improve the neural network itself. But it's kind of a very isolated space, so you can more easily navigate. And then the rest of the search stack has still been on its own and running things. Along the way, there have been various new technologies, so starting with transformers. I think that's the biggest one that, in the end, introduced all the Gen.AI world.

11:55But we were reaping the benefits of Transformers on search long before all the stuff came in. And we were open about it. So we have announced publicly the systems like BERT, like MOM. And they have been able to transform the search and ranking into a much better place. And again, these systems were built in kind of an isolation as well. Just like the SafeSearch systems, I think these systems were also built in isolation as the new signals. and these new signals were supporting the whole ranking infrastructure and was one more thing on top of everything else. Hopefully that makes sense. That makes sense.

12:37And I mean, if you look at it, the new AI features are kind of also, they're integrated, but they're also somewhat isolated as in like there's an AI overview that lives in its own space and AI mode is a completely different way of searching. so they are kind of also independent of the rest of the search, even though they use the rest of the search infrastructure and search stack and ranking systems, right? Would you say that's the case as well? Or is that completely different from previous systems? Yeah, let's maybe start with AI overviews because that's where I think this holds the most still.

13:15Because if you think of AI overviews, like this is your normal search, with perhaps a few fan outs. I just introduced a new term. I probably should. Please explain that. I think it's, you know, the experts out there, I don't think it's like, probably many of them have heard about it. But anyway, a fan out is when you have your own search query, but then we might identify some additional search query that will yield the results that can be relevant for your original search query as well. And then we can fork and in parallel do the retrieval for multiple search queries that can all come back into one original, more complex query that you gave in.

14:04And so as I initially said previously that we do see longer queries. This is also we can help and understand the kind of more directions of what you were initially typing. So we launch multiple queries. Now we get all of this retrieved back, and then AI Overviews is combining from an interesting selection of these results and making a summary from what you can see in those results. So in a sense, the whole retrieval system, the whole ranking system is the old style, the old school and that one is the AI overviews is a feature that stamps on top of this and operates on its own in this I this is the kind of the isolated space for the AI overview where it combines and it's really fascinating what the language models have been able to do but yes it can combine like text that it sees on these sources, on the snippets, the titles, etc.

15:12And an additional context it can get out of those pages and then make a really nice summary in the end. And I really like that. And I think that also goes back to what you said earlier, that the behavior changes and queries get longer and more complicated. Because I remember back in the days when, I don't know, the word was still monochrome or something. When I searched, even on Google, I searched kind of keyword like restaurant, vegetarian, Zurich. And then over the years, that became more conversational as in like vegetarian restaurants in Zurich, which is already a change. And now, nowadays, I ask questions or I type in queries that are so much more vague, and I still get usable results, like based on dietary restrictions which restaurants would you recommend now for a lunch in zurich and then you get like a bunch of stuff and and it works because of these fan out queries it asks like a bunch of queries that i don't have to ask myself anymore to get to the right result and what i find myself doing is i'm asking questions where i don't even know what what a good question is right beforehand you would sit in front of google and think like how do i how do i even look for this there's an effect in, I don't know, let's say like there's a physical effect and I, ah, what was the name of that?

16:37So you would try to like find the name of the effect first and then Google for the specific effect once you had the name. And now you're like, what is the physical effect that makes water glow when there's radiation there? And then it kind of figures it out for you. And I think that's one of the possibilities of features like AI overview, right? So from AI overviews, what was the motivation and the idea behind then going further towards AI mode? Yeah, no, I agree completely. These are exactly the nice examples of the way how search has evolved with AI overviews and eventually also AI mode. But all the capability of understanding your intention with some vagueness or, I mean, even if you're more detailed.

17:23yeah, I want a vegetarian restaurant that serves falafels and that has stuff. You should be able to get this that's open now, near me, like all the kind of context that you're getting. True, I didn't think of that. But yeah, even if you have more details, you now get better results. Yeah, so either if you have vague query or if you have actually more details. So both of this seems to work better. and, you know, clearly this doesn't stop there yet because what we're seeing with the large language models, they're able to gather a lot of information on their own, right? And so they're able to, like, things like, what is the capital of France?

18:08You don't really need to kind of do the search for it, right? So this is, you know, one part of, like, it's all in parametric memory of the model. And so AI mode is able to communicate with you in, obviously, it's like even longer queries or longer discussions because it also enables you to do the multi-turn thing. And I mean, you have like different tools that do all that, right? So like with Gemini being like Google's version, but obviously others like JGPT, et cetera, have been there. And we do see that users like that. So the users like the conversational aspect, the user like to communicate longer, and so on.

18:52So AI mode is kind of a search's answer to that. And we have also seen, obviously, not every user in the world is going to some of these chatbots. And obviously, AI mode is kind of a part of search. So the users of search might actually want to use that and see how it's like. and you do have also the option to transition from the AI overviews to AI mode if you want to kind of explore more and have like a longer conversation and more detail. So I think it's an overall really, really nice addition. And I get myself like many times entering query on search or maybe directly into AI mode or like going to the AI overviews and say, maybe I want like a longer conversation and then I'll go to AI mode.

19:35AI mode is also still using the search, right? So it does have its own fan outs. It does have the linked results and citations as well. So it is kind of, in essence, still based on this kind of standard concept of how we do things on search. But on its own, it has a kind of a bigger, well, like the infrastructure is new and it has kind of bigger ownership or like it's no longer an isolation of it. It's like the AI mode, it runs on search, but it also has a bigger platform for its own. I'm still processing the fact that, yeah, of course, it works in both directions. It also works with if you have more details.

20:19And I just like the ability to have multimodal search, and I think AI mode just adds to that, really, and that's pretty cool. 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 like 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?

21:10What is it that we do to make sure the ecosystem thrives with these new features? Yeah, the ecosystem impact and like, I think, as you said, I've been on two, three search central lives, like twice in Zurich, once in Madrid. 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, I think 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.

22:14So if, for example, you're selling something, you have a product or a platform, you have some subscriptions, et cetera, you clearly will, if you are providing value to your clients, like they will continue coming to you. We were talking about restaurants, right? Obviously, if you're like putting a menu, et cetera. So yeah, the users will eventually come as well to either your restaurant. So they will like go over and see. So in the AI-centric or AI-oriented system, I think those kind of bringing the value still continues. But just like in kind of the previous evolutionary or revolutionary steps, like on how the media has been disseminated, thinking about the newspapers, the radio, the TV, like the internet, all the stuff, like all of these things also kind of remain to be in this world, but people needed to continue providing value because if you don't provide value, nobody's going to buy your like a newspaper or book or like nobody's going to listen to the radio or to the podcast.

23:16But so I think everybody, like including all of us, like there is a lot of questions, right? Like is AI going to take our jobs and so on? I think we all need to continue thinking, like, 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 kind of this is one of my recommendations to all the SEO professionals and site owners and, like, 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.

23:53Now, obviously, I don't think we would over here recommend the best possible way is to just multiply all the content and just generate because it's cheap and easy and now we're going to generate. 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 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 the AI can help you better understand your data. Maybe AI can help you understand the competition potentially better as well. And so on. So clearly, this is something we can advise.

24:33I find that really interesting because I'm seeing a lot of excitement at the same time, a lot of varying in the community, in the ecosystem. And I think it is like that because on one hand, it democratizes a lot of stuff that has been traditionally difficult to do or just cumbersome to do. At the same time, some people have misunderstood whatever it was that they are trying to accomplish or to provide to be these cumbersome bits and only these cumbersome bits, right so to give you an example um when it comes to let's say uh writing articles about i don't know lifestyle or technical topics because i'm more like a geek so i'm reading more technical things right i really enjoyed when people were giving me like interesting details of technology from the days past much older than i am so i i wouldn't have any touching points with technology from the 60s or the 70s.

25:37And someone was like, hey, did you know that the displays in old hi-fi devices worked like this? That was a really interesting article. But obviously, they also went and explained what their experiences were with new technology as it came out and as they were provided with samples sometimes even. And that was interesting. But eventually, that into them effectively, 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.

26:15It says it on the box. You don't have to tell me that this is now a three gigahertz processor. It says it on the box. Thank you. And I had like a key moment when I was buying a joystick back in the days for computer game and I didn't know what force feedback was and that's effectively like you have like a different resistance and it might like move and vibrate the device if there's like any shaking happening in the 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, oh, that means that this joystick has force feedback.

26:58Right? 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. Like you don't have to spend as much time to kind and I rattle off the spec sheets into a more readable human conversational form. But chatbots do that. So you don't necessarily have to do that on your website anymore. But maybe you have tested it, and you found it to be particularly good for your use case or particularly unfit for your use case.

27:35And then you can share this insight that AI doesn't have. It doesn't know. It hasn't used the technology. It doesn't know this. But you do. So you're the expert, and I might be coming. if you're using your electronics the way that I use them, I might be interested in your opinion. I might not be interested in this other person's opinion because they are using the electronics differently. But that's fine because there are other people who are using their electronics the same way as they do, just not me. So I think there is still enough space online for different outlets and people and opinions and experiences.

28:09But 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. Yeah, I absolutely agree. I get often kind of nervous when I see the kind of AI-style reports. Obviously, internally, we want to use these tools. They help me understand the documentation more easily. They can ask questions like, Notebook has been a fascinating tool that can, in a couple of minutes, explain a complicated thing. So yeah, I do believe there is still a need for the human touch on top of all of that.

28:56I do think we need to understand the capabilities of the tools, but in the end, us providing the value, us making sure that, yes, we're bringing something to the table, and I think that's where we want to focus. But yeah, are you using the coding tools? Interestingly enough, yes. And that's exactly where this stuff comes in so handily. So the code base in Google is huge because it has a lot of stuff and you've seen it yourself. And just a couple of days ago, we stumbled upon a specific piece of code and it was going through like lots of layers of indirection and abstraction to do something. And we had a hypothesis where this is going in the end, but we didn't know.

29:39So we asked our internal tools, like, so we found this thing that does this thing, but where does the information actually go? So it was basically like, we found this method that tells us how big an image is. Where does this information come from? Does it have to download it or does it use, like, the image in index for this? and we could have found that ourselves by going like 20, 30 minutes through abstraction layer after abstraction layer to finally get to where it's coming from or we just ask the system and it's like, oh, this is coming from here and that was the right spot and we're like, oh yeah, okay so it comes from where we expect it to come from cool, that's good to know so it is useful, it does help it makes things faster, right?

30:22doesn't replace us making the effort of figuring out if what we're doing makes sense in the first place and if it takes the right trade-offs and if it's the right choice. Those things, I think, are not that automatable or AI-able yet. Yet. Maybe yet. That might change, right? But I think these tools are useful. But yeah, you're absolutely right. It depends on how you use these tools. But yeah, and on top of that, I think there's always risk of introducing a bug that you don't understand and so on. So I think the whole discussion of how is the software of the future is going to be maintained by the AI and will remain human maintainable or understandable.