The Best SEO Podcast: Defining the Future of Search with LLM Visibility™

SEO’s New Frontier With Duane Forrester

• MatthewBertram.com

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We push past rankings and traffic to map the real skills SEOs need to influence AI answers. Duane Forrester explains the machine layer, vector embeddings, semantic density, and why structured data is a must if you want reliable retrieval.

• AI reshapes marketing and elevates SEO’s role across the business
• Good SEO foundations as the prerequisite for AI performance
• Writing for chunks with high semantic density
• Structured data and entity clarity to validate facts
• Vector embeddings as the new alignment target
• KPIs beyond rankings: retrieval confidence and zero‑click presence
• Why LLMs.txt lacks adoption and what matters instead
• Practical tracking of AI answers and trend analysis
• The gap between search engines and LLM information retrieval
• Learning paths to keep pace with faster platform updates

Guest Contact Information:

Website: duaneforrester.com
LinkedIn: linkedin.com/in/dforrester
Twitter/X: x.com/DuaneForrester

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With over 5 million downloads, The Best SEO Podcast has been the go-to show for digital marketers, business owners, and entrepreneurs wanting real-world strategies to grow online. 

Now, host Matthew Bertram — creator of the LLM Visibility Stack™, and Lead Strategist at EWR Digital — takes the conversation beyond traditional SEO into the AI era of discoverability. 

Each week, Matthew dives into the tactics, frameworks, and insights that matter most in a world where search engines, large language models, and answer engines are reshaping how people find, trust, and choose businesses. From SEO and AI-driven marketing to executive-level growth strategy, you’ll hear expert interviews, deep-dive discussions, and actionable strategies to help you stay ahead of the curve. 

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SPEAKER_00:

This is the unknown secrets of internet marketing. Your insider guide to the strategies top marketers use to crush the competition. Ready to unlock your business full potential. Let's get started.

SPEAKER_03:

Howdy, welcome back to another fun-filled episode of The Unknown Secrets of Internet Marketing. I am your host, Matt Bertram. Uh, what an exciting time we live in. It's uh 2026, and it's like the birth of the internet has happened again with AI, and AI is working its way into everything, and AI has really affected marketing in a big way. We were probably one of the first disrupted industries in that. And um, I wanted to bring on a special guest. This is going to be kind of like a masterclass, guys. So uh get your get your notebooks out there. Uh, I have the one and only Dwayne Forrester uh on online. So, Duane, welcome to the show.

SPEAKER_01:

Matthew, thank you.

SPEAKER_03:

And for anybody that's uh been been in the industry a long time, sports guy, um, you know, he's he's cut he's been doxxed and he is out there uh uh and he is standing by his name and his work, and he's done some great things for some big companies, uh like Bing, Yelp, um, you know, sports betting. Was it MGM? Was that was that who you're with or who was it with?

SPEAKER_01:

So I started my career with Caesars Palace Casino.

SPEAKER_03:

Caesar Palace, that's what it was, right?

SPEAKER_01:

And then um moved on to sports betting, which was not them, um online, and um ended up leaving that company, went to Microsoft for a decade, moved on to YXT, and you know, I've been deep into AI since then, and it's been eye-opening, invigorating, frightening, exciting. Um, there are several more adjectives that go in that line, and and and I'm sure your listeners have several of their own to add there as well.

SPEAKER_03:

Yeah, that thank you for the correction. Yeah, yX. I'm looking at the back of your book here, right? Um, being, of course, and uh really schema.org when you helped launch that, I thought that was uh huge. And that's where um, you know, I started hearing your name. And so um I am I'm just super excited to have you on. Uh, I've been going through your book, I got all kinds of questions or kind of deep dives of things that I think would be helpful for other people listening because I can tell you when we started talking about vector embeddings and chunkings, um I certainly started to feel like I was outside my scope of like, okay, I'm gonna have to learn some.

SPEAKER_00:

So that feeling.

SPEAKER_03:

So some some some new things because this is not what I was taught or or any other words. I was like, what is that? So I need to do that. Like, what is that? And so um, you know, I one of the things to kind of tee up this conversation, and we can take it wherever we we talked for for a while prior to this. Um, but uh uh in his book on page 149, um uh basically we were talking about what it takes to be an SEO today and like where that that's moving to and the new skill sets you have to have. And and and really, you know, when I look back at being an SEO at working with a larger organization, you don't have a lot of control, right? Like so you have so you're your certain lane or your certain inputs, but you can't impact everything. And when you get in to where it's at today, it it has to be um part of the DNA of the company to move the needle. Um, and and I've seen a lot of SEOs get frustrated at bigger companies and try to kind of teach everybody everything and then they move on, and then the next company doesn't understand, and like with AI, with entities, right? Yeah, like there's no like you can go do this over here and this doesn't impact it over here. And so I think the role of an SEO, whether the title gets changed, which I know there's a lot of debate about that, but like needs to be elevated and they need to have more control, and and they they really have all the data, which is shocking to me when you talk to like traditional uh mediums or uh marketers, like they love all the data. And so so I think SEOs need to elevate themselves to kind of uh have full visibility of what's going on. Um but one of the things you said, and I want to read this for everybody, uh the the the required dedicated expertise today requires optimizing for retrieval confidence and chunking structure, not the same as optimizing landing pages for human conversion. Understanding vector embeddings and semantic similarity scoring is not the same as running a keyword gap analysis. Tracking brand presence and zero-click AI answers is not the same as monitoring rankings and traditional search. So basically, which I'm seeing it with all the tools, you gotta throw out what you knew to a certain degree and come up with maybe new KPIs, or as you kind of put it, there's an additional layer on top of good SEO. So it AI doesn't protect you from bad SEO that you have tech debt you gotta make up.

SPEAKER_01:

Yeah.

SPEAKER_03:

But you know, but but now you gotta you gotta you gotta go that extra mile. There's that extra um uh like sphere of influence that you have to tap once you do the basic stuff or the foundational stuff.

SPEAKER_01:

I'll I'll I'll put it to you this way, Matthew. The title of the book is the machine layer, and it's not accidental, right? Like, you know, you you've read it, you're part way through the book. Um, you know, at some point you'll have to tell me if you actually think it's worth the money you spent on it, because I'm I'm dead curious. The authors don't we don't like we don't get to get that feedback, you know. Um, but one of the key pieces of this is, and I talk about it in the book, right?

SPEAKER_03:

It's like so Dwayne, leaving you a review, okay, on Amazon, and then and then doing a semantic citation with us together for the eat experience with me in the picture of the book is actually worth it.

SPEAKER_01:

So you see everyone, you see what we're doing here, right? Like, you know, um, but it's it's like and I I frame it in the book like you know, you're going from high school to university, you're going from you know, um, a graduate degree to a master's degree. Um, you can put whatever example you want on it, but but you're leveling up with what we're doing today. Now, look, you know, the spokespeople at the engines will tell you differently. The engines themselves seem to be putting out different information right now, possibly contradicting.

SPEAKER_03:

Yeah, I still have to do that. And it's a moving target, right? Like it and it keeps, yeah.

SPEAKER_01:

This is the point, okay? It is a moving target, it is an absolutely a moving target. So, um, like I built something in this book that I wished was here two years ago because everything is moving so fast, and and this is a challenge. Like, like I did a survey, I looked at jobs that were posted for SEOs, and I took, I don't know, the better part of 300 jobs, and I looked across every one of those jobs, and I, you know, basically came up with 2% of those jobs are talking about AI in relation to SEO. And I sat and I thought to myself, so if these other people are hiring someone, let's just say their intent is that two years from now that employee is still with them. Um, where exactly is this person developing these skills around understanding the AI environment? And I'm purposely avoiding talking about optimizing for AI because there's there's a very clear step of understanding how LLMs operate, what they do, what they don't do, that is it is different than the easy understanding of search. Like everyone alive today knows what search is. Even though a billion active users daily use ChatGPT, they could not accurately tell you what it is and what it does in the background. You need to know you've gone through this, Matthew. You've taken a couple of classes, you're about to get some certification that I'm kind of a little bit jealous of. Um, like you're but you're doing these things, and you're doing these things on a very fast-moving train. So, like, it doesn't stop at every station. If you want to get on, it's difficult. Like, you've got to run and leap and hold on, and eventually you find your set, your your footing, and you're like, okay, now I can understand this. Then you start to see how it applies to everything you knew before that, which brings me back to a very important point that you made. Um, you can't suck at SEO and excel with the AI environment. And I'm speaking like as a business, if your SEO presence, if your execution of SEO is not good, better, or best, if it's mid-level or lower, you are not going to perform in AI answers. It's that simple. It'll be a rare viral moment that you get called forward. Whereas people who actually have a very good solid foundation, very good solid reputations, relationships, they are going deep on things. Um, I released a um, I released a um we'll call it a recently.

SPEAKER_03:

Is that what you're talking about? The substack?

SPEAKER_01:

So, yeah, this I I released a substack, but I gave my my readers a specific process from a white paper that came out in November that was very specific about looking at product in AI and ways to influence the product being brought forward. And they identified a bunch of them. I created an eight-point plan for it and said, hey, if you're a subscriber, you get it, it's free, here you go, right? So it's there if anybody wants it. Um, but my point behind this is not that anybody should go get that. My point is we're now starting to see the very beginning of a white paper based on a methodology that's shared and there was a test and there's empirical data behind it. I don't know whether it's right or wrong, good or bad, if it's repeatable. I'm telling everyone, you should go try this and tell us if it works. Because I can tell you right now, without that, a lot of people are guessing their way forward. And and if you're being told chunking doesn't matter, geo equals SEO, SEO equals geo. Look, I'm here to tell you as somebody who worked inside one of the largest search engines. Um, it's my experience, and based on my knowledge, that is not accurate, that is not the direction you want to lean in. You need to understand this yourself and how it applies to you because it is different.

SPEAKER_03:

So I want to ask somewhat you can you can give me uh an open-ended answer, but I want to ask somewhat of a pointed question because um I don't know the answer, right? So I I know that like qualifying for like fan out terms when you're ranking, you want to rank in that top 100, right? So you rank in that top 100. And I did see some data early on that there was a correlation with the um the AI bots or the chat GBTs or of the world, like whatever you want to call um the the the the machine layer um of which you know defining that like a search engine is not like a big LLM and like what the differences are, I think might be might be useful because I think that there's some some kind of correlations of uh some of the formulas and things that they do, but it is is uniquely different. But I thought okay, so you you you're in the top 100 and you're showing up, and then you're like, okay, AI answers. And then there's like this kind of direct correlation between the different uh chat bots or whatever of the answers they showed. And then I I believe it was it was either HRS or SEMrush came out with a big study that said, you know, 21% or something like that of all the callbacks are are not even indexed. Okay. And so we know that Google and other search engines are not respecting like do fall, like they're sucking in all the information and trying to make a determination about it. And I'm assuming that the you know, the the AI is doing the same thing, it's got access to everything. And it's like and it's making assumptions. So one of the things you even talked about too was how do you measure influencing through through a framework or something like that, how the AIs understand and process it, but without giving you the citation for what they're doing. And and and I I I like that, you know, that that was very interesting to me. And that's you know, trademarking and you know, there's there's there's ways to kind of claim that territory, but that's not something that I was ever taught or was taught about when we're talking about SEO. And and now you're going, well, as an entity strength, like the trust signals that are coming up, how do I, how do I know if I'm gonna show up there or not? And and you got these tools that are now you know scraping it daily and telling you like in the average, but it's like you gotta under you gotta understand to your point how these things work because to give you a tip and a trick on how to influence it and optimize it, which it seems like it's a moving target. So what's working now is not gonna work. They're getting smarter. So if you're spamming everybody, like that's only gonna work for so long, and then you're gonna be holding the bag going, like, I don't know what happened. Like it's worth it to now today to just do the work the right way and kind of claim that land and get in that long-term data set. Um, but but then I'm even seeing with the grounding with like Chat GBT when it started saying, like, hey, like so so it has to be very, very current uh articles that are something like uh 10 months or is what I read. And so there's all these different factors that you know, I mean, but then now the AIs do call really authoritative stuff that are hugely old, right? And and Google does the same thing. So there's a lot of like competing information, and it's like, and it's so so I mean, I would love like okay, to show up in the AI answers, you know, what's what's like a frame in which people need to just think about it, maybe in kind of a short snapshot.

SPEAKER_01:

Okay, so um look what you're describing is is you know the norm, right? Like in the absence of clarity. Um, first off, let me describe it this way. We are back in the Wild West. Uh, that's where SEO started. And if you've been an SEO for more than five years, you will recognize the feeling. You'll recognize the feeling of you've gone from essentially walking down a corridor with artwork and lighting and plush carpeting and chairs to stop and rest in, because this is the structured world that you lived in as an SEO working with a known entity, which is your direction. And somewhere along the way, you opened a door and you stepped out into a desert, and there is absolutely nothing. And some people thrive in that environment. They say, This won't kill me. I have what I need between my skills and the raw entity of this desert, I will be able to survive. Other people look at that and go, I need a checklist where like I don't have everything I need. Like it's and that is completely normal. That feeling is real, okay. And in fact, that that feeling is the core premise of the book, is about going from fear to actualization. So, yeah, you're uncomfortable now, but I'm gonna give you a bunch of frameworks you can use to build comfort around. And that's you'll get there as you read it. Um, but but the reality is that look, there's a lot of stuff that you should have been doing. We touched on like where you're doing good SEO or you do mediocre SEO. Okay. Um, one way that I would cut that is do you have structured data in place? And you know, a lot of people can raise their hand and they're gonna say, Oh, yeah, I got it. And I'm gonna go, okay, did you use every piece of structured data that you could across every area of your website that you could? Oh, well, no, I only used it here. You know what? Mediocre SEO. Good SEO starts at you've done all of the work, not some of the work. And that's really what we're seeing, right? Is this leveling. Now, I'm picking on structured data because it's an easy one and people understand it. Okay.

SPEAKER_03:

Well, I also want to throw it out there that people are even like I know I know your opinion on it, but and I would love to to deepen that because I've heard people waffling saying, Well, I don't know if that's really important or not. And and I've heard you say it's absolutely a hundred percent important, but like I've seen it on LinkedIn even recently. People are like, Oh, like, I don't know, it's that, and I'm like, it it's a it's a defined structure that they understand, and it can, and you're telling them, hey, this is what I want you to believe, and then they can go verify it. It gives them like a starting point to to cross-verify it. Like, I'm like, why would this not be important? You're basically telling it, this is what I want you to think. Go believe me if I'm telling you the truth or not. That's what I'm here.

SPEAKER_01:

Um, I have the unique position of having been there when that was launched and understanding why and understanding how it was used and for what reasons. If you want to skip it, I will say thank you very much for making my life easier. Because if your approach is I don't need it, it doesn't matter, I'm happy to have less competition. Thanks for you know, stepping out of the race. I appreciate it. Um, but again, you know, you do you.

SPEAKER_03:

So like I what about uh I I just saw something recently, and maybe it was uh uh uh clickbait, but it was like uh uh what is lm.txt doesn't matter.

SPEAKER_01:

No, so here's the thing um the problem with LM LLM.txt, and I I just put out a substack uh maybe a couple of months ago on this, I did a deep dive on it. Um, the problem isn't what it's attempting to do or how it does it or the wording or anything like that. The problem is adoption and trustworthiness. So when robot.txt, schema.org, sitemap.xml, when those are all launched, they were launched with explicit backing from the search providers. So the platforms themselves had a vested interest in it and said, We will follow this, we will agree with this, we want that. Not a single platform has come on board. This is a private effort on LLMs.txt. It is someone who has technically their own vested interest involved with it, but I don't believe that's their point. I believe their point is we need something different than a robot TXT. And if no one's gonna create it, I will create this and give it to you. At one point in my career, I wanted to know if I was being paid well, so I started the industry's first salary survey. I had a vested interest in understanding if I was compensated well, but then I ended up giving a survey to the entire industry that was used for a decade to benchmark jobs. So there you go, right? Um, I get people do things altruistically or just, you know, not necessarily for their own purposes. And I think that that's where this came from. Now, do I think that it has value? Not so much. Um, none of the engines are on board. Google typically you hear these cycles where they come up with this, you know. Oh, we don't back this, they say it. And if you look, there's a there is a cycle, there's a timeline around which these messages get repeated to us. And um, you know, Google likes to do things on their own, right? They always have, and for good reason, like they have the resources, whether it's people, intelligence, money, foresight, whatever. Like they they are well equipped to roll their own and just like do their own thing. So it shouldn't be a surprise to anyone that Google comes out and says, Yeah, we're not gonna support that. And and that's fine. Um, it doesn't mean that it's not valued. And I would be shocked. Look, if I worked for Chat GPT and they said to me, Um, hey Dwayne, um, we have our own crawler, um, it's got to go out there and gather stuff, like, you know, how should it interact with the world? You come from search, you know crawlers. What should we be doing? I would be telling it to look at LLMTXT, and I would be gathering every LLM TXT I could as the platform, and I would be creating an average signal across thousands of those instances. And then I would say, which of these signals benefits me and which of these harms me? And then I would be in favor of supporting the things that benefit me and ignoring the things that harm me. And so after that, I might consider telling the world that I support this. Problem being that if I come out and say I support it, everyone hears that as a binary yes or no. Even if you explain, here are the 18 things I support. If you say anything else, I ignore it. For years, I battled the argument that um robot TXT was a controller for crawlers, and everyone was in this do crawl. What's the proper do crawl? What's the proper do index tag? How do I write this? And I'm like, you don't. The assumption is the crawler will crawl everything, that's its job. It's the only reason it exists. If you let it go on its own, that's what it will do, it will crawl everything. And some of those crawlers are strong enough to break things, they will crawl that hard and that fast and that insistently. And robot TXT is a way to say, Don't harm me, don't look in here, don't waste your time, whatever it is. But it's strictly a blocking mechanism, it is a no object, it is not a yes object. And for 15 years, the debate has raged about oh, you have to have do crawl, you have to have due index, you have to have these things, and it's like, no, you don't. If you just put a robot.txt up there with nothing on it, but the file is there, you find all of the clue it needs, right? Like literally an empty file is permission. So so we're in the same situation.

SPEAKER_03:

So just because to tie it all up, what did you ever think of uh the humans.txt file?

SPEAKER_01:

Um, listen, uh to me, this is like we're starting to veer into territory of should we debate geo or SEO or AIO? Look, we got more important things. I agree. I agree just for the record. Yeah, geo is used across 183 industries as an acronym that means different things. It is not a good acronym for you as a search marketer to show up at a meeting and start tossing around because there is a very higher than average chance that somebody in that room has a different meaning attached to it, and you will create friction in that room. They will either mistrust you, they will be confused by you, which is a form of mistrust, and therefore you won't get invited back, or they won't listen to you. Or best case scenario, everybody smiles at you when they leave the room, they go, dude, didn't even know what Geo meant. And everyone else at the company laughs about it because they know what it means to them. Just be careful with that, right? Like SEO might get confused for a Korean surname, but nothing else.

SPEAKER_03:

Yeah, I mean, all these semantic anchors in all these different directions is creating a lot of noise and a lot of confusion. And and I I feel like a lot of people are debating on the the the title of it, not actually like what's actually changing and the work in it, you know. Um I would love uh like kind of going back to to to what we talked about, maybe maybe talking about some KPIs, right? Like what are the right KPIs? Because the current KPIs of traffic, um of uh keyword rankings are thrown out the window in my book. Uh I don't find much use in them anymore.

SPEAKER_00:

Yeah.

SPEAKER_03:

I mean, they're they're they're a point, but they're not enough. Like them on their own are not helpful.

SPEAKER_01:

So I'm just I'm calling this up right now, right? Because this is really important that we look at some of these. A couple of caveats. And I look, I outline all this in the book, obviously, and I talk about this in my substacks, right? But like just for people that are listening or watching the show, um, some of the data that we are gonna talk about and we are gonna say is important is not available to you. It is data that is only inside the actual platform. So proprietary to ChatGPT, perplexity, claude, Gemini. And those platforms are not gonna share that, right? I expect sometime on the other side of never, Google will start sharing this in Search Console. Um, and I don't think that the other entities that I mentioned, I don't think that they that it will even hit their radar to share anything. So, like forget the the equivalent of Search Console or Webmaster tools from OpenAI. Just don't see that happening. There's no upside to them for for chasing that. Um, but these are things that we know are being used internally at these platforms, right? So, for example, semantic density score, okay. Um correct me if I'm wrong, Matthew, but you brought up semantic earlier, and you were kind of like, is it that big of a deal? Is it that different? Maybe it's not that different. That was the direction we were leaning in at that moment in the conversation as a question around as a question, you know.

SPEAKER_03:

I I mean, I when Bert came out, and Bert's part of my last name, right? Right, you're already dug into it, and and uh, you know, the semantic spano is uh is is quite important. Um, and there's a lot of there's there's a lot of uh uh yeah, I I think yeah, so keep going.

SPEAKER_01:

So here is the important part when you hear somebody say semantic density and you hear them talking about semantic search, those are two entirely different things. The word is the same because the meaning is the same, but semantic density is actually something that's looked at inside these systems. So within your chunk, are you actually deep in knowledge? Do you provide everything I need within that chunk to understand that topic, that question, that answer, that entity, whatever it is?

SPEAKER_03:

Yeah, we were yeah, we we were talking about like I think when people are writing content, they they kind of string it out where where they're they're given like little nuggets throughout the article because they're looking at the article in summation, but that's not how AI looks at it, it'll cut it off.

SPEAKER_01:

Here, here's the problem, okay? It's easy, it's easy for me and everybody to sit there and say, uh, yeah, no, that's not good. Don't write long form, and and you know, like you want to avoid that, you want to chunk and blah, blah, blah. And then it's super easy for a search engine to sit down and say, Don't do that. That's not a great human experience. And if you look at both of those on a bell curve and you put them both out at the outside edge, you are correct. Like both of those statements are true on their own at the same time. Um, however, you can find that blend. Now, this is where, as a content writer, your job is not to sit down with ChatGPT and say, Here are my parameters, give me 500 words on this topic, and then you take those and edit those. And at the end of the day, you go, Wow, I created 11 new articles today. First off, you didn't create 11 new articles today, unless the definition of creating is file, save as, save as new title name, then you created it. Yes, I agree. However, in no way is diminished the importance of the human in this loop. In fact, it underscores the importance of the human in this loop, okay? Because your job is not whether it's long form, short form, bulleted points, a list, that's not your job. Your job is to understand I have an entity, and on this page, I have identified nine entities, it's arbitrary number, it could be any number. Okay, if the page was a page about you, there would be one entity, Matthew, repeated many times, but there would be a lot of topics in relation to Matthew. Okay, he is a competitive water skier, he enjoys modeling in his spare time, like all of these things are entities of their own in relation to you, okay, association versus another page, which is all of the greatest podcasts that have SEO people on them. Okay, let's just say that number is nine. Each one of those, as they are described, has to live in a world where the description about that object has everything that not only the human needs to know about it. Why did it make the best list? Who are these people? What is their background? What are you giving me that others aren't giving me? So on and so forth. But you also, by doing that and bringing it forward for the machine, you create a better experience for the person, right? The example I always go to is um, I go to this example on um on um product search, okay. Um, a few years ago, I wanted a new coffee maker. And um I'm kind of a unique guy, like I want something different, right? Like I'm not just gonna run over to Walmart, buy a coffee maker, and then like you know, a year and a half later, run over to Walmart and buy another coffee maker because that one crapped out. Like, I want something different, unique. Problem is when you go looking for that stuff, what you start to learn with coffee makers is different unique means tall glass pieces, and these things don't fit under a counter, like on your counter, but not below the cabinet, right? Which then makes me realize I need measurements so that I know how big your coffee maker is, so I know if it fits. And you, as the manufacturer, give me the size of the shipping box because that's what the factory in China supplies to you. So that's what you put into your metadata about the product, which then goes into every product feed as the size and dimension of the product, but I'm not putting your box under my cabinet. Uh, you can take three to five inches out of that for packing material, and that's so I need that data, and people don't do that, they don't write that way. Another problem that you fit or you find is people write for humans. We write emotionally. There's a lot of like trying to get a hook in there, right? Almost exclusively, these hooks are ignored by machine learning systems. They are not emotional, they are not nostalgic, they are not interested in feelings, they are interested in the facts, they are interested in objectivity, they are interested in um a concurrence of opinions. So everybody loves it, everybody hates it, that type of thing. Um, but that to them is just more data. They don't actually believe you want an example of this, okay? Go to Amazon, go dive in on the reviews of a product that you want to buy and read the AI overview that Amazon provides you, okay? Because they tell their AI, go through all the reviews, come back and tell us what it is.

SPEAKER_02:

Yep.

SPEAKER_01:

I would say 40, maybe 50% of the time, people love it because of its height, and other people complain about how tall it is. It's like one, two in their summary, because a lot of people complain about it and a lot of people like it. And you're sitting there going, This is useless because it's a data engine. It's just looking over a block of time, a block of occurrences, and saying, Love, hate, boat equal, okay, I should put both of those in. Here you go. And then you, as the human, are looking at it, going, You just gave me two pieces of information that contradict each other. What's the point of the overview? Like it, you know, that's your reality. So, so when we really get down to it, look, creating content matters, still matters, will always matter. Who you're creating it for, you got to pay attention now. And to your point earlier, Matthew, you were kind of touching on this. Each one of these platforms behaves differently, they all value different things for different reasons. And the settings that they have across their systems, the weighting and then the temperature. Okay. So weighting would be your left to right balancing, and then temperature is your vertical on that. And everything is moving at that same time. I can't do vertical and horizontal same time. Can't do it. Um, so but that they're all moving in that at that same time in real time when it's being called, and you, as the writer, have to figure out how to create content that positions you to slide both of those to the maximum so that you will get included. And then congratulations, you've done something you don't even know you've done it because how do you know if it's you they're talking about?

SPEAKER_03:

Well, you know, when I when you said that, like what that made me think of is like I I saw a a platform do this pretty pretty, I thought it was a pretty good tactic if you understood what what the intent is is what you're trying to do is in the long-term memory or in the inf uh like the the the operational heading, not just the prompt of the AI, the memory, uh to to get your product in there, but their recommendations are gonna be based on all their past searches, whatever it's remembered about that person. And so, you know, whatever, like if you and I search the same thing, you're gonna get a different answer than I, you know, unless there's like no other choices in that category of what we're looking for.

SPEAKER_01:

So be careful with that because there are examples of in very defined niches with excellent content coverage, you will see similar things popping back. So, like, look, a part of search has always been popularity, right? Like, and you know, whether engines like to admit this or not, time and resources get assigned to specific verticals, okay? News, weather, sports, all these big verticals where everybody goes every day and consumes that content, that is more important than bedazzling roller skates. As a vertical, I think we can all agree that even if we balloon this up to just roller skates, that's a much smaller area of the internet with many fewer people engaged in it than the news. So, so if it's you as an engine or as a platform applying resources, and it's really important, the engine applies human resources, engineers. The platforms are applying AI systems that cost tokens, which translates to real dollars. Either way, the company is investing money, okay? So you can't say, Oh, it's just AI, that's cheaper than a human. Ah, not at that scale, it's not like it definitely is not. And when you apply those things, you don't apply them all to solve the problem for roller skates, you apply it to solving the problem for news and for focusing in those areas. So it's we're gonna see that. We will see things overlap on those odor edges. That's my point.

SPEAKER_03:

So, what I what I took away from what you said was different categories, there's different amounts of resources that are are meant to crawl the internet and to also figure out what's most relevant, like news on one end of it versus maybe roller skates on the other end of it. So if you're in the data for roller skates, right, and in in in long-term memory, and you're launching a new product or whatever, and you don't have that authority, it could take a lot longer to show up. And there, I mean, there's a moat there that you might have to do a lot of work before you start showing up because a lot of resources weren't applied to update that category.

SPEAKER_01:

Is that is that I'm gonna redirect that, Matthew. Yeah, and I'm gonna define this a little better. Okay. It's not about some categories are slow, some are fast. Okay. If you have a problem and it affects consumers of news, you're gonna put resources to fixing the problem or refining something in that category because it's a scale issue. It affects many more people much more frequently. If that problem only exists in the roller skate niche, you're less likely to say, everybody show up and go fix that problem because that one affects a smaller number of people. The trajectory with which your new product is the same between the two of them. It's not gonna matter. Same core algorithm, it's the same weightings and temperatures and whatnot, right? Um, it's not like okay.

SPEAKER_03:

So there's not, there's not like you're categorized here, and then no, no, no, no.

SPEAKER_01:

And like way early, early on, when um, and this is gonna stretch a lot of people back, right? But like when Google was first launched and and MSN search was first launched, right? Like they had verticals, right? You would click on a tab and go into search specific for news for autos and whatnot. At that time, you still had groups of people assigned to work in those areas, and you might have more people on one team than another team based on volume of traffic and problems to solve. Conceptually, you still have that kind of verticalization happening, but we've long ago moved, and this, you know, I talk about this in the book, right? Like, search is not just a what if lookup table, it's really complex, right? Like, I encourage people to go look up what BM25 means and how it's utilized, look up what BERT is and how it's utilized and when it started to be applied. You will see that the current iteration of what I consider to be traditional search is really advanced. Like it's really advanced, right? No one's just gonna go out and create a search engine today. Like, that's we're way beyond that. Um, it where we are in traditional search, it can almost see LLMs from where it is. The LLMs are an entirely different animal, though. What they do is entirely different. And in fact, you could actually say, and it would be completely valid, I think. Look, the LLM is not about search, it never has been about search, it's been about information retrieval. And if you want to argue that search and information retrieval are the same thing, okay, then these are the same things. I argue that the execution is the difference, not the meaning of the word, but how you do it is what creates the difference. Different process, different math, different requirements.

SPEAKER_03:

Yeah, we were talking in the pre-interview, is like LOM and machine learning, there was no resources put in it because no one thought it was gonna work.

SPEAKER_01:

Well, look, you know the story, right? Like 20 years ago, everybody was claiming they were doing it. 10 years ago, everybody was claiming it was doing it, and everyone else was looking at them going, You're nuts. Five years ago, someone came out and said, We're gonna do this, and a few people went, hmm. Four years ago, boom, yeah, TPP3, and the world changed. And and and suddenly all those people who were nuts are all now billionaires, multi billionaires, they're at the cutting edge, they're you know at the front end of everything. They made Google play catch up. I mean, I don't know what to tell you about a movement.

SPEAKER_03:

Well, this is just why this is my argument on why it's not the same. Because you you said it. The the process is it it it might give you an answer at the end, but how it computed that answer that technology did not exist before now. So there's no way to say this is the same thing as this, and I'm just gonna do the same thing here to do that. I'm like, I mean, I've learned how search engines work and I've learned how LMs work, and they don't work at all. Like, I mean, search engines you said were black box, but like I'll tell you LLMs are a whole different category.

SPEAKER_01:

Look, I'll I'll put it to you this way um search engine, and you could use whatever you know verb you want or adjective you want in here, right? Like it forces, it encourages humans to do work. Okay. A search engine means I still have agency because a search engine says, Hey, look, you gave me diddly squat for information, but here's what I think you want based on that, because I did a lot of smart calculations in the back end and and am I close? And then you, as the human, go, well, hang on, let me read through your list of stuff, and I'll pick one that I think is the most enticing. Okay, that's the engine forcing the human to complete the final mile. The human brain is wired to conserve calories. That's why we got smart as a species and started with tools. That's why we had the industrial revolution, that's why we have LLMs today. We are about preserving energy that in our core, in our genetic makeup, we are programmed to burn fewer calories whenever possible. Now, might not seem like a big deal, but first off, your brain burns the most calories per any organ in the human body. So there you go, right? It's big, it's thirsty, it sucks down a lot of resources, and it kind of knows if I can offload stuff, I'll do that, right? Like, you know, that's easier for me. It's a survival thing. So search telling us to go look through one or two pages of links and determine in this mess which one is the best. I'm burning energy, I'm having to think. All I wanted was an answer. Zero click comes along. Now, all of a sudden, all of that stuff gets summarized, chop right at the top. Here's the answer to your question. Marketers panic, and here we are. Guess what? It's only expanded its footprint, it's continuing to expand its footprint. And LLMs are the next logical step in that. I have a personal digital butler that I get to yell my request at, and it comes back and goes, I got you, bro. Here you go, here's your answer. And it says it with such authority that the lazy part of our brain kicks in and goes, I'll trust that. That sounds confident. Okay, first off, be careful because what you've done as a human being is you've handed your agency on decision making to something that does not even understand your interest in the moment, can't possibly, because you didn't communicate it. Because again, humans are lazy and we suck at communications. So, like there's a lot going on there, right? And so it's really difficult then as a search marketer to sit down and say, Yeah, I trust the output. I can use like there's so much here that still needs to mature. A billion consumers a day don't care because they ask a question, they get an answer, and they move on with their lives. And here we are.

SPEAKER_03:

So the risk in decision making is a whole new thread that I would love to go down out of the because that that is where where I have a lot of interest. To your point, like you you very eloquently said it is like people are like, oh, uh, LLMs aren't that much of search, right? Like, so just focus on SEO. And I think everybody's giving lip service to it because it's not that big of a percentage yet. And when I saw what was happening, and like people crossed that Rubicon, people are not really going back. Maybe they're going back to Google to verify, right? So just uh okay, let me let me double check, let me see if I'm missing anything. But but ultimately, most people are lazy, right? Like if you just want to put everybody out there in an aggregate. And so they're gonna go to this. And so this is gonna eat search, like you wouldn't believe. And when Google launched, like, what are they focused on now? They're focused on YouTube, right? Like that's like they because they know the mark. And I felt like even AI overviews were like just like holding the water back of everybody moving over, like we got to keep people here. And then it's funny when I've talked to people over the last year, they're like, Yeah, I'm using AI, I'm using those AI overviews. They're like, I'm using AI by looking at the overviews, and they are, I guess.

SPEAKER_01:

You know, here's here's a universal truth that I think everyone is going to come to understand. Um, so I don't think that chat GPT necessarily like you're not going to see Google's market share slide and chat GPTs go up, because chat GPT isn't tracked in the same vertical, in the same bucket as a search engine. So you can still get 90% of your traffic from Google, but if the size of that search bucket is cut in half because the volume of searches goes down overall, and all of that volume moves over to an AI-powered system, you will still see that 90% of your traffic is coming from Google. You will still see that they have 90% market share. The problem here is that now the market share in search is less valuable. And it's my prediction that Google being every search engine, they know this because you can't fight human behavior. Okay, look, it took Google nine years to reach what I think of as market saturation, okay, around the billion user mark. Um, it took three and a half years for ChatGPT to do that. So, like the world is a very different place, okay? And more and more consumers, you know, I I relate this story sometimes. I had a friend of mine, he went to um his daughter's school one day, and he had to stand in front of the class and tell the class, like, this is what daddy does, right? And, you know, he's like, Oh, I help businesses get more exposure on Google and rank at the top of the list on Google so that you know they're bigger, they're more successful, and everyone makes more money. And one of the kids in the class raised her hand and said, Can you explain to me what Google is? I see my parents using it and I hear them talk about it, but I don't know what it is. These are 10-year-olds. So these are kids with phones, with devices, they're internet natives, and she's 10 years old, really doesn't have a frame of what Google is and the value it provides. That's a very real thing that every tech company is facing and fighting. So, yeah, AI overviews, stop gap measure until we find a way to monetize AI overviews because we still need traffic and search and those ads because that's where all the money is from. And you slice it for whatever reason you want, right? Like that's one reason. There are a whole lot of other reasons. There's infrastructure, there's tech deck or uh um tech debt, there's like all kinds of reasons and rationales why it makes sense. There's also a lot of people who still want to click on actual links, so you can't just flip a switch and make a move. But what if you woke up one morning and the entire industry that you are attached to, a$22 billion a year industry that is so deeply threaded into every single business on earth now? What if they woke up one day and said, We're changing direction? We think that's more important, and we're all gonna go talk about that and focus on that and not do other things. That look, that right there, red flag, flashing warning light, danger, danger. Like, I get it, it's very real.

SPEAKER_03:

That that was my fear, right? That was my fear, and I started using you know chat GBT and going, like, I don't understand how this works, I don't understand how this is working, but I'm using it more and more and more. And I just every everybody's gonna move that direction. And and now you're talking about which we don't have time for, but like the the bot economy, right? Like these things are gonna have their own agency to be able to do different things and buy and sell.

SPEAKER_01:

And the whole, yeah, the whole agentic thing is like look, if you think you're gonna jump into agentic, you really better go back and score yourself on SEO because in order to get to agentic, you have to go through AI. And if you sucked at SEO, you still got so much to learn and do before you can grasp and wrap your head around AI, which will then prepare you for agentic. It's not there aren't shortcuts anymore. This is the wild west. Go and learn, right? Like you have to go and learn these things.

SPEAKER_03:

Yeah. I mean, you you said everything was moving so fast, and you you have to jump. I mean, I'm doing trainings almost every night just to just to keep up with what's going on. I would love, I I know we're getting close to time here, um, but I would love to talk about vector embeddings. I think a lot of people have a misunderstanding about that. And and you you've done a really good job and and of how you talk about things. You're a great teacher. So I'd love for you to kind of share with how people should be looking at that.

SPEAKER_01:

Okay. For vector embedding, I want people to think of a compass. Okay. Um, in theory, and we're gonna generalize here because as somebody who uses compasses in one of their hobbies, I I know the discrete difference, but generally speaking, a compass will point due north or magnetic north. So it's pointed in a direction. Now, that compass is pointed on what you can consider to be zero. Anything to the left or right of it is close, but not perfectly lined up. The only things lined up are on zero. So if I give you a 0.1 or a negative 0.1, you know you're left or right, you know you're a little more or a little less aligned. That's vector embeddings. Every single entity and object is cre is taken, and we're gonna separate vector from embedding here for a moment. The embedding is the mathematical representation. So everything, Matthew Bertram, is converted into a number, and that number then is stored in a vector database, which is different than a knowledge graph and vastly different than a traditional database. Okay. I'm gonna throw some words at you all, and I want you to go look these up because I'm not gonna explain them, but they are companies that will help you understand. Weaveate, pine cone, super base. They sound weird. Go look that stuff up. You're gonna explore a whole other world, right? It's it's a rabbit hole, so get hydrated and dive deep. But the embedding is a mathematical representation of an entity or an object or a thing or a word or or of anything. Um, that number then exists in a three-dimensional space floating there. The vector is directional, the vector is your north, your south, your west, your east, your somewhere in the middle. And that's going through everything, always. Vector embedding refers to how close is that number to the number created for the query. So, what shoes does Matthew Bertram like? That's our query. There are very few things that line up directly on the zero line for that and give you an overlapping vector embedding. And even then, it'll be really hard to get it perfect because the words you use, the syllables, uh, it's slightly variant, but you'll end up with the same thing. He likes hush puppies, so there you go. And everything to the side of that, he likes Merrills, he likes Nike's, he likes Adidas. All of these things get farther apart. The vector changes. You're one degree, your two degree, you're three degrees off zero on that compass as that changes. And the reason that changes is hush puppies are comfortable shoes for older people. That's Matthew's choice. Merrills are hiking shoes for active people, still shoes, but the database knows that that's a different thing. Adidas is very much into sports and specific sports. So now you're three or four degrees off alignment. So still talking about shoes here, and maybe Matthew's got a pair because he can't get rid of them because he relives his youth, you know, once every month, and that he won't give up his Adidas, right? But but that's the whole point behind vector embeddings. So your your job now as an SEO, how close to the target can I get? How close to that line can I get? It's not a bullseye. You are not attempting to hit a stationary spot on the wall. You are attempting to chase a meteor hurtling through space, and you want to come up as close to behind it to land on it as possible as you can. And there are points given for close. So can you get in line? Can you get close? It's a three-dimensional game now. I saw somebody, um, somebody sent me a 3D, it was a gif of a vector embedding in vector space, and it was amazing because it was a representation of as somebody wrote the query, every character that was added to it was another point in this thing. And it was this really weird kind of started spiraling, flattened out, then came up another layer and turned in on itself, and then stopped when the enter button was hit because now the whole query was represented. But it's not a straight line, it is a weird corkscrew in three three-dimensional space. That's an invector embedding of a question.

SPEAKER_03:

Gosh, what I was going through this. Yeah, well, that that just reminds me of math, uh in the Oxford program that I took, uh, where it was like entropy, and like where's the yeah, and I think that that's where's the end? Where's the start? Yeah, like and and and depending on what you do, where it would fall.

SPEAKER_01:

Um here's what worries me, Matthew. I wrote an article about the math behind all of this, and I titled it the math behind this, and I put the image was warning math ahead, and it was one of the least engaged with articles that I wrote all of last year, which is shocking because on my Substack the people there are following for SEO, and I'm like, so I tell you this is about math, and you you bounce. I'm sorry, your future is all about math. And if you don't like it, I I sincerely think what we are about to see as an industry is we are gonna see a whole lot of the industry is about to take a left-hand turn off the highway and go in a whole new direction, and a bunch of people are just gonna keep hurtling toward the horizon and they don't fall off, they don't, you know, like the end is there, it's all very polite and calm, but it's just like a big parking lot, and there's nothing special about it. Whereas everybody else turned left, and then you got to backtrack and turn right to try to catch up. Like, you know, just come left with us and learn. That's the future.

SPEAKER_03:

So, so they're they're like they're mainly content people, right? Like, I think uh there's content people that masquerade as SEOs, and there's a lot more math of a lot of checklist SEOs, though, right?

SPEAKER_01:

Like people who run around create, like they they create checklists by aggregating checklists from others, and then they sell based on a chat GPT output that I can do this audit and it's more in-depth than anybody else's audit, and somebody pays them for that, and then you know, like it's there's a whole lot of that going on that it's not gonna happen because to your point earlier in our conversation, I can't give you a checklist, even if I give you a checklist. Would like I get in my book, I give checklists for things like here's a 90-day plan for learning all this stuff, right? Here's a way to talk to your executive about this stuff. Like, I get practical checklists around that, but I can't give you a practical checklist on how to rank in an LLM today because first and foremost, I don't have AI model success rate metrics, cement semantic density scores, zero click surface president. I don't have an understanding of machine validated authority from the machine. These did they don't share any of this. I can't see inside their database to understand if I'm actually getting covered. Like, am I being crawled properly? Like, I can't see any of that. A retrieval confidence score. I I want it, I know what it means, but but I need their data to help me understand.

SPEAKER_03:

Oh my gosh.

SPEAKER_01:

Yeah, you know, like like we're not there yet, right? Although I outline all of that, obviously, and I give you know understanding and and what the meanings are of these things, and I also break them out into buckets so that people understand look, here's what you can use immediately, here's what you can go start doing today. And a lot of this is manual now. Do you want to know how you're performing and how you're you're ranking, misusing that word, in an LLM? Well, you better carve out time, you know, with some Gatorade because you're gonna need four hours a week to sit there and pound queries in and capture what the full response looks like and create a spreadsheet manually so that you can track that over time. Pro tip do it, do not worry about how you're gonna track what you're seeing in it. Track it all, and then once a month, hand the spreadsheet to ChatGPT and tell it, find the trend lines, tell me what things are, you know, interesting insights from this, and then it'll bring that back to you and go 43% of this says this, and 18% of this, but something I see that's growing is this, and and it'll give you a starting point. That's the key thing because they'll tell you what's not going to give you the starting point. Traditional SEO tools, those are not gonna give that to you right now.

SPEAKER_03:

Well, I think that's a good place to end. I would love to branch off into a number of different topics and conversations, and you know, you start like, you know, so I'm I'm working on Langchain right now and some orchestration with agents, and this is all new. Like it never, you know, not a developer, had to learn it, had to start at the beginning, had to, you know, I didn't even really want to talk about AI because how do I talk about it unless I was an expert? And so I had to go back and relearn everything, rego through everything that I knew about SEO and try to connect those dots and build an even stronger foundation. And and I really think the the machine layer's done that for a lot of people and helps connect those dots a lot faster. Um, and so I would encourage everybody to go check it out. Um and and I may grab a copy. I would love to get a signed copy by it, right?

SPEAKER_01:

And uh we'll come to use it and we'll we'll we'll grab copy and I'll write something nice in it.

SPEAKER_03:

Fantastic. I would love to also have you back on and talk about as we keep progressing, like where it's going, because the thing that we haven't talked about that that I'm seeing as the biggest issue, and we'll kind of maybe leave this for the future, is I'm sick of using all these different tools and then trying to triangulate in my head what the answer is. Okay. And all of these need to use data pipe. And APIs to come together to give the LLMs all the data they need to give me the best answer. And then we talked about the trust and the judgment of like, is this data correct? Uh, and and what is the risk if it's not correct? Like, I think that that's where things are going and it's moving so fast. And you got all these agents that are getting spun up and are doing all these things and having agency. And like, I mean, they're still making big mistakes. Like, there's still a lot of big mistakes still happening. And so, from an enterprise deployment standpoint, it's scary of how fast we're moving and how much new technology is out there. And like, okay, the developers are working on their thread, and then you got the SEOs that are working on their thread, and then you got a lot of people just using it but not understanding it. There was like a uh I was I was at an event and there was a threat, uh cybersecurity threat um internally at a company and they got charged a ton of money because the developers were using it inside and didn't notify, and it was grabbing data and point. So it's just it's it it's it's the wild west. And there's so many different facets and factors to look at it. And I feel behind every day. I um I'm so totally thankful that I was able to have this conversation and and share it um and and shed some light on it. And I I really feel like now's the time for community for not everybody to protect what they know because it's all out there and to kind of share it and to come up with some some new frameworks and some new fundamentals to look at stuff that are maybe guided by the community. Because like you were talking about doing all those tests, a lot of people that I know don't have time. They're trying to execute for a client. And then there are some big thought leaders that are publishing huge studies, and like then I'm like trying to save that. I've built a little scraper to save all that to then look at that information because every time I log into LinkedIn, like my head spins, and I'm like, I'm missing so much information, it's moving so fast, and then I got to figure out okay, where's my learning plan? What do I need to know? How do I add additional value to it? And I mean, it it's it's fast, it's fast and furious right now.

SPEAKER_01:

So um it is crazy. Um I will I will give you the pro tip. Um, if you think this is moving fast, you need to really understand that updates that happen at the platforms like ChatGPT and Claude and whatnot, they produce and push out updates more than twice as fast as what we're used to as SEOs and Google doing updates. So Google doing updates has created a cadence. ChatGPT's cadence is more than twice that fast. So, first off, I think don't worry if you can't keep up with every single thing, because what's important is that you know how to get back into that pipeline, get back into the hose, and you're like, okay, I'm not entirely lost here, right? Like you get some of it. Um, I think what's incredibly important for people though is just don't lose faith on it. Like, use those platforms. I did this last night, I sat down with ChatGPT and I said, Hey, look, I'm sick of scrolling through the news because there's always a bunch of crap in there that I'm not interested in. And it doesn't matter how much I train my news feed, it still puts crap in I'm not interested in. Can you act as a news scraper for me? And if I ask you that I'm interested in a particular topic from a particular angle, can you go find that information? Right? So every day at whatever time, and yeah, I'm interested in what's going on in Iran, and it was like, oh, yeah, no problem. And it went and got all my stuff. And I'm like, yeah, but there's another angle to this that I'm curious about that this stuff isn't talking about, which is the military positioning and posturing around what's happening, and you know, because uh I am a military buff, you don't do things without positioning assets beforehand, and it's somewhat easy to see those positionings happening these days. So I want to know about that. But it's like I want ChatGPT to go get what I want it to go get for me and bring it back to me. You can use the same thing in gathering information about AI, you can just create a prompt that you keep in Word that you keep adding people's names to that you want to hear from, and you go back in and you just copy, paste, and it's like, go get me news from these people, has to be within the last seven calendar days. Today is this date. These platforms they don't have a clock, they do not have a clock, so they don't understand what day and time it is. So if you just say, get me something current, good luck. Like current is highly mobile in their mind. But if you tell them today is January 20th, it is you know 11:22 a.m. Pacific time, find me things that are within seven days of right now. You've given it something it can then take out and stamp against everything and do a comparison on and bring you back more useful information. And you can also tell it, take every one of those that's more than 100 words and summarize it for me. So, like use the system against itself. That's the key here.

SPEAKER_03:

I love it. Well, Dwayne, it's been a pleasure to have you on. Thank you so much. Is there anything that you're working on? Uh, you we talked about your Substack, you sent it to me. I'll put it in uh the show notes for sure. Uh, I'll put a link to your book uh there. Um, just I want to give you an opportunity to kind of share, share any last words with the audience.

SPEAKER_01:

I well, I just first off, thank you for having me on the show. Everyone, thank you for listening. If you're still here with us, this is awesome. Um, I do have frameworks. So if you want to hit my website at Dwayneforster.com, a bunch of the frameworks from the book are there. They're free for anybody who wants them. Um, most of the frameworks are still in the book, though. So I will encourage you to take a look at that. If you're interested, grab a copy. I would deeply appreciate it. Um, and obviously, if folks want to get in touch, feel free to reach out. I'm on all the socials and I'm easy to track down.

SPEAKER_03:

Wonderful. Well, everyone, thank you so much for listening. Please leave a review. Uh, it helps us follow, like, share, Shaiko, uh, as we we've talked about. Uh, until the next time, my name is Matt Bertram. Bye bye for now.

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Matt Bertram

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