AEO vs GEO vs AIO: How AI Search Picks Who It Names

Everyone's talking about AEO, GEO, AIO right now, but most of what you hear is confusing or just straight up wrong. But while companies are still arguing about acronyms that buyers have already moved, half of B2B software buyers now start their research in an AI chatbot instead of Google. And when your buyer asks ChatGPT who they should hire, it doesn't give them 10 blue links. It names three or four companies. And if you're not one of them, that deal just went to a competitor and you'll never even know it. No click, no visit, and nothing in your analytics. So, in this video, I'll break down what these terms actually mean, how AI decides what companies to recommend, and how you become one of them, even if you're up against much bigger competitors. I'm Frederik. I'm founder of Danish Lead Co and SourceRank AI . We've been working on getting brands recommended by AI since ChatGPT first came out at the end of 2022. And since then we've optimized quite a few B2B brands for it. So everything in this video comes from what we've seen when we run real buyer questions through ChatGPT , Gemini, Claude, Perplexity and Google every week, not theory. Right? So first of all understanding like the difference between Google and these AI models. The basic difference is when you search on Google, it shows you you know 10 links on the first page something like that and then you have to do the judging. So you have to judge you know which of those to actually go in and then you know look at those pages. But AI essentially does that step for you. So it actually does those uh searches or it searches its internal knowledge base which is practically like a web in itself. And the difference there is as well instead of it just being a keyword or short search that like users do on Google, it also already has a bunch of context because most of the time it's not the first time that they've used you know that AI model ChatGPT or whatever. So then it does its own searches which is called fan-out queries most of the time but now they're actually starting to do more and more like just use their own knowledge base which is essentially all the information that it's been trained on. So then it doesn't even go out and actually search the web, but it still uses articles that are on the web that is just saved in its knowledge base. The difference between the two is essentially just that the knowledge base is not updated as often. So then it goes and then it actually reads these lists. It reads the articles. It reads the directories. It reads the reviews online and then it judges them and then it names a few that it thinks is the best fit for you. So now essentially AI does the judging that you used to have to do on Google. And it doesn't just do the judging. People also trust it. People like practically trust it more than their own bomb. So when someone shows up uh where ChatGPT has recommended your company, they practically come up half sold because they already trust that you're the best company in the bunch or at least one of the best companies in the bunch. So if I open an incognito tab and search on Google best outbound agency for boutique investment banks, Danish Lead Co which is our company shows up as uh the first results and actually quite a few of our pages and as you can see like this page is even a listicle that we've written where we rank ourselves as the top agency and where we compare ourselves to a bunch of other competitors like Belkins, CIENCE, Martal, SalesRoads um essentially you know, a bunch of other agencies that also do outbound for PE and M&A deal um origination. And if I then open a ChatGPT without actually logging in and I ask it the same question, we actually come up as the first one here as well. DealSource Systems , that's our subbrand for PE and M&A. And then it links to like it essentially gives a recommendation here of a bunch of other uh companies here. And the interesting thing here is that these are not the same. Like it's not using the same articles. Um, and it's not the same uh companies that are showing up here compared to the Google search that we did before. Like none of these are the same. So this just shows just because you did well on SEO doesn't mean you do well on AEO or on AI visibility. Okay. So what's actually the difference between these different acronyms? AEO, GEO, AIO, uh, and there's a bunch of others. So, AEO stands for answer engine optimization. It means actually optimizing your website or whatever your online presence to be recommended, meaning being mentioned by these AI models. GEO stands for generative engine optimization, which stems from a Princeton paper from 2023 and is also widely used. I think those two are the like the most widely used. And here you're essentially referring to actually being cited so that these AI models picks up your articles or your website and actually uses you as a source, but not necessarily that you're being recommended by these AI models. So that's the primary difference. And of course, we want to be optimizing for both, but at the end of the day, it doesn't really make a difference if they're using your, you know, site as a source. You want to be recommended by these AI models because that's what generates meeting or deals or generates traffic to your website and eventually revenue. And then there's AIO which I think is less used and mostly just people that are mispronouncing AEO but most of the time it refers to Google's AI overviews which uh is essentially a feature. Um, so when you search on Google now, they have this AI overviews which is at the top before it actually shows all of the usual links and all of the usual websites. The important thing here is that they have different names, but it's the same goal. Like you're trying to be uh recommended by these AI models to generate revenue. And Google is saying that they see AEO whatever as the same thing as SEO. But that's Google and that's of course because Google is a search engine and these models that they've built are built on top of that. But ChatGPT and Claude and Perplexity, they're their own models and they're not a search engine. So why is it that just because you're ranking on Google, it doesn't necessarily mean that you're being recommended by these AI models. Essentially, it's because the AI models, as I mentioned before, they use their knowledge base. So that all the information that they're trained on which is not as upto-date or they use their own uh search engines. So when they go out and make web searches they use their own like searches agents to do that. So for example ChatGPT primarily uses Bing because they have a collaboration partnership with Bing and Claude uses Brave which is like also a search engine but a smaller one that I don't I think most people don't use but it's essentially built for these AI models. Copilot of course uses Bing because that's the Microsoft world and then there's Gemini which of course uses Google and then on top of that on top of the fact that they're you know using different search engines they also do different fan-out queries. So when they do the searches, they also search for different things cuz they don't just, you know, you don't just give it like one phrase and if you do, it doesn't just go and search for that one phrase. It goes and searches for a bunch of phrases and then it finds all the most relevant articles and, you know, websites and directories from that. So of course that leads to different results. And in fact, Ahrefs actually did a study on this and it's only 8% of what ChatGPT mentions or sites that is even ranking top 10 on Google for the same queries. So for example, if I go to an incognito tab and I search for outbound lead generation agency for M&A advisory firms and I scroll past all the sponsored results, then it's showing Hypergen, GrowthFlare, ProxDeal, Pearl Lemon Leads . If we then go to ChatGPT and then we ask the same question, it's only actually one of them which is the first one here that's the same uh that's also mentioned in that search Hypergen. So obviously Hypergen is doing pretty well here but all the others are different and are not like even ranking at all on Google on like top anything. Okay, so a quick one. If you want to know if your website is even ready for this, we built a free AI visibility scan. basically checks whether AI crawlers can actually read your site and your structured data and whether your pages are actually answering uh the questions that buyers ask. It takes a couple of minutes and you'll find the link in the description. So, how does these AI models actually decide who to recommend or who to mention? So, the core is that the AI models are essentially trying to do the same thing that you would do in general. They just do it more thoroughly. So the core uh like basis of all of it and what you like what I want you to keep in mind like always is you're trying like you should be trying to make it as easy for the AI to understand exactly when and why to recommend you and when not to. And in general, specificity beats size. Meaning you can beat some of your like some of the bigger competitors out there if you can win some of the more specific things where you in general are better than they are. So for example, if you're an M&A firm, like you specialize or you have a specific case study from working with a behavioral health clinic and you have that on your website and you've focused on that specific sector, which there's still like plenty of deals to be made there. If that is very specific on your website and you have specific pages that answer the questions that those um people might be asking, then the AI is much more likely to recommend you or at least recommend you as one of the options alongside, you know, some of the bigger players simply because you are so specific. So even though you might not have that many reviews or you might not be listed that many directories or your domain score might not be that good, it still mentions you because you answer this specific question. So when a 56 year old behavioral health clinic owner goes and searches and ChatGPT goes out and does a bunch of fan-out queries, it finds those specific pages. So you know, for example, if you had a case study with a 50-year-old or 60-year-old person who like owner of a behavioral health clinic that just sold. So it's that level of specificity that's essentially how you beat the the large players, the the big agencies and the big companies out there. So, you want to be writing these detailed case studies where essentially going through, what was the problem, how did you solve it, what was the outcome for the like client you solved it for? Because those types of case studies, it generally do super well with these AI models because they're super specific. And then you want to be creating these honest comparison reviews. So, for example, like the reviews where they're ranking for us both on ChatGPT and on Google. You're essentially creating these comparison pages where you're comparing uh yourself to like the biggest competitors out there, but do it in a way where it's still honest and where you're actually like you're simply saying these are the things that they're doing. These are the things that we're doing. And in general, you can go into more depth about what you do and why you do it well, you know, because you know all of your processes and everything that you do where of course you won't know all of those things for your competitors. And then the thing that most people miss or most people don't really do is getting on the sources that these AI models are already using. So all the articles that it's already using as the context, as the basis of the recommendations that it gives. So, for example, for our competitors, and we actually get a bunch of competitors reaching out, for example, for a comparison page like this, wanting to be listed on this comparison page. And that's essentially what you should be doing as well, reaching out to the articles and to the sources, to the like comparison pages and listicles and like whatever it might be, um, directories so that you know, you're essentially pitching yourself as being listed on those because you know those are already being used. So if you can get on those, then that's almost like a surefire way of actually being recommended by the AI models and also ranking on Google for that matter. And then lastly, you want to have the same description everywhere across the web. So everywhere that you're mentioned or your company is mentioned, it should essentially like describe the same thing. It doesn't have to be the exact same wording, but the amount of clients we work with where then they have like an old LinkedIn company page or they have a profile and clutch or some directory or review site which with the wrong uh industry or wrong founded date or whatever it might be. And all of that essentially just lowers the AI's confidence in your company and what your company does. So the more coherent that can be, the more confident the AI is of course going to be in actually recommending you because it feels like it knows exactly who you are and what you do, which comes back to the first point. And then there's foundational technical things like adding schema to your website and FAQ and FAQ markup and uh llms.txt and all of these things that can make a difference as well, but it's not what is going to like that isolated is not going to make you suddenly be recommended by these AI models. That's all the things above. So essentially if I go to again ChatGPT or Google actually let's just use ChatGPT and I search for a super general query where like there's thousands of people every single day searching for this best B2B lead generation agency and we'd love to rank for it but it's practically impossible because it's such a general question so there's no way that we can win on like specificity on that. So here all the big players are ranking Belkins and CIENCE and Callbox all of these super big agencies. And if I then go and make one of these very specific queries such as outbound agency for private equity deal sourcing then now suddenly the firms that it recommends um and again here it both recommends Danish Lead Co and DealSource Systems. So here it like recommends completely different um companies and here these searches are the ones that we can win and they're also the ones that are actually like the highest value for us because this is also the client where we perform the best and we're essentially winning this query because we have these subpages that it's citing where we're literally saying this outbound deal origination for private equity firms and that's the only way that you win these queries. If you want the full picture where we run real questions your buyers ask through ChatGPT, Gemini and the rest and show you who gets recommended instead of you, book a call and then we'll have that audit ready for the call. So, a big thing in this and something that I think many people actually miss is not trying to optimize for these super generic questions. Unless you're like a huge company, unless you're, you know, Goldman Sachs or whatever, then of course you can optimize for those as well because you have like the overall presence and you're mentioned so so many places on the web. But when you don't have that, be strategic about the queries and the keywords that you're actually trying to win. So what we essentially look at is where's most valuable to you. So where do you have the best clients that generates the most revenue for you and where you have the best case studies and where in general like do the best service to them. And then also we look at like the actual queries and the market and where is it actually possible to win those queries. And then of course that's the aspect of also not being too specific because if if you end up being too specific and there's like one person searching for that every year then of course it's not high value either. You want the perfect balance between people actually searching for it and it being super specific so you can actually win it. So most people are optimizing for prompts that they simply can't win or they actually don't make money from. And then there's the part of actually implementing all of this and starting to produce this content. And and of course here it's like it's stupid not to use AI in this day and age, but the way that you use AI is like the difference between ranking tomorrow and continuing to rank over the next 5 years. So essentially, if you're lazy and you just don't give it anything to actually work with, you don't give it anything proprietary about you, any specific case studies about you or any like intellectual property, anything that will make that post or that page that it creates different from all the other people that are also lazy and goes and asks it to do the exact same thing. then it might rank tomorrow because there's like not that many people that that has done it yet, but it's not going to keep ranking because there's so many people that are starting to actually do all of this content now. And then another mistake is producing a bunch of content that is not actually bottom-funnel. So, it doesn't actually answer the buyer queries. It answers all the research queries, all the like how-tos. And like a great example of that is Sopro, which is an agency as well. They rank for 1,350 Google keywords, which is great. The problem is it's not named by ChatGPT in any of the queries that we track. For example, for the ones that they would want to be listed for, like best B2B lead generation agency, but it uses all of their blog posts and all of their like pages as sources. it just doesn't actually recommend them because they haven't actually optimized for the buying queries and for actually being recommended. They've just optimized for being cited and being like the sources that the AI models and Google uses. And then the last mistake is not actually tracking it. So like most people don't actually know who these AI models are recommending instead of you or what the sources are that it's using to come up with its queries. So, you want to be essentially tracking hundreds of prompts, like potential prompts that your ideal customers would be asking these AI models through all of these AI models on almost a daily basis to actually see like who's it recommending today? Uh why is it recommending those meaning like what sources is it using and where are the places where you can actually go in and beat them? So, where there's not that great articles already written. So you can see it like fumbling a bit and actually like the ones that it recommends are not really the best ones and those are then the ones that you want to go in and win. So understanding how AI search works is important but knowing concepts is very different from actually getting your company mentioned when buyers ask ChatGPT for recommendations. So that takes knowing which questions are even worth winning where your gaps are and which sources the AI trusts in your market. So, if you want to see where you stand today, start with a free scan or book a call and we'll run your buyers' questions for you. Both links are in the description. Then watch our next video, the complete guide to AI search optimization, because that's where you start turning AI search into a real acquisition