How to get discovered in AI search

AI search is changing how people discover information and how brands need to think about visibility. Daniel and Chris talk with Liam Dunne and Ben Moore, co-founders of Discovered Labs, about the shift from traditional SEO to AI search, what happens behind the scenes when an LLM generates an answer, and why retrieval and citations don't always tell the whole story. They dig into AI visibility, Reddit strategy, query fan-out, representation in model weights, and consensus. They also look ahead to a world where AI agents don't just discover websites, but interact with them and take actions on behalf of users. Featuring: Liam Dunne – LinkedIn https://www.linkedin.com/in/liamdunne05/Ben Moore – LinkedIn https://www.linkedin.com/in/benkarlmoore/Daniel Whitenack – Website https://www.datadan.io/, GitHub https://github.com/dwhitena, X https://x.com/dwhitenaChris Benson – Website https://chrisbenson.com/, LinkedIn https://www.linkedin.com/in/chrisbenson, Bluesky https://bsky.app/profile/chrisbenson.bsky.social, GitHub https://github.com/chrisbenson, X https://x.com/chrisbensonLinks: What AI models know about 1,000 B2B SaaS brands before web search https://discoveredlabs.com/research/ai-model-brand-recall-studyDiscovered Labs https://discoveredlabs.com/Sponsors: Midwest AI Summit: Join AI practitioners on October 15 in Indianapolis for practical sessions, hands-on discussions, and real-world AI solutions. Use code PracticalAI20 to save 20% on your registration. https://midwestaisummit.com/#ticketsFramer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalai http://framer.com/practicalaiResources and Events: Register for upcoming webinars here https://practicalai.fm/webinars! Prior Webinars from our partner Prediction Guard https://www.youtube.com/playlist?list=PLRpRdFmBfdF6boDgAD3nNaLm8Yx4KN2VtMidwest AI Summit 2026 https://midwestaisummit.com/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Speaker 00

  Welcome to the Practical AI Podcast, where we break down the real-world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind-the-scenes content, and AI insights. You can learn more at practicalai.fm. Now, onto the show.

• 0:41Daniel Whitenack?

  Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I am CEO at Prediction Guard, and I'm joined, as always, by my co-host, Chris Benson, who is a Principal AI and Autonomy Research Engineer. How are you doing, Chris? Hey, doing great today, Daniel. How's it going? It's going great. It's good to see you. You're visible to me. And that's an interesting part of the topic today. An awkward segue into how do things become visible to us on the internet these days, which seems to be increasingly through AI platforms, AI chat interfaces,

• 1:24Daniel Whitenack?

  answer engines, whatever you call them. And today, we're privileged to have with us Liam Dunn and Ben Moore, who are co-founders at Discovered Labs, to talk through some of these things. Welcome, Liam and Ben. Great to have you.

• 1:38Speaker 02

  Good to be here. Thank you.

• 1:41Daniel Whitenack?

  Well, for those that maybe are less familiar with this topic in general around AEO, GEO, answer engine optimization, AI visibility, whatever kind of term is around this, and maybe there are differences between those terms. But for those that aren't as familiar, could you all give us a context for what those things mean? And then also, how you're involved in those topics day to day? What you're doing at Discovered Labs? Which is the context that you're doing some of the work that we'll talk about?

• 2:17Speaker 02

  Cool. Yeah, so I would say, and everyone's got a different opinion on this,

• 2:23Speaker 02

  feel free to take mine with a pinch of salt. So I would say AI search is the broad category. And then within that, you'd have answer engine optimization. Some people say, which is AEO, some people say GEO, generative engine optimization. I view those as the same thing, and I just call it AEO. Honestly, for a very simple reason, there are a lot of venture-funded companies that have spent a lot of money on that term. And so I'm just gonna fly behind them and lean into it.

• 2:59Speaker 02

  The background of us, how we're involved with this. So we're co-founders at Discovered Labs. It's an organic search agency. So we provide end-to-end services. Now, organic search for us splits into two buckets. You've got traditional SEO, and you have AI search. So traditional SEO, we want our clients to be at the top of Google whenever people are searching related keywords. AI search, we want our clients to be appearing inside LLMs in a way that they want to be. And so we view that as the AI search side of things.

• 3:30Daniel Whitenack?

  Gotcha. And I guess maybe, for context, people might be familiar with SEO, search engine optimization. And is it? Maybe help orient us. Is SEO, is that still a thing? Is this topic basically replacing that? Is it kind of? How do the two interact? I mean, this is always, I guess this is always a fluid thing. But if I am a company, this, you know, I'm a company in 2026. Like, what is most important? And how are people thinking about the effort they're putting into maybe traditional SEO versus these topics?

• 4:15Daniel Whitenack?

  Do you have any thoughts there?

• 4:16Speaker 02

  I do. Feel free to. But, you know, I can waffle too much, so feel free to ask me questions. I'll go deeper. But ultimately, you won't hear me say that SEO is dead. It's not an opinion I hold. I just think that the space has grown. We're doing the same three jobs that's on-page. So looking after your owned website, we're doing off-page, which is building brand authority. And we're doing technical, which is making sure agents and Google Systems can access your website and understand information. It's the same three jobs. For me, it comes down to a matter of tactical priorities. And I think this is where people get lost.

• 5:01Speaker 02

  I do think there are some things, like depending on what the goal is, right? Like, to rank number one on Google for a commercial keyword, you're probably going to use different tactics than if you were to optimize for a surface area like ChatGPT. Again, you're gonna be doing the same jobs. You're gonna be creating content. You're gonna be building the brand of your company. You're gonna be optimizing for technical SEO. But the tactical priorities is where it changes. Like, for example, what does good content actually mean from a perspective of Google versus ChatGPT? So ultimately, SEO is still a thing. It's now expanded. We now have AEO. Those together, I call organic search. We're doing the same three jobs, but the tactical priorities

• 5:46Speaker 02

  have just changed a bit, I would say.

• 5:47Daniel Whitenack?

  Could you talk a little bit about, like, if you are a business owner out there, and you're marketing, you have your website out there, you've got your social, how has this changed? Like, if you were to take a snapshot of the industry, and a long time ago, I was in the digital marketing industry for a while. And that was obviously before AI came thing. And you were focused on these things minus the AI engine optimization. And so how has that changed? Like, how is the thinking of the marketing department changing to accommodate the fact that you now have this whole way that people are going out and searching? And all of us are AI-first. That's very different from maybe 15 years ago.

• 6:32Daniel Whitenack?

  How should business owners listening be thinking about that in terms of how they approach the whole thing?

• 6:39Speaker 02

  Yeah, so broad topic.

• 6:41Daniel Whitenack?

  It is.

• 6:42Speaker 02

  So I would say, no, no, no, it's all good. So I would say the first thing is buyer behavior. So people researching and consuming information inside LLMs has changed buyer behavior. And so the symptom of this is people will see that, hey, like, where's all my organic traffic gone? And it's because that organic traffic, a big chunk of it, was created by people consuming information on your website. But if people are consuming information inside an LM, the LM is the new website visitor. They're taking that information. They're chewing it up, and they're spitting it back out to the user inside an LM. And so you've now lost those clicks. So I think that's the first thing.

• 7:26Speaker 02

  And so the implications there are, well, how do we measure that? And that's really challenging. I'm a marketer. Ben is the engineer.

• 7:37Speaker 02

  And measurement has always been like a nightmare for marketers. And so this concept of zero-click research has been around for a while. I think AI has just sped that up a bit. We've been dealing with that on social platforms. I think now organic search is starting to feel that. So definitely a change in buyer behavior. You know, it's probably very similar as when the mobile came around. I think we're seeing some changes there.

• 8:06Speaker 02

  And then also, I kind of touched on it there. There is now just a new visitor to your website. So websites, for the last however many decades they've been around, have really been optimized around the human visitor. That's why we have user experience. How do we make the navigate? That's why websites look the way they do today, right? Like, how do we shape the navigation bar? Like, conversion rate optimization, all of these things of the human visitor.

• 8:35Speaker 03

  Whereas now it's these agents visiting your website. And so I think there's some interesting considerations there of, well, what are they looking for? And what state does your website need to be in for them to be able to access information and understand you? And so I think there's some considerations there. I don't know where this ends. I know there are a lot of companies out there that are building towards this. I you have like one version of your website for humans, one website version for agents. But I'd say those are like the, and then, then obviously there's a bunch of implications like downstream of all of those things. So does that answer your question? I have to go to deep, deeper into it.

• 9:16Chris Benson?

  No, it's a great start. I appreciate it kind of laid the landscape out there. And I know Daniel has one, but after that I got plenty more.

• 9:24Daniel Whitenack?

  Yeah, I think my, what triggered in my mind, Liam, as you were talking about that is, is that measurement piece? One of the things that I think I appreciate about your all's approach and both from a, both from a company standpoint, but also a research standpoint is you're interested in knowing actually how the mechanics work around these things and understanding it at a deeper level. Ben, I'm, I'm wondering if kind of turning to that technical side, you've published a variety of research over time and you'll have more coming. Heard a little bit about that before the interview and we were talking, but for example, like citations or mentions in these answer engines or in AI systems.

• 10:09Daniel Whitenack?

  From a technical standpoint, like obviously I, as a human can go into chat GPT or Gemini or Claude or whatever, and I can test out some prompts and see what's cited. How from a technical standpoint, like what's required from the technical standpoint to actually have, have a scaffolding and a mechanism that you can track your, like, what does it mean to track your visibility in these systems over time? What are the relevant things? Like I mentioned a couple of things, citations, mentions, like what are the relevant things that you're looking at? And at a high level, like what does it take to put the right tooling in place to actually measure and track that?

• 10:51Speaker 03

  Yeah.

• 10:53Speaker 03

  Yes, as Liam said, it's a big question, but it's a great one. So I would say this is probably the thing that has probably shaken a lot of the SEO industry at its core a lot. In a sense that everyone kind of showed up, you know, in the past with traditional SEO and said, look, great. Let's like kind of color by, is it like paint by numbers kind of thing? Like you fill in the colors and we do this, we do that. Everything is great, everyone's happy and no one really actually thinks about like what's going on here. And you could go into like, essentially unless there was a Google core update, essentially like the rankings were essentially very deterministic and you had a set of keywords. And it was like pretty finite, pretty manageable, all pretty controllable. And again, paint by numbers. And now we've kind of like gone into a system and go really deep on this as needed.

• 11:38Speaker 03

  But we've gone to a space where there are sources of like a non-determinism or stochastic elements that people need to start modeling around. And the SEO industry, and we've seen it as a whole, doesn't have like great statisticians or like great data science literacy, just being blunt. And if you're any serious like statistician or control engineer, you would look at the system you're probing. And you would say, well, like, what are the requirements on the signals I'm trying to measure here in order to bound the noise on these, these, these, these, these measurements essentially? And the, and the quantities you're trying to measure. And then you would say, okay, well, then you'd set up your model of the system.

• 12:22Speaker 03

  You, you like essentially a lot of these LM agents are essentially gray boxes. They're not totally black boxes. There are research papers out there, there are hints in the, the, in the network traffic and some of the, if you can decrypt the packets, there's also in information there. And there's also like distilled models. So when I looked at the training data, you can also figure out like some information from the distilled models. And so the thing is to set up basically, basically experimentation, a kind of engine or pipeline, whatever you want to call it. And then say, well, how do I bound the, you mentioned their citation rate, mention rate, share of voice? How do I bound the uncertainty on those metrics? And what do I need to bound to basically bring that uncertainty under, let's say, 5% margin of error? And to be honest, no one in the industry is really doing this.

• 13:05Speaker 03

  Or even are you thinking about if people go on, they buy a profound or some other thing? And they're like, here's my prompts, let's go away, go do this. A profound aren't interested in it either. They just like to send you something. And then everyone says, oh, look, number goes up. How lovely. And then the SEO industry has like an incredible ability to point at numbers going up and claim that it was them. And then as an also incredible ability to point at a number that's going down and claim it's Google core update.

• 13:38Speaker 03

  So yeah, I just think in general, I can go into like how we think about this and how like the various systems are like displaced here and how we think about bounding this randomness. But in general, you need to approach it like, how am I going to model this system that I know hints about? And with the data we have, and then how do we bound that, that basically uncertainty on those quantities? We're trying to measure for, as you mentioned there, Chris, like for the business that has whatever they're selling, you know, gardening tools, whatever it is, right? That's, that's, that's what people need to be getting to. And no one's really talking about that. And I said that it does take a lot of time. But I also think we've got to the point where it's like, we've got to like stock market dynamics. Like there was a change in Reddit recently.

• 14:20Speaker 03

  And, you know, no one also really knows, is it meaningful? So lots of times we have to come to our clients and say, so let's say, I'll give you a pro example, right? Yeah, they'll be like, you'll probably have CNN on the background there. And someone will say like, or ABC News, whatever it is. And then they come down, they say, oh, Dow Jones is down 5%. Everyone freaks out. And if you actually look at the, basically the standard deviation over the last year, you'd be like, well, this is not statistically significant. As a very basic check, like it's very basic, but we need to print news, so we do that. And the SEO community has a bit, bit around that. And also people just want to like get eyeballs on stuff. But again, you know, a lot of people waste a lot of marketing hours and a lot of engineering time around things that aren't statistically significant. And that's a very basic thing that we can check. Like that's not like, that's not rock science, but there's a very simple example.

• 15:04Speaker 03

  Anyway, that's why I think we're like, kind of like state of the tradition SEO where we are now and where people are freaking out around it and where we need to add.

• 15:16Daniel Whitenack?

  I don't know about you, but I'm sick and tired of showing up at AI or technology events and immediately realizing that they're just going to be full of sales pitches or things that aren't useful for my day to day. I have more than enough to fill my time and I really need to focus on bringing back value to my company and my day to day work. That's exactly what the Midwest AI Summit is about. October 15th in Indianapolis, Indiana. We, we as the podcast are partnering as a media partner with the Midwest AI Summit. And there's just an amazing amount of practical value that you'll get if you attend the Midwest AI Summit.

• 15:59Daniel Whitenack?

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• 16:38Daniel Whitenack?

  So check it out at Midwestaisummit.com and use the code PRACTICALAI20 for 20% off registration.

• 16:50Daniel Whitenack?

  So Ben, I want to ask a follow up question. You did a great job at kind of laying a foundation for maybe the, some of the things that we should be, some of the things that we should be thinking about statistically as we approach this topic. I'm wondering if you could help our listeners understand, like if I put in a prompt to one of these systems and a brand shows up or a thing that I want to show up, shows up. How, what are the mechanisms by which that can show up? And I know you've done some research across like citations and web search versus Reddit versus training data.

• 17:31Daniel Whitenack?

  Before we get into any of those, like very specifically, could you help us understand like what, what do those, like what happens behind the hood? How might I show up mechanically in, in one of these systems?

• 17:43Speaker 03

  Yeah. So let me dig into it. Just before I do, I would say to anyone that anyone listening for sure, do not run down a rabbit hole of being like, this is this one prompt and I would love to rank on it. You want to basically model like it from like basically a topical domain standpoint and get into more of that. But basically, yeah, what happens is essentially your prompt is fed in. It's, it's tokenized and then ingested by the LM. And then there's a essentially a reasoning stage, right? So basically the transformer basically has, has embedded your prompt and now looks at all those tokens and starts generating its like reasoning stage, base arts, training weights. And basically it has a, it's running this like forward pass.

• 18:26Speaker 03

  And there's typically two sources of randomness. And the reason I mention this is kind of important. It's like the kernel function. So there's like floating point error in GPUs. That's one source of randomness. And the second source of randomness is the actual temperature itself, like deliberate temperature randomness that generates the next token. And then the final part of this is like, it's also, I was speaking with Liam about this is now, there is now fingerprinting on these LM. So they'll also nudge and create hashing functions in the background to know where the content came from. Anyway, so it goes through this reasoning phase. Let's say you say like the best, I know gardening service in, let's say Dublin, Ireland. I'll use that example. And it'll then start reasoning and saying, oh, it'll have a certain set of criteria. It'll like do some reasoning before it makes any, you know, queries with its retrieval engine.

• 19:11Speaker 03

  And it's exposed to a series of tools and one of them is also going to be the retrieval engine. I'm saying a series of tools because when we get to agents, they could be exposed to a whole host of other things that you're not aware of. Then it accesses this like retrieval engine creates a set of basically query fanouts. You've probably heard the term.

• 19:29Speaker 03

  And then pulls back in basically a set of initial candidates essentially for to be pulled into the the context window. But before that happens, essentially, they look basically, it's not always exactly the same algorithm, but DeepMind had an AGREE style algorithm, it's called. And basically, it's a way to avoid hallucinations. And so, once they pulled in these, let's say, let's say forty to sixty sources from various places. It's done, let's say, another set of analysis on it, which is is almost across the board. It's very similar to the AGREE algorithm from DeepMind. It's very similar in Anthropic's models. It's obviously, it's in Gemini's models and also ChatGPT. And we've seen this in our own analysis.

• 20:11Speaker 03

  And also Claude moved to it recently when they moved to Fable and Fable five point one. Essentially, what it's doing now, trying to see like, well, what is the consensus? So I can basically use like basically majority voting system. So I don't make a mistake or I don't have that hallucination. And then it says, okay, cool, great. This is our kind of like distilled set of contexts we want to work with. Now, like, let's say, generate the final response essentially from that cleaned up context. And this actually kind of ties back to like Reddit, people freaking out about the final citation. And again, the way I look at like AI visibility, everyone's like, oh, we'd love to know about citations that are mentioned in the final output. But there's all this other part before we got there. So I talked about, you know, the weights, how you're showing up in the weights. And we did some research on this recently. How is the model reasoning?

• 20:56Speaker 03

  Like, what is it thinking? Like, when it makes like core decisions around your domain? Or be that bottom of funnel, top of funnel, whatever it is, it has a criteria it's going through. It's internal logic, just like a human would, essentially. It's not a human, but you get my point. And then finally, you have like your retrieval engine in a response. But you really need to be looking across all of those four pillars in order to dominate your domain. That's how that's how we look at it for all of our clients.

• 21:20Daniel Whitenack?

  There are lots of things to dive into, but before we do that, I'm curious. I actually want to go back to Liam for a second and and like, as I'm listening to Ben explain these things, the thing in my my head was kind of something that you were saying early on about, you know, how it changes behavior. And and I'm curious, as a marketer, I like the ability to go back and forth between the the technical and and the the human behavior side. How does how is that changing how the human, you know, it's not now that you have the agent going and doing this? What does that mean for the human that now has this this proxy in the agent going out and doing that? And how is that changing the behavior and potentially the transactions that are following up on that?

• 22:06Daniel Whitenack?

  Just to kind of come full circle for a moment.

• 22:08Speaker 02

  Yeah, so I guess I'll look at this from like demand and supply side. So from a demand side, the buyer, I think this is just a much better deal, right? Because if we if we look at search behavior pre LLM, trying to find relevant information on Google is just a nightmare, right? Because, you know, us marketers just ruin everything. And you'd look for things and like, you just get the list of ten URLs that you then have to go visit and research.

• 22:36Daniel Whitenack?

  Right.

• 22:37Speaker 02

  And whereas if we compare that today to like chat based LLMs, it's more conversational. So I could be like, hey, I'm Liam. This is my company. It's in this industry. We're doing this revenue. We're really struggling. These are our core constraints at the moment. We're looking for this solution. These are the attributes of the solution, you know, we're looking for a piece of software. Must have a free trial, must charge monthly, must integrate with HubSpot or CRM. All of these requirements, I can front load that. And then the LLM does its thing and it comes back with personalized recommendations. Now, I'm not saying that marketers are, you know, that's our job to influence those recommendations,

• 23:17Speaker 02

  but it's just a far better deal than like clicking through all those websites and finding the information myself. Like me personally, I just use it for everything, right? Date night, you know, meal prep, like buying software, everything. So from a demand side, I think it's it's a greater deal. And so therefore, I think more people are going to take the deal. From a supply side, the people who are the vendors, I would say, I probably look at this from two ways. So number one, if people are front loading all of that context, that now creates a really long tail distribution of queries that those models are using. It's no longer just best code email software, which is what people would search on Google.

• 24:00Speaker 02

  It's all of these entities and things are kind of hanging, you know, appended on to the end, right? The integrations, the the business model, all of these unique requirements. So that creates this long tail distribution of queries. Now, the great thing about long tails is they're less competitive. And so I think this has really been where the opportunity has been over the last year is where ultimately, I would simplify it down to relevancy be authority. Now, there's some nuance in there, but this is really where the edge was. And this is where, say, if I'm competing against HubSpot, who has dominated Google serps

• 24:41Speaker 02

  for all the money keywords I'd love to rank for, well, now I can create relevant content for those super niche queries that my buyer is searching inside an LLM. So I think there's that perspective is the long tail distribution means we can create relevant content that targets there. And then I think there's the authority consideration is, okay, well, if everyone has relevant content, how does the model then reason over who to trust? Who does it decide who to trust? Historically, it was traditional SEO. You know, we look at PageRank. It's, you know, how we know Google assesses authority of pages and domains.

• 25:23Speaker 02

  Again, it's it comes down to consensus. It's not as weighted, is my understanding. You know, Google, Google have publicly said this, it's not as weighted nowadays.

• 25:34Speaker 02

  What's like the LLM's version of of that? And this is what seems to be changing a lot at the moment, right? Because marketers use a bunch of tactics, they work well. Everyone starts using those tactics. Law of shitty click throughs kicks in. And so then they diminish. And then we need to find the next edge. Or like, you know, that these vendors like patch, patch the vulnerability, basically. This is basically what we're doing is we're exploiting systems, you know. SEO is just exploiting Google's systems.

• 26:05Speaker 02

  So I think there's like a whole thing there of like, well, how do I ensure that my brand, my brand's information is chosen as a candidate and and the position I want is communicated back to the user inside the LLM? Like, that's how I'm I'm viewing it at the moment, from like a marketing perspective.

• 26:22Daniel Whitenack?

  Yeah, that's that's really helpful. And I guess to that point, there can be, I I you mentioned this, Liam, like, or maybe it was Ben, like, trying to get people to think more about like, oh, here's the prompt I want to optimize around. And I know something that like people have talked about to me from various aspects is, oh, you need like a Reddit strategy to to be to to do great in AI visibility. And we were we were chatting a bit about this before the conversation came about. But I know you all have done some research, both earlier research on like what actually gets cited in these LLM systems, but then more recent research around the the weight that

• 27:05Daniel Whitenack?

  and the weight and the importance of kind of a Reddit strategy and how that surfaced visibly versus influencing some of that behinds that behind the scenes activity that you were mentioning before, Ben? Do you want to just kind of help us understand some of the research that you've done there? And maybe that Reddit piece or or other things that kind of influence that that citation that are that are relevant in in relation to Reddit?

• 27:33Speaker 03

  Sure, yeah. So on the Reddit front, so obviously, as I mentioned, there's obviously the kind of like the fourth pillar of like AI visibility, the actual response, and there's the reasoning or retrieval stage. And then, but we did a piece of research on the ChatGPT retrieval engine at the time. And it allocated like, but I think almost a third of its basically retrieval slots for Reddit. So if it could find relevant threads for for that topic or that query, it would basically inject that into the context window at the time. And then obviously, as it does, like, as it gets those those Reddit threads, let's say, thirty percent of its retrieval, let's say sixty slots, twenty percent of them are allocated to Reddit. Most of those were actually rejected at citation time.

• 28:18Speaker 03

  So they basically are kind of used to ground the model and formulate its thinking, but often not actually cited in the final response. And then there's also been, so that's one aspect of it. And then there's also also the long form Reddit, like AMAs, questions and Reddit training data is also used in the the training data itself. And I think it's maybe around, it's it's typically around the oral HF

• 28:45Speaker 03

  stage where it's like a reinforcement, right, from human feedback. And that's basically golden training data for them for that. And we actually see when we looked at this, there is traces of basically Reddit showing up in the ways, particularly from ChatGPT, as well as Gemini. When we looked at the basically the open source models, it's not like super present, but you can see it, it's it's there. So with those in mind, I think, you know, as I said, people say, oh, well, citations have dropped. It's kind of like saying, you know, it's one signal on the engine, I would say, but it's not everything. And ultimately, if you wanna, as I said, if you wanna basically, you know, move the narrative for your brand or own in a certain direction, you need to look at all sides,

• 29:29Speaker 03

  the reasoning engine, the retrieval side of it, as well as the response and the weights.

• 29:35Speaker 03

  So yeah, I would say Reddit is still very much a big player.

• 29:39Daniel Whitenack?

  Yeah. And maybe just to check my understanding here, if I'm understanding what you're saying, this would be like, oh, and that retrieval and query fan out stage, let's say I retrieve 20 sources and 10 of them are from Reddit saying a very similar thing. And maybe one of them is from TechCrunch talking about the same, like it confirms the 10 Reddit things. It could be that the answer engine uses those 11 sources as confirming the same thing. And that's what it's going to talk about, but it doesn't cite the Reddit things. It cites the TechCrunch thing because it's like, I don't know, for whatever reasons behind the scenes it cites. Do I have the right understanding here?

• 30:23Speaker 03

  Exactly. Yeah, that's exactly it. So essentially it's like at that, basically, once we retrieve those, that set of, let's say, 20 sources you mentioned, and let's say 10 on Reddit, there's a series of processes that go on, like someone that could be like re-ranking versus the query. And then there's also some later research around, basically like domain, it's already where ChatGPT wants to send user itself. But yeah, that can, all of those can basically compare to say, look, we've used Reddit to ground the answer here. We're confident to move forward with this one. But we haven't, we haven't actually cited it. That's, that's exactly what's going on there.

• 31:02Daniel Whitenack?

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• 32:22Chris Benson?

  Ben, I wanted to do a quick follow-up on what you were just talking about. And that's kind of that, that relationship between retrieval and citation a little bit. And so it seems like they, like intuitively, I might have thought that there was that, that there was a, you know, a positive relationship between them and that, you know, that retrievals would lead to citations. You just talked about the fact that, you know, that isn't necessarily the case. Could you talk, I mean, it seems like that's a significant thing. Like if you're, I would guess that if you're a marketer out there and going with that intuitive approach, that that would be kind of a substantial pivot that you'd have to make to, to kind of accommodate that.

• 33:05Chris Benson?

  Could you talk a little bit about, you know, like what that means? What does it mean, the fact that the retrieval and the citations don't necessarily line up that way? And Liam, I'd also love to hear what you have to say about that on the, on the marketing side.

• 33:19Speaker 03

  So, yeah, so what I would say is, yeah, it is a big, a big change. Cause typically it's like, look, again, like, you know, traditional SEO would say basically, look, oh, query an index, we get something back, then it's reasonably stable minus a Google core update. And now we have essentially, yeah, multiple, multiple steps, multiple query fan outs in that. And not only that, we have like re-ranking involved as well as like the LLM basically critiquing the information itself in this agree style algorithm that I mentioned. So, yeah, I think basically just to, just to kind of link back all the possible sources, that citation time, we talked about, about like basically citation time optimization to say like, well, how do we end up in the final citation? And then one of the things is obviously consistency, like or consensus.

• 34:05Speaker 03

  So if I have, if I have this long tail query, basically that one of the query fan outs we've been retrieved on, what other supporting materials are out there that are likely to get ranked on that query to support it? So that's like consensus. We have a number of audits we do on this, et cetera. And then the other side of it is, so once I pull in that, like even before, so I pull in that, that, that set of sources that basically add consensus, that'll help. And also you wanna avoid saying, well, let's say, let's say there's some like anti-consensus information out there. You don't wanna, you wanna avoid that building up. So let's say there's something about your brand where it's actually like negative, let's say reviews. And it seems to be like it's published in multiple places. That'll be a good example. That's actually one thing we've built out a lot here is AI perception. And one thing we've found is actually quite interesting is that like,

• 34:48Speaker 03

  if there's blank space, you could almost publish, you can almost get anything cited. So for instance, let's say certain industries are very, very sensitive about publishing pricing. And we found this works like insanely well if you publish numbers that even like yardstick directionally correct. Because there's blank space and the LLM doesn't wanna hallucinate essentially. So that's just a good concrete example of it. And similarly, like if a competitor isn't talking about those, you can also like do something similar to kind of capitalize on it. And the second thing is like, well, even when it basically perceives that information it's got from your brand, like how are you, how are you showing up in the weight? So it might have a very, it might have a very negative sentiment or around your brand.

• 35:32Speaker 03

  And if it's looking at data from your brand, then let's say, for instance, it's quite funny when we did the model rank analysis, profound as like a really negative sentiment in the score.

• 35:43Speaker 03

  And essentially that's gonna impact then a citation time. So like, and it's gonna even come down to simple things, like what is the name of your brand? So if your brand is like a, a word that is like, let's say, already existing English word in the dictionary, for instance, that can be an issue, or if it has negative connotations with it, or it doesn't align with the, like basically overall, let's say it's a security platform and it's like openly or something like that, that could have a negative effect.

• 36:11Speaker 03

  So there, there is multiple points there, both on the retrieval side, consensus, as well as like how the model is, like basically the sentiment it has towards your brand. And also what, so when these models are trained, coming back to the weight side of things, it's all about co-occurrence, right? So there's this attention function basically where it's basically going along, looking at all the tokens in a certain space and it's associating you. So if I have like, let's say, guardingtools.com, what are the other entities I've related to as I've gone through the training data? And then when it comes to citation time, it's like, well, that also influences like, okay, well, well, this, you know, we're talking about X. So essentially like you, let's say with the, the entity shows up that's related to you, you're more likely to get retrieved again. So it's all like essentially like an embedded form of a knowledge graph.

• 36:56Speaker 03

  But typically, most of these indexes from Google, et cetera, are built on knowledge graphs. But now with an embedded form in this transformer layer, but you gotta try and assess that. So I'd say basically there are three areas, well, or primarily two areas. The weights and how it's reasoning on those weights for you. And then the consensus around that and the weights in there, factual accuracy, sentiment and co-occurrence. That's how I think about it. But yeah, it could be like, it's not, it's not easy to, it's not easy to operationalize that and then like start weighing that for, for like, if you just get profound or something like that.

• 37:29Daniel Whitenack?

  Liam, I'm, I'm curious. So Ben just described kind of, of course, there's this whole chain of things that influences what eventually shows up in the actual response. Everything from the, the model weights to this query fan out, to the agreement algorithm, the, the, the naming, all of those things, co-occurrence. I'm wondering, like over time, obviously this industry of AI visibility is evolving, right? And so at a certain point, maybe it was like, oh, you need FAQs on your website. This is how, like the easiest way to, to show up.

• 38:09Daniel Whitenack?

  Now, now you're kind of thinking across all of these stages of, of the process. So how do you, when, when you're engaging with new, with new clients, different brands, like what is, are there any generic kind of takeaways around like the strategy of where, where you start and what, what is kind of near term and, and long-term important for your brand? So that you're hitting all of these things, but also you're able to make progress quickly? Any, any thoughts on that?

• 38:44Speaker 03

  Yeah. So.

• 38:47Speaker 02

  Um, I would just say three words and then I'll, I'll, I'll dig into them. So I think it's Sure. relevancy, consensus, consistency, right? And all of this, you could also put into buckets of like tradition, like again, same three core jobs I, I said at the beginning. So, and, and it really depends on what your current state is. Like, do you have a website today? That's going to be super important. And like, all the foundational stuff really matters. Like the, I used to run a paid, that paid ads agency. And I was like, man, why does some companies like really succeed with ads and, and others don't? There's lots of factors that go into it, but one of the key aha moments I had is messaging.

• 39:27Speaker 02

  Is like the companies that really succeed in marketing, they, they have, they have a really defined ICP. They really know what their product does and they're able to map that to clear messaging. And they have really good differentiators. And I think that's really important in AI search. Is like, what are those terms you want to be associated with? Like, what do you actually do? And how do you translate that into a language that people are going to be searching? And I think that's how you create that relevant content. But you can't create good content if you don't actually know who it's targeted to or, or what you do. And so I think that's like the relevancy piece. I think that's how you ensure that during that query fan out, these LLMs are coming across your content.

• 40:08Speaker 02

  Now there's, we can go deeper on that. Like, I think you, you shouldn't just be posting blog content. You need a website, you know, your service pages, like, you know, all of that good stuff. I think then consensus is, and Ben covered this in, in enough detail, is basically, my content is like me throwing my hat in the ring, like, hey, we have relevant stuff over here. Consensus are all the votes of confidence. Like, why should we choose this company? Why should we, why should we trust them? Mark has always asked me like, well, hey, where should we focus our efforts? And my answer is everywhere. Really what we're talking about here is it, it's a, a budget and time constraint, right? Like if, if, if, if these, if, if budget and time weren't a consideration, then be everywhere.

• 40:51Speaker 02

  If they are a consideration, well, that's really going to depend on like industry. We're quite bullish on places like Reddit. One of the advantages of Reddit is a lot of B two B companies don't really know how to do it well. And so just like because of that, you, you can gain an edge. I think also doing just traditional, you know, digital PR. I think building a presence on social channels is super important. If I was to, you know, gun to my head, where should I focus? I'd bet on YouTube. It's a, it's actually a native search channel. It's owned by Google. I think it's going to be around for a while.

• 41:29Speaker 02

  I don't think you should, I, I'm, I'm personally not that bullish on LinkedIn as a search channel, as a social channel, absolutely. We're, we're on there. I really believe in it, but for search activities, I don't know.

• 41:43Speaker 02

  I think also building customer advocacy. So we operate in the SaaS industry. That's going to be your G two's of the world, right? Getting people to say positive things about your brand on the internet is always going to be a vote of confidence for consensus. And then consistency, this is kind of where it closes the loop. What I kind of opened the thought with is, you need to get really clear on what you want to be known as, as an entity, as a company, right? So if you go to our website, you're going to see we use similar words across the place because I want those to be embedded in, in these models and, and the world.

• 42:21Speaker 02

  And so whether it's your activities on Reddit, you're posting videos on YouTube, you're posting content on LinkedIn, even like your company LinkedIn profile. You know, the content on your website and on your blog, always presenting yourselves with a consistent message. Where I think this is like, and so like, one of the, the key contrasts here is if we look at like link building and SEO, the big check in the box was, hey, we got a link, we've got a do follow link, um, you know, we're gonna, get some link juice back to our website. In AI search, I actually think the blurb surrounding, even if that link didn't exist, the blurb surrounding you as an entity is far more important. Because like, who are you as a company? We want that to be really dense, right?

• 43:06Speaker 02

  Like I want, if, if an LLM picked this paragraph and put it back inside to an answer, are we being positioned competitively? Like, we're thinking about it from that perspective. So that's like consistency. Now, we can get into like the tactics and all of that stuff, but it really depends on like your current state. Because some people, we can talk about Reddit and all these tactics, but some people might not even have clear messaging, right? They, they might, they might just have a website with a few paragraphs of text on it, right? And so then your priority is going to be slightly different. So it does depend on where you're at.

• 43:38Daniel Whitenack?

  I, I'm curious with, with kind of that, with the focus on Reddit and, and the potential there with, you know, like what, what tends to make, you know, one Reddit thread more likely to surface than another? Are, are some of the, are some of the signals that you would have that are like, you know, upvotes and comment count and, you know, user reputation? Do those correlate with retrieval? You know, are those, are those, you know? And, and so when you're looking across different threads on Reddit, you know, does that make a substantial difference? How do you think about that?

• 44:13Speaker 02

  Yeah. So I'm not going to speak in absolutes because I think these things are always changing. What we have observed is,

• 44:21Speaker 02

  in engagement in terms of upvotes, um, we haven't seen that correlate with increased citations. What we have seen highest correlation with is the actual content itself, right? So I, I always like to break things into like, uh, uh, easy ways to understand because that's how my brain works. But if we just look at this from the principle of user prompts, LLM, LLM does query fan out and it's looking for information. Contained within those queries, there's lots of things and entities, right? So if I go back to that prompt example, SaaS founder, like doing this in revenue, in those query fan outs, it's containing all these entities, right? And so I want those to be contained within my content on Reddit.

• 45:04Speaker 02

  And so we found very often that the passage being extracted from Reddit is like 50% down the page. And the comment has like one upvote or two upvotes and it's because the passage was relevant. Now, I think that might change over time, right? Because similar to how relevancy is super important, but authority is becoming even more important. I think these models are going to keep similar, like, you know, how Google always releases a core update is, you know, they, it's, it's a cat and me, cat and mouse game between these providers and marketers who are constantly trying to catch up. And I know there are commercial agreements between OpenAI, Reddit, Reddit, Google. And so maybe, um, things like upvotes, maybe even the history of those accounts.

• 45:49Speaker 02

  Things like that might factor into it. I don't know the complexities of that. Might change over time. But yeah, just what we, we've observed so far is the overlap between what the user is searching and, um, the, the content on Reddit.

• 46:04Daniel Whitenack?

  Liam, you, you started getting us towards kind of like how things may or may not change, how, how they're evolving over time as we get kind of close to an end here for, for this conversation. I'd love to close out by just asking each of you, maybe circling back to Ben to start. Each of you, like, as you look forward, what, what is kind of top of your mind? Either in terms of like, oh, there's like a huge opportunity here for, for people that are willing to, to jump into it. Or something that's like, oh, there's a really open challenge here. We haven't figured it out yet. There's more work to be done in this area. And either of those areas, um, like what are you thinking about as you end your days?

• 46:49Daniel Whitenack?

  Or as you start out in the morning, that's kind of top of your mind going into this, this next phase of work?

• 46:56Speaker 02

  I'll let Liam go. He's ready to go. So, hmm, I'd probably say two things.

• 47:04Speaker 02

  So offsite, um, like, how are those third party earned sources gonna influence how I am or our clients are perceived inside LLMs? I think there's just so much change happening there. And ultimately, brand is the final moat, right? You see this plastered everywhere is like, and so, um, I really think that's where things are going to change a lot. And so I'm always thinking about that. I'm always thinking about, you know, our clients only have certain budget and time they can allocate. Um, and they're on strict timelines.

• 47:45Speaker 02

  And so how do we increase the probability that the bet they make is gonna have, um, you know, the highest expected value? So I'm always thinking about that. And the second area.

• 47:58Speaker 02

  Is agent accessibility? So I think everyone at the mo- or for the like, everyone, I'm speaking in such generalities, but I think everyone's been focused on discoverability, right? Like, how do we get my content discovered? And I think there's so many edges to gain there. This is, this is going back to my original point. It's like, walk before you can run. But we're quickly moving to a place where it's not just going to be agents crawling your website and retrieving information. They're actually going to be taking actions on your website. And we've already seen evidence of this. We have clients reporting to us that, hey, an agent, an AI agent booked a demo last week. We're seeing things like that happening.

• 48:43Speaker 02

  And so then that just, again, websites have historically been optimized for humans. Humans, if your website takes a little bit to load or like your form, your demo form is confusing, it's okay, we can figure it out, right? We can, you know, use our intelligence, we can figure it out. It's not fun and it's going to impact conversions, but we'll figure it out. Agents, are they going to wait around to figure it out? I think there's actually some standards that, and we've seen some movement here from Google. And so I think that's going to really impact how websites look and behave. And I think that's really exciting because I don't think anyone's looking there yet. I know Ben wants to research more in this area. I'm kind of holding him back a bit because I don't think it's sexy enough just yet.

• 49:28Speaker 02

  But I, I see that definitely as the next frontier. And, you know, we have clients that are generating a lot of conversions, you know, tens of thousands of conversions per week. And a lot of their users are coming via command line interface. So they're saying, hey, I want to build X. And then the agent is the one going out there and coming back like a private procurement team. And, and like, not just saying, hey, I recommend these tools. They're saying, hey, I installed these tools for you and they're good to go. I think that expands the surface area that we need to look at. So yeah, I'd say those two is, is what I'm thinking about.

• 50:03Daniel Whitenack?

  Awesome. Yeah. Ben, anything to, to add there?

• 50:06Speaker 03

  Yeah, definitely with, as Liam said, with the agents, I just think it's moving from like a, I always describe it as like a read-only environment to a read-write environment is where we're headed. And as Liam said at the very start of this conversation, it's just a better deal. It's a better deal to get all the, all the context in one place, just talk to an agent. It would be even better deal if you didn't have to fill out the form. It would be an even better deal if you didn't have to take a slot in the calendar, yada, yada, yada. Get the idea. And so that's for sure where we're heading. I think, you know, we've seen evidence of this. And if you look at the Google Web C P program, like this is going to, it'll probably, it'll be very, very slow and overnight, go boom. And that's, that's what I expect to happen just because it's human, it's just human behavior.

• 50:51Speaker 03

  It's just easier. It's just easier with like lowest friction path. That's where we tend to. And then I would say, I, I do think like to date, we've been very focused around, basically like, okay, like here's some query fanouts and here's a citations. How can I map them up? And we're kind of like playing this, this, a little bit of a, most marketing teams are playing this game of like, here's my comp and I'm now ranked for kind of thing. But I think people are going to start focusing more on like the weights and what's driving the weights longer term. Because two, two reasons is that, well, the, the marketing in general is like sophisticated. People are becoming more aware of how these systems work. The second thing is that the training cycles are actually coming down.

• 51:36Speaker 03

  So when we first started, it was like cool, we have like a new version of chat G P T once every like nine, 12 months, right? And now obviously these engines are getting trained like on a weekly and bi-weekly basis at the top layers. So that even like the top layers are being retrained and being tuned. And then the backbones are being retrained every, let's say, three months or so now. And so they are more influenceable. And if you can get influence or imprinting those tokens, it's worth like orders of magnitude more than, than being in a, in a, in a, in a single context window. Having said that, you ultimately want both and it's not easy to get into that training data. But that's where I see, I see things heading in terms of otherwise outside of that.

• 52:19Speaker 03

  Yeah. Offside is going to be a big thing. I think of being able to, because essentially what, kind of, a kind of overall trend is that Google is having to, or all these, all these agents are having to avoid basically like dead internet theory, right? They're trying to avoid training on like basically model collapse, training on their own systems. And so they're looking at all this content out there and that's part of what the fingerprinting release is. It's part regulatory, it's part, it's probably a part IP play, it's part like multiple plays they have out there. But it's, it's, it's ultimately, it's one of the reasons I also think they have is they don't want to train on their own. So if you, if you post a Reddit with like a generated Claude comment, they will know that. They look up their hashing function, very simple.

• 52:59Speaker 03

  Boom, the probability of this being AI-generated from our model is very high. And they just, they, they, they essentially, I think that's going to ratchet off. And I think there's going to be a lot of cleaning and basically all these like, the, basically the, the protection of human level context. And actually when OpenAI started out, they have this manifesto online to, they wanted to offload the verification of text to third parties, like TrustPilot is a good example, in partnership with them. They'll, they'll keep trying to do that and I think they'll keep trying to up the ante here. So it'll get harder and harder to basically evade them. So you need to keep climbing the edges around that. So that's where one other kind of like, let's say, AI content, bot content, I guess,

• 53:42Speaker 03

  or bot behavior in general is going to become, the human signal is going to become very valuable there, but also emulating it will too.

• 53:50Daniel Whitenack?

  Makes sense. Yeah. Well, I'm, I, I was writing down furiously in the background a few things that you all mentioned kind of throughout. I need to level up on my understanding, things are just moving so fast. I really appreciate both of you joining us to like, with your expertise, really helping us and our listeners understand this topic. I would encourage everyone listening to go check out Discovered Labs. In our show notes, we'll link some of the research that we talked about here. We'll link their research page, which is, which is really great. Thank you so much, Liam and Ben, for joining us. Looking forward to having you back on the show sometime when everything is different.

• 54:28Speaker 02

  Thank you so much. Thanks for having us.

• 54:30Speaker 03

  Thanks, guys.

• 54:30Speaker 02

  Cheers. Bye bye.

• 54:38Speaker 00

  All right. That's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X or Blue Sky. You'll see us posting insights related to the latest AI developments and we would love for you to join the conversation. Thanks to our partner, Prediction Guard, for providing operational support for the show. Check them out at predictionguard.com. Also, thanks to Breakmaster Cylinder for the beats and to you for listening. That's all for now, but you'll hear from us again next week.